In the face of proliferating cyber threats, CTOs are increasingly asked to get involved in cybersecurity management, especially when the CISO/CSO role is absent.

However, with conflicting priorities, limited workforce, and finite budgets, it’s often hard to determine the best course of action. 

Our team suggests focusing on the following cybersecurity trends to achieve early, tangible outcomes and secure greater stakeholder support for a well-rounded cybersecurity program.

Cybersecurity Trends For Short-Term Gains and Long-Term Advantage 

Corporate cyber-resilience takes time. To build a staunch security perimeter, you need to introduce process improvements, people management initiatives, and technology innovations.

Moreover, your cybersecurity program must be proactive, not reactive, meaning that it has to prevent cyber attacks and breaches from happening rather than solely improve your incident response speeds and abilities to deal with present-day threats. Because the stakes are getting higher in terms of financial losses. 

Source: IMF 

To design an effective cybersecurity program that protects your company today and prepares it for tomorrow’s threats, we recommend that CTOs focus on the following six areas: 

1. Zero trust security model adoption
2. Human-centric security design
3. Multi-cloud security and risk management
4. AI and machine learning for cybersecurity
5. Security of AI systems and applications
6. Cybersecurity workforce upskilling

Zero Trust Security Model 

Zero Trust model requires all users, inside or outside the organization’s network, to be authenticated, authorized, and continuously checked for security configurations and posture before accessing applications and data. It’s backed by well-established standards like  NIST 800-207 and CISA’s Zero Trust Security Model. 

A traditional perimeter-based security model prevents threats from breaching the network perimeter. The Zero Trust model does that and promotes extra controls for data security inside the network, including firewalls, access and identity management (IAM) tools, public key infrastructure (PKI), security information and event management (SIEM) solutions, and enforced policies to control user and device behavior inside the network.

From the beginning, zero trust requires every user to verify their identity, thereby authenticating users and regulating access to corporate resources.  It also uses advanced access controls to strengthen authentication for network devices and servers.

Zero Trust enables monitoring user behaviors and enforcing policies to prevent risks posed by distributed operations, such as remote workforces, BYOD policies, hybrid cloud services, API-based connectivity, and shadow IT.

Source: Oracle 

According to Gartner, 63% of companies globally have implemented a zero-trust strategy, addressing half or less of an organization’s environment and mitigating one-quarter or less of overall enterprise risk.

The Zero Trust model requires new cybersecurity expertise and investments in the right technologies, but it drives tangible outcomes. After adopting it, Akamai consolidated its security technology portfolio, eliminating legacy technology and reducing IT appliance and process overhead for new employees and authorized third parties. The company also reported cost reduction due to more efficient IT resource allocation and greater employee productivity.

Human-Centric Security Design 

Six in ten CTOs believe human error is the biggest cybersecurity threat facing their organization.

Indeed, a lot of hacking tactics exploit the “human factor” — employees’ gullibility, inexperience, or nonchalance. Vulnerabilities also arise due to staff’s reluctance to comply with security policies like using two-factor authentication or strong passwords. Likewise, many people lack awareness about the dangers of opening suspicious attachments or installing unauthorized apps on their devices.

Source: Gartner 

Employees must be the first line of defense, not the easiest leeway for hackers. Hence, your cyber program must drive measurable behavior change, not just promote checkbox compliance.

In 2023, NIST presented a Human-Centered Cybersecurity Program — a new security paradigm designed to “champion the human in cybersecurity”. Apart from addressing the usability of security technologies, it also promotes understanding and adapting to human behavior and psychology.

Instead of strict policies and punitive action, human-centric cybersecurity addresses individual needs and provides ongoing support to employees. It assumes the usage of user and entity behavior analytics (UEBA) to understand workforce behavior and its root causes. UEAB data helps identify policy and process shortcomings and design better controls. On a higher level, UEAB also aids in threat detection and ongoing security monitoring. 

Based on the established behavioral baseline, you can design better role-based access control (RBAC), granting users the right degree of privileges and access permissions. A good RBAC mirrors your organizational structure. Establish different security roles based on the users’ functions and responsibilities and link these to specific security rights. Each role must have just enough permissions, following the principle of least privilege. Introduce role hierarchies to allow lower-level roles to inherit permissions from higher-level roles, simplifying access rights management. Popular IAM solutions like Microsoft Entra ID (former Azure Active Directory), Okta, and CyberArk allow easy automated implementation across the organization and ongoing policy fine-tuning from one interface.

Lastly, consider implementing passwordless authentication. Password reusing is a common reason for breaches, while constant password resets add extra work to your IT service desk and create user frustration. Passwordless methods like FIDO2 security keys, magic links, and biometrics are more user-friendly and secure. Adopters report 2.6X faster sign-in times and 25% fewer password reset requests from users. 

These human-centric security design principles eliminate cybersecurity-induced friction, promote secure employee behaviors, and help reduce the risks of cyber breaches. 

Multi-Cloud Security and Risk Management Strategy 

Multi-cloud is now the dominant operational model, used by 89% of businesses. Indeed, this approach has many advantages — access to best-of-breed technologies, improved latency, and enhanced disaster recovery options among others. 

Source: Flexera 

Yet, multi-cloud operations also pose new security risks. A 2024 State of Multi-Cloud Security Report by Microsoft found some glaring problems:

• Identity control. Microsoft Entra Permissions Management found 209 million identities across its customers’ clouds in 2023. Among the 51,000 permissions granted (up 22% from 2022), only 2% were used and 50% were high-risk.

• Cloud-native application and infrastructure security. In 2023, 65% of cloud code repositories had source code vulnerabilities for an average of 58 days. Moreover, the average company had 351 exploitable attack paths. 

Businesses must do more to secure multi-cloud environments. CTOs could address the problems by implementing DevSecOps practices, which shift security to the software planning, design, development, and testing stages. A shift to the left improves alignment between development, QA, DevOps, and security teams on work processes and objectives. This ensures new cloud applications are developed in consideration of potential threat vectors, security vulnerabilities, and compliance requirements.

When shifting security left (towards the beginning of the SDLC), teams can identify potential security risks early on and build for every integration into the CI/CD pipeline. Teams automatically check for software dependencies and change impacts on the security performance. Apart from the standard unit and integration testing, DevSecOps promotes static application security testing (SAST) for every new build and container scanning. On the deployment side, DevSecOps promotes Dynamic Application Security Testing (DAST) — a type of black-box testing designed to probe running applications for potential vulnerabilities. OWASP has a detailed guide that further explains the key principles of DevSecOps.  

At the infrastructure level, better multi-cloud cloud security posture management (CSPM) is necessary. Lack of visibility into multi-cloud environments leads to bloated permissions, unprotected workloads, resource inefficiency, and compliance issues, magnifying security risks. CSPM tools like Azure Arc unify security and compliance management across clouds by establishing a centralized resource inventory. It also helps monitor all connected clouds, distribute policies, perform patch management, and monitor cloud costs.

AI and Machine Learning for Enhanced Cybersecurity

Cyber security teams are understaffed and overworked. Seven in ten security specialists work weekends globally, according to a BitDefender survey. Long hours and high stress increase the teams’ intention to quit: 71% of UK cybersec professionals plan to look for a new job in 2024.

CTOs risk losing critical cybersecurity competencies. Rampant tech talent shortages also delay hiring times, meaning that many roles can be left unfulfilled for months unless you’re using an alternative approach like strategic vendor partnerships. 

AI and machine learning (ML) cybersecurity solutions can reduce the mounting workloads security teams face by streamlining menial work. For 48% of security professionals, AI adoption is the top initiative for 2024, and here’s how they plan to use the technology.

• Alert prioritization. Security specialists face hundreds of alerts daily, many with low-value information due to outdated, overmatched, or over-sensitive system heuristics. This leads to alert fatigue, detachment, delayed responses, and overlooked threats. Half of companies plan to use AI for cybersecurity alert prioritization. Algorithms can triage alerts based on severity, potential impact, and relevance, helping teams focus on critical areas first.

• AI-assisted cyber threat intelligence. AI/ML solutions process large security telemetry data volumes and provide human teams with more accessible insights. New-gen AI assistants like Security Copilot, FortiAI, and Elastic AI assistants help query data with spoken commands and assist in case investigation with prompts.

• AI-enhanced SAST: Continuous static application security testing is an important DevSecOps principle. Yet developers often struggle with using security technology to assess their code. 45% think development teams will benefit from using AI for security reviews. GitHub introduced a code scanning autofix feature that provides an AI-suggested code fix alongside an alert directly in a pull request. Users can review the vulnerability explanation and the fix suggestion, specific to their use case, and decide on the next steps.

AI also has immense value for threat detection and prediction. Algorithms can be tuned to identify suspicious hostnames, IP addresses, and file hashes, suggest new rules, based on known cyber-attack patterns, and supply extra intelligence for  Tactics, Techniques, & Procedure Reports (TTP). Half of the MixMode survey respondents said AI adoption improved their security posture by better-prioritizing threats and vulnerabilities, and 46% say AI tools help identify application security vulnerabilities more effectively.

Security of AI Systems and Applications 

AI adoption spans well beyond the security function. Almost three-quarters of businesses are adopting generative AI fast or very fast, with 66% implementing it across functions. Greater accessibility of AI systems can boost productivity. However, AI systems can also amplify existing security risks or create new ones.

AI systems must be properly secured to prevent privacy and copyright breaches, data leakage, and direct adversarial attacks.  The French National Commission on Informatics and Liberty (CNIL) outlined the main risk factors for the security of AI systems: 

• The nature of data, submitted to an AI system (e.g., sensitive corporate data or personal user information) 

• Control over the models, toolchain, and data (e.g., open-source models may contain compromised components; fine-tuning processes may introduce potential security flaws to the system, etc.)
   

• System access modalities (e.g., open-source distribution of the model, web access interface, or SaaS access increase the attack surface)
   
• The intended use context for the AI system: (e.g., an  AI credit scoring solution will require more input to ensure its robustness versus a gen AI tool for generating presentation template). 

The multi-dimensionality of this risk is reflected in emerging AI regulations (mentioned in our Generative AI trends post). Regulators are adapting compliance requirements based on the AI system’s intended use and potential risks.

The baseline for AI security is continuous monitoring. Implement AI observability dashboards to supervise model performance in training and production. Tools like Grafana and Prometheus collect telemetry data across the model lifecycle. Precisely, track key metrics like:

• Data quality, volume, distribution, drift, and dependencies 
• Infrastructure utilization and saturation rates 
• Model accuracy, precision, recall, and explainability 
• For deployed models —  availability, latency, reliability, 

Additionally, you should integrate automatic data quality checks and privacy controls into data ingestion pipelines for all incoming data. Similarly, ensures protection of the data storage. Use firewalls, encryption, and secure data transfer protocols to minimize the odds of any leaks. 

Comprehensive monitoring helps detect both drift in model performance as well as signs of malicious intrusion. 

Cybersecurity Workforce Upskilling

Novel threats and security technologies demand new staff competencies. Over one-third of C-suite leaders identified cybersecurity and AI as top investment areas for staff training this year. Role-based, ongoing cyber awareness training should be part of your human-centric security program for business users.

Technical staff, in turn, could benefit from more advanced security certification programs. Almost 90% of leaders would be willing to pay for employees to get certified in cybersecurity. Certified professionals bring new knowledge to your organization and help discriminate best practices across the workforce. 

Working with a strategic technology partner is another option for obtaining missing cybersecurity competencies. An experienced advisory team can help implement new security architectures and processes for threat detection, investigation, and prevention. For instance, at 8allocate, we help businesses implement threat intelligence collection systems and proactive security measures as part of a custom-built security architecture. In addition, our team can provide security awareness training to educate your workforce on best practices and adopt appropriate behaviors. This way, you get air-tight protection against all threats without competing for scarce security talent. 

Final Thoughts 

Proactive, zero-trust cyber security is supplanting reactive, perimeter-based measures. 

CTOs can reduce mounting pressures from frequent security scares, long incident response times, and high program costs by implementing a new architecture, where security processes and policy enforcement are (semi-)automatic and analytics cover the entire IT estate.

While adopting these latest cybersecurity trends certainly requires hefty initial investments, proactive programs are also bringing measurable ongoing ROI in the form of reduced downtime, streamlined compliance, higher workforce productivity, and minimal chances of suffering a data breach. 

Get in touch with the 8allocate team to learn more about building long-term cyber resilience. 

Frequently Asked Questions

Quick Guide to Common Questions

Why is cybersecurity a top priority for CTOs today?

As cyber threats evolve, CTOs are taking on greater responsibility for security, especially in companies without a dedicated CISO. Cybersecurity is no longer just about protecting data—it ensures business continuity, regulatory compliance, and resilience against emerging risks. A proactive approach is essential to prevent breaches before they happen.

What is the Zero Trust security model, and why should companies adopt it?

Zero Trust operates on the principle that no user or device should be trusted by default, even if they are inside the corporate network. Every access request is continuously verified, reducing the risk of unauthorized access and insider threats. Unlike traditional perimeter-based security, Zero Trust assumes that breaches can happen anytime and ensures that security measures remain active across all environments.

How can businesses implement Zero Trust security effectively?

A successful Zero Trust strategy requires strong identity and access management, continuous monitoring of user behavior, and strict authentication controls. Companies must enforce multi-factor authentication, segment their networks to limit lateral movement and implement real-time security analytics to detect potential threats.

What role does human behavior play in cybersecurity, and how can companies reduce risks?

Human error remains one of the biggest causes of security breaches. Employees often reuse passwords, fall for phishing scams, or bypass security measures for convenience. A human-centric security approach focuses on making security practices intuitive and non-intrusive. Using passwordless authentication, monitoring behavior with analytics, and integrating role-based access controls can significantly reduce human-related security risks.

How does the rise of multi-cloud environments affect cybersecurity?

Many organizations now use multiple cloud providers, leading to security challenges such as fragmented identity management, inconsistent policies, and visibility gaps. Cloud misconfigurations are among the leading causes of breaches. To improve security, companies must unify their cloud security posture, standardize identity and access policies, and implement automated compliance monitoring across all cloud environments.

How can AI enhance cybersecurity?

AI is transforming cybersecurity by automating threat detection, analyzing vast amounts of security data, and identifying suspicious behavior in real time. It helps prioritize security alerts, reducing the burden on security teams and allowing faster responses to critical threats. AI-driven security solutions can detect vulnerabilities before they are exploited, providing an additional layer of protection against sophisticated cyberattacks.

What security risks are associated with AI itself?

AI-powered systems can introduce new vulnerabilities, such as data poisoning, adversarial attacks, and unintentional biases in decision-making. Protecting AI applications requires robust model governance, continuous performance monitoring, and strict controls on data access. Organizations must ensure that AI models are trained on high-quality data and regularly audited to prevent potential exploitation.

Why is cybersecurity workforce upskilling essential?

With cyber threats evolving, organizations need skilled professionals to stay ahead. However, the talent shortage in cybersecurity is a growing challenge. Investing in employee training, supporting cybersecurity certifications, and leveraging external experts can help businesses bridge the gap. AI-driven security tools can also reduce reliance on manual security processes, easing the burden on existing teams.

How can CTOs balance cybersecurity investments with business needs?

Security investments must align with business objectives without slowing innovation. By focusing on foundational security strategies such as Zero Trust, AI-driven threat detection, and proactive risk management, CTOs can strengthen cybersecurity without disrupting operations. Demonstrating the ROI of security initiatives—such as cost savings from breach prevention and improved compliance—helps secure executive buy-in.

Artificial Intelligence has evolved into a strategic imperative for modern businesses, capable of tackling complex tasks at a scale beyond human capacity. Driven by rapid innovation and enterprise demand, the global AI market is skyrocketing – valued at roughly $214.6 billion in 2024 and projected to reach $1.33 trillion by 2030. This explosive growth (about 35.7% CAGR​ over the period) underscores how indispensable AI has become across industries.

At the same time, the past couple of years have brought AI into the mainstream of daily operations. The public release of OpenAI’s ChatGPT in late 2022 marked a tipping point that familiarized millions with AI’s potential, spurring a wave of business adoption. In a 2023 Forbes Advisor survey, 56% of business owners reported using AI for customer service, making it the most common use case, followed by cybersecurity and fraud detection (51%) and digital assistants (47%)​. Companies are also increasingly deploying AI for tasks like customer relationship management (CRM), inventory optimization, content creation, product recommendations, accounting automation, supply chain optimization, and recruitment assistance​. In short, AI is quickly permeating virtually every business function as organizations seek efficiency and competitive advantage.

Yet investing in AI doesn’t guarantee success. Even as about two-thirds of organizations are boosting their AI budgets​, a similar proportion struggle to move beyond pilot projects and fully integrate AI into production​. In many cases, the stumbling block isn’t the technology itself. Still, the lack of expertise in implementation – challenges around data quality and shortages of skilled talent are consistently cited as top barriers to AI success​. The missing piece is often guidance rather than tools: companies need the right strategy and know-how to bridge the gap between promising prototypes and scalable solutions.

This is where specialized AI development partners come in. For businesses without deep in-house AI experience, partnering with expert AI solution development companies can make all the difference. These firms help navigate technical complexities, apply proven methodologies, and integrate AI effectively to deliver real business value. The following section highlights 10 top AI solutions development companies driving innovation in this space. While they may not all be household names, each has a proven track record of delivering AI-powered solutions across industries. From custom machine learning software and data-driven analytics to intelligent automation, these companies offer the skills and services organizations need to harness AI’s potential and turn it into impactful results.

10 top AI solutions development companies:

1. 8allocate

8allocate is an AI solutions development company that helps businesses accelerate growth, streamline operations, and enhance decision-making.

Founded in 2015 and headquartered in Tallinn, Estonia, with R&D centers across Central and Eastern Europe (Poland, Ukraine, Romania) and Latin America, we empower organizations in FinTech, EdTech, Construction Technology, and other high-growth industries.

8allocate builds AI-driven solutions that transform operations, unlock new efficiencies, and enable real-time decision-making. From automation and predictive analytics to Agentic AI and scalable cloud-based platforms, these solutions provide a strategic advantage by seamlessly integrating AI into business processes, ensuring adaptability, resilience, and sustained growth.

Key services: AI Consulting, AI MVP Development, Custom AI Solution Development, AI Agents Development

2. InData Labs

InData Labs specializes in data science consulting and AI development, offering custom solutions in predictive analytics, natural language processing, computer vision, and big data analytics. Founded in 2014 and headquartered in Vilnius, Lithuania, their services range from AI strategy consulting and data engineering to custom AI-driven software. InData Labs focuses on enabling data-centric innovation for clients globally.

3. Neoteric

Neoteric provides custom software development and AI implementation, specializing in generative AI, natural language processing, and machine learning. Founded in 2005, the company has completed over 300 projects, helping businesses build intelligent digital products, automate processes, and optimize operations. With offices in Poland and the USA, Neoteric serves finance, manufacturing, and retail industries.

4. ThirdEye Data

ThirdEye Data provides AI and big data solutions, helping enterprises integrate advanced AI and analytics worldwide. Founded in 2010, the company focuses on machine learning, predictive analytics, natural language processing, and data engineering. Their expertise includes AI consulting, deep learning development, fraud detection, and IoT analytics, serving startups and large corporations across various industries.

5. Softarex Technologies

Founded in 2000, Softarex Technologies provides custom AI and robotics solutions, enabling digital transformation through intelligent automation. With experience across healthcare, finance, and other industries, their services include AI and ML tools, robotics, computer vision, data analytics, and full-cycle software development, maintaining strict quality and security standards such as ISO 9001:2015 and SOC 2.

6. Markovate

Markovate is an AI consulting and development company specializing in Generative AI solutions tailored to optimize workflows and drive measurable business outcomes. Founded in 2015, they offer AI-driven systems like chatbots, computer vision applications, and blockchain solutions, servicing diverse sectors, including healthcare, retail, travel, and fitness. Their proactive and client-focused approach ensures alignment with specific business objectives.

7. Alltegrio

Alltegrio, founded in 2012, offers end-to-end AI solutions, including strategy consulting, generative AI, machine learning, data annotation, conversational AI, and big data engineering. With over 350 specialists across offices in the USA, Poland, and Ukraine, they focus on customized AI solutions for healthcare, logistics, fintech, and retail sectors, enhancing business operations and driving efficiency.

8. Devtorium

Devtorium is a versatile AI solutions provider offering services from AI product development and machine learning to SaaS, eCommerce, and no-code platforms. Established in 2011, the company supports startups and enterprises alike, providing flexible collaboration models such as IT outstaffing and fixed-rate projects. Headquartered in New York, Devtorium prioritizes quality, security, and risk management throughout AI solution delivery.

9. AI Superior

AI Superior, founded in 2019, is a Germany-based AI services company providing pragmatic, data-driven solutions focused on real business value. Their offerings include AI consulting, machine learning development, process optimization, and niche specializations like geospatial AI and LLM-based applications. Their scientifically rigorous approach ensures AI investments translate into measurable business outcomes.

10. OyeLabs

OyeLabs is an AI-focused software company delivering custom AI and machine learning solutions. Their expertise includes conversational AI (chatbots), NLP, data analytics, and software product engineering. Founded in 2016 and based in India, the company also offers blockchain, IoT, and web/mobile applications services, aiming to drive growth and efficiency for startups and enterprises alike.

Criteria for Selecting an AI Development Company

Choosing the right AI development partner is a crucial step that can greatly influence the success of your AI initiatives. While many companies offer AI services, businesses should evaluate potential partners against clear criteria to find the best fit. Key factors to consider include the provider’s technical expertise, relevant domain experience, proven reputation, security practices, and the overall cost versus value of their services. In fact, industry research shows that factors like AI expertise, data privacy/security, pricing, support, and time-to-value rank among the top criteria in AI vendor selection​. Below is a structured look at these criteria and why each is important:

• Expertise and Experience: Assess the company’s depth of AI expertise and track record with AI/ML projects. An ideal partner will have a team skilled in machine learning, NLP, computer vision, and other AI domains, with years of hands-on experience. Look for a portfolio of successful AI solutions they have delivered. Extensive knowledge and technical proficiency ensure the partner can handle complex AI challenges and use the latest tools and frameworks. Remember that expertise is often the most icrucialselection criterion when choosing an AI vendor​, so prioritize firms that demonstrate strong capabilities and innovation in AI.
  
• Industry & Domain Knowledge: Consider the partner’s experience in your specific industry or domain. A company with domain knowledge will understand your business context, regulatory environment, and unique challenges. Vendors who have solved problems similar to yours can provide tailored AI solutions that align with your needs​. For example, an AI firm familiar with healthcare data privacy (like HIPAA compliance) and medical workflows would be invaluable if you’re in healthcare. Domain expertise accelerates the development process and ensures the AI solution effectively addresses real-world use cases in your field.
  
• Reputation and Track Record: Research the AI development company’s credibility and client satisfaction. A strong reputation is usually built on a history of delivering results. Check case studies, client testimonials, and independent reviews (e.g., on Clutch or GoodFirms) to gauge past performance​. A solid portfolio with measurable outcomes is proof of the company’s ability to execute projects successfully​. Don’t hesitate to request references from past clients – speaking directly with organizations that have worked with the vendor can reveal insights about the firm’s reliability, communication, and problem-solving approach. In short, choose a partner with a proven track record and positive reputation to minimize risk.
  
• Security and Compliance: Data security is paramount when outsourcing AI development, as AI projects often involve sensitive business data. Evaluate how the company handles data protection, privacy, and compliance. Do they follow industry standards and regulations (such as GDPR, HIPAA, etc.)? A trustworthy AI partner should have strict security protocols – for example, robust encryption, access controls, and regular security audits​. They should be able to articulate their policies for preventing data breaches and safeguarding intellectual property​. Additionally, inquire about confidentiality agreements and how your proprietary data or algorithms will be protected. Prioritizing security-conscious firms will ensure your AI project doesn’t introduce undue risk.
  
• Cost vs. Value: Finally, consider the cost-effectiveness of the partnership and the value delivered. Pricing for AI development can vary widely, so it’s important to understand a vendor’s pricing model and what’s included (development, deployment, maintenance, etc.). Avoid choosing the lowest bid – the cheapest option may lack the expertise or quality you need​. Instead, weigh the value you expect to gain against the cost. A slightly higher upfront investment can be worthwhile if the partner’s solution will drive significant improvements or ROI for your business​. Look for proposal transparency and ensure you’re only paying for what brings value. The goal is to find a partner who offers the best balance of cost and outcome – delivering impactful AI solutions efficiently, not just cheaply.
  

Businesses can make an informed choice by evaluating potential AI development companies against these criteria. An ideal AI partner will demonstrate technical excellence, relevant experience, trusted reliability, strong security ethics, and a clear value proposition for the price. Taking a structured, analytical approach to selection helps ensure the chosen AI firm aligns with your business goals and can deliver a successful project.

Benefits of Partnering with AI Development Companies

Partnering with a specialized AI development company offers numerous advantages over trying to build AI solutions with an in-house team. Experienced AI firms bring expertise and resources that can significantly accelerate and enhance your AI projects, often with greater efficiency and cost-effectiveness. Here are the key benefits businesses gain by working with AI development companies:

• Deep AI Expertise: AI development companies are staffed with professionals who live and breathe AI. They offer specialized knowledge across machine learning algorithms, data science, and emerging AI technologies. This concentrated expertise means they can tackle complex AI challenges and find innovative solutions more effectively than a general in-house team. By tapping into this knowledge pool, you gain access to the latest best practices and research in AI without having to train your own staff from scratch. In short, you’re hiring an experienced team that can deliver cutting-edge AI models tailored to your needs.
  
• Cost Efficiency: Working with an AI development firm can be more cost-efficient than building an internal AI department. Developing AI in-house incurs significant costs – recruiting scarce AI talent, paying salaries and benefits, and investing in infrastructure and tools. An external AI partner already has these resources in place, allowing you to pay for just the services and time you need. This reduces overhead costs and avoids long-term staffing expenses​. Moreover, a specialized firm is likely to get the project done right the first time, saving money that might be lost on trial-and-error by an inexperienced team. Overall, outsourcing AI development often delivers a better return on investment by achieving results faster and with fewer missteps.
  
• Operational Efficiency & Focus: Bringing in an AI development partner lets your organization maintain focus on its core business operations. The external team handles the heavy lifting of AI research, prototyping, and implementation, freeing up your internal teams from tasks outside their expertise​. This division of labor leads to greater operational efficiency – your employees continue to drive value in the areas they know best (like product development, sales, or customer service), while the AI experts concentrate on building the solution. Additionally, AI firms usually have established development processes and agile methodologies that keep the project on track. They can often execute AI projects more swiftly and systematically, which means less disruption to your daily operations and quicker realization of benefits.
  
• Faster Time-to-Market: Leveraging an experienced AI development company can significantly accelerate your project’s timeline. These firms come equipped with pre-built frameworks, tools, and experience from prior projects, which allows them to ramp up quickly. Instead of your team spending months just learning and experimenting, an external partner can hit the ground running. They know how to avoid common pitfalls and can iterate rapidly. The result is that AI-powered products or features reach the market sooner. In today’s fast-paced environment, this speed is critical – being first or early with an AI innovation can be a significant competitive advantage. By collaborating with AI specialists, businesses can shorten development cycles and respond to market opportunities faster than if they went solo​.
  
• Scalability and Flexibility: AI development companies offer scalability that is hard to match in-house. As your project grows or requirements change, an external partner can quickly allocate more experts or adjust the skill mix to meet new needs. Need an extra data scientist for a few weeks? Or do you want to pivot from one machine-learning approach to another? A seasoned AI firm has the breadth of talent to scale up, down, or reassign as necessary. This flexibility ensures that the AI solution can evolve with your business. You won’t be limited by the fixed skills of a small internal team. Instead, you gain a scalable resource that can handle everything from developing a small AI pilot to expanding it into a full production system. This is especially beneficial for long-term AI strategies – your partner can continue to support larger deployments, integrations, and iterative improvements over time.

In summary, partnering with an AI development company allows businesses to leverage top-tier AI expertise and infrastructure without the burdens of building it all in-house. It brings cost savings through efficient resource use, improves operational focus by offloading specialized tasks, hastens time-to-market for AI solutions, and provides the flexibility to scale innovation as needed. With these benefits, companies can implement advanced AI capabilities more confidently and effectively, positioning themselves for greater innovation and competitive growth.

Conclusion

AI has become an indispensable component of modern business operations, driving more intelligent decision-making, automation, and enhanced customer experiences. For organizations looking to stay competitive, leveraging AI is no longer a luxury – it’s a strategic necessity. However, realizing AI’s full potential requires the right expertise and execution. This is why partnering with a dedicated AI development company is such a strategic move. It allows businesses to infuse world-class AI skills and technologies into their products and processes quickly and with lower risk, than attempting to develop everything internally. A strong AI partner brings experience, efficiency, and innovation to the table, ensuring that your AI initiatives align with your business goals and deliver tangible results.

Companies such as 8allocate, recognized by Clutch among the top AI software development firms, offer proven expertise and a successful track record in delivering impactful AI solutions. As you consider your next steps in AI adoption, we encourage you to treat AI development as a collaborative journey. Choosing the right partner will not only help you build the solution, but also guide you in strategy, data management, and integration – accelerating your transformation into an AI-driven organization.

If your business is ready to harness the power of AI, consider reaching out to us for support. We offer deep AI expertise, a robust portfolio of successful projects, and a client-centric approach to development. Whether you are just starting with an AI idea or scaling up an existing solution, our team is here to help you succeed. Contact us to discuss your AI development needs and explore how we can turn your vision into reality.

Frequently Asked Questions

Quick Guide to Common Questions

Why should businesses partner with an AI development company rather than building an in-house team?

Partnering with an AI development company gives you immediate access to specialized expertise and established infrastructure, significantly reducing project timelines and startup costs. Unlike building an in-house team—which requires extensive hiring, training, and infrastructure investment—external firms bring proven experience, best practices, and faster integration. They also offer scalability, allowing businesses to quickly adjust resources according to project needs.

What key criteria should I evaluate when choosing an AI partner?

The most important criteria include AI expertise and experience, domain-specific knowledge, proven track record and reputation, robust data security practices, and cost vs. delivered value. Prioritize firms with demonstrated success in delivering projects similar to yours and strong client feedback, and verify their security protocols for handling sensitive data. Lastly, ensure their pricing structure aligns with your budget and the expected business outcomes.

How critical is industry-specific experience when selecting an AI development partner?

Industry-specific experience is highly valuable. A partner familiar with your sector will understand your business challenges, regulatory requirements, and operational nuances, enabling them to deliver more tailored solutions faster. While general AI expertise is essential, industry knowledge significantly improves solution relevance, efficiency, and effectiveness, particularly in highly regulated fields like healthcare, finance, or fintech.

How can I verify an AI company’s credibility and expertise?

Check the company’s portfolio and detailed case studies, looking specifically at measurable outcomes from past projects. Review independent ratings and client testimonials on platforms such as Clutch or GoodFirms. Directly contacting their references for feedback on reliability, communication, and problem-solving is also advisable. Additionally, consider any relevant certifications or awards that validate their industry standing.

Is outsourcing AI development typically cost-effective compared to in-house development?

Yes, outsourcing AI development is usually more cost-effective than building an internal team, primarily due to lower upfront and ongoing costs. In-house AI teams require continuous investment in talent recruitment, training, salaries, and infrastructure. Outsourcing allows you to control costs by paying only for needed services, often achieving faster time-to-market and a higher return on investment through quicker, more accurate project execution.

How does an AI development firm integrate its solution with my existing systems?

Experienced AI firms begin integration planning early, carefully assessing your existing technology stack—such as databases, applications, and APIs—to ensure compatibility. They typically build custom interfaces, middleware, or APIs, enabling seamless integration. Extensive testing is conducted in staging environments to ensure a smooth transition into production. Collaboration with your internal tech team ensures minimal disruption and efficient, secure integration.

What happens if my project requirements change or expand during development?

Good AI development partners expect and accommodate changes through agile methodologies. They handle evolving requirements by adjusting project scope, adding features to future sprints, or scaling their team up or down as needed. Transparent communication, clear change-management policies, and regular check-ins ensure that any adjustments are smoothly incorporated without negatively impacting project outcomes.

What kind of ongoing support should I expect after the AI solution is delivered?

Reliable AI companies typically offer comprehensive post-deployment support, including issue monitoring, bug fixes, system updates, and ongoing model optimization or retraining. Many also provide training and clear documentation to empower your internal team. Confirm the terms of support in advance, such as response times, availability (24/7 vs. business hours), and long-term maintenance arrangements, to ensure consistent system performance and reliability.

How long does it generally take to implement an AI solution with an external partner?

Timelines vary based on project complexity, data readiness, and specific goals. Smaller or well-defined AI projects, like proof-of-concepts or pilots, might be completed within weeks to a few months. Larger-scale implementations involving extensive data preparation, custom model training, integration, and testing typically require several months up to a year. A reputable provider will clearly outline a realistic timeline with milestones before starting, helping you manage expectations and planning effectively.

Do I need a fully developed AI strategy or prepared data before approaching an AI development firm?

Not necessarily. While having clearly defined goals and some prepared data can accelerate the initial phase, one advantage of partnering with an AI firm is their guidance in refining your strategy, assessing your data readiness, and developing solutions collaboratively. Good providers will assist you in clarifying objectives, collecting and preparing necessary data, and designing an effective AI roadmap, even if you begin with just a general concept or business challenge.

What is the typical cost of hiring an AI development company?

The cost of hiring an AI development company varies based on project complexity, the technology used, and the vendor’s expertise. For example, mid-sized AI projects often range from $15,000 to $50,000+, while complex custom solutions involving extensive model training and integration could exceed $100,000. Always consider the provider’s experience, quality, and the overall value they offer beyond just initial price points.

Generative AI has taken the world by storm and is here to stay for the long term. 

Goldman Sachs Research expects AI investment to approach $100 billion in the US and $200 billion globally by 2025. Over the long term, AI-related investments could further increase to 1.5 to 2.5% of GDP in developed countries. 

Every business is now certain that Gen AI will transform their operations, but far fewer know exactly how that will happen. Market dynamics shift fast due to technological advances and emerging regulations. However, our team is fairly certain that several generative AI trends will remain prominent for the next two to three years. 

6 Generative AI Trends to Capitalize On 

Source: Sopra Steria Next

The generative AI market is at its liminal moment, with first movers like OpenAI, Microsoft, and Anthropic capitalizing on the growth momentum. But larger opportunities are yet to come.

Enterprises are expected to spend $40 billion on Gen AI in 2024 and over $150 billion by 2027, according to IDC. Top spenders are financial services companies (25%-30%), healthcare (15%-20%), consumer goods and retail (15%-20%), and media and entertainment (10%-15%).

In terms of use cases, businesses are the most likely to deploy Gen AI for marketing, sales, product development, and IT functions. McKinsey reports that adoption rates have more than doubled in a year for marketing and sales, and 50% of enterprises have adopted AI in two or more business functions, up from less than a third in 2023.

What we see today is the emergence of a new market, driven by both Gen AI infrastructure providers, application developers, and service companies — and we expect the next six trends to shape it over the next two to three years. 

The Rise of Fine-Tuned LLMs 

Open-source large language models (LLMs) like GPT, LLAMA, and BERT offer new opportunities for streamlining knowledge management tasks. But most users will tell you that most tools are good for high-level tasks and lack specialized knowledge.

Coding copilots can reduce time spent on routine tasks like code documentation or simple code generation by 35% to 50%. However, time savings are minimal for high-complexity tasks or those where developers lack knowledge of the technology. Likewise, benchmarks of open-source LLMs for code refactoring tasks showed that 30% of the time AI models fail to improve the code. In some cases, the tools also changed the external behavior of the code in subtle but critical ways. 

Moreover, the usage of public Gen AI tools has raised valid security concerns. Sensitive data can be leaked to open-source models and stored indefinitely or used for other model training, violating privacy laws. Commercial Gen AI tools have guardrails for data training but are prone to direct adversarial attacks. Hackers exploit techniques like prompt injections, model extraction, and dataset poisoning to tamper with the solutions.

To address these shortcomings, enterprises are choosing to fine-tune open-source models and run them on private cloud instances.

Source: Andersson Horowitz 

Fine-tuning adapts a pre-trained model to a specific task or domain through partial training. Model weights are updated based on new datasets, leveraging pre-training knowledge to improve performance.

For instance, the Low-Rank Adaptation (LoRA) technique inserts trainable low-rank matrices into specific model layers to tune performance on certain datasets without significant computational resources. This selective improvement enables quick model adaptation and performance gains.

According to the PredictBase Fine-Tuning Index, fine-tuned models outperform open-source LLMs in tasks like legal contract review, medical text classification, or code generation using the corporate’s internal codebase. 

Source: PredictBase

Still, the fine-tuning process isn’t without its challenges. Adopters must also create a process for model development, testing, and deployment (MLOps) and optimize their IT infrastructure for a fine-tuned LLM.

The new model has to be integrated with other business systems to ensure access to domain-specific data. This isn’t a straightforward process, with roughly 90% of the fine-tuning efforts being related to data management. 

Gen AI Applications Will Focus on Differentiation 

2024, so far, has been generous for new LLM releases. 

GPT-4 Turbo has an updated knowledge cutoff of April 2023 and enhanced data processing. The Claude 3 model family (Haiku, Sonnet, and Opus) outperformed GPT in cognitive tasks like math problem-solving and knowledge Q&A. Google released Gemini 1.5 with experimental long-context understanding features. Stability AI launched Stable Diffusion XL Turbo with real-time image creation and editing capabilities.

API-based access to more capable, scalable, and cost-efficient LLMs allows for more fine-tuning and new product development. Yet Gen AI’s open-source nature also intensifies market competition. 

Many vendors in the Gen AI space simply offer a user interface to an open-source model or a lightly fine-tuned version with marginal performance improvements. New Gen AI products must have a tech edge over others and a unique value proposition to stand out. 

We believe that the apps that innovate beyond the “LLM + UI ” formula and significantly rethink the underlying workflows of enterprises or help enterprises better use their own proprietary data stand to perform especially well in this market.

Andersson Horowitz

Enterra Solutions differentiates by combining a proprietary Autonomous Decision Science^TM (ADS^®) algorithm with generative AI capabilities to support enterprise data processing operations. Enterra’s platform consists of interconnected business applications with a shared analysis, optimization, and human-like decision-making layer. By consolidating and operationalizing data from a multitude of connected applications, enterprises gain a 360-degree view of their operations.

Amira Learning, in turn, developed a custom algorithm for its intelligent reading tutor using results from over 100 studies and field trials. By combining human expertise with technological advances, Amira built an app as effective as a human tutor and more effective with English Language Learners.

Additionally, Gen AI product companies can differentiate by offering easier deployment, flexible hosting models (cloud vs on-premises), and tighter integration with the existing enterprise application stack. 

The Most Promising Gen AI Opportunities Will Be In Automating Semi-Structured Tasks 

Gen AI tools already proved their chops with simple, linear tasks like writing, image generation, or coding. But there’s also a huge vista of opportunities for automating processes that need human judgment, which were previously beyond deterministic AI reach.

Such judgment-based tasks include insurance claims processing, legal document reviews, medical billing, customer sentiment analysis, and loads of other operational processes. The latest advances in Gen AI make this type of cognitive automation possible and profitable.  

Source: Sopra Steria Next

For instance, RobinAI speeds up legal report generation, review, and analysis by 5X. Developed with Cambridge’s Investment Management team, the AI copilot for lawyers combines Anthropic’s Claude LLM with proprietary contract data and machine learning to automate tasks with high precision.

Maven built an artificial general intelligence (AGI) platform to automate customer center operations. Unlike regular customer support chatbots, Maven’s AI agents use a fine-tuned GPT-4 model connected to an enterprise-grade search engine. The system parses millions of knowledge articles to generate an answer, and a scoring system cross-checks the output against several documents to ensure accuracy before providing a user output in 50 languages. With this architecture, Maven AGI can resolve 93% of customer inquiries autonomously.  

Building cognitive solutions for vertical use cases, however, comes with unique hurdles. 

For one, companies will need access to high-quality, variable training datasets for fine-tuning, which may be hard to obtain in regulated industries.  In many cases, a robust human quality review system is necessary. For example, Robin AI has in-house legal experts to validate model outputs.

Secondly, there’s the issue of data security. Plug-and-play gen AI apps need access to corporate data sources for accurate response generation. Organizations with high data silos and outdated processing architectures will need to improve data management strategies and supporting infrastructure before integrating Gen AI products. This leads us to the next point… 

Gen AI Adoption Will Trigger IT Infrastructure Modernization 

Ambition is high among enterprises, but adoption is hard. An MIT survey found that 76% of enterprises experimented with generative AI in 2023, but only 9% adopted the technology widely.

Legacy systems and subpar IT processes stall Gen AI adoption. In the same study, fewer than a third of respondents rated each of the eight IT attributes as conducive to rapid AI adoption. In particular, businesses lack capabilities in the areas of data quality/accuracy assessments, model hallucination identification, cyber risks mitigation, data privacy protection, large data volume processing, data storage infrastructure, computing infrastructure, and real-time data capture and analysis. 

Source: MIT Technology Review

What’s even more curious is that companies who have already experimented with Gen AI deployments have less confidence in their IT infrastructure than their peers. In other words, Many leaders underestimate the degree of groundwork required to prepare for Gen AI adoption. 

At 8allocate, we always recommend starting with an assessment to understand the state of your IT infrastructure better. Our consulting team helps determine the critical areas of improvement, optimization, or incremental modernization at every layer — from hardware and network configurations to cloud services and data management. 

Effectively, we help you create a more conducive environment for developing and deploying Gen AI solutions by adding the missing guardrails and capabilities. These may include optimizing your data storage systems (data cloud lake and warehousing), introducing real-time streaming pipelines to accelerate fresh data delivery to the models, adopting metadata management tools, and developing better governance practices for data management and analytics. 

Gen AI applications are latency-sensitive, so finding the best architecture for storing, analyzing, and interconnecting large data sets and business logic is challenging but possible with an experienced technology services partner.

Gen AI Services Market is Emerging 

Gen AI solutions require not just new technology infrastructure but also people with unique skills. Finding AI talent, however, proves to be as feasible as debugging a software app with zero documentation. 

Almost 70% of businesses struggle to attract enough talent to manage AI-based systems. The shortages are unlikely to end soon. IDC expects over 90% of companies globally will continue to experience IT skills shortages by 2026, leading to $5.5 trillion in losses due to product delays, impaired competitiveness, and loss of new business.

To gain the missing competencies, leaders are increasingly looking outward — towards the emerging market of Gen AI tech services providers. 

Emerging market for services relating to gen AI/AI could be worth more than $200 billion by 2029.

McKinsey

Tech services providers, specializing in AI/ML implementation, can help businesses address their lack of experience in developing and deploying Gen AI solutions. Apart from addressing the gen AI talent gap, external providers can bring cross-functional expertise in building AI solutions for different domains (e.g., FinTech or ESG) and deliver process guidance, for example, in the areas of AI security or machine learning operations (MLOps). 

As studies have shown, few companies are at a high stage of AI maturity. Substantial gaps remain in data management readiness and process maturity for deploying POCs and assessing their impacts on business operations. Technology services providers can assist customers with the necessary foundational transformations and speed up the adoption of Gen AI without sacrifices in security or compliance — the two growing areas of importance. 

AI Governance Will Soon Become Mandatory 

Although 75% of CEOs say that trusted AI is impossible without effective AI governance, only 39% have sufficient Gen AI governance in place today, according to an IBM survey. 

That’s problematic as emerging AI regulations call for precise control over data quality, system transparency, robustness, and security. 

The EU Council greenlighted the AI Act in May 2024, which established a ‘risk-based’ approach to regulating private and public AI systems on the market. Over 400 AI-related bills are at different stages of the discussion across 44 states in the US, as well as the federal Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence. Canada is also working on a federal Artificial Intelligence and Data Act (AIDA), while the UK and Australia both introduced voluntary AI guidelines. 

Generally, policymakers adopt a risk-based approach to regulating AI, which level-sets the applicable regulations with the intended use of an AI system, along with its risk profile. This is the case of the EU AI Act and Canada’s AIDA. 

Yet, some countries are also taking a dual approach, issuing both cross-sectoral and sector-specific compliance requirements. The cross-sectoral provisions establish a baseline framework with which all AI systems can comply. However, extra guidelines may also apply to businesses using AI in specific sectors and/or for specific use cases. Singapore’s Model AI Governance Framework, for example, provides general guidelines for the ethical use of AI for all businesses. However, financial companies must also comply with an extra regulation on  Fairness, Ethics, Accountability, and Transparency (FEAT) in the Use of AI and Data Analytics from the country’s Monetary Authority. 

For businesses, the emerging regulatory landscape further aggravates the need for better auditability and explainability of AI systems, especially those intended for regulated industries like finance, education, or healthcare. Again, this requires prudence with AI system security and monitoring, as well as an effective data governance strategy for providing the necessary disclosures. 

Conclusions 

Generative AI became a major theme in boardroom discussions this year, but the real breakthroughs are still ahead. Aspirations for adoptions and Gen AI product development are high, but the maturity levels of people, processes, and infrastructure levels are still lagging. 

Companies that will focus on improving their foundational AI capabilities before launching all efforts into gen AI solutions deployment will emerge as winners in the long term. Likewise, new market entrants that will invest in fine-tuning their own models and achieve better integrations with enterprise workflows will outpace those offering “wrapper” solutions over open-source LLMs. 

As legal compliance becomes mandatory, AI governance, security, and explainability will also distinguish the leaders from the laggards. Capabilities assessments, combined with careful planning and targeted investments in the right area, will be key to long-term success. 

Create and refine your Gen AI adoption strategy with 8allocate’s help. As part of our AI implementation service, we help global businesses create a conducive environment for deploying custom AI solutions and ensure their topmost accuracy and security. Contact us to learn more about our engagement process. 

Frequently Asked Questions

Quick Guide to Common Questions

What are the key generative AI trends shaping 2024 and beyond?

The most impactful generative AI trends include the rise of fine-tuned LLMs, Gen AI applications shifting toward differentiation, automation of semi-structured tasks, IT infrastructure modernization, the emergence of Gen AI service providers, and growing AI governance requirements. These trends will define how enterprises adopt and scale AI technologies.

How will fine-tuned LLMs change the AI landscape?

Fine-tuned large language models (LLMs) provide higher accuracy, domain-specific knowledge, and better security compared to general-purpose models. Enterprises are increasingly customizing open-source models like GPT, LLAMA, and BERT for legal, financial, and healthcare applications to enhance performance while ensuring data privacy.

Why is AI infrastructure modernization critical for Gen AI adoption?

Many organizations lack real-time data processing, scalable cloud infrastructure, and robust cybersecurity—all essential for deploying Gen AI at scale. Without modern IT environments, AI models suffer from slow processing, security vulnerabilities, and data inconsistencies, delaying enterprise adoption.

How will AI governance impact businesses using Gen AI?

AI governance is becoming mandatory, with regulations like the EU AI Act and emerging US federal guidelines enforcing risk-based compliance. Businesses must ensure AI security, bias mitigation, model transparency, and auditability to remain compliant and trustworthy.

How does 8allocate help businesses implement generative AI solutions?

8allocate specializes in AI implementation, fine-tuned model development, and AI-driven business optimization. We help organizations modernize IT infrastructure, secure AI systems, and integrate Gen AI into business workflows—ensuring scalable, high-impact AI adoption.

Technology leaders face a critical choice: build a custom AI solution development from scratch or leverage an off-the-shelf AI offering. AI capabilities are increasingly embedded in software products (Gartner projects 80% of software vendors will embed AI by 2026), meaning off-the-shelf options abound. At the same time, competitive pressure is driving companies to develop unique AI models that set them apart. This article provides a strategic framework to help CTOs, product heads, and innovation leaders decide between custom AI and ready-made AI – balancing cost, time-to-market, data control, and business impact.

Understanding Off-the-Shelf AI Solutions

Off-the-shelf AI solutions are pre-built, general-purpose AI tools or services available for immediate use. Examples include cloud AI services (e.g. image recognition or NLP APIs from Google, AWS, Microsoft), pre-trained models like GPT for text generation, or AI features embedded in enterprise software (CRM, ERP add-ons, etc.). These solutions are developed by third parties and offered via APIs, SDKs, or platforms that companies can integrate with minimal effort.

The primary advantage of off-the-shelf AI is speed and convenience. Organizations can deploy AI capabilities quickly without having in-house data science teams. The upfront investment is typically lower – often a pay-as-you-go or subscription model – making it attractive for teams with limited budgets or those wanting to prototype and achieve quick wins. Off-the-shelf tools also come “pre-tested” and maintained by the vendor, so reliability and updates are handled externally.

However, these ready-made solutions are built for broad use-cases, not tailored to any single business. By nature they are generic – which means they might not capture the nuances of your specific industry, data, or processes. There can be limits to customization, and you often have less control over how the AI works (since the vendor’s IP and model architecture remain a black box). Data privacy and compliance can also be concerns: using a third-party AI service might involve sending your data to an external cloud, potentially unacceptable in highly regulated industries or when dealing with sensitive proprietary data. Finally, while initial costs are low, off-the-shelf AI can incur ongoing usage fees and may not scale cost-effectively if your usage grows significantly.

Understanding Custom AI Solutions

Custom AI development means building an AI system specifically tailored to your organization’s needs. This could range from training a custom machine learning model on your proprietary data, to developing a unique AI algorithm or even a full bespoke AI software application. Custom AI often involves assembling a team of AI engineers or partnering with an AI development firm to design, train, and deploy models that solve your unique business challenges.

The obvious benefit of custom AI is fit and ownership. The solution can be crafted to exactly align with your business logic, data specifics, and objectives – resulting in potentially higher accuracy and effectiveness for your use-case than a one-size-fits-all tool. You also retain full IP ownership of a custom-developed model and complete control over its behavior, updates, and integration. This level of control is crucial if the AI capability is core to your competitive advantage or must conform to strict internal policies. Many executives see custom AI as a way to differentiate. Additionally, data stays in-house – a custom model can be trained and run on your controlled infrastructure, enhancing security and compliance (especially important in sectors like finance, healthcare, or defense).

The trade-offs of custom AI are the higher upfront cost, longer timeline, and complexity. Developing a quality AI model is resource-intensive, often requiring large datasets, experimentation, and specialized expertise. There is a risk of project failure if not managed well (e.g. model accuracy might fall short, or it might not make it into production). Initial costs for custom AI development can be 30–50% higher than using an existing tool, but it can also yield efficiencies long-term. In other words, custom AI is a strategic investment: you pay more and wait longer up front, but you build an asset that can pay dividends in IP value and tailored performance over time.

Off-the-Shelf vs Custom: Pros and Cons

Let’s summarize the pros and cons of each approach. Use these as talking points when evaluating your decision:

Off-the-Shelf AI – Pros:

• Fast Time-to-Value: Ready-made AI tools can be deployed in days or weeks, allowing you to start getting results immediately rather than spending months on development. This quick time-to-market lets teams focus on using AI outputs rather than building algorithms from scratch.
  
• Lower Initial Cost & Effort: No need to hire a full AI team or invest heavily in infrastructure – you leverage the vendor’s investment. The upfront cost is limited to subscription or usage fees, making it easier to get budget approval for pilots. Small and mid-size businesses often find this the only feasible way to adopt AI initially.
  
• Proven Technology: Established AI services from big providers have been tested across many scenarios. They continuously improve their models and handle maintenance. You benefit from a “battle-tested” solution used by others, which often means fewer bugs and edge-case issues (a vendor product is pressure-tested across multiple customers).
  
• Regular Upgrades: Vendor-provided AI tools typically come with upgrades and new features. As AI research advances, providers often update their models (e.g. adding more languages, higher accuracy algorithms) and you get those improvements “for free” as part of the service. This allows you to stay up-to-date with minimal effort.
  

Off-the-Shelf AI – Cons:

• Limited Customization: An off-the-shelf solution is built for the average use-case, so it may not fit your specific requirements out-of-the-box. In practice, many teams find they must heavily tailor or workaround generic tools. That means using a “ready” AI often isn’t plug-and-play – your developers may end up writing lots of glue code or your users may have to adjust their processes to the tool, rather than vice versa.
  
• Data & Privacy Concerns: With third-party AI APIs, you typically have to send data to the vendor’s cloud or use their environment. This can be a non-starter if you deal with sensitive data (customer financials, personal health information, trade secrets, etc.) and must comply with regulations or strict internal security. Many regulated enterprises cannot use external AI services due to data residency, GDPR/CCPA, or IP risk – unless the vendor offers on-premise options (which often come at a premium cost).
  
• Integration Challenges: Off-the-shelf tools might not seamlessly integrate with your legacy systems and workflows. For example, a pre-built AI might output results in a format that doesn’t match your database, or it might not connect easily with your internal applications. These integration costs and complexities are sometimes underestimated when choosing a quick external solution.
  
• Ongoing Costs & Vendor Dependency: While easy to start, the lifetime cost of an off-the-shelf AI can grow steep. You may pay per transaction or API call, meaning as your product scales, the fees balloon. You are also dependent on the vendor’s roadmap and pricing – if they change their service or experience downtime, your product is affected. In a worst-case, if the vendor goes out of business or discontinues the product, you’re left stranded. Essentially, you are trading some long-term control for short-term convenience.
  

Custom AI – Pros:

• Tailored Performance: A custom AI solution can be designed to excel at your specific task, potentially outperforming generic tools. You can incorporate proprietary data that your competitors don’t have, and tune the model to the nuances of your domain. This often translates to better accuracy or more relevant insights for your users. For example, a custom AI model built for your finance platform could be tuned to detect fraud patterns unique to your business, which an off-the-shelf fraud API might miss.
  
• Competitive Advantage: Because you own the IP and unique capabilities of a custom AI, it can become a long-term competitive moat. Rather than using the same AI as everyone else, you’re developing unique features competitors can’t easily replicate. If AI is core to your value proposition, investing in a custom approach can set you apart in the market.
  
• Full Control & Compliance: With custom development, you maintain full control over the technology stack, data handling, and updates. This means you can ensure the system meets all security, privacy, and regulatory requirements from the ground up (as opposed to trying to fit a third-party tool into those requirements). Companies in sectors like banking, healthcare, or government often choose custom AI to guarantee compliance with laws and internal policies. Control also means you can modify or extend the AI as your needs evolve, without being constrained by a vendor’s feature set or update schedule.
  
• Better Long-Term ROI: Although initial costs are higher, custom AI can be more cost-effective over the long run for serious use-cases. Since you’re not paying per prediction or API call to someone else, the operational costs can be lower at scale. More importantly, the investment you put in becomes an asset on your balance sheet – you own the solution and can reuse or commercialize it. If you have a long-term horizon, building your own AI can pay off financially and strategically.
  

Custom AI – Cons:

• High Initial Cost & Effort: Building AI is not cheap. You’ll need skilled data scientists, engineers, possibly ML cloud infrastructure, and lots of data preparation. Many projects also require extensive experimentation and tuning to reach acceptable accuracy. This translates to significant upfront cost and use of internal resources. For some organizations, especially startups or those with tight budgets, this may simply be out of reach initially.
  
• Longer Time-to-Market: A custom AI project can take months or even years from idea to production deployment, depending on complexity. In fast-moving markets, that delay could mean missed opportunities or letting competitors launch AI features before you. Off-the-shelf tools, by contrast, might allow you to launch an AI-powered feature this quarter. Thus, there is an opportunity cost to the slower delivery of custom solutions – you must be confident the wait is worth it.
  
• Project Risk and Maintenance: Not every AI initiative succeeds. There’s a risk that after investing time and money, the custom model might underperform or the project might be deemed infeasible (for example, lack of enough quality data). Moreover, once deployed, a custom AI system requires ongoing maintenance: model updates, monitoring for data drift, bug fixes, scaling the infrastructure, etc. You assume all the risk and responsibility for the solution’s success. If key AI staff leave the company, continuity can also become an issue. Organizations must be ready to commit to the lifecycle of supporting a custom tool, whereas with a vendor solution much of that work is handled by the provider.
  

Key Factors to Consider in the Decision

How do you actually decide between off-the-shelf and custom AI for your product? The right choice depends on several key factors in your context. Below are the criteria enterprise leaders should evaluate:

• Use Case Uniqueness: Start by assessing how unique your problem is. If it’s a common challenge (e.g. general image recognition, standard chatbot queries), an existing AI tool likely covers it well. But if your use case involves specialized data or proprietary processes, a custom model may be necessary to achieve high performance. Ask if a generic model can meet your accuracy/quality bar – if not, that leans toward custom.
  
• Data Availability & Privacy: Consider the data you have and any sensitivity around it. Off-the-shelf AI requires you to fit your data into someone else’s model (or send your data to them). If you have ample proprietary data that gives you an edge, you might leverage it in a custom model to gain superior results. On the flip side, if you lack sufficient data, a pre-trained model might be a faster way to start. Also weigh regulatory/privacy constraints: industries with strict data control (finance, healthcare, public sector) often steer toward custom or on-prem solutions to keep data in-house.
  
• Budget and ROI: Determine your budget for AI and the expected ROI. Off-the-shelf has lower upfront cost, making it easier to dip your toes in AI without large capital expenditure. Custom requires a larger investment – so justify it with a clear business case (e.g. long-term cost savings, revenue growth, IP creation). If AI is mission-critical to your product’s value or revenue, that usually warrants investing in custom development. Conversely, for a minor feature or experiment, buying may yield adequate ROI. Perform a rough cost-benefit analysis over a 3-5 year horizon: sometimes the subscription fees of off-the-shelf might actually exceed the one-time build cost, tipping the scales to custom in the long run.
  
• Time-to-Market Needs: How urgent is your AI capability? If you face competitive or internal pressure to deploy something quickly (e.g. “we need an AI feature in next quarter’s release”), off-the-shelf is undeniably faster. Custom builds take time – there’s no cutting corners on model training and validation. If speed is a higher priority than perfection, using an existing service or model can get you there. However, if you have the luxury of time and what you’re building will be a core offering for years, investing the time to get it right with a custom approach can be worthwhile. Some organizations also take a phased approach: use an off-the-shelf solution to launch quickly, while in parallel using AI MVP development to validate and then developing a custom model to replace it once ready.
  
• In-House Expertise & Resources: Evaluate your team’s capabilities. Do you have (or can you hire) AI talent to build and maintain a custom solution? If not, are you willing to engage an AI consulting partner like 8allocate’s AI Consulting or development services to fill the gap? If you lack AI expertise and can’t easily obtain it, options like AI outsourcing strategies can support an off-the-shelf solution or a custom build via a trusted vendor as the pragmatic choice. Also consider your IT infrastructure – building and hosting AI may require scalable cloud setups, MLOps pipelines, and data engineering work. Make sure your organization is ready for that commitment.
  
• Scalability & Flexibility: Think about the future. Will the AI solution need to adapt and grow with your business? Off-the-shelf tools offer scalability in a technical sense (the vendor will handle scaling to more calls or users if you pay), but scalability of requirements is another matter. If you anticipate the need to evolve the AI’s functionality, custom might be better – you can modify the code or model as needed.
  
• Strategic Importance: Finally, align the decision with your strategic priorities and the criteria in AI solutions that drive business growth. Is AI a core driver of your product’s value proposition or competitive strategy? If yes, you should treat AI development as a core competency and invest accordingly (custom build, internal talent, etc.). If AI in this context is more of a supportive utility or a “nice-to-have” feature, it might not justify a heavy investment – using an off-the-shelf service could be sufficient. Also consider risk tolerance: some CTOs will prefer owning critical tech internally (to de-risk vendor issues), while others are comfortable outsourcing non-core tech. Your company’s philosophy on build vs buy will inform the AI decision as well.
  

When to Choose Off-the-Shelf AI

To distill the above factors, here are scenarios where an off-the-shelf AI solution is likely the better choice:

• Early-stage or Pilot Projects: If you’re just dipping your toes into AI or need to prove value quickly, using off-the-shelf tools is ideal. They let you validate use-cases with minimal investment. For example, a product team might plug an existing NLP API into a prototype to test if a feature is viable before committing resources to custom development.
  
• Common Problems with Available Solutions: When your problem space has been well addressed by existing AI services, it’s wise to at least start with them. Use off-the-shelf if, say, you need standard OCR, sentiment analysis, basic recommendations, or other ubiquitous functions – reinventing the wheel in these cases often adds little value. The solutions on the market have likely matured and offer good accuracy from day one.
  
• Tight Deadlines and Limited Budget: If leadership expects an AI-driven feature next month, or the budget cycle doesn’t allow a big upfront spend, off-the-shelf is the practical route. It’s better to deliver something functional quickly via a third-party service than to over-promise a custom build that takes 6+ months. Off-the-shelf can be a bridge to immediate ROI. (You can always plan for a custom roadmap later once you’ve secured initial success and perhaps more budget.)
  
• Lack of AI Resources: Companies without data science teams or AI engineers on staff will find it challenging to execute a custom project. Off-the-shelf comes with support and abstracts away the complexity. It’s also useful if your engineers are not familiar with AI/ML – they can call an API without needing to understand the underlying math. Until you build up internal AI competency (or partner with an expert vendor), it’s sensible to buy rather than build.
  
• Non-Core Functionality: If the AI capability you need is ancillary to your main business, off-the-shelf is usually sufficient. For example, if you want an AI transcription for meeting notes in your software, that’s not your core IP – using a third-party speech-to-text API is perfectly fine. Save your development dollars for the areas that truly differentiate your product.
  

In these cases, the benefits of speed, lower cost, and reduced risk with off-the-shelf AI outweigh the benefits of a bespoke solution. However, remember to plan for the limitations – ensure you have a mitigation plan if you hit the ceiling of the off-the-shelf tool’s capabilities (whether that means negotiating with the vendor for features, or earmarking a future custom build).

When to Choose Custom AI

On the other hand, consider a custom AI development path when these conditions apply:

• Strategic Differentiator: If the AI is central to delivering value in your product and a source of competitive differentiation, you should strongly consider custom development. For instance, a startup offering AI-driven medical diagnostics would likely invest in proprietary models tuned to their novel approach, rather than using the same public model every competitor has access to. Owning the IP and knowledge becomes a strategic advantage.
  
• Unique Data or Domain: When you possess unique datasets (e.g. years of niche industry data, or domain-specific user behavior data) that can feed an AI model, a custom solution can leverage this “data moat.” Public models might not be trained on such data and thus won’t perform as well. Your custom model trained on proprietary data can achieve results that off-the-shelf simply can’t match – whether that’s higher accuracy or entirely new insights. This often applies in B2B and industrial settings (for example, a logistics company building a custom AI on its supply chain data to optimize routes better than any generic tool).
  
• High Requirements that Generic AI Can’t Meet: Sometimes the required accuracy, latency, or integration level is beyond what off-the-shelf tools offer. Perhaps a generic model gets 85% accuracy but you need 95%, or the API response time is too slow for a real-time system, or it can’t be deployed on-premises where you need it. When off-the-shelf falls short in performance, custom is the route to push beyond those limitations. You can design the system specifically to hit your targets (albeit with sufficient investment).
  
• Regulatory and Compliance Reasons: Industries under heavy regulation (finance, healthcare, government) often find that building custom AI is the only way to meet all compliance criteria. If you need to ensure complete data sovereignty, auditability of algorithms, and adherence to specific standards, you’ll likely have to build it yourself (or have a solution built to your specs). Off-the-shelf vendors might not offer the certifications or deployment models you require (for example, a cloud AI service might not be deployable in a secure on-prem environment). Custom AI gives you the freedom to architect the system in whatever compliant way needed – e.g. training on de-identified data within your private cloud, adding audit logs, bias mitigation strategies specific to your policies, etc.
  
• Long-Term Cost Benefit: If you project high usage volumes at scale, do the math on build vs buy costs. In many cases, heavy ongoing use of an external API becomes more expensive than building an in-house solution. For example, if you expect millions of AI inferences per day, the third-party fees might far exceed the cost of running your own model on cloud instances. At that tipping point, investing in custom development (and perhaps using open-source models to start) can save money in the long run. Additionally, each dollar you invest in custom AI is building equity (IP, know-how) inside your company, rather than paying it out to a vendor.
  
• Integration and Control: When an AI solution must deeply integrate into your existing systems and workflows, custom development is often easier in the end. You can design the interfaces exactly as needed, ensure compatibility with legacy data formats, and generally avoid the kludges that sometimes come with forcing an external tool into your ecosystem. If you’ve experienced friction with off-the-shelf integration in the past, you know the hidden costs that can entail. With custom AI, you control how it plugs in from day one. Furthermore, you won’t be subject to a vendor’s roadmap – if you need a new feature or tweak, your team can build it without having to lobby a third-party provider.
  

In summary, choose custom AI when the value of a tailor-made solution significantly outweighs the convenience of a ready-made one. It’s a commitment to invest in a long-term asset. Custom AI development is most effective when guided by a strategic partner—designed and built to align precisely with your product vision, data, and compliance requirements.

Finding a Middle Ground: Customize Off-the-Shelf AI

It’s worth noting that the decision doesn’t have to be strictly binary (build vs buy). Hybrid approaches are emerging as popular ways to get the best of both worlds. For example, you might start with a powerful open-source model or off-the-shelf AI service, and then customize it to your needs through techniques like fine-tuning or transfer learning. This way, you’re not building from a blank slate (saving time and cost), but you also aren’t limited to vanilla outputs.

Many companies follow a “shaper” approach: take a pre-trained model (say, OpenAI’s GPT or an open-source transformer model) and fine-tune it on your proprietary data. This yields a custom-tailored model without requiring you to train one from scratch. It does require some ML expertise and investment, but far less than a full custom build, and you still retain control over the tuned model. Another approach is Retrieval-Augmented Generation (RAG), where a generative AI is combined with your private knowledge base – the base model remains off-the-shelf, but you augment it with custom data on the fly. This can often achieve custom-like results in tasks like Q&A or recommendation without extensive retraining.

When adopting a hybrid strategy, consider it “buy to accelerate build.” You leverage existing AI components (frameworks, models, cloud infrastructure) as the foundation, and concentrate your custom efforts on the layers that matter most to your business. The result can be a shorter time-to-market than pure custom, and better fit than pure off-the-shelf. The key is to have a clear view on which parts of the AI stack you want to differentiate versus where you’re happy to rely on standard solutions.

Conclusion: Align the AI Solution with Your Business Strategy

Deciding between custom and off-the-shelf AI is a strategic choice that should be guided by your business goals, constraints, and the value AI brings to your product. There is no one-size-fits-all answer – some organizations may even adopt both approaches for different projects. The golden rule is to ensure the AI approach aligns with your broader strategy. If speed and proven results are paramount, off-the-shelf will likely serve you best. If long-term differentiation and owning core technology are critical, investing in custom AI will be worth it.

Keep in mind that adopting AI (in any form) is not a set-and-forget endeavor. Success requires proper integration, change management, and continuous improvement. Many companies struggle not with the initial choice of tool, but with how to effectively implement and scale AI solutions for real business impact. Whether you buy or build, have a plan for user training, data governance, monitoring model performance, and iterating as you learn.

Finally, don’t hesitate to seek expert guidance. An experienced partner can assess your situation objectively and recommend the optimal path. At 8allocate, we offer vendor-agnostic AI Consulting to help you evaluate build vs buy decisions, as well as deep expertise in developing custom AI solutions when needed. The decision you make today will influence your product’s trajectory for years to come – by making it thoughtfully, you’ll ensure your AI investments truly advance your business objectives.

Frequently Asked Questions

Quick Guide to Common Questions

How do I know if my project needs a custom AI model or if an off-the-shelf AI will do?

Start by evaluating the requirements and performance needed. If your use-case is very unique or mission-critical – requiring high accuracy tailored to your proprietary data or processes – that leans toward a custom model. Also consider data privacy (sensitive data often rules out third-party tools) and integration needs. On the other hand, if there are reputable off-the-shelf solutions that cover your problem and you need a quick, cost-effective result, try those first. It can help to prototype with an off-the-shelf AI and measure the results. If it meets your goals (accuracy, speed, etc.), you may not need to build custom. If it falls short, you have evidence to justify a custom development effort. Engaging in an AI feasibility assessment (for example with an AI consultant) can systematically determine which path fits best.

Is off-the-shelf AI more cost-effective than custom AI?

In the short term, usually yes – off-the-shelf tools avoid the heavy up-front costs of development. You pay a subscription or usage fee which is spread over time, making it easier on cash flow. For initial deployment and low-to-moderate usage, off-the-shelf is generally cheaper. However, if your usage scales up significantly or you plan to use the AI for many years, the cumulative subscription costs can rival or exceed a one-time build cost. Custom AI has a high upfront cost but lower incremental costs thereafter (and no ongoing vendor fees). There are also intangible ROI factors: custom AI might generate more value by aligning exactly with business needs, whereas a less-optimized off-the-shelf solution might not generate as high a return. In summary, off-the-shelf is cost-effective to start, while custom can be cost-effective to stay (long-term), provided the AI is core to your business.

Can off-the-shelf AI solutions be customized to our needs?

To a limited extent, yes. Many off-the-shelf AI platforms allow some configuration – for example, tweaking parameters, choosing models, or inputting your own training data to fine-tune (if the vendor supports custom model training). Some “AutoML” services let you train a model on your dataset without coding. These can give you a middle ground: you’re using the vendor’s infrastructure but with your data to get more customized outputs. However, you are still bounded by the platform’s capabilities. If your needs are very specific (e.g. a new algorithm or a very distinct data domain), you might hit the limits of how much an off-the-shelf system can be adapted. In practice, many teams start with off-the-shelf and then push those tools as far as they can – if too many workarounds or customizations are needed, that’s a sign the off-the-shelf tool is being stretched beyond its intent. That’s when moving to a truly custom solution makes sense.

What about using open-source AI models?

Open-source AI models (like various models from Hugging Face, OpenAI’s open releases, etc.) are a great option to consider in this decision. They are essentially off-the-shelf in terms of being pre-built, but they give you more control than a closed third-party API because you can host and modify them. Using an open-source model can be a cost-effective way to get started – you avoid vendor lock-in and can fine-tune the model to your data. It’s a bit of a hybrid approach: you “buy” (actually freely obtain) a base model, then “build” on top of it. Many companies leverage open-source as a stepping stone to full custom models. Keep in mind you’ll need the in-house skills to deploy and possibly train these models, and some open-source models may not match the top performance of proprietary ones without significant tweaking. Additionally, ensure the model’s license and data sources are compliant with your use (some open models have restrictions or use data that might not be suitable for all applications). Overall, open-source AI can drastically lower development cost and time, but you take on the responsibility to integrate and maintain it – so weigh that like you would a custom project.

In an ideal world, hiring software developers in-house is a good way to gain the competencies you need to accelerate digital transformations and build competitive software products. However, in the real world, you must weigh the benefits of having the right people on payroll against the ongoing costs. 

Global tech talent shortages have caused salary acceleration and increased time-to-fill for in-demand roles to over six months. Prolonged hiring times, in turn, limit your ability to adapt to the market dynamics and ensure effective business scaling. Large hiring volumes can also create bottlenecks in other business areas — onboarding, project management, and infrastructure management — causing a ripple chain of disruption. Lastly, budget constraints also hinder your ability to attract the best talents with specialized skill sets. 

Learn how much it costs to hire software developers internally — and how you can optimize this cost (and maximize the value) with an alternative talent sourcing strategy. 

How Much Does it Cost to Hire a Software Developer In-House? 

The total costs to hire one software engineer can be as high as $248,000 for the first year, after factoring in recruitment costs, salary, bonuses, taxes, benefits, personal equipment, software licenses, onboarding, and training costs. Other costs, apart from recruitment expenses and onboarding, are recurring (data sourced from open sources in the US). 

Recruitment Costs 

Recruitment costs are on the rise due to global tech talent shortages. Despite layoffs remaining in the news headlines, most businesses still retain a substantial tech workforce. Among the 2024 Tech Talent Survey respondents, 34% plan to maintain the same headcount, and 37% plan to hire more tech specialists this year. 

High competition for tech talent extends hiring time. Organizations spend an average of 5.4 months on technical recruitment, according to the Linux Foundation. Over 64% of companies need more than 4 months to fill one open technical position. 

Understandably, time-to-fill for senior positions and in-demand technical skill sets is even longer. Hiring an Al/ML Engineer takes 6.1 months on average, a Data Analyst / Data Scientist — 5.9 months, and a Cloud Architect / Engineer — 5.8 months on average. 

Prolonged time to fill drives up talent acquisition costs as your company needs to spend more on job advertising, employer branding, and sign-on bonuses. In some cases, you may also need to pay extra in employee referral bonuses or relocation costs to secure the ideal match. 

However, extensive recruitment marketing still doesn’t guarantee the best technical hires as you also need to spend extra on pre-employment assessments. Interviewing for new technical roles is challenging as you may not have internal staff to properly assess the candidate’s credentials. 

In fact, recent research in the UK found that 74% of businesses don’t use any candidate testing, and 32% use non-technical professionals to interview tech talent. As a result, 24% of hired candidates don’t have the appropriate technical skills for their role. 

The Cost of a Bad Hire is Steep

The cost of a bad hire is steep: 15%-21% of the employee’s salary, depending on seniority. This can significantly impact a company’s budget and productivity, as a bad hire fails to deliver the expected performance and necessitates additional recruitment and training expenses. Using a technology service partner to source, interview, and onboard new tech talent can substantially reduce the risks of poor fits. These partners typically have specialized expertise and a more efficient recruitment process, leading to better hiring outcomes. Moreover, you can recuperate some HR costs. On average, recruiting process outsourcing (RPO) is 1.6x cheaper ($311,220 vs $525,480 annually) than having an internal recruitment team. This cost efficiency can make a significant difference, especially for companies looking to optimize their hiring processes and reduce financial risks associated with hiring errors.

Employee Salary and Benefits 

Pent-up talent supply steers salary growth. In 2024, 62% of hiring managers increased starting salaries for cybersecurity, cloud, AI and machine learning, and general software engineering roles to attract better talent. 

The average base tech salary across the US stands at $111,193 annually, per Dice. That excludes extra benefits, overtime pay, employer taxes, and any profit-sharing schemes. Although some roles also have even higher salary brackets: 

Source: BridgeView 

Emerging pay transparency laws across states also force employers to disclose salary ranges in all job postings. According to Indeed’s Hiring Lab, more than half of all jobs on the platform in August 2023 included salary range disclosures. This definitely favors the candidates who want to “shop around”, but puts employers with more limited budgets at a disadvantage. 

Still, despite salary growth, only 49% of tech professionals are satisfied with their current compensation, which forces HR teams to offer extra perks to attract and retain tech staff. And employees expect a good mix of primary (health insurance, paid time off, pension plans) and secondary benefits (remote or flexible schedules, training and education, work-from-home stipend). 

Source: Dice

The high bar, set by big tech firms and venture-funded scale-ups makes it challenging for other firms to keep up. In the UK, 44% of businesses have difficulties matching competitors’ packages (pay and benefits) and thus attracting the best talent. 

Expanding your tech talent search to new geographies may help. Three Central Eastern European countries — Poland, Romania, and Ukraine — have a combined tech talent pool of over 870,000 professionals. Still not as heavily heavy-handed by Big Tech players (although some do have regional offices), CEE countries have a burgeoning tech ecosystem and ample, multi-skilled tech talent. 

The salaries are also on the more moderate end of the spectrum. Gross annual salaries for senior engineering talent are in the $60-85K range, while mid-level specialists can be hired for $40-$55K annually. 

Equipment and Software 

To get an accurate cost of hiring a new software developer, you should also factor in all the extra spending on their tools of trade: hardware, software, and office space in case you’re hiring for on-site or hybrid positions. At the same time, over half of remote tech workers also expect a work-from-home stipend or extra allowances for covering coworking fees. 

Although hybrid or fully remote work helps companies save on facility costs, it also drives spending in other areas, namely business software. An average company now runs between 100 to 1,000 SaaS applications on their network, with spending on software set to increase by another 13.9% through 2024. 

Overall, office space, equipment, and software costs can add another $10K-$12K in annual costs per hire, on top of what you’ll be spending in terms of salary, benefits, and employer taxes. 

HR training and development

A new hire becomes fully productive in their role within 8 to 26 weeks. People in senior positions usually need more time to get settled, while junior staff may require extra training to get into full grove. Effectively, organizations are losing money during the onboarding period because the new employee costs more than they generate with their work. 

Onboarding also requires extra labor efforts from your HR team and the new employee’s peers and managers, who’d be guiding them in their work. This can lead to further reductions in productivity, especially if you bring several new team members at around the same time. 

New tech employees may also require extra training on corporate workflows and policies and perhaps even upskilling programs. To address talent shortages, 43% of IT leaders choose to hire inexperienced staff and invest in upskilling them. This, however, further increases the costs of new hires and extends their onboarding time, which creates another overlooked cost center — opportunity cost.  

Bonus: Opportunity Costs

Talent shortages and prolonged hiring time create operational inefficiencies: 38% of organizations say that recruitment issues have delayed new IT projects. Delays, in turn, can cause revenue losses, jeopardize your market share, or affect investor interest. 

According to the law of increasing opportunity cost, the opportunity cost will increase for other projects with every resource allocation decision toward your primary objective. When one software engineering project requires more effort and oversight, this will come at the expense of other initiatives. For example, when your core teams are already busy dealing with legacy systems and maintaining existing infrastructure, you cannot prioritize new product development initiatives to accelerate revenue growth. 

Short-staffed software engineering teams are also forced to plow through the growing mountain of work, which may lead to some “corner-cutting” and accelerate technical debt accumulation and product quality issues, further increasing opportunity costs. 

The Alternative to Hiring Software Developers In-House: Strategic Talent Partnerships 

Given the steep costs and long timelines of hiring full-time tech talent, many leaders are choosing an alternative approach: technology services partnerships. 

At 8allocate, we’ve created a client-centered partnership model, rooted in a strategic alignment between your business goals and our capabilities in delivering stellar technology solutions. Our goal is to support you at every stage of your journey — whether you’re just starting with an MVP or looking to scale existing solutions and build stronger software engineering processes. Our services solve challenges, help our clients unlock new revenue enablement models, and incorporate the latest tech innovations into their businesses. 

Our cross-sectoral expertise and strong ties with educational institutions across CEE allow us to match your operational needs with value-based offers tailored to your objectives and budgets. When you need specific expertise or want to expand into new markets, we support scaling your workforce. 

As a strategic ally, we’re also committed to driving improvements at process and technology levels through knowledge exchanges, process guidance, and collaborative innovation, which assumes co-investment in new promising ideas. This way, you gain access to missing organizational competencies and address skills gaps, without shouldering high overhead hiring costs.

Here’s a comparison of in-house vs partner-based hiring costs of a senior software engineer: 

Extra Benefits of Strategic Technology Partnerships 

Beyond ensuring access to mission-critical tech talent, strategic partners also add value in other business areas. 8allocate team can help you align your technology roadmap with your business goals and provide process guidance, designed to enhance your teams’ capabilities.

So that your organization gains the following benefits. 

Cross-Functional Skill Sets 

The technology landscape is changing at breakneck speed, and with it, the IT roles linked to them are evolving too. For businesses, this translates to the constant need to either upskill/re-skill existing talent or hire specialists with new/niche skill sets (cue the above-mentioned recruitment expenses). 

At 8allocate, we’re not facing the same constraints thanks to a large talent pool and the teams’ cross-functional experience. Our staff has shown their mettle on projects in a variety of industries — ranging from EdTech and FinTech to logistics and ESG — and different programming languages. Apart from retaining qualified staff with knowledge of different tech stacks and technologies, we also invest in their training (so that you don’t have to). 

At any project stage, we can supply the missing expertise and provide resources to scale your teams effectively to respond to new market opportunities and accelerate growth with advanced technologies. We believe in true partnership, where each side has a vested interest in solving problems, maximizing value, and establishing new avenues for innovation. 

Process Efficiency 

While having the right people in the right role is crucial, so are the right engineering processes. Poor backlog prioritization, manual deployments, and lack of streamlined cloud infrastructure management create operating issues down the road and diminish the value your tech teams can break. 

Since we’ve been in the IT business for over a decade (and have had unique opportunities to look at software engineering processes at Fortune 500 enterprises and nimble scale-ups), we understand the common barriers and drivers among different businesses. 

As part of every client engagement, we offer proactive process guidance, aimed at improving your software engineering culture and productivity. This may include help with continuous integration (CI) and continuous delivery (CD) pipeline setup or assistance with the implementation of automated quality assurance (QA) processes. On a higher level, we also offer assistance with building effective data management and governance processes, implementing cloud infrastructure monitoring, and robust cybersecurity programs. 

Our goal is to help you prevent project delays, infrastructure cost overruns, and rework volumes to hit the plateau of software development productivity. 

Ad-Hoc Consulting 

IT professionals are not the only ones affected by the lack of understanding of emerging technologies. Over 57% of CEOs admit that their leadership teams lack AI skills and knowledge. Another survey found that 67% of senior executives aren’t comfortable using advanced analytics tools and choose to go with their ‘gut feeling’ instead. 

Lack of confidence and awareness at the helm makes it harder to promote digital and data literacy at the grassroots. This factor also complicates ongoing digital transformations as leaders struggle to identify the best areas for investment and thus capture ROI. 

We help companies overcome these challenges by embedding technology consulting into our services. Our senior partners can assume an interim CIO/CTO role to help your business deal with the more strategic issues of aligning your technology roadmap with your business goals, emerging technology use cases evaluation and implementation, as well as digital transformation planning and execution. 

Our end-to-end technology services are engineered to optimize your current IT operations while strategically aligning your organization for future growth and innovation.

Conclusion 

Lack of experience with emerging technologies, unfamiliar market landscape, complex system integrations — a lot of factors can intensify the costs of new software development.  Many of these cannot be effectively addressed by merely hiring more people. They require more profound process changes and strategic technology guidance. 

Investing in the right areas and making changes in the right areas are the keys to achieving your long-term vision sustainably. 8allocate operates in your orbit and ensures that you receive the right support at every stage of your business growth. 

Contact us to learn more about our approach to building value-driven technology partnerships. 

Frequently Asked Questions

Quick Guide to Common Questions

How much does it really cost to hire an in-house software developer?

Hiring an in-house software developer involves multiple hidden costs beyond salary. These include recruitment fees, onboarding expenses, benefits, equipment, software licenses, and ongoing training. Additionally, a bad hire can cost up to 21% of the employee’s salary, making in-house hiring a high-risk investment.

What are the main challenges of recruiting software developers internally?

Companies struggle with long hiring cycles (5-6 months on average for senior roles), rising salaries, and talent shortages. Many businesses also lack internal expertise to properly assess candidates, leading to mis-hires and productivity loss. These delays can cause project setbacks and missed market opportunities.

How do opportunity costs impact business when hiring in-house?

Long hiring cycles and skill gaps delay IT projects, slow product launches, and increase technical debt, impacting revenue and competitiveness. Over 38% of organizations report that recruitment bottlenecks have delayed critical digital transformation projects.

What are the alternatives to in-house hiring for software development?

Many companies partner with AI solution development companies to access top-tier AI engineering talent, specialized expertise, and scalable development teams. This reduces hiring risks, accelerates project delivery, and ensures seamless access to AI, data, and cloud expertise without long recruitment cycles.

We proudly announce that 8allocate has been honored with the 2024 Spring Clutch Global Award. This prestigious recognition highlights our dedication to driving digital transformation and unlocking growth opportunities through strategic technology solutions. The Clutch Global Awards showcase companies that deliver outstanding client service and make a significant impact on a global scale.

This award underscores our recognition as a Clutch Global Leader for service excellence. We are committed to delivering next-level solutions, driving client success by aligning cutting-edge technology with strategic business goals, and empowering our clients to achieve sustainable growth. Our approach is deeply rooted in delivering tailored, high-performance solutions that address each client’s specific needs while upholding our core values of excellence, flexibility, and respect.

We have empowered numerous clients to thrive and achieve their business goals by consistently delivering measurable results and significant client impact. Our success spans various industries, including FinTech, HealthTech, and EdTech, enabling organizations to unlock new growth opportunities. This recognition from Clutch highlights our unwavering commitment to excellence and innovation.

Clutch, the leading global marketplace for B2B service providers, plays a crucial role in recognizing industry top performers. Through rigorous evaluations based on client feedback and industry expertise, Clutch helps business leaders make informed decisions when selecting trusted partners. Our inclusion in the 2024 Clutch Global Awards reaffirms our status as a trusted partner in technology innovation.

Contact us for more insights into how 8allocate can help your business achieve its digital transformation goals. Let’s build innovative solutions together, focused on growth and success.

Although IT budgets are growing, they’re not always advancing tech innovation. Almost a third of CIOs admit that budget increases are mainly due to infrastructure modernization and migration projects.

Just like physical equipment, digital technology is prone to aging. Legacy, on-premises software systems lack the performance capabilities of modern cloud architectures, resulting in frequent downtime, clunky user experience, low operational efficiencies, and high maintenance costs.

Legacy solutions also limit your ability to benefit from the latest technologies like big data analytics, artificial intelligence, and IoT due to poor compatibility. This, in turn, undermines your ability to stay competitive in the changing markets. 

At some point, leaders need to make a choice — modernize or replace legacy technology. In this post, we explain how to decide between different approaches to legacy software modernization and when to do so. 

7 Main Approaches to Legacy System Modernization

When it comes to legacy system modernization, business leaders often picture a sort of “surgical removal procedure” — you take out the old component and plop in a new one, and everything starts running twice as well.

But such scenarios are uncommon and oftentimes, carry operational risks. For example, banks hold on to legacy core banking systems, and even those overnight batch processing impede real-time transaction processing because a full replacement can create operational, compliance, and security risks. 

More commonly, businesses opt for one of the following approaches to legacy system modernization:

Encapsulation

Encapsulation “packages” the legacy applications’ data and functions as independent services available via an API. Effectively, you externalize some of the app’s features and can then integrate or embed these into other applications. For example, you can gain access to transactional data from a legacy CRM and integrate it into a modern digital experience management platform. 

Encapsulation often requires the least engineering effort. However, it may not solve underlying scalability problems or application performance limitations. For example, if you create APIs to get data from legacy flat file mainframe databases, you’ll still have limited concurrency due to the mainframe’s I/O bottlenecks, resulting in slow response times under heavy load. 

Rehosting 

Rehosting, also called “lift and shift,” means migrating an application to another type of infrastructure (new physical servers, virtual machines, or cloud) without changing its code or features. 

This strategy can result in some hardware consolidation and a shift from CAPEX to OPEX spending in the case of the cloud. However, rehosting mostly solves the resource problem (i.e., the app gets more computing power and extra capacities for data storage) but doesn’t always lead to better app performance. 

Because you’re migrating an app ‘as is’, you’re carrying over all the technical debt and underlying inefficiencies without fully benefiting from the cloud capabilities.  Likewise, rehosting rarely solves integration problems with more modern tech stacks, as you’d still need to create some form of middleware solutions. 

Replatforming 

Replatforming assumes bringing the app to a new runtime platform with minimal changes to the code to ensure its compatibility with the new environment, most commonly the cloud. Like rehosting, replatforming brings marginal scalability, reliability, and performance improvements. 

Since the core app architecture and core remain mostly unchanged, many of the legacy limitations will still be present. The process can also be time-consuming and error-prone if you need to migrate a lot of data, for example, as part of migrating from one e-commerce platform to another. But when done right, replatforming can help address some of the issues by upgrading the applications’ operating system, database, or some of its components. Moreover, you get access to new platform features and capabilities.

Refactoring 

Refactoring optimizes and improves application code before migrating it to a new environment (e.g., a cloud platform). It’s the most common strategy for eliminating technical debt and improving factors like availability, scalability, security, and maintainability.

Code refactoring allows incremental quality improvements and incorporation of new features. For example, developers may separate payment logic from the core transactional platform into a separate service built with cloud-native technologies to increase transaction processing volumes and speed.

Refactoring generally improves application performance, reduces technical debt, and enhances codebase maintainability. However, it can be tedious for large, monolith applications with limited documentation. Since refactoring mainly addresses non-functional requirements (not the app’s external behavior or user interfaces), the updates may not be apparent to end users, making it hard to justify the investment.

Rearchitecting 

Rearchitecting an application involves substantial changes to its code and structural design — how components interact and what principles guide their design — with the goal of obtaining better capabilities. For example, transforming an on-premises monolith application into a cloud-based microservices based one. 

With re-architecting, you gain the most benefits in terms of scalability and maintainability, as you’re essentially creating improved guardrails for its business logic and strategically tackling problematic areas. In the case of microservices, modularity also enhances fault isolation and allows the most tech stack diversification (i.e., each microservice can be written in a different programming language or with a different front-end framework if that makes sense). 

On the downside, application re-architecture requires expertise with both the legacy and the modern tech stack. The process also requires meticulous planning to minimize operational disruptions. Furthermore, managing the coexistence of old and new architectures during the transition period can be resource-intensive. 

That’s why many business leaders involve external technology partners (like us!) to help plan and deliver the project in iterations. As part of our legacy software modernization service, we bring the necessary expertise to create new reference architecture patterns, perform code updates and data migration, and provide comprehensive quality assurance to ensure that the modernized system meets performance, security, and reliability requirements. 

Rebuilding

A rebuild involves dismantling and rewriting the legacy application from scratch while preserving its scope and specifications. Effectively, you salvage the application features and business logic but place these on an entirely new technology stack. 

Rebuilding an application can make sense when the initial app design contains some fundamental flaws, or the technology stack is severely outdated. But you still want to have a proprietary version of this application instead of purchasing an off-the-shelf solution as a replacement. 

Replacing

Full application replacement with a completely new solution, built according to the new software specifications, is the most drastic measure. Naturally, this approach is the most cost- and resource-intensive, but it may be the best course of action when legacy technology no longer has strategic value for your business and erodes a good fraction of your technology budget. 

Each of the above application modernization strategies differs in terms of “invasiveness” — the degree to which you’d meddle with the application code and key structural components. Likewise, there are tradeoffs in each case. 8allocate team can help you decide on the optimal strategy and execute the modernization project. 

When to Incrementally Modernize Legacy Applications 

Legacy software is an ever-growing cost center and an operational nuisance. 

Outdated technologies hinder employee productivity, user experience, and have a hefty total cost of ownership (TCO) due to the need for specialized talent and constant “workarounds” to extend their lifespan. Especially if the official vendor support has long ended. Subsequently, half of business leaders name “challenges with technology infrastructure” as the chief internal barrier to cost control. 

Here are cases when incremental software modernization makes the most sense: 

You’ve Maxed Out System Scalability 

By design, legacy monolithic architectures have limited throughput as all the application components are bundled together. The entire application must be scaled to handle the increased load. For example, if only one database in your ERP needs more storage, you can’t just scale his database — you need to scale the entire application, requiring greater labor efforts and overpayment for computing resources. 

Likewise, because all monolith application components share the same resources (CPU, memory, and I/O), you’ll hit the ceiling at some point regarding scalability. When that happens, the app components start competing for the same resources, resulting in resource contention and complexities in load balancing. 

Oftentimes, such scalability problems emerge when an MVP product, designed as a monolith, starts gaining traction and the system can no longer handle increased usage.  

If that’s your case, you’ll need to incrementally upgrade to service oriented architecture (SOA) or straight to microservices. 

The optimal approach to decomposing and scaling such a monolith application will depend on its design pattern (and your business and tech requirements). In some cases, it may even pay off to keep a modular monolith — a microservice-like architecture where the application is split into loosely coupled modules with a distinct domain or bounded context but some shared resources (e.g., a shared data tier). 

Source: Medium

Amazon, for example, recently reverted to a modular monolith architecture after microservices for its video monitoring services as it delivered superior performance and 90% cost reduction. 

Your Codebase Has Grown Too Complex 

Legacy codebases, spanning millions of lines of code, make it more difficult for developers to understand and troubleshoot code. Bugs and system vulnerabilities go unnoticed, causing costly issues in production or, worse, data breaches. 

Bloated codebases also prompt developers to come up with their own solutions to various coding problems rather than following the existing standards. As a result, you get a lot of “spaghetti code”, which no one understands what it does and is afraid to tinker with for the sake of crashing the entire system. Code complexity results in further tech debt and all its consequences of poor system performance, security, and scalability. 

Organizationally, large codebases also undermine software development productivity. Developers are more likely to experience merge conflicts, get frustrated with long code compilation time, and spend a lot of time retesting the same code for integration issues. As a result, you have slower release cycles, limiting your ability to release patches fast and deploy new product features. 

You can improve the code quality by refactoring and re-architecting. Sha Ma, VP of software engineering at GitHub, shared that when modernizing legacy applications, she always insists on conceptually separating the code and data based on feature functionalities before creating new microservices. 

This allows the team to design smaller services with easier-to-read code, quicker ramp-up time, and easier troubleshooting. Effectively, the developers no longer need to know and understand the entire legacy product codebase to be productive. Instead, they can focus on improving and extending the features of one microservices, which they know in and out. 

You Want to Increase New Feature Deployment Speeds 

Time-to-market for new features is pressing, and legacy systems aren’t conducive to continuous feature deployment. Because monolith architectures require full re-deployment to incorporate new releases, new code deployments sit in the pipeline for days, and major system updates require planned downtime. 

Likewise, the need for constant maintenance undercuts the time for innovation.  A CISCO survey found that half of the time, developers are being dragged into ‘war rooms’ to solve application performance issues rather than invest their time in developing new software applications. 

Limited deployment capabilities were a major bottleneck for Airbnb circa 2015. At that time, the home-rental platform was built as a monolith Ruby on Rails application. A team of 200 engineers was deploying 200 commits to production daily to respond to the market demand. But they also experienced around 15 hours on average of time when the Monorail was blocked, recounts Jessica Tai, a Senior Software Engineer. 

To address the problem, Airbnb went on a mission to migrate to service-oriented and microservices-based architecture over the next four years. By incrementally re-architecturing and upgrading its platform, Airbnb managed to increase its throughput speed to over 10,000 deployments a week while also increasing its workforce to over 1K people, getting new code to production faster and more reliably.

Your Want to Experiment With Innovative Technologies 

Legacy monolith applications aren’t compatible with many more modern technologies like real-time big data analytics, machine learning, computer vision, and generative AI, among many others. By design, a legacy app may not support machine learning workloads’ computational and storage requirements. Likewise, batch data processing limits data throughput speeds, required by modern data processing and analytics tools. 

Encapsulation is one option for solving this data management issue. With the help of APIs, you can stream data from legacy apps to a cloud-based storage system (e.g., a data lake or a data warehouse), from where it can be queried by cloud-native analytics tools and ad-hoc ML models. However, refactoring and rearchitecting may be necessary if you want to improve the applications’ performance and/or extend its functionality. 

You can also achieve greater fault isolation through microservices, ensuring that bugs in individual app components don’t affect the entire system, resulting in costly downtime. 

Lastly, you can achieve greater system performance by combining different tech stacks in a microservices architecture, picking the best technologies for each purpose. For example, you may develop a user authentication service with Node.js to benefit from its lightweight and asynchronous capabilities, while the content recommendation service can be built with Scala and Apache Spark to handle large-scale data processing and machine learning algorithms. This approach also lets you address the UX issues of a legacy app, as you can experiment with different front-end frameworks and designs. 

Your Customer Experience is Lagging 

The complexity of legacy systems is the most significant barrier to meeting customer needs and expectations, according to the ICMI study. From missing self-service capabilities to planned application downtime, obsolete technology undercuts employee and customer experience. 

Dated applications don’t have the best interfaces,  alienating users with complexity and low utility. Many also lack modern features consumers have come to expect from businesses, like instant payment processing or a high degree of service personalization, resulting in further frustration. 

Businesses that don’t make amends risk losing their market share. By 2028, financial institutions can miss out on 42% of extra payments-related revenue annually unless they upgrade to modern paytech platforms. The stakes are similar in other industries. 

Incremental modernization helps you progressively improve your product’s technical performance and address CX frustrations with new features. For example, by re-architecting its systems, a payment services provider can enable real-time transaction updates and integrate AI capabilities like real-time fraud detection or automatic risk management. 

When To Fully Replace Legacy Software  

Incremental system modernization is a more common route for salvaging legacy technologies. In several cases, rebuilding or replacing a legacy application makes more sense. 

The Software Nearing End of Support 

Large software vendors like Microsoft, SAP, and Oracle, among many others, also feel the financial squeeze of maintaining legacy software products as these divert resources away from innovation. 

For example, Microsoft has already relinquished support for several Dynamics CRM editions (2011 to C5 2014) and will end support for Dynamics CRM 2014 to 2016 in the next couple of years, encouraging users to adopt cloud-based Dynamics 365. 

End of support means the vendor will no longer provide updates, security patches, and tech support for the product, leaving the software vulnerable to:

• Security risks 
• Interoperability issues 
• Bugs and glitches 

This increases maintenance workloads for the engineers, forcing them to come up with workarounds to keep the system safe and running. At the same time, systems past the expiry date create operational issues as they become unstable, prone to downtime, and data loss. 

You’ve Discovered a Critical Vulnerability

Legacy systems may contain critical bugs hidden deep within the bloated codebase. Likewise, when the system contains no longer supported technologies (e.g., components in Python 2) it becomes prone to new security threats as there’s no official patch available to address the gap. 

Vulnerabilities create a backdoor for hackers. A Gartner survey found that end-of-life systems with critical vulnerabilities were the root cause of data breaches in healthcare. Although replacing a tech system is expensive and somewhat challenging, it’s better than dealing with the aftermath of a security breach. 

The System No Longer Aligns With Your Tech Roadmap

Considering just how much the business models have changed, companies may find themselves with redundant or outdated systems in their portfolio, like a legacy CMS (e.g., Microsoft FrontPage) or an outdated accounting (e.g., Peachtree Accounting). 

Likewise, some early product features, deployed at an MVP stage, may have shown little traction and generated little value for end-users. For example, Microsoft scaled back Cortana’s consumer-facing features and instead focused on embedding its capabilities into Microsoft 365 apps.

When the tech systems no longer align with your business objectives, retiring them and redirecting the resources toward more strategic objectives makes sense. 

Final Thoughts 

Leaders are often reluctant to let go of legacy software because of the perceived status quo and loss aversion. But the sunken costs only get higher when you promulgate modernizing legacy products. Technical debt is like credit card debt: The longer you keep it, the more overwhelming and expensive it becomes to take corrective actions. 

Legacy software modernization indeed has its challenges. But it also sets the path for business model innovation and new revenue enablement through modern technologies. More secure, reliable, and feature-rich systems drive in new customer audiences and improve your position in the market. 

If you’re not sure how to proceed, talk to our team. We can offer a candid, expert take on how to maximize the value of your tech portfolio without creating operational disruptions and critical downtime. 

Frequently Asked Questions

Quick Guide to Common Questions

How to decide between modernizing or replacing a legacy system?

If the core business depends on the system but limits scalability, performance, or integration with modern tools, incremental modernization is the best approach. If security risks, maintenance costs, or outdated technology stacks are blocking growth, a full replacement may be necessary.

What are the key risks of maintaining a legacy system too long?

• Security vulnerabilities due to lack of updates and compliance risks.
• Rising operational costs from maintaining outdated infrastructure.
• Scalability bottlenecks, limiting growth and feature development.
• Incompatibility with cloud, AI, and real-time data processing.

Which modernization approach works best for improving scalability?

Rearchitecting or refactoring is often the best option if scalability is the primary concern. Breaking a monolithic system into microservices or cloud-native architecture allows for incremental scaling while reducing system dependencies.

How can AI and analytics be integrated into a legacy system without complete replacement?

Encapsulation via APIs allows data extraction and integration with AI-driven analytics or machine learning models without overhauling the entire system. This is a common approach for companies wanting real-time insights without major disruptions.

What are the biggest challenges in migrating a legacy system to the cloud?

• Refactoring code to ensure compatibility with cloud-native environments.
• Minimizing downtime during migration.
• Optimizing costs, as simple rehosting doesn’t fully leverage cloud benefits.
• Ensuring regulatory compliance in cloud-based data handling.

How to reduce modernization risks while keeping operations running?

A phased approach—migrating components in stages, prioritizing critical pain points, and running parallel systems—minimizes disruption. Proof-of-concept deployments also help validate the impact before full rollout.

How does 8allocate help with legacy system modernization?

8allocate assesses current architecture, technical debt, and business priorities to recommend the best modernization path—incremental upgrades, re-architecture, or complete rebuild—while ensuring a seamless transition and minimal risk.

We will have to build our own digital capability on top of the technology that we have adopted. Because every company is a software company

CEO of Microsoft, Satya Nadella

Modern businesses use front-end software to improve the purchase experience, enable omnichannel delivery, and introduce new product or service capabilities. In the middle- and back offices, cloud computing, data analytics, and artificial intelligence (AI) boost employee productivity.

Players from non-digital industries borrow ‘playbooks’ from software companies to unlock new revenue channels and achieve greater scalability. General Motors generated about $2 billion from in-car subscription services in 2021 and aims to reach $25 billion by 2030. Rolls-Royce doubled its operating profits in 2023, with a substantial fraction of revenue now coming from its growing portfolio of digital products, including asset management software, engine health monitoring solutions, and digital twins.

According to McKinsey research, 70% of top-performing businesses use proprietary software to differentiate themselves from their competitors. A third also monetizes the software directly. 

As technology becomes more crucial for business growth, CIOs’ roles are evolving. Tech leaders are expected to help develop business strategy, establish competitive differentiation with technologies, and participate in re-engineering business processes. In the 2023 State of the CIO survey, 71% anticipate a greater immersion in business strategy over the next three years.

Source: 2023 State of the CIO survey

However, the Ops workloads didn’t disappear either. Most are also expected to handle system modernization projects, security management, and other IT infrastructure management tasks. 

IT outsourcing, in its classic sense, only helps address talent capacity and cost issues. A staff augmentation vendor may supply hard-to-find skill sets or deliver a scoped volume of services bound by tight SLAs. Yet, such a vendor management approach fails to account for the growing need for wider transformations: faster adoption of data analytics across functions, new security practices to protect AI systems, or improved ESG performance. 

Such transformations are complex to handle in-house. Leaders lack experience with emerging technologies but want proven best practices and certainty in implementation. A recent survey found that 57% of executives “lack confidence in their leadership team’s AI skills and knowledge,” for example. 

What tech leaders need today is strategic support in transforming IT from a back-office function to an engine of innovation and business growth.  

Transactional IT vendor management doesn’t help address the wider issues of business transformations. It merely focuses on farming out certain workloads. However, by changing approaches to IT services acquisition, business leaders can acquire the missing skills and the partners’ experience in engineering processes, digital product development, and modern infrastructure management practices.

Our Vision of Value-Driven Technology Services Partnerships 

Over the years, 8allocate has worked with dozens of clients, ranging from Fortune 500 enterprises to scale-ups and rapidly growing startups. 

The most successful partnerships have been ongoing for over six years, with our team earning the spot of a strategic, long-term technology partner (rather than a service supplier) because we:

• Develop client knowledge. We’re not just showing up when it’s time to foot yet another bill, but whenever we spot an opportunity for internal process improvements, a nascent market risk in your industry, or a new tech-led opportunity for revenue diversification. 

• Collaborate with agility. Markets move at breakneck speeds. We aim to anticipate the new ebbs and flows to help you move with agility. As one of our clients said: “[8allocate] skates to where the puck is going to be”. We take pride in maintaining high responsiveness levels thanks to Agile project management practices and effective change management approaches. 

• Communicate transparently. We like to maintain an open line of project-specific discussions and approach tough conversations honestly. Strong communication is the bedrock for effective roadmap alignment and cultivation of synergetic relationships. 

These principles form the pillar of our service models, which help our customers jump-start new digital capabilities, improve software development velocity, and gain more strategic insights to drive business innovation.  

Long-Term Vision Realization

We’re operating in a new business climate where companies compete as ecosystems. The average company now has 27 ecosystem partners, according to BCG, ranging from OEM manufacturers and distribution partners to software providers and technology research facilities. Amazon, for example, has 67 core partners in logistics, finance, media, and telecommunications. The most successful partnership ecosystems also span multiple countries. 

Source: BCG 

Recognizing this new business reality, 8allocate aims to build affinity ecosystems with our clients, ensuring strong alignment in expectations, goals, and service delivery. By operating closely in your orbit, we can effectively supply the right knowledge, skills, and expertise as your company enters a new period of growth or faces market turbulence. 

We help strategize and implement multi-year technology roadmaps, ensuring that your organization progresses, invests in the right priorities, and progressively improves its maturity levels in different business areas: new product development, data analytics, cloud-native operations, cybersecurity, and IT infrastructure maintenance. 

With our help, you can direct resources toward digital initiatives that will bring the highest ROI and help you secure a strong market position for the long term. The added benefit of such strategic partnerships is flexibility. At different stages, 8allocate can supply different competencies, acting as an advisory and a polyvalent implementation partner. 

We combine strong competencies in software architecture and UX/UI, critical at the prototyping stage, and strong software engineering, data, and cloud acumen, critical for building and scaling new digital products or modernizing legacy systems. Likewise, our partners gain access to AI, ML, and data science expertise whenever required, as well as a variety of other technical competencies.

C-Level Strategic Advisory 

When it comes to digital transformation, 61% of leaders wish their technology function could better explain the potential and value of new technologies. Poor collaboration practices, skill gaps, and a risk-averse culture are among the other common barriers.   

As part of our strategic consulting service, we offer C-suite level advisory to help sharpen your vision and determine key priorities for tech-led transformations. While the technology budgets keep increasing, leaders aren’t always focusing on the right priorities. Two in three businesses experience software purchase regret and two in five states that poor software choices made them less competitive, according to Gartner. 

We offer on-demand access to senior tech experts to consult on the impacts, risks, and ROI of different technology acquisitions. So that you can better understand how emerging technologies can influence key business metrics and become a driving force behind new revenue streams. 

As a global technology partner, we have also cultivated competencies across markets and industries. Having partnered with startups, scale-ups, and enterprise-level companies, we also understand the challenges each business faces at different growth stages. Our advisory services are tailored to support your operations today while also building reliable long-term plans to avoid being blinded by unforeseen risks.

Co-Investment in Innovation

Software products that took years to develop can now be produced in several months. Emerging technologies are also forcing businesses to change gears and often venture into the unknown. Because of AI’s disruptive impact, over 60% of organizations must rapidly evolve their data and analytics operating model. 

At 8allocate, we help clients explore innovative ideas at faster speeds and with greater confidence by sharing the risks of minimal product development (MVP). Our team can rapidly prototype new ideas to validate market assumptions and product use cases, sharing the costs and risks with your organization. We helped one of our clients from the construction industry prototype a digital twin platform in two months with a minimal budget using our Agile framework. This helped the company sign on early clients within just four months instead of a year and get extra cash flow to fund scaling.

Effectively, we offer access to our decades in digital product development. An ability to bounce ideas with senior tech and product leaders to sharpen your vision, proven methods for user research and hypothesis validation, and the ability to establish solid guardrails for future product scaling from the first day. 

Addressing Talent Shortages 

By 2027, Gartner expects that in most industries, 50% more will be spent on IT contractors compared to in-house staff. A number of businesses have been at a disadvantage competing with tech firms for IT talent. 

Despite the recent layoffs, 90% of leaders still rank recruiting and retaining talent as a moderate or major challenge. Retention is another problem, as 64% of tech workers plan to look for another job in 2024, knowing their skills are in high demand. 

At 8allocate, we help clients secure a link to critical talent and expertise by avoiding talent wars in the local markets. With us, you gain access to an 870K+ CEE talent pool and flexible staffing, which allows you to escape slow hiring cycles and prolonged time-to-market for new initiatives. 

Obtain missing competencies from our staff bench to launch new PoC initiatives without worrying about ongoing talent retention. We helped our clients successfully scale from a 5-person to a 45-person distributed team and acquire missing competencies in machine learning, data science, and cloud engineering, among other areas. 

Expertise Sharing With Your Teams 

Engineering excellence is the grand sum of ‘trial and error’. Few companies start with a high degree of DevOps maturity or strong data management best practices. Only 39% of large enterprises rank their IT staff as “very capable” of cloud implementation, management, and operations, mostly because the engineers were originally hired to support legacy applications. 

As a strategic partner in technology innovation, we get to hone our skills across a wide portfolio of projects and technologies. We’ve learned which approaches work best and what practices can perpetuate poor outcomes. Being embedded into your organization allows us to effectively transfer our knowledge to your teams, helping them acquire new skills and adopt the best practices in engineering and project management. 

Expertise sharing helps accelerate maturity in various IT functions—from data analytics and cybersecurity to cloud cost optimization—and achieve sustained improvements without going through common trials and errors. “Whenever 8allocate saw an inconsistency or an error in our specification, they suggested how to build certain features differently so we could save on our development budget. They also recommended effective tools and methods to facilitate and streamline our project, which helped us choose the right approaches and technologies”, shared one of our clients in the Energy sector. 

Global Scaling Support

New market entry is a natural step for companies seeking revenue growth and diversification. Yet, it also requires extra substantial resources for market research, regulatory compliance, product localization, and the ability to support a much larger user base potentially. 

8allocate helps businesses enter new markets without carrying exorbitant costs. Our flexible service models allow you to launch with minimal resources and quickly acquire extra resources once you’ve gained a strong foothold. 

As Marc Andreessen put it in an interview with McKinsey Quarterly, businesses are always in a hill-climbing mode. 

As you climb the hill, you refine the products and proliferate them to a mass market. Of course, every market has its’ S curve’ of adoption, but it might take a long time to top out, and the plateau might be really big.

Marc Andreessen

The valleys in the S curve include crossing the chasm of the technology adoption lifecycle to win over the early majority of users and ensuring long-term product profitability in the new market. 8allocate can provide on-demand strategic support and resources at every stage of your journey, helping you optimize your cash flow at critical stages and accelerate product development speeds to reach the plateau of mass product adoption. 

Corporate Engineering Practices and Productivity Improvements 

Over 56% of companies are building new products faster than ever, but 80% are also looking for ways to increase their speed, according to a Protolabs survey. Manual, redundant steps in engineering processes reduce software development productivity. Instead of staying in the flow, your engineers constantly switch tasks, spend unnecessary time in meetings, or get blocked by the slow pace of decision-making. The outcomes are well-known: prolonged time to market, low efficiency, and lost business opportunities. 

Our team can audit your current engineering processes and flag opportunities for optimization. Whether you want to advance your Agile project management, introduce a greater degree of infrastructure automation with DevOps, or advance to platform engineering, we’d be delighted to support you.  

Value-Based Engagements

The collaborative strategic partnership model also calls for new engagement models. Traditional IT outsourcing billing models like ‘fixed-price’ or ‘times & material’ no longer serve the buyers or the suppliers well, as they don’t promote interest in shared risk/reward.

Value-based engagements add an outcome-based component to the partnership. It better reflects the hard-to-quantify add-ons a partner brings— tech expertise, product development acumen, process excellence — that lead to tangible business impacts. EdTech company GoIT increased the number of weekly active users from 74% to 86%, and the average Net Promoter Score increased from 60 to 75 by proactively suggesting new solutions to the customer. A user-friendly e-commerce platform we’ve developed for a B2B services company generated a 30% sales increase. 

A value-based engagement model promotes scope flexibility, as neither party is as strictly tied to optimizing the number of billable hours. Instead, we focus on driving the real business impact of our services. 

Contact our team to learn more about the complementary capabilities we can provide for your business. 

Supply chain bottlenecks, customer behavior shifts, and competitive pressures force businesses to reinvent operations. Digital technologies, in particular, have become significant enablers for business model transformations but also sources of disruption. 

Leaders realize that their technology investments now directly impact their companies’ viability. A 2024 Annual CEO Survey from PwC found that 45% of leaders worry that their company will not be viable in a decade if it stays on its current path.

Tech-led transformation has thus become the core pillar of business strategy. Yet, the link between tech investments and specific business outcomes — revenue growth, higher customer satisfaction, bigger market share—is not always apparent. 

In this post, we’d like to show how leaders can ensure a strong alignment between new tech investments and business objectives, using technology roadmapping. But first, let’s recap the basics. 

What is a Technology Roadmap? 

A technology roadmap outlines the technologies and organizational capabilities a company must develop to meet overarching business objectives. Spanning two to five years, it helps:

• Formulate and document a long-term vision for digital transformations
• Identify key enablers and blockers in the strategy
• Prioritize strategic technologies and capabilities
• Coordinate activities across the organization
• Establish transparency and clear communication

The main goal of a technology roadmap is to level-set new technology acquisitions with business goals and identify key steps on their journey. The roadmap focuses the C-suite and management efforts on the most important tech priorities. It answers the “what”, “why”, and “when” in your initiatives, helping leaders better allocate resources, establish realistic timelines, and monitor execution. 

How To Align Your Technology Roadmap With Business Objectives 

A well-designed technology roadmap pegs technology initiatives to specific business outcomes like improvements in customer experience or new revenue enablement. 

The problem, however, is that leaders often struggle to ‘connect the dots’ between specific investments and desired outcomes. Because of that, we often see headlines proclaiming that “yet another emerging technology fails to deliver ROI.” 

Indeed, measuring ROI from new technology acquisitions is challenging because we’re looking at more than a mere technology shift. New acquisitions often trigger wider operational and business model transformations, generating direct and indirect returns across multiple teams and processes. 

To identify and multiply these positive impacts, we recommend using the following technology roadmapping best practices. 

Identify Your North Star Metrics

Technology is a means to an end, not an end unto itself. For this reason, cloud migration or AI adoption cannot be a standalone goal. Such initiatives should be tied to specific business outcomes to measure and claim ROI.

Leading companies use the North Star Metric as the main measure of success for their tech initiatives. Depending on the industry and business model, a North Star Metric can be “revenue growth”, “profit margin”, “transaction volume”, “daily active users”, or “number of paid subscriptions”. Choose a metric that, if it increased today by 2X, would indicate that your company has truly made it. 

Precise, measurable metrics focus everyone’s attention on a shared objective. As a decision-making tool, North Star Metric helps prioritize initiatives and better quantify their impacts. By 2025, 80% of CTOs with business-outcome-focused technology roadmaps will see a 20% increase in employee and customer satisfaction scores.

Determine Your Position In the Market 

Emerging technologies can change the industry stakes in a matter of months. Buy now pay later (BNPL) companies quickly capitalized on the growing adoption of digital payment methods and e-commerce, eroding the profits of traditional banks. Software as a service (SaaS) companies rendered perpetual software licenses a thing of the past. 

Understanding the key shaping forces in the digital markets is critical to determining the general industry direction—the prevailing trends and shifts that will shape its future trajectory. 

By understanding the overarching trends, leaders can exercise better judgment when it comes to: 

• Emerging customer needs, unaddressed by other entrants 
• Proliferating risks, endangering market share 
• Opportunities for differentiation from competitors 
• New partnership opportunities with emerging players 
• Necessary business model transformations 

Using this knowledge, leaders can better align their technology roadmaps with the target objectives and carve out a stronger position in the market. 

To illustrate the above, let’s take the language learning market. The latest EdTech trends suggest an increased focus on using data science for user analytics and generative AI for course authoring and content development. However, different players use the same technology uniquely to secure a competitive advantage. 

Duolingo maintains its position as a self-paced language-learning app. In 2023, it retired its Classes feature, which allowed users to host or attend live language classes. Instead, the company doubled down on optimizing self-paced learning using data analytics strategies. Duolingo heavily invested in improving its machine learning algorithms for language learning assessments and was among the first to incorporate a GPT-4 based assistant into its premium plan. 

Babbel, on the contrary, is switching from a self-study app to a blended learning model. Following the re-branding, the company announced more Babbel Live classes, engaging magazine content, and podcast series. The company also heavily invests in data analytics initiatives to monitor the impact of any product changes on learners’ experience, optimize course content, and improve user retention. But unlike Duolingo, Babbel channels its technology investments into personalized live learning to thrive in the market. 

Both EdTech companies share the same niche—language learning—but have a different vision of their position in the industry, backed by respective business objectives. Duolingo aims to maximize the total number of daily active users and engagement to increase conversions to premium subscriptions, while Babbel directs more efforts toward maximizing paid user acquisition and retention. 

Know Your Blockers 

You have your target metrics and a vision for your business’s desired future state. But the speed and success of your technology transformation journey will also depend on your starting position — the as-is baseline. 

Every company’s starting position is determined by its data, talent, operating model, and technology portfolio. Each of these factors can be either an enabler or a blocker. 

Factor	Enabler	Blocker
Data	Scalable data lake architecture for aggregating customer insights	Existing data pipelines cannot support real-time analytics
Talent	Competent cloud engineering function	Missing data science and ML skill sets
Operating model	Cross-functional, autonomous product teams	Siloed communication between Product and Engineering teams
Technology portfolio	Cloud-native mobile application	Monolith application design with limited scalability

Blockers usually include gaps in certain capabilities, monetary constraints, and operational inefficiencies. They increase operational risks, extend project timelines, and undercut ROI. Many of these can (and should) be addressed on your roadmap through specific initiatives like migrating to a microservices architecture and adopting DevOps to support the core product. 

The challenge, however, is that when one blocker goes, others emerge. For instance, cloud migration could solve the problem of poor scalability and curb some tech debt, but it may raise cost optimization challenges. 

A clear future vision for your company’s position in the industry, backed by North Star Metrics, helps determine which blockers to address first and which can be acceptable tradeoffs at any given stage. For example, sometimes you may need to delay releasing new product features and repay technical debt first to avoid undermining user experience or facing huge IT infrastructure costs. Likewise, it’s a risky move to launch a public-facing AI system if you lack experience in AI security. 

Place all known blockers, present-day, on your technology roadmap and prioritize them by severity. Flag critical gaps preventing you from reaching the next milestone. Then, consider strategies for resolving them. Include these as sub-steps on the roadmap.

Analyze Your Tech Portfolio 

Few businesses start with a ‘clean’ technology slate. Your current IT portfolio and system design already possess certain inefficiencies. These may include mounting technical debt, increasing infrastructure costs, or technology sprawl — overspending on redundant and poorly compatible technologies. 

To detect and assess the impacts of such issues, you’ll need to update your IT asset inventory. Map all physical and virtual systems (infrastructure, applications, networks, etc.) you currently have. Assign each asset an owner, designation, and value level based on its role in your company’s strategy.

By consolidating IT asset data, it becomes easier to identify gaps in your tech portfolio and flag redundant or legacy assets. For example:

• Identify necessary hardware upgrades
• Consolidate duplicate licenses
• Address system vulnerabilities
• Formalize new tech adoption requirements

Emerging technologies like AI or IoT come with technical prerequisites. For machine learning, that’s a reliable, cloud-native data architecture. Industrial IoT deployments require secure, low-latency networking at the installation site and experience with edge computing. Without understanding how these technologies will slot into your current infrastructure and what degree of changes are required, it’s impossible to evaluate the adoption costs and estimate subsequent ROI properly. 

As part of our strategic consulting service, 8allocate helps leaders create reference architectures for adopting new technologies based on initial tech portfolio assessments. Our specialists identify areas needing improvement and create architecture solutions for your domain. The roadmap also includes a technology life cycle management sub-map that tracks current and planned changes to applications and infrastructure, ensuring steady progress across the key initiatives. 

Formalize a Technology Implementation Plan 

A detailed description of your ‘as is’ state helps you understand the present role of technology in your business and identify areas of strengths and inefficiencies. North Star Metrics, combined with a strong industry vision, help you describe the future ‘to-be’ state. A technology implementation plan, in turn, bridges the gap between them. 

Unlike a broader technology roadmap, an implementation plan offers a close-up view of current and planned projects, their statuses, and priorities. Many initiatives will be interdependent and thus prone to blockers and possible setbacks. 

A common challenge with technology implementation, however, is that it usually has no clear finishing line. Once you’ve embedded AI into one process, you may want to explore other use cases or focus on upgrading data infrastructure for real-time processing.

Successful teams usually adopt a continuous interaction approach, where one successfully completed tech milestone evolves into the next or breaks down into several ancillary initiatives.  Such an approach allows you to test different hypotheses and progressively refine your approaches. Ongoing experimentation and incremental improvements add up. For instance, the Microsoft Bing unit increased revenue per search from 10% to 25% annually by rolling out incremental monthly product improvements (rather than spending a year on delivering one big product update that may have flopped). 

Maximize Technology Adoption Rates 

The culprit of the technology implementation stage is full adoption. As mentioned already, new technology doesn’t automatically start bringing dividends. It has to be used by the majority of your employees and/or customers to bring you closer to the target objectives. 

Typically, most tech initiatives start as pilot projects, limited to one team or business process. A pilot deployment allows validating the initial assumption and securing ‘quick wins’ to demonstrate value to other stakeholders. As the “pioneering” team starts demonstrating better performance, others will be enticed to adopt the new solution. 

For instance, Spotify started experimenting with Agile software engineering practices with a small group of employees back in 2009. Through trial and error, the team progressively figured out the optimal team structures, workflows, and cultural practices to scale Agile across 30 teams and 3 cities by 2012. 

Spotify also used a similar approach to building out its platform engineering practice — by supporting grassroots initiatives and encouraging contributions from different business units. Over time, Spotify addressed more and more developers’ frustration, eventually building out an internal platform boasting an impressive 99% voluntary adoption rate. 

To promote greater technology adoption across business units, determine what matters to each category of adopters. Next, appoint the main sponsor — a person who’ll champion the change and rally others to join. Prosci research found that having a highly effective sponsor increases the likelihood of success by almost three times. 

From there, keep open communication with the project sponsors. Ask about the bottlenecks and challenges they’re facing regarding adoption and aim to resolve these proactively. 

Revisit and Refine Your Technology Roadmap

A technology roadmap is an evolving document. It should be refined based on the evolving 

market conditions, changes in the industry, and learnings from the pilot and scaled initiatives. 

Revisit your roadmap every quarter and reassess all the in-progress initiatives through the length of:

• Relevance: Does this project still make sense? 
• Risks: Are there any new problems to plan for? 
• Resource: Do we need to add extra resources to succeed? 

Periodic reviews help ensure that you’re staying on the right course and making steady progress towards the target objectives. Set regular check-in meetings to review task completion, confirm timelines, and reassess resource allocation.

Regular reviews, combined with agility in change management and execution, also help leaders rapidly change gears during turbulent periods. When the pandemic hit, Airbnb managed to quickly shift focus from its core product development to new offerings like “digital experiences” and quarantine stays. By reacting rapidly to the dire market conditions, Airbnb stayed afloat and successfully IPOed by the end of the year.

On the other hand, companies that refuse to adapt and innovate eventually see their demise. Kodak was well aware of the rising popularity of digital cameras among consumers. (Heck, they invented the first digital camera in 1975!). But instead of investing in the new technology, the management chose to continue making money from film sales (while these lasted) — a decision that led to the company’s bankruptcy in 2012. 

The bottom line: Businesses that try to battle the market rather than capitalize on its changes hardly ever succeed. Regular roadmap reviews help avoid dumping resources into inconsequential initiatives.  

Final Thoughts 

Like “painting by numbers”, a roadmap provides a better sense of direction for the organization and helps leaders prioritize the right projects at the right time.  By identifying North Star Metrics and understanding the position of the market, leaders can make informed decisions about which technology investments to prioritize to achieve the target business objectives. 

As a communication tool, a technology roadmap also helps convey the value to all stakeholders and communicate priorities downstream, ensuring faster technology adoption among the employees. This helps ensure that technology investments directly impact the company’s profitability and contribute to its long-term competitiveness. 

Create and refine your technology adoption roadmap with 8allocate’s help. As part of our strategic consulting service, we help leaders achieve better alignment between their tech investments and key business metrics. Contact us to receive a personalized consultation. 

Frequently Asked Questions

Quick Guide to Common Questions

What is a technology roadmap, and why is it important?

A technology roadmap is a strategic plan that aligns technology investments with business objectives over time. It helps companies prioritize innovations, optimize resources, and maintain a competitive advantage by defining short- and long-term digital transformation goals.

What are the key components of a well-structured technology roadmap?

A comprehensive technology roadmap should include:

• North Star Metrics – Clear indicators of business success.
• Market Positioning – Competitive landscape analysis.
• Blockers & Risks – Identifying technical and operational gaps.
• Technology Portfolio Assessment – Optimizing existing solutions.
• Implementation Plan – A phased rollout with success metrics.

What are common mistakes when aligning technology with business objectives?

• Investing in technology without clear business impact.
• Ignoring market trends and customer needs.
• Lack of collaboration between IT and business teams.
• Overlooking scalability and long-term costs.

How can businesses measure the success of their technology roadmap?

Success can be tracked through:

• Operational Efficiency – Faster workflows, reduced downtime, and cost savings.
• Customer Impact – Higher engagement, lower churn, and improved satisfaction.
• Financial Performance – Revenue growth, optimized expenses, and clear ROI.

How can companies ensure the successful adoption of new technologies?

• Start with pilot programs to validate solutions.
• Provide structured training for teams.
• Monitor adoption rates and address usability challenges.
• Ensure alignment between technology and business goals.

What factors should be considered before integrating AI or automation?

• Data Readiness – Ensuring structured, high-quality data.
• Business Impact – Identifying clear ROI-driven use cases.
• Scalability & Integration – Compatibility with existing infrastructure.
• Compliance & Security – Addressing governance and ethical concerns.

How often should a technology roadmap be updated?

It should be reviewed quarterly or biannually, depending on:

• Industry changes and competitive shifts.
• Business growth and evolving priorities.
• New opportunities in AI, cloud, or cybersecurity.

In a perfect world, tech leaders have unnerving board support, unlimited budgets, and a bountiful talent pool. But in reality, it’s the opposite.

Although 93% of IT leaders expect budget increases this year, 83% also believe they will need to achieve more with less, a CIO Pulse survey found. A significant portion of budgets are tied to supporting legacy technologies and rising cloud bills. Tech talent scarcity, lingering economic uncertainty, and limited VC funding mean CIOs must squeeze every ounce of value out of the available resources. 

However, constraints aren’t bad for innovation. On the contrary, research suggests organizations benefit from a healthy dose of limitations, challenging them to be more creative. As former Googler and Yahoo CEO Marissa Mayer said in 2006: “Creativity loves constraints,” citing an example of adapting the Google Search engine to have a memory footprint of less than 8 megabytes to run on 90% of computer models at that time.

Instead of bemoaning the inconvenience, innovative companies reframe adversity as an opportunity. In this guide, we’ll discuss the main constraints tech leaders face and strategies to overcome them.

Slow Response to Market Changes 

Markets move faster than most businesses. Prolonged decision-making, lack of clear leadership, siloed communication, and suboptimal business processes prevent companies from delivering new products faster and capitalizing on market momentum.

Incumbent financial institutions often sit out on the emerging FinTech trends. Buy now, pay later (BNPL) companies like Klarna and Affirm took advantage of consumers’ decreasing interest in credit cards by offering a simpler, more convenient, and transparent alternative. It’s unlikely that banks didn’t notice the 30-50% decrease in auto loans, mortgages, and credit card applications happening at that time. Rather, they were slow to act.

If companies don’t move fast enough, they get outcompeted by more Agile players. Almost 65% of banking leaders admit that slow digital transformation has resulted in losing new customers.

Businesses need to rewire legacy operating processes and org charts to become more efficient. A Gartner survey found that 55% of CIOs have embraced an operator mindset, where they handle digital delivery responsibilities and engage other C-roles as sponsors in other areas of business. Thanks to this approach, CIOs achieve better digital initiative outcomes and perform better in general IT management.

Leaders also focus on improving software development productivity through:

• Cross-functional collaboration
• Adoption of Agile project management
• CI/CD pipeline implementation
• Fast feedback loop setup
• Use of coding copilots and automation technologies

JP Morgan Chase builds new products with engineering, support, legal, and marketing working together. Each contributes ideas, market observations, and operational insights, recorded as technical requirements and user stories. This change in ideation had a big impact on the speed and flexibility of the product development cycle, according to the company. By introducing Agile project management, the bank also decreased the time-to-market for new product features from over a year to under six months on average.

Pressure to Deliver ROI From Tech Investments

New tech trends emerge yearly and are greeted with initial stakeholder excitement. But in a tight economic market, many also demand a demonstration of profitability before authorizing bigger budgets.

CIOs are being tasked with an increased demand in delivering business value from technology initiatives without additional resources

Gartner 2024 CIO Agenda

This puts extra pressure on CIOs as quantifying the impacts of digital transformations isn’t always straightforward. Traditional metrics don’t always reflect an accurate picture, especially if success criteria weren’t level-set with the business case. It’s not always easy to anticipate a market response to an innovative product released 12+ months after the initial market and user research.

Doing product discovery before committing resources is a good strategy for acquiring market and customer knowledge, formalizing product requirements, and avoiding technical feasibility gaps. At 8allocate, we’ve developed a product discovery framework to validate product-market fit, prioritize product features for the first release, define project timelines, and estimate budgets with high accuracy.

To minimize risks, start with a minimal viable product (MVP) to quickly collect market insights. MVPs validate commercial viability, market demand, customer willingness to pay, and product usage frequency. It helps demonstrate early value and secure extra financial support.

Also, an MVP helps collect product metrics to guide strategy and determine revenue-boosting features and areas for optimization to improve product performance and adoption rates.

For greater accountability, set a North Star Metric — a top-level guiding metric to optimize — to better direct your efforts and report on the outcomes. Lenny Rachitsky, an author and former product lead at Airbnb, points out that most companies choose to focus on one or two of the following North Star Metrics:

• Revenue: The total money generated, measured through KPIs like ARR and GMV. A focus metric for ~50% of companies, including Airtable, Coda, Coinbase, Github, and Shopify.
• Customer growth: The total number of paid users, measured through KPIs like marketshare, paid enabled users, monthly transacting users, etc. A focus metric for ~35% of companies, primarily using a marketplaces or platform business model, e.g. JIRA, Slack, Spotify, and Webflow. 
• Consumption growth: Intensity of product usage, measured through ‘number of orders’, ‘messages sent’, ‘content viewed’, etc. A focus metric for ~30% of paid-growth driven businesses like Lyft, Airbnb, and Plaid. 
• Engagement growth: Total active product users measured through MAU and DAU. A focus metric for ~30% of companies with premium products or relying on ads, e.g., Instagram, X, Strava, Coda, and Hubspot. Also used to measure the success of internal products.
• Growth efficiency: Cost-to-profit efficiency, measured through LTV, CAC, or profit margin. A focus metric for ~10% of companies like Calm, Casper, and Hims.
• User experience: The degree of customer satisfaction with the product, measured through NPS or customer health scores. A focus metric for ~10% of companies, differentiating through UX like Superhuman, Slack, and Robinhood. Used as a supplementary metric to optimize customer retention.

A strong focus on a North Star Metric adds ‘creative constraints’ to your product strategy. It forces everyone to assess decisions through the prism of “Will this really help improve our GMV or number of DAUs?”. 

Lenny Rachitsky recounted that when working at Airbnb, the North Star Metric was “nights booked.” It was a broad metric for shaping the product roadmap, but it forced the team to come up with different ideas for ancillary measures that help optimize the North Start. For example, increasing the conversion rate increases the amount of nights booked, and so does adding more home listings, or improving the search experience. 

Diminishing IT Budgets Due to Technology Sprawl

IT budgets increase yearly, but the spending is mostly on operations rather than innovation. The main reasons for the 2024 increase are security improvements and rising technology and services costs. Investment in emerging technologies comes afterward.

Source: Foundry

Tech leaders now manage extra-large IT estates. The average enterprise has over 1,000 apps in the portfolio, a 25% increase from 2021. All of the applications require regular maintenance — patching, updates, vulnerability scans, and cybersecurity proofing.

Ongoing technology sprawl limits companies’ visibility. The majority (63%) of companies aren’t sure where their data resides. Moreover, 41% of employees use shadow IT — to acquire and/or create technology outside of the IT department’s control.

Lack of visibility is problematic as “forgotten” assets become hidden cost centers. Over half of businesses say aging technology and technical debt waste IT budgets, while 42% say redundant applications are also a problem. Invisible assets become breeding grounds for security vulnerabilities as they’re not regularly patched and updated. After five years in production, 70% of applications have at least one security flaw. Left unaddressed, it can become a gateway for hackers.

To reclaim budgets, refresh your IT asset inventory. Collect data about servers, VMs, and apps with modern asset management solutions like AWS Systems Manager Inventory. The tool also helps implement unified cloud resource configuration and usage policies and locate assets to reduce overspending and under-utilized resources. A detailed inventory of hosted applications can help locate redundant products or licenses and harmonize usage.

Once you have a detailed tally of your assets, assess the portfolio’s cost performance and security status. Run a vulnerability scan to locate unpatched applications and unprotected endpoints. Analyze existing security controls and tools, then consider additional cybersecurity measures. Assess the TCO of new security investments against the costs of a breach or containing the risks.

Expanded Cybersecurity Radar 

From 2022 to 2023, data breaches increased by 20%, and by 2027, the global cost of cybercrime can reach $23.84 trillion. In the meantime, CIOs continue to experience cybersecurity talent shortages, get pressed by new regulatory demands, and are thus oftentimes forced to resort to reactive rather than proactive cybersecurity measures.  

The better news is that despite the constraints, CIOs can come up with better solutions for cyber-security. A research group from MIT Sloan found three key reasons for heightened cyber risks: 

• Cloud misconfigurations
• New ransomware attacks
• Increased vendor system exploitation

Misconfigurations happen due to unfamiliarity with cloud technologies. Engineers lack knowledge of the product ecosystem and keep permissive ‘default’ security settings. To avoid extra costs, some companies skip purchasing extra cloud security tools (a common oversight). To avoid such scenarios, partner with an experienced cloud engineering team to help you configure secure architectures, implement appropriate controls, and set up security monitoring.

Ransomware attacks are increasing and proving effective. Unmitigated application vulnerabilities create an easy attack path. Employees remain susceptible to phishing and malware attacks. Many attacks are automated, allowing hackers to stage ‘spray and pray’ campaigns for a low cost. Better security monitoring, improved access and identity management (IAM) practices, and regular data backups can minimize ransomware attack risks.

When hackers can’t directly breach your security perimeter, they try going through a ‘backdoor’ — a vendor system with lower security protection connected to your infrastructure (e.g., a payment processor or an accounting app). To avoid this, carefully assess all your IT purchases.

Lastly, AI systems pose novel cybersecurity threats and need new protection mechanisms. To avoid data disclosures or poisoning attacks, audit your toolchains, enforce strong security policies, and apply data anonymization techniques.

Ongoing Workforce Challenges

The tech job market remains in turmoil. Layoffs continue in the US, yet most CIOs still struggle to find the talent they need. In a Deloitte survey, 90% of tech leaders said recruiting and retaining talent was a moderate or major challenge.

An CIO survey, conducted at the end of 2023, by Evanata also found that 49% of leaders feel it’s harder to fill open roles than last year. In both cases, the overarching issue is that companies struggle to hire for senior and specialized positions.

“We aren’t getting people coming in with backgrounds in AI, ML, automation, cloud security, cloud infrastructure, etc. In fact, we really want to improve our security and take it to the next level, but we aren’t getting enough talent,” one executive mentioned. 

That’s hardly surprising since most businesses prioritize AI adoption, security improvements, and cloud adoption. Everyone is after a narrow segment of the tech workforce, exhausting local talent pools.

In such cases, it pays to borrow a principle from Blue Ocean Strategies: Stop trying to beat the competition and make it irrelevant instead. Expand your hiring strategy to untapped regions like Central and Eastern Europe (CEE). Ukraine, Poland, and Romania have an 870K+ tech workforce, growing year-on-year. Over 60 Ukrainian tech companies are registered as active in the field of artificial intelligence — and an even wider number operate in adjacent areas of big data analytics and data science.  Over 200 vendors in Poland offer cloud consulting services, and Romania recently became the host country for the EU’s new Cybersecurity Competence Centre (ECCC).

Strategic IT partnerships with software engineering partners can help businesses close skill gaps and ensure an ongoing supply of the right talents through joint workforce planning initiatives. Moreover, leaders can gain a wider range of support with engineering process optimization, product development, and new technology investment assessments, among other areas. 

Participation in ESG Strategy 

ESG reporting is now a regulatory requirement in 30+ countries and territories, including the US, the UK, and the European Union. CIOs are now expected to drive these initiatives. A Deloitte survey found that 27% of CIOs are expected to provide the necessary data and 31% to contribute new tools.

While environmental, social, and governance (ESG) initiatives add value to the business, their implementation is challenging to orchestrate. Data silos, in particular, are a major constraint. ESG insights are often trapped in multiple business systems, making it hard for compliance officers to fulfill disclosure obligations or quantify the impacts of recent initiatives. Moreover, data collection and analysis for ESG reporting is often ad-hoc and done manually. In fact, most barriers ESG practitioners face are related to data in one way or another.

Source: Workiva

Tech leaders can help advance ESG initiatives by driving more resources toward improving data management processes to have a single source of truth for all data and establish audit-proof reporting processes. Then implement ESG analytics and reporting platforms to digitize and automate the reporting process.

Beyond that, CIOs can contribute to reaching ESG targets by identifying new investment and improvement areas. AI opens ample opportunities for improving ESG metrics through optimized energy management, predictive maintenance, and real-time risk intelligence. Tech leaders can inspire sustainable business practices and advance ESG goals.

Final Thoughts 

As the adage goes: “If you wait for perfect conditions, you’ll never get anything done.” Smart tech leaders know how to work with what they have (or how to change their circumstances effectively). The above tactics provide a good starting point for strategizing.

For personalized help, contact us. At 8allocate, we help leaders build better engineering processes, refine product ideas, and commercialize emerging technologies through advisory and hands-on engineering services.

Frequently Asked Questions

Quick Guide to Common Questions

How can tech leaders drive innovation with limited budgets?

Tech leaders can optimize budgets by focusing on high-impact initiatives, leveraging automation, and eliminating redundant technologies. A detailed IT asset audit can reveal cost-saving opportunities. Shifting to cloud-native and AI-powered automation can also help streamline operations while reducing overhead.

Why do companies struggle to respond quickly to market shifts?

Slow response times stem from rigid decision-making, siloed operations, and legacy technology. To stay competitive, organizations should embrace Agile methodologies, cross-functional collaboration, and real-time analytics to speed up decision-making and execution.

How can companies ensure a high ROI on tech investments?

Tech investments should align with a clear North Star Metric—whether it’s revenue growth, customer acquisition, or operational efficiency. Leaders should prioritize projects with measurable impact and use MVPs (Minimum Viable Products) to validate demand before scaling.

How can IT teams manage growing technology sprawl?

The average enterprise has over 1,000 applications, leading to inefficiencies and security risks. Tech leaders should conduct regular IT asset reviews, eliminate underutilized tools, and consolidate redundant applications to reduce maintenance costs and enhance security.

What strategies help organizations strengthen cybersecurity under budget constraints?

Focus on proactive threat detection, security automation, and strong identity and access management (IAM) practices. Investing in cloud security tools, zero-trust architecture, and AI-powered anomaly detection can enhance security while optimizing resources.

How can companies overcome tech talent shortages?

Instead of competing for the same limited talent pool, companies should expand their hiring strategies to global markets like Central and Eastern Europe (CEE). Partnering with software development firms for dedicated engineering teams can also provide access to niche expertise on demand.

What role does AI play in optimizing enterprise IT operations?

AI can automate IT workflows, improve data-driven decision-making, and enhance predictive analytics. It helps organizations optimize cloud costs, streamline DevOps processes, and strengthen cybersecurity through intelligent threat detection.

How can CIOs contribute to ESG initiatives?

CIOs play a key role in ESG (Environmental, Social, and Governance) compliance by implementing real-time ESG tracking, optimizing energy consumption with AI, and ensuring ethical AI deployment. Data-driven sustainability reporting tools can also streamline compliance efforts.

How does 8allocate help businesses innovate despite constraints?

8allocate is an AI solutions development company. We help businesses accelerate growth, streamline operations, and enhance decision-making through AI-driven solutions for high-growth industries like FinTech, EdTech, and Construction Technology.