Since the start of this decade, digital technologies have proven to be a major market force. Cloud computing, data analytics, and artificial intelligence (AI) have already had a substantial impact on consumer expectations, operating models, and revenue generation capabilities. 

These technology advancements (and many others) would have been impossible without continuous innovation in the IT sector. 

As we enter the new year, the 8allocate team decided to look into the latest trends in software engineering. In this post, you’ll find a data-backed summary of the new themes in the market, covering the technology and strategy aspects of software development. 

Software Engineering Trends for 2024

Last year was a roller-coaster for the tech sector. After a massive growth spree, most companies had to adjust their appetites as their growth didn’t fully translate to profitability. 

Yet, despite operational challenges, accelerating inflation, and ongoing layoffs, the majority of business leaders (94%) plan to increase investment in IT and emerging technologies in 2024 to drive ongoing innovation and growth.

More specifically, our team believes that the following software engineering trends will dominate the business agenda in 2024. 

Platform Engineering Will Help Improve SDLC 

Enterprises have a sizable tech portfolio. More technology inevitably leads to higher total cost of ownership (TCO), interoperability issues, and a larger carbon footprint. 

To optimize operating costs, leading teams are switching to platform engineering — a software development paradigm, which assumes using a shared engineering platform, designed to promote common tools, reusable components, shared services, and common knowledge for software development teams. 

Source: Gartner  

Effectively, platform engineering builds upon the DevOps best practices of continuous integration (CI), continuous delivery (CD), infrastructure as a code (IaaS), and increased collaboration. But, unlike DevOps, platform engineering focuses on improving the developer experience, not just the end product quality. 

Platform engineering promotes the creation of standardized toolchains and workflows that enable developer self-service aka allow your software engineers to create, improve, and modernize new apps faster using tools from an internal developer portal (IDP). 

Platform engineering teams perform essential “housekeeping tasks”, ranging from automatic infrastructure provisioning and workflow automation to proactive management of IDPs. 

Source: The State of Platform Engineering Report 2023. 

By offering a ‘golden path’ with all of the tools required for product development, testing, deployment, and management, the IDP facilitates developer self-service. This allows developers to complete these tasks without having to master individual tools, which can reduce cognitive load and boost efficiency.

Nike, Nordstrom, and Starbucks attribute a substantial part of their software engineering productivity to platform engineering practices. Overall, adopters report improvements in system reliability (60%), greater software developers’ productivity (59%), and better workflow standards (57%). 

Apart from improving team performance and software quality, platform engineering also helps minimize waste, both in the form of unproductive fiddling with manual tasks and computing resources overuse, leading to better ESG performance. 

Accelerated Adoption of Industry Cloud Platforms 

Cloud adoption has been going strong since the mid-2010s. In 2023, global businesses spent over $563 billion on public cloud services, including PaaS, IaaS, SaaS, and DaaS solutions. 

Cloud-native operations have already proven more cost-effective, environmentally friendly, and innovation-oriented, paving the way for new application development. 

Source: MIT Technology Review

Until recently, however, cloud technology has been industry agnostic, offering a standard set of capabilities, each company had to configure to meet their operating demands. In response, some vendors are now evolving their offerings to industry cloud platforms. 

An industry cloud platform consists of cloud solutions, tailored to the needs of a particular industry, such as healthcare, retail, finance, or AdTech. It’s purpose-built to meet the industry requirements for data storage, auditability, and security, plus provides a combination of composable SaaS, PaaS, and IaaS services to support specific use cases. 

Industry cloud capabilities can range from optimized data architectures and new development tools (e.g., for data fabric) to a collection of pre-installed SaaS apps on managed cloud infrastructure. For example, Salesforce Health Cloud is a HIPAA-compliant platform, pre-furnished with patient management software and healthcare analytics. Snowflake, in turn, released a Media Data Cloud, with data architecture, adjusted for the needs of AdTech businesses. 

By 2027 as many as 70% of enterprises will use industry cloud platforms, up from just 15% in 2023. If you’re looking to transition to cloud-native operations, the 8allocate cloud engineering team would be delighted to advise you on the optimal choice of cloud platforms, as well as facilitate the adoption. 

New AI Frameworks like RAG Will Accelerate Gen AI Development 

At present, only 5% of market leaders have implemented Gen AI in production at scale, but the majority (62%) name investments in this area as a top organization priority, according to Wavestone’s 2024 Data & Analytics  Survey.

In particular, business leaders are looking for ways to improve their data analytics capabilities and new frameworks such as retrieval-augmented generation (RAG) help make that happen.

Originally introduced by Patrick Lewis of Facebook AI Research, RAG is a collection of techniques and methods that enable generative AI models to fetch facts from external sources. 

Effectively, RAG allows large language models (LLMs) to access extra data, which wasn’t part of its training or parameter optimization to provide more accurate user replies. RAG models can scan specific corporate documents to look up data or browse indexed documents on the Internet to provide users with the freshest data and cite sources for the output. RAG also helps provide better responses to ambiguous user queries and minimizes the occurrence of model hallucinations. 
Initially, RAG techniques were primarily focused on optimizing the end-to-end pipeline of retrievers and generators. However, RAG is now shifting towards a more modular approach where retrievers and generators are separate entities that can be optimized independently. This allows for greater customization of knowledge sources, retrieval techniques, and the selection of LLMs, according to a 2024 survey.

Source: arXiv

Last year, we saw plenty of new open-source RAG frameworks emerge, ranging from native ones like Parent Document Retriever to more complex, modular patterns like RAG Fusion. 

At the end of 2023, Microsoft also launched a commercial, enterprise-grade RAG solution for deploying corporate LLMs — and we’re likely to see more offerings from large tech vendors in 2024. 

Practically speaking, RAG dramatically improves the quality and relevancy of LLM outputs, allowing organizations to create custom “Copilots” and “GPTs”, trained on internal data and vetted for accuracy in performance. 

AI Will Further Increase Software Development Productivity

Generative AI tools made a strong entrance in 2023, with Microsoft releasing GitHub Copilot and Microsoft Security Copilot, Amazon — CodeWhisperer, and Replit — Ghostwriter (not to mention all the coding use cases of ChatGPT).  

Generative AI tools have already demonstrated value across various software engineering tasks, ranging from debugging code to brainstorming different application design ideas and scanning software for security vulnerabilities. Scientific tests have proved that some AI tools are capable of professional-level performance on different coding tests. 

Although Gen AI usage is still relatively low among software engineering teams, the technology potential is massive. 

GitHub estimates that AI software developer tools can add an equivalent of an extra 15 million “effective developers” to worldwide capacity by 2030. In monetary terms, this translates to a global increase in GDP of $1.5 trillion. 

Apart from making software engineering teams more productive, Gen AI tools can also help developers improve their coding skills, while 4 out of 5 developers believe that AI coding tools will make their team more collaborative.

Gartner also predicts that in the next several years, organizations will embed gen AI into a wider range of software engineering processes: 

• 30% of enterprises will implement an AI-augmented development and testing strategy by 2025. 

• 60% of website and mobile app design efforts will be automated with Gen AI by 2026 

• 15% of new applications will be auto-generated by AI without a human in the loop by 2027. 

By reducing the learning curve, gen AI can also entice more people into tech careers and empower a new generation of citizen developers, helping organizations close the persisting skills gaps. 

On the flip side, however, new approaches will be required to quality-check AI-generated code outputs. Likewise, AI tools can also become a new vector of attack for malicious actors, meaning such systems will require extra security measures. 

IT Outsourcing Will Continue to Remain a Key Strategy for Talent Acquisition

Business leaders continue to grapple with tech talent shortages. Globally, 75% of employers still report talent constraints, with IT, data, and engineering being the hardest roles to fill, according to a ManpowerGroup Employment Outlook Q1 2024.

Source: ManpowerGroup Employment Outlook Q1 2024 

With an almost uniform commitment to AI development, getting competent professionals has become increasingly hard, especially in mature markets. Gartner expects that the demand for tech talent will greatly outweigh the supply until at least 2026. 

Over the past years, software development partnerships have become one of the key strategies for overcoming talent constraints in the local markets as we shared in our eBook. 

To gain access to the required competencies and advance software development projects, 76% of businesses rely on third-party IT service providers. In 2024, this synergy will further increase. 

According to Deloitte, over 60% of shared service providers (aka outsourcing partners) have increased the scope of provided services and opened new local offices in 2023, in response to greater demand. 
Central and Eastern Europe (CEE), in particular, has strengthened its position as the top region for hiring software developers. Poland, Ukraine, and Romania have over 870,000 IT specialists, with over 10K of new graduates joining the industry annually.

Strong tech acumen in areas such as cloud computing, big data analytics, ML, and AI among others, complemented by close cultural and geographic proximity to Western markets and improvements in the overall business climate, made CEE one the fastest-growing global tech hub. 

Microsoft, Amazon, IBM, Facebook, Snapchat, and Google have already established local delivery centers in the region. Open AI expressed interest in opening a regional office in Poland, while Netflix has almost finalized the deal. 

That said, the demand for tech talent in CEE is still lower than the supply, and software engineering services are more cost-effective. If you’re looking for ways to accelerate time-to-market for new software products or acquire the missing tech skill sets, look into the opportunities CEE has to offer. 

Companies Will Expect to See Impact, Not Just Innovation from New Technologies 

Emerging technologies carry a massive transformational potential, but business leaders are feeling more pressed to see the actual ROI. In fact, “lack of confidence due to missing ROI” has been cited as the top blocker of digital transformations among enterprises. 

In 2024, we expect more leaders to focus on determining the impact of new technology trends on their business model and ability to generate more revenue. Not all new tech investments can transform the business model per se. Some may only upgrade existing capabilities (e.g., increase data storage capacity) without creating significant business impact (e.g., delivering better analytical insights). 

CIOs must also recognize the gaps that exist between the expectations of business leaders and the reality of what IT departments can deliver. When your organization doesn’t have a mature data management capability, for example, developing new AI solutions will be an uphill battle. Likewise, different machine learning methods come with inherent advantages and constraints, not to mention specific skill sets AI teams require.  

To avoid such scenarios, business leaders should focus on assessing new technology investments through the lens of their software engineering capabilities and the impacts these can bring to their operating models. Practices like the discovery phase in software development, technology readiness level (TRL) assessments, and capability maturity model integration (CMMI) among others can help in this area.

Engineering Your Way to Success In 2024 

Business leaders face increasing demands for innovation, enterprise-wide transformation, departmental technology, and data analytics capabilities.

To meet these (often conflicting) priorities, leaders will need to focus on establishing better IT talent-sourcing strategies and improving their ability to execute the promised initiatives. To get through the backlog of corporate IT projects and accelerate new product development, leaders increasingly rely on software development partners in 2024. 

Likewise, new engineering best practices like platform engineering and greater usage of AI developer tools can increase software engineering teams’ velocity and efficiency. New technology solutions like industry clouds and RAG are further easing the adoption curve of respective technologies, allowing businesses to deliver greater and faster impacts. 

If you’re looking for ways to improve your software development capabilities in 2024, reach out to 8allocate. Our company provides end-to-end software engineering services with dedicated teams, as well as ad-hoc technology consulting services. Harness new market opportunities with an experienced software engineering team. 

Frequently Asked Questions

Quick Guide to Common Questions

What are the key trends shaping software engineering in 2024?

Software engineering is evolving with platform engineering, industry cloud adoption, AI-driven productivity tools, and IT outsourcing. Businesses are focusing on improving software development efficiency, leveraging AI frameworks like RAG, and ensuring that emerging technologies deliver measurable impact rather than just innovation.

How is platform engineering changing the software development lifecycle (SDLC)?

Platform engineering streamlines software development by creating standardized toolchains, reusable components, and internal developer portals (IDPs). It enhances developer productivity by reducing cognitive load, improving system reliability, and promoting self-service infrastructure. Companies like Nike and Starbucks report higher efficiency and workflow standardization through platform engineering adoption.

What role does AI play in improving software development productivity?

AI-powered coding tools like GitHub Copilot and Amazon CodeWhisperer assist developers in debugging, code generation, and security scanning. By 2030, AI-assisted coding is expected to add the equivalent of 15 million new developers to the global workforce. However, AI-generated code requires rigorous validation, and organizations must adopt security measures to prevent vulnerabilities.

Why is IT outsourcing becoming more critical for software engineering?

Tech talent shortages persist, with demand for AI, cloud, and software development roles far exceeding supply. Many businesses are turning to IT outsourcing to access specialized talent quickly and scale their software development efforts. Central and Eastern Europe (CEE) has emerged as a leading hub for outsourcing, offering highly skilled developers with expertise in AI, big data, and cloud computing.

The convergence of artificial intelligence (AI) with the Internet of Things (IoT), AIoT, is revolutionizing multiple sectors by facilitating more intelligent automation and informed decision-making processes. Effective data collection from IoT devices is crucial for training AI models and enhancing decision-making in AIoT applications. By 2024, AIoT applications will greatly improve productivity, safety measures, and consumer interactions across various industries, including retail, manufacturing, and healthcare. This article explores the impactful transformative applications of AIoT and their effects in practical scenarios.

Key Takeaways

• AIoT is transforming industries such as healthcare, manufacturing, and retail by enabling real-time monitoring and autonomous decision-making, ultimately improving operational efficiency.
• In smart cities, AIoT applications enhance urban living through improved traffic management, security, and public safety using innovative technologies like computer vision and big data analytics.
• Edge computing plays a critical role in AIoT by allowing data processing closer to its source, resulting in faster response times, reduced bandwidth usage, and improved security in various applications.

Key AIoT Applications Across Various Industries

The fusion of AI and IoT is not merely a technological trend but a driving force behind significant advancements in various sectors. AIoT applications leverage the vast amounts of data generated by IoT devices to optimize operations and make informed decisions. AIoT systems leverage this combination to autonomously make decisions that elevate operational efficiency while offering capabilities for real-time monitoring. By incorporating AI into IoT frameworks through AI and ML implementation and integration, these systems provide critical insights used in predictive maintenance, quality control, and resource management.

Smart Cities: Revolutionizing Urban Living

Smart cities are at the forefront of AIoT adoption, utilizing advanced technologies to enhance urban living. AIoT systems facilitate real-time traffic analysis, congestion management, and crowd control, leading to more efficient urban management. Cities like Singapore and Barcelona are leveraging AIoT to monitor traffic patterns, reduce congestion, and optimize public transportation routes, significantly improving the daily commute for millions.

Traffic Management and Public Safety

AIoT systems use computer vision technology to detect traffic incidents—such as accidents or congestion—in real-time, allowing city authorities to respond more quickly and effectively. Additionally, AIoT significantly enhances smart video surveillance, improving security and public safety. By analyzing data from connected and wearable devices, AIoT enables cities to respond proactively to potential threats, ensuring resident safety.

Energy Management and Sustainability

The integration of AIoT in smart cities extends to energy management. Smart grids powered by AIoT can automatically adjust energy distribution based on real-time demand, reducing waste and lowering costs. For example, AIoT-enabled street lighting systems can adjust brightness based on pedestrian and vehicle presence, cutting energy consumption significantly.

Industrial Internet: Driving Efficiency and Innovation

The industrial internet is another area where AIoT is making a substantial impact, particularly in predictive maintenance, quality control, and resource management. AIoT systems, enhanced by machine learning algorithms, empower industries to operate more efficiently by providing real-time insights that allow for proactive decision-making, reducing downtime, and optimizing operations.

Predictive Maintenance

AIoT systems continuously monitor machinery for signs of wear and tear, using AI algorithms to predict potential failures before they happen. Machine learning capabilities enhance predictive maintenance by utilizing advanced algorithms to uncover insights and make informed decisions. This proactive approach reduces downtime, lowers maintenance costs, and extends the lifespan of equipment. In the oil and gas sector, for instance, AIoT-enabled sensors can detect leaks or pressure anomalies in pipelines, allowing for immediate intervention before a catastrophic failure occurs.

Quality Control

AIoT is also transforming quality control processes across industries. Machine learning models enable real-time data processing, allowing AIoT systems to detect defects and anomalies more effectively. By automating inspection processes through advanced image recognition technologies, AIoT enhances the accuracy and efficiency of quality control. AIoT systems can detect defects and anomalies in real time, enabling prompt corrective actions and ensuring that high-quality standards are maintained throughout the production process.

Case Study: General Electric (GE) implemented AIoT across its jet engine manufacturing facilities. By analyzing real-time data from thousands of sensors, AIoT predicts maintenance needs and quality issues before they arise, reducing unplanned downtime by 25% and significantly lowering operational costs.

Healthcare: Enhancing Patient Care and Operational Efficiency

In healthcare, AIoT is transforming patient care and operational efficiency by enabling continuous monitoring and personalized treatment. AIoT in healthcare involves the integration of mechanical and digital machines to enhance patient care and operational efficiency. By integrating AI and IoT, healthcare providers can offer more tailored care, improving patient outcomes and enhancing the operational efficiency of healthcare facilities.

Continuous Patient Monitoring

AIoT-powered wearable devices provide healthcare providers with continuous, real-time monitoring of patients’ vital signs. This capability allows for the early detection of potential health issues, leading to faster interventions and better patient outcomes. For example, AIoT systems can alert doctors to irregular heartbeats or sudden drops in blood oxygen levels, triggering immediate medical responses that can save lives.

Personalized Treatment Plans

AIoT systems also play a crucial role in developing personalized treatment plans. By analyzing data from wearable devices, AIoT provides insights into how patients respond to treatments, enabling healthcare providers to adjust plans in real time. This leads to more effective treatments and improved patient satisfaction. Additionally, AIoT helps monitor patient adherence to treatment regimens, ensuring medications are taken as prescribed.

Operational Efficiency

Beyond patient care, AIoT enhances healthcare facilities’ operational efficiency. AIoT systems manage resources such as medical equipment, ensuring they are always available when needed. They also predict when supplies will run low, enabling timely reordering and preventing shortages.

Case Study: The Mayo Clinic implemented AIoT to monitor ICU patients in real-time. By continuously tracking vital signs and predicting potential complications, the system reduced ICU stay times by 20%, improving patient outcomes and optimizing the use of hospital resources.

Enhancing Operational Efficiency with AIoT

Operational efficiency is crucial for the success of businesses across sectors, and AIoT has become instrumental in achieving this. It does so by equipping systems with the ability to analyze data and make decisions in real-time. The fusion of AI and IoT brings advanced features such as automatic data generation, pattern recognition, and valuable operational insights. By harnessing these capabilities for predictive maintenance, AIoT empowers proactive measures against system failures, promoting safer and more efficient operations.

Predictive Maintenance: Proactive Problem-Solving

Predictive maintenance is a crucial application of AIoT in industries where real-time analytics help identify and address machinery issues before they lead to breakdowns. AIoT devices continuously monitor machine parts, ensuring efficient operation and preventing unexpected failures. This proactive approach minimizes operational costs and significantly reduces downtime, improving overall operational reliability.

Case Study: A global automotive manufacturer implemented AIoT sensors across its production line to monitor the performance of robotic arms. The system detected a decrease in efficiency in one arm, predicting a failure that would have led to costly downtime. By addressing the issue preemptively, the company saved an estimated $2 million in potential losses.

Quality Control: Automating Precision

Quality control is another area where AIoT is making a significant impact. By automating inspection processes through advanced image recognition technologies, AIoT enhances the accuracy and efficiency of quality control. AIoT systems detect defects and anomalies in real-time, enabling prompt corrective actions and reducing the likelihood of defective products reaching customers. This automation improves operational efficiency and enhances customer satisfaction by ensuring the consistent delivery of high-quality products.

Case Study: A leading manufacturer uses AIoT to inspect circuit boards during production in the electronics industry. The system identifies microscopic defects that human inspectors might miss, reducing the defect rate by 40% and improving the reliability of the final products.

Energy Management: Driving Sustainability and Savings

AIoT systems play a vital role in enhancing operational efficiency through meticulous energy management. They provide continuous insights into energy consumption, facilitating superior oversight and resource administration. By examining real-time consumption patterns, AIoT solutions can fine-tune energy usage, leading to notable reductions in expenses and minimizing the ecological footprint.

Case Study: A large commercial building in New York City implemented AIoT to manage its HVAC systems. The AIoT system monitors occupancy levels and adjusts heating and cooling in real-time, resulting in a 30% reduction in energy costs and a smaller carbon footprint.

AIoT in Smart Home Devices: Transforming Daily Life

By combining AI with IoT, smart home experiences are being revolutionized as devices gain the ability to learn from and adjust to user preferences. This fusion bolsters the capabilities of smart appliances, enabling better automation by understanding user habits, which boosts convenience and enhances performance for daily activities.

Smart Appliances

AIoT-enabled smart appliances analyze user behavior patterns to personalize functionalities and improve user satisfaction. These appliances autonomously adjust their functions based on real-time data analysis, leading to more personalized interactions and optimized performance. By actively monitoring user habits and adjusting settings accordingly, AIoT-enabled appliances enhance efficiency and convenience in households.

Case Study: A smart refrigerator equipped with AIoT technology can track how often the door is opened and adjust its cooling settings to conserve energy while keeping food fresh. It can also monitor expiration dates, suggest recipes based on available ingredients, and even notify users when staple items are running low. This not only enhances convenience but also ensures more efficient use of resources, showcasing AIoT’s potential in daily life.

Home Security Systems

The integration of AIoT into home protection systems transforms security management by elevating the capabilities of smart video surveillance and instantaneous data analysis. This advanced technology refines conventional video management platforms, providing automated recognition of individuals, objects, and events that bolster home safety and homeowner reassurance.

Case Study: A smart doorbell with AIoT capabilities can distinguish between family members, delivery personnel, and potential intruders. It sends real-time notifications to the homeowner’s smartphone, enabling immediate action. This advanced level of security is becoming increasingly essential in modern homes, offering robust and reliable protection.

Energy Efficiency

AIoT devices consistently track energy consumption within households, offering immediate insights that assist in refining how energy is utilized. Automated adjustments to settings can be executed by smart thermostats and lighting solutions according to occupancy and tailored preferences, ensuring an economical approach towards energy use.

Case Study: A smart thermostat with AIoT learns a homeowner’s schedule and adjusts heating or cooling accordingly. Over time, it can recommend energy-saving adjustments that lower utility bills while maintaining comfort. This intelligent energy management reduces costs and supports environmental sustainability.

Edge Computing in AIoT Applications: Enhancing Performance and Security

Edge computing is crucial in AIoT because it enhances operational efficiency through localized data processing, especially when integrating AI into existing platforms. This approach minimizes latency and expedites decision-making, which is essential for applications requiring real-time responses. By decentralizing computation to where data originates, AIoT systems deliver immediate insights and reactions, markedly boosting performance and system reactivity.

• Faster Data Processing: Edge computing facilitates the processing of data at a local level within AIoT applications, yielding response times that are almost instantaneous and paving the way for real-time insights and actions. This is extremely valuable in situations where decisions must be made quickly, such as in autonomous vehicle operations or industrial automation processes.
• Reduced Bandwidth Use: By processing data at the edge, AIoT systems reduce the amount of information that needs to be transmitted across networks, preserving bandwidth and lowering operational costs. This is especially critical in applications that produce extensive datasets, such as those for smart cities and industrial environments.
• Enhanced Security: Processing crucial data on-site through edge computing bolsters security by diminishing the risk of data breaches while information is in transit. By handling sensitive information close to its origin rather than transmitting it over potentially vulnerable networks, edge computing helps protect against cyberattacks.

Case Study: In a smart home security system, edge computing analyzes video feeds locally, sending alerts to the cloud only if a potential threat is detected. This approach minimizes the exposure of sensitive data, making AIoT applications more secure and trustworthy.

AIoT in Retail: Enhancing Customer Experience and Operational Efficiency

The retail sector is experiencing a transformation with the adoption of AIoT technologies. AIoT facilitates real-time inventory tracking and management, significantly reducing instances of stockouts and overstock situations. Retailers can dynamically adjust stock levels based on demand forecasts, optimizing resource utilization and enhancing operational efficiency.

• Inventory Management: AIoT enables real-time monitoring of inventory, allowing retailers to maintain optimal stock levels and reduce instances of stockouts. This supports making agile adjustments aligned with demand predictions so that products are in supply precisely when consumers require them. Through refining inventory management, AIoT technologies help cut down on surplus goods and slash operational expenses.
• Customer Insights: AIoT provides deeper insights into customer behavior and preferences. By analyzing data gathered from IoT devices, retailers can create highly personalized shopping experiences and tailor marketing strategies to individual customer needs. This leads to more effective marketing strategies and enhanced customer satisfaction.
• Automated Checkout: AIoT streamlines the checkout procedure, minimizing customer wait times and improving convenience. Technologies like automated scanning and payment systems increase transaction speeds, resulting in a more efficient checkout process.

Case Study: An AIoT-enabled checkout system in a supermarket uses computer vision to scan items as they are placed on a conveyor belt, automatically calculating the total and processing the payment. This reduces customers’ time in line, making shopping more efficient and enjoyable.

AIoT in Transportation: Revolutionizing Mobility and Logistics

AIoT technologies are transforming the transportation industry, significantly improving safety, efficiency, and user experience. Intelligent systems leverage data analysis from a multitude of IoT sensors to advance traffic control and elevate public service quality.

• Fleet Management: AIoT technologies enhance operational efficiency in fleet management by enabling predictive maintenance with AI and digital solutions for logistics.This allows timely actions to avert vehicle breakdowns. AIoT systems, equipped with sensors, can predict machine failures approximately 36 hours before they occur. This significantly lowers both downtime and operational costs. Adopting this proactive strategy improves vehicle upkeep, boosts resource utilization, and elevates overall operational effectiveness.
• Autonomous Vehicles: AIoT stands as the fundamental technology behind autonomous vehicle systems, which facilitate secure navigation without human intervention. Autonomous vehicles, equipped with an array of sensors, cameras, and AI algorithms, gather and analyze data instantaneously to make independent navigational decisions. This integration enhances safety, reduces human error, and improves the overall driving experience.
• Traffic Management: AIoT systems significantly improve traffic flow within smart cities. AIoT applications alleviate congestion and promote smoother transportation by employing smart sensors to gather real-time information. The effectiveness of AIoT in managing urban transit underscores its capacity to revolutionize city navigation and improve living standards within intelligent urban environments.

Summary

AIoT represents a pivotal leap in technological advancement by fusing artificial intelligence capabilities with IoT technologies. This combination is reinventing numerous sectors, from elevating efficiency within industrial environments to reshaping smart home functionality, heralding an era of enhanced connectivity and intelligence. The role of edge computing in this synergy is substantial, as it accelerates data processing speeds while reducing bandwidth requirements and bolstering security.

AIoT holds extraordinary promise for spearheading innovation and improving our lives. By harnessing the strengths of both AI and IoT together, we have the potential to craft intelligent systems that can learn autonomously, adapt dynamically, and make decisions independently—resulting in operations that are not just more effective but also highly reactive. Embracing AIoT now positions industries on the cusp of witnessing dramatic shifts and advancements at home, in business, and throughout urban landscapes.

To stay ahead in the rapidly evolving landscape of AIoT and meet regulatory security requirements such as EU AI Act compliance, businesses must act now. Whether you’re in retail, manufacturing, healthcare, or any other industry, the time to integrate AIoT into your operations is now. Contact us today to learn how our expert team can help you leverage AIoT to transform your business, enhance efficiency, and drive innovation through custom AIoT solution development.

Generative AI. Metaverse. Advanced robotics. Blockchain — every year, all of us get bombarded with futuristic tech trends and predictions of yet another “big thing” making a disruptive impact over every industry. 

What many lists forget to address, however, are the implications of new technologies on business operations: the use cases, ROI, and tangible impacts they can make today and tomorrow — and that’s exactly what we want to focus on in our analysis. 

Main Tech Themes for 2024: Trending Technology and Their Real-World Impact 

For several consecutive years, innovation has been the central theme for businesses, with 80% naming it a “top-tier” priority. Emerging technologies, in turn, have become the key tenet for business model transformation, new product development, operational process improvements, and innovation in other business areas. 

Based on the market analysis and conversations with our clients, the 8allocate team expects that the following themes will shape the technology landscape in 2024 and beyond. 

Digital Identities Will Become a Strategic Area of Interest 

A digital identity is a combination of unique attributes and credentials associated with a person’s virtual presence. A unique username, an email address, an IP address, or a combination of authentication methods can be used to identify users (or more recently — assets) online.

Source: World Economic Forum.  

In 2024, the digital identity landscape will evolve in response to new privacy regulations and growing concerns about cybersecurity. 

By mid-2024, Google promises to sunset third-party cookies. Advertisers will no longer be able to track conversions and identify users across domains. In response, the AdTech industry doubled down on developing new first-party data infrastructure, creating proprietary solutions for user identity resolution (e.g., Liveramp ID) or shared user identity graphs (e.g. Unified ID 2.0 solution). 

Brands, in turn, also double down on collecting first-party audience data (like purchase history and demographics data) to stitch together unique customer identities for advertising and marketing purposes. Both of these trends are increasing the demand for new data infrastructure, including customer data platforms (CDPs) and data clean rooms (DCRs) as an alternative to “walled data gardens”, created by Big Tech players like Google and Facebook. 

On a wider scale, governments worldwide call for greater digitization of people’s identification documents. The European Union plans to roll out a digital identity system powered by biometrics, which would allow users to open bank accounts, make payments, and hold digital ID documents. The system provides businesses with more reliable data for KYC and AML purposes, resulting in greater digitization of healthcare and financial services. 

In countries, where the governments aren’t as active on digital identities, private companies take the lead.  Capital One, Wells Fargo, Chase, and Bank of America, for example, launched Authentify — a joint identity verification product, that allows all participating businesses to perform KYC and securely exchange sensitive banking data.

New digital identity solutions can also address the cybersecurity shortcomings of traditional authentication methods like passwords (which aren’t effective and hinder UX). Passwordless authentication and identity-based single sign-on (SSO) solutions will be the next evolutionary step for cybersecurity. 

Microsoft, for example,  already launched Entra Verified ID —  a security product, based on decentralized identity standards. Instead of having identity data distributed across countless apps and platforms, Microsoft wants to build an environment, where people and organizations are more empowered to control how, when, and from whom their data is accessed.

Fundamentally, 85% of global executives agree that digital identity is no longer just a “technical issue”; but a “strategic business imperative” for their organizations. 

First, there’s an opportunity to create new value pools and innovate applications of digital identities. Secondly, digital IDs will soon be central to every business’s operations. Some companies will experiment with developing their digital identity solutions, while others may be looking for ways to integrate existing solutions into their products. For example, to bolster security or embed their offers into adjacent products (e.g., offer financial services to independent contractors, working on gig platforms). 

Strong Sustainable Agenda for Technology 

Environmental, social, and corporate governance (ESG) are the three pillars, shaping future business models, and more lately — technology investments. 

Gartner predicts that by 2027, 25% of CIOs will see their compensation linked to their sustainable technology impact. Many leaders are exploring the applications of emerging technology for ESG to evaluate the benefits and estimate the ROI. 

By applying AI to corporate sustainability, organizations can generate $1.3 trillion to $2.6 trillion in value through extra revenues and cost savings by 2030.

BCG

Virgin Atlantic, for example, developed a new coaching application, teaching pilots to fly more effectively from a fuel burn perspective. Powered by historical and real-time insights, the application helps optimize routes for different airplane models and assists the captains in making the most sustainable choices. In 2019, Virgin already reduced CO2 emission rates by 17% per revenue tonne kilometer (RTK) and aims for another 15% gross CO2 reduction per RTK by 2026. 

Siemens, in turn, developed a proprietary supply chain management (SCM) sustainability platform, which collects operational data on carbon reduction initiatives,  corporate responsibility self-assessments (CRSA), on-site audit results, and other sources to inform decision-making.  Thanks to greater visibility into their supply chain and advanced big data analytics, Siemens achieved major progress in ESG: 

• Ensures ethical goods and service purchases from 140 countries, while maintaining full supplier compliance with the internal code of conduct. 

• Reduced supply chain emissions by 1% in 2023, compared to a 2020 benchmark, despite increasing the purchase volumes by 33%. 

Apart from investing in ESG solutions development, we also expect accelerated investments in climate tech. According to McKinsey, 12 sets of climate technologies will enable around 90% of the total carbon abatement needed by 2050, but only 10% of these are commercially mature. 

Source: McKinsey 

In other words: there are plenty of promising opportunities for new product development in the climate sector, especially in the domain of carbon capture, circular economies, and renewable energy infrastructure management. As we also wrote, in our previous post many of the new climate tech products and ESG initiatives will be enabled by AI.  

AI Trust, Risk, and Security Management (AI TRISM) Enters the Spotlight 

Artificial intelligence had a breakthrough year. Generative AI apps demonstrated a new degree of speed, efficiency, and “intelligence” we can expect from software. 

However, with more AI applications entering the production stage, concerns about AI trust, risk, and security increase. According to Gartner, 66% of business leaders worry about the risks of mass Gen AI availability carriers, especially regarding data privacy, information validity, and breacher opportunities. 

AI trust, risk, and security management (AI TRiSM) emerged as a new framework for managing these risks. By combining governance principles and better techniques for model development, AI TRiSM promotes the creation of fairer, more reliable, and robust AI systems. 

The goal of AI TRiSM is to provide AI development teams with better tools for model explainability, risk management, and security orchestration — and many players are already working in this domain. 

Google released a Secure AI Framework (SAIF) in 2023, offering best practices for mitigating risks specific to AI systems such as data poisoning, prompt injection, and backdoor extraction of confidential information from the training data. 

Startups like Fiddler and Aquarium, in turn, launched observability platforms for AI models, offering out-of-the-box access to model monitoring, performance analytics, data quality, and bias assessment tools. 

As new AI regulations emerge, we also expect a stronger focus on AI explainability and greater growth of explainable AI (XAI) solutions. The demand for XAI is already high for AI products in finance and education, where regulators demand greater model transparency. 

Amsterdam-based Deeploy, for example, offers access to an integrated development environment (IDE) for responsible AI development and deployment. CitrusX, in turn, just closed a $4.5 million seed funding round to build out its end-to-end AI explainability platform. 

Looking ahead, we expect more frameworks and products to emerge in the XAI and responsible AI spaces, especially as the regulatory pressure increases. 

New AI Regulations Will Shape the Competitive Landscape 

At the end of 2023, the EU approved the first legal framework for AI: the AI Act. The policies have received a mixed response from the development community, with many commenting on its limiting policies for Gen AI apps (and foundation models in general). Mistral AI and Open AI both said that the AI Act could push their companies out of the EU markets. 

On the other side of the globe, the Biden Administration issued an Executive Order on AI, with some 150 requirements federal agencies must meet to advance safe, secure, and trustworthy AI.

Overview of the main AI regulations around the world 

Source: BCG 

The federal US regulations on AI are less stringent. But individual states have the power to change the stakes. In 2024, California and Colorado plan to adopt new regulations for automated decision-making in the context of consumer privacy. These will apply to AI systems, trained on consumers’ personal information, or those that collect it. They also give users an option to opt out of AI usage, which can affect AI products for insurance or hiring, for example.  

Industry analysts expect more AI regulations to emerge globally in 2024, with some arguing for the need for a “global AI agency” — an independent, neutral entity to oversee AI implementation similar to how International Civil Aviation Organisation (ICAO) manages the aviation industry or CERN — nuclear research. 

But before that happens, AI companies will have to face a disjointed world of regional regulations, varying a lot between the US, the EU, the UK, and counties in other regions. For multinationals, this can be a period of heightened compliance risks, while AI startups in markets with favorable regulations will grow much faster than their bureaucracy-ridden peers. 

Stronger Focus on Data Management

In 2020, over 47 zettabytes of data were created — by 2035, data generation volume will increase to 2,142 zettabytes per annum. 

The rapid growth of data volumes, variety, and velocity presents a unique challenge. Rather than being a cause for concern, however, it’s an opportunity to revamp data management practices.

95% of global executives agree new data architectures and strategies are required to manage the dramatic changes to their organizations’ data landscapes.

Accenture

Over the past years, we’ve seen innovative companies transitioning to cloud-native data storage locations (data warehouses and data lakes) and building up orchestrated data pipelines for processing data in near real-time. 

Data mesh architectures now emerge as the next evolutionary step, with companies implementing decentralized, domain-oriented, self-serve designs to streamline data transformation, improve data accessibility, and improve cyber-resilience by eliminating the single point of failure from data systems designs. 

Similarly to microservices architecture, data mesh combines lightweight services to expose more data to consumers or business applications. Data gets separated by functional domains and treated as a product, which enables new data analytics use cases.  

Apart from greater data accessibility, new data architectures also promote better data governance. Over 90% of business leaders agree data transparency is becoming a competitive differentiator for their organizations. The Open Banking movement paved the way for a new generation of personal finance management products and digital banks, for example. 

Improved data transparency can also inform innovative product development. Australian manufacturer Brambles, for example, used its supply chain data to create a new range of circular products for logistic companies. Thanks to robust analytics, the company can also estimate over 80% of its product’s environmental impact at the design stage. Not only did Brambles improve its sustainability, but it also helped its customers reduce carbon footprints. 

Source: Brambles 

In 2024 and onward, we expect a greater focus from business leaders on their data management infrastructure, especially if they plan to launch new ML/AI solutions.  By prioritizing good data governance, leaders will also face less regulatory pressure and reduce their exposure to cyber risks. 

In other words: investing in data management infrastructure is not just a necessity but also an opportunity to gain a competitive advantage in the market. 

 Quantum Computing is Changing Cybersecurity

Quantum computing is a longer-term tech trend, as the technology is still far from being mainstream in most domains. But its earliest impacts will likely be seen in cybersecurity. 

Industry experts predict that within the next five to thirty years, quantum computers may be able to break all current encryption standards like RSA and ECC. Most think there’s even a 50%-70% chance of this happening in the next five years.

To ensure data privacy and security in the future, we’ll need to develop quantum-resistant security algorithms today. The US National Institute of Standards and Technology (NIST) plans to issue the first standard for “post-quantum cryptography” (PQC) in 2024. 

On the other hand, quantum systems will also need new security mechanisms. Quantum processing can’t be monitored in the same ways as classical computers. Hence, we’ll need new tools for assessing the accuracy, safety, and security of quantum workloads. Also, we’ll need to create new mechanisms for protecting multi-tenant and distributed quantum computers as the technology scales — and this will be an existing new frontier for cyber sec companies. 

Translating Technology Innovation to Business Impact 

In 2024, technologies like digital identities, big data analytics, and AI will be further commoditized and embedded into more business products and corporate workflows. Greater access to data and analytics, in turn, will help leading businesses accelerate new product development, improve ESG metrics, address regulatory concerns, and stave off cybersecurity risks. 

If you’re trying to determine the optimal role of emerging technologies in your business and identify the key benefits, reach out to 8allocate. Our company offers a full cycle of technology implementation services — from initial ideation and consulting to use case validation through product discovery and software engineering with dedicated teams. Make more informed decisions with our strategic support and cross-domain technology expertise. 

At the start of the 20th century, the Industrial Revolution transformed how we live and work, accelerating the speed and volume of goods we produce and scientific innovation. 

In the 21st century, digital technologies have created breakthrough economic opportunities (and entire new industries), powered by cloud computing, mobile connectivity, advanced analytics, and more lately artificial intelligence (AI). 

Now it’s time to think about the future we’re building. Leading organizations no longer apply digital technologies to improve operating costs or organizational speeds. Instead, they’re sizing up their potential to drive better ESG outcomes to be ahead in the emerging net-zero economies. 

Tech-Led ESG Trends for this Decade

ESG strategies should be in the CIOs’ purview. After all, at its core ESG is a data problem, requiring businesses to first establish the current benchmarks and then chart the path forward to more sustainable operations. 

The technology role will only expand from an axillary tool for ESG data collection and reporting to a central pillar for new operating models and sustainable products. 

Our team has identified eight major trends, showing the massive potential of applying digital technologies to ESG opportunities. 

1. CleanTech Will Become a Major Economic Sector 

CleanTech (or ClimateTech) is an emerging sector of technologies, aimed at reducing the negative environmental impacts through energy efficiency improvements, sustainable use of resources, or or environmental protection activities. 

Cumulatively, the CleanTech sector can expand to $650 billion, surprising the oil market’s value by 2030. Rapidly developing CleanTech segments include:

• Clean and renewable energy 
• Circular economy solutions 
• Food supply chain optimization 
• Bioshpehre preservation 
• Cabron measurement and reduction 
• Green mobility 
• ESG data analytics 
• Sustainable fuels 

Decarbonization of the transportation sector, for example, obviously accelerates the demand for electric vehicles (EVs), but also ancillary growth pockets: 

• EV fleet management applications 
• Multi-modal transportation management systems 
• EV charging infrastructure and management software 
• Better traffic management solutions 

Effectively, leaders in the transportation space are switching to cloud-centric, platform-based operations. To support the emerging smart mobility sector, transportation companies will need to adapt their systems to:

• Collect big data, incoming from connected vehicles and smart road infrastructure (edge IoT devices, road sensors, CCTV cameras). 

• Securely operationalize it in the cloud and transmit it back to road players with low latency to enable new applications like smart traffic light signals, remote traffic management, or dynamic road pricing among others. 

This transformation process will require a mature data management strategy, as well as expertise with cloud, IoT, and big data analytics technologies. — areas, where 8allocate could help.   

A green transportation sector will also accelerate transformations in the energy sector. Europe, for example, will need an extra 324 TWh of electricity to power a fleet of long-haul EVs.  That’s without factoring in the consumption of passenger EVs and public transportation. 

New types of energy generation systems will be required, plus better solutions for energy greed management — and many market players are already working in this domain. 

Ogre, for instance, created an AI-driven platform for energy management, which can generate accurate demand forecasts, for connected renewable energy sources (wind turbines, solar panels, hydro stations). Thanks to real-time big data analytics and AI-based predictions, plant operators can forecast consumption to reduce financial losses from procurement errors, plus detect anomalies and future technical losses to reduce risks of service interruptions. 

Overall, AI can help the energy sector process massive volumes of data to identify new patterns in energy demand and areas of operational inefficiencies. 

2. Wider Adoption of Circularity Practices 

The traditional linear economy has proven to be unattainable. Growing demand for new housing, more energy, products, and services inflate the demand for raw materials and resources, which are available in finite quantities. 

That’s why companies and global economies at large (like the EU) are bullish on introducing better circularity practices — and digital technologies will play a key role in this process. 

Circular economy helps extend the materials’ life cycle through smart re-usage. Data analytics, combined with new connectivity technologies, help organizations track material use across the supply chain and flag opportunities for reuse, recycling, and refurbishing. 

IKEA, for instance, plans to become a circular business by 2030. To do so, the manufacturer released a data-driven Democratic Design tool, which evaluates all new products through five dimensions: function, form, quality, sustainability, and low price. Design that balances all five of these elements is considered democratic and sustainable, and thus ready to hit the shelves. 

The tool also helps IKEA calculate each product’s lifespan, based on the data about product usage and handling during ownership. Moreover, the company plans to include built-in features for convenient reuse, refurbishment, remanufacturing, and recycling in its new range, to promote further sustainability. 

By intelligently using data, IKEA increased its circular capabilities of the lowest-scoring products (to 36%) and maintained full circular capability (100%) for the highest-scoring products. 

Apart from design optimization, emerging digital technologies like machine learning and parametric 3D modeling can help businesses in the manufacturing and construction sectors select and source more sustainable materials, better track material provenance, reduce energy consumption during production stages and suggest new opportunities for material reuse and recycling. 

3. ESG Benchmarking, Based on Real-Time Data 

ESG disclosures are becoming mandatory for publicly traded companies around the world, but meeting these requirements is challenging. 

ESG reporting requires data from multiple sources, including human capital, greenhouse gas emissions, supply chain, and transportation among other areas of governance. These insights are produced and stored in multiple transactional systems, which lack interoperability. 

New approaches to data management and analytics will become the key areas of focus for organizations. Modern ESG analytics solutions can help businesses determine their current posture and track progress toward new targets. 

Downer Group, for instance, went with IBM Envizi ESG Suite to centralize its ESG-reporting efforts. The analytics platform tracks the company’s GHG emission levels and energy consumption across 300 operating sites via a single interface. It also helps measure the progress toward specific ESG goals such as a 50% reduction in GHG emissions by 2023 against a 2020 baseline, plus automatically report on progress to regulating authorities. 

IDC predicts that 75% of large enterprises will adopt ESG analytics and reporting platforms by 2024 — and generate sustainably driven cost and competitive advantages. For software vendors, this translates to a higher demand for ESG-focused analytics features, as well as self-service BI tools for benchmarking. So new ESG software development can be a promising area for new product development. 

4. AI Takes Charge of Operations Management 

While data analytics helps companies set benchmarks and measure ongoing progress, AI and ML can help locate new opportunities for ESG improvements and suggest prescriptive actions for higher operational efficiencies. 

For example, 8allocate recently helped a major US logistics company deploy a mobile AI-powered application for automatic container label recognition, which helps the company process dispatches faster. 

KDDI, Japan’s second-largest communications service provider, in turn, uses AI to improve its energy management. Based on Nokia AVA for Energy Efficiency, the platform can assess energy demand and traffic patterns in real-time and automatically adjust the amount of power being consumed by Radio Access Network (RAN) resources. The system also self-coordinates across multiple neighboring cells to deliver the best power savings within a coverage area. Thanks to it, KDDI reduced power consumption by 50% in low-traffic environments and by up to 20% per cell. 

Other emerging use cases of AI for ESG include Facility management with digital twins,  

predictive risk intelligence, transportation route optimization, and optimized workforce management among others.  

5. Urban Management Will Receive a Tech Makeover 

Overcrowding is becoming a major problem.  By 2030, there will be 43 megacities with a population of 10+ million. Such densely populated areas will require better mobility networks, improved traffic management, and smarter planning for new construction projects. Climate risks will also require faster, more consolidated responses from city managers 

Digital technologies can help design and maintain smarter, greener, and more inclusive cities of tomorrow.  UNDP’s Strategic Plan for 2022-2025, in particular, calls for exploring new applications of digital technologies to increase cities’ capabilities for withstanding heat waves, flooding, and other adverse climate events. 

The city of Toronto developed an online Heat Vulnerability Index to alert residents about potential exposure and direct them to nearby cooling centers. Such maps can be created with real-time data through partnerships with meteorological agencies and installations of temperature sensors around urban areas. Madrid recently installed a network of 27 meteorological sensors to measure temperature, humidity, and other parameters across the city and better coordinate urban services in response to climate events. 

Boston-based startup Tomorrow is taking weather prediction a notch further with the help of AI. Its AI weather engine relies on data from the first commercial weather radar satellite to aggregate the latest information and provides users with hyperlocal forecasts and highly probabilities weather modeling capabilities. The solution provides companies in the transportation, aviation, mining, and defense industries among others with high-fidelity weather data and predictive risk modeling to make critical operational decisions. 

AI can also help cities be more inclusive for people with disabilities. The AI Group, set up by the city of Amsterdam, uses image recognition software to detect barriers (e.g., broken sidewalks) on Google Street View images and then deploy urban services to fix the issue. 

Finally, a combination of advanced analysis and 3D modeling provides urban planners with a richer spatial context. 3D digital models are more affordable to create for new urban projects, plus they allow rapid prototyping and can be effectively re-used for multiple projects. By using 3D models, planners, architects, and developers can iterate on new projects faster and evaluate the project’s impact on the local communities using real-world data.  So this is an area also ripe for new software development. 

6. Carbon Reduction as a New Driver for Public Cloud Migration 

Cloud adoption has many operational advantages, with carbon reduction being one of them. 

Top cloud service providers (CSPs) already power a significant portion of their operations with renewable sources. For four consecutive years, Google has been matching 100% of its global annual electricity consumption by data centers with purchases of renewable energy and is currently working on establishing on-prem renewable infrastructure. 

Microsoft plans to shift to a 100% supply of renewable energy by 2025. AWS plans to power its operations with 100% renewable energy by 2025 and already has 90% of the electricity consumed, attributed to renewable sources. 

Apart from equipment and maintenance costs, on-premises data centers also have a significant carbon footprint attached to them. Cloud adoption can help eliminate it. John Mennel, managing director at Deloitte, says that cloud computing adoption can save companies up to 80% on energy costs. 

In other words: CIOs can contribute to sustainability goals by selecting CSPs with a low carbon footprint and adopting efficient migration approaches — an area of expertise at 8allocate. 

7. Accelerated Human Capital Development 

Pervasively, companies are facing critical skills shortages and can’t access enough specialized talent, especially with IT and AI competencies. 

In Europe, three-quarters of companies have difficulties with sourcing workers with appropriate skill sets. That makes sense given that 44% of people in the EU and a third of Europe’s workforce lack basic digital skills. Gartner also found that even with the recent wave of tech layoffs in the US, tech skills remain in short supply. 

There’s no universal solution to resolving skills shortages. Rather this process requires a combination of human capital development and management practices organizations can (and should!) take to nurture a future-proof workforce.

This can include outbound actions such as expanding talent sourcing strategies to new markers to overcome local shortages. CEE countries like Ukraine, Poland, and Romania are promising hotspots, with large talent pools and reliable software development partners. 

Internally, organizations are also sizing up different technologies to help with talent acquisition and development. IBM designed an AI-powered decision support tool that helps HR teams better plan compensation. The platform analyzes dozens of internal and external data sources (e.g., stats from the Bureau of Labor Statistics) to provide recommendations. The tool is already used by thousands of first-line managers across IBM offices. 

Wealth management firm BakerAvenue, in turn, uses a DEI analytics platform to identify better career paths for its female employees. With the tool’s data-driven recommendations, BakerAvenue increased the number of women in leadership positions by 2X and the total number of women in its workforce by 40%. 

Emerging technologies also help deliver better workforce training, helping new employees safely use complex equipment and follow the required operating procedures. 8allocate, for example, recently helped implement an employee training solution for Hololens 2 glasses, powered by Microsoft stack for AI, and 3D modeling, which substantially improved employee training experiences. 

8. Greater Supply Chain Efficiency and Transparency 

The past several years have uncovered the fragility of global supply chains in the face of adverse economic, geopolitical, and health events. On average, supply chain disruptions now cause between 6% and 20% of revenue losses. 

Moreover, global governments and consumers alike are also calling for more sustainable supply chains, penalizing companies with problematic practices in human rights, working conditions, and ethicality. 

In the last year alone, over 117 sanctions have been imposed on companies found of using forced labor of Uyghur populations, resulting in 2,325 shipments denied and 988 pending for a total value of $826 million, according to the U.S. Customs and Border Protection.

On the other hand, consumers are ready to pay a 2% to 10% price premium to companies, exhibiting greater supply chain transparency. This is particularly important for the food, apparel, and manufacturing sectors, which bear the brunt of consumer dissatisfaction. 

Better supply chain analytics can help bring more visibility into the global supply chains, promoting better governance of downstream suppliers, plus more sustainable sourcing, transportation, and processing of goods.  

Patagonia, for example, has been successfully running a Supply Chain Environmental Responsibility Program for a couple of years now. The company uses industry-wide tools, such as the Higg Index, as well as third-party certification programs, such as the bluesign® system, to demonstrate supplier compliance. The company also boasts an advanced Material Traceability Tracer, which allows it to clearly communicate the provenance of all sourced materials.  

Other adopted supply chain analytics such as Nike offer real-time, public insights into individual factories, including full data on the labor force, type of products made, and more.  

The UK retail chain Marks & Spencer also has a similar interactive map of its food and apparel manufacturers.

To achieve the same level of transparency, businesses need to implement real-time tracking, monitoring, and verification of incoming supplies. Investing in streamlined data management then unlocks opportunities for more advanced analytics. 

For example, using AI to reduce operational waste. Inventory management systems with predictive analytics capabilities can dynamically adjust inventory levels, based on the demand trends to avoid overstocks. By analyzing vehicle telematics data, companies can also optimize transportation routes, reducing fuel consumption and GHG emissions. 

Lastly, supply chain traceability solutions allow streamlined reporting and compliance with ESG standards, leading to greater consumer and investor trust. 

To Conclude 

By 2050, digital technologies can reduce global emission rates in the most polluting sectors by 20%. 

That said, carbon reduction and clean energy generation represent only a fraction of new opportunities in the ESG sector.  More sustainable, digital-led product and building design, powered by digital twins, real-time transport optimization, immersive employee training, and greater supply chain transparency — there are plenty of green growth vectors tech-led companies can explore to make the world a better place.
8allocate would be delighted to further assist you with developing novel ESG and ClimaTech solutions through proactive support with product discovery, technology consulting, and project implementation with dedicated development teams.

Frequently Asked Questions

Quick Guide to Common Questions

How are emerging technologies shaping ESG strategies?

Emerging tech plays a critical role in sustainability efforts. AI and data analytics enhance real-time ESG reporting, energy optimization, and supply chain transparency. Companies are leveraging predictive analytics and automation to track environmental impact and improve compliance.

How does AI improve ESG benchmarking and reporting?

AI-powered ESG platforms automate data collection across multiple sources, reducing manual errors and improving auditability. Businesses can track carbon footprints, energy consumption, and regulatory compliance in real-time.

How do companies use AI to enhance supply chain sustainability?

AI helps optimize logistics, reduce emissions, prevent waste, and increase traceability in supply chains. Predictive models identify more sustainable sourcing strategies and detect compliance risks with ESG standards.

How does technology support carbon reduction initiatives?

AI-driven energy management systems, cloud migration, and operational efficiency improvements help businesses lower their carbon footprint. Predictive models adjust energy consumption in real time to minimize waste.

What role does AI play in workforce sustainability and talent development?

Companies use AI-driven workforce planning and training models to bridge skill gaps and improve productivity. AI supports DEI (Diversity, Equity & Inclusion) analytics, workforce retention strategies, and fair hiring practices to create sustainable human capital policies.

Why is cloud adoption becoming a priority for ESG?

Public cloud providers are increasingly powering data centers with renewable energy. Cloud migration helps companies cut infrastructure-related emissions, scale operations efficiently, and reduce IT energy consumption.

How do predictive analytics and automation improve ESG compliance?

Automated compliance tools track real-time ESG metrics, flag risks, and generate accurate reports based on international sustainability frameworks, ensuring transparency and accountability.

Environmental, social, and governance (ESG) is the focal point of attention among business leaders. 

ESG disclosures are already mandatory for publicly traded companies in a number of countries in Europe, North America, and APAC. The new EU Corporate Sustainability Reporting Directive (CSRD) will further expand the scope of ESG reporting for EU-based and non-EU companies with subsidiaries in the region starting in 2024. 

That said, ESG is more than just another compliance requirement. It’s a broader movement towards establishing more equitable, sustainable, and future-oriented business operations. And strong ESG practices are directly tied to improvements in financial performance. 

The problem, however, is that business leaders don’t always understand how they can improve ESG performance — and that’s where artificial intelligence (AI) with its robust data collection and analytics capabilities can help. 

How AI Helps Improve ESG Practices 

AI and ESG may seem like an unlikely pairing at first glance. After all, AI systems consume a substantial amount of natural resources (e.g., electricity for computation, water for data center cooling, etc) and are sometimes viewed as a threat to diversity, equity, and inclusivity.

But the truth is: AI can do much more good than harm for ESG when built on responsible data practices and AI data governance. Advanced algorithms can help companies measure their carbon footprint with higher effectiveness, identify gaps in workforce diversity, and improve corporate governance. 

In fact, by applying AI to corporate sustainability, organizations can generate $1.3 trillion to $2.6 trillion in value through extra revenues and cost savings by 2030.

Our team has lined up the most promising applications of AI for ESG: 

1. Energy management 
2. Air quality monitoring 
3. Carbon footprint measurement
4. DEI optimization 
5. De-biased recruitment 
6. Risk intelligence 
7. Financial inclusion 
8. Automated compliance 

1. Improved Energy Management

AI, combined with on-prem IoT devices and digital twin systems, enables a better understanding of energy consumption at the facility and real-time optimization. 

By using data from smart sensors, utility companies, and other types of installed sensors, organizations can create virtual replicas of managed buildings. Then apply machine learning algorithms to run different predictive scenarios. For example: model energy usage under different occupancy scenarios or forecast utility savings under different HVAC settings. 

Flex2X by Grid Edge helps forecast the building’s energy profiles and identify opportunities for shifting demand to cheaper and lower carbon times of the day. The platform combines data from the energy management system with external data sources (e.g., weather forecasts) to make predictions and suggest optimization scenarios. Pilot deployments have shown that Flex2X can deliver:

• Over 10% in annual savings and revenue generation for managed buildings
• Carbon reductions of up to 40%  through load-shifting and efficiency measures

Several other companies are also experimenting with dynamical energy optimization including Ogre, C3.ai, and Energiency. With commodities costs rising year-on-year and net-zero targets nearing, the AI-driven energy optimization market will likely expand even further, accelerated by generative AI trends in sustainability analytics.

2. Air Quality Monitoring 

Air pollution accounts for 12% of the global burden of disease. In response, global governments are allocating ample funding for implementing clear-air policies and solutions. Air quality monitoring, in particular, is among the highest priority areas and one where AI can make an impact. 

AI algorithms help process data on air quality at higher speeds, detect anomalies with more efficacy, and provide human analysts with more detailed insights for timely action. The United Nations Environment Programme (UNEP) recently partnered up with IQAir,  a Swiss air quality technology company, to create a new global air pollution monitoring platform, code-named GEMS. 

IQAir aggregates data from 25,000 air quality monitoring stations in more than 140 countries and uses algorithms to provide real-time ratings on air quality and extra insights to inform health protection measures. 

This use case of AI for ESG can lead to systemic changes in how air pollution is managed, leading to major societal improvements.  However, it’s a challenging one to implement since it requires a mature data management strategy, as well as AI/ML expertise — two areas where 8allocate can help when it comes to developing ESG solutions. 

3. Carbon Footprint Measurement 

Carbon footprint calculations are critical to understanding and reporting on the sustainability of your operations. But it’s also a labor-intensive task as you need to pull accurate data on:

• On-site industrial activities
• Area of facilities and percent of occupancy
• Facility energy use such as electricity, gas, coal, oil, and solar
• Corporate travel such as plane, rail, vehicle
• Waste generation 

…And do so across multiple sub-stages of your product or service lifecycle and supply chain. 

Machine learning algorithms are actively being used to measure carbon emissions of different types of operations. Johns Hopkins Institute created an AI-powered system for measuring greenhouse emissions from vehicles. The team combined remote sensing technologies and computer vision to capture the signatures of emissions from visual satellite data, as well as road network data and population stats. 

Because some areas had fine-grained emissions and vehicle count data, the team could train the algorithms on ground-truth data on vehicle activity. Then apply the model predictions first at the country level to produce accurate estimates for road transportation emissions of the top 500 emitting cities worldwide.

IBM, in turn, released a smart cloud carbon calculator, designed to help companies spot high emission patterns in their IT workloads and provide insights for emission reduction strategies. AWS has a similar Carbon Footprint tracking Tool, designed to report on carbon emissions, generated by the company’s cloud usage and run different modeling scenarios.  

Rapid digital transformations including cloud adoption and advanced data analytics adoption often come at the cost of an increased carbon footprint. So such IT-focused carbon calculators come in handy for companies looking to achieve net-zero across their entire value chain. 

4. Better Diversity, Equity, and Inclusion (DEI)   

Pay equity, racial diversity, greater inclusivity — organizations still have a long way to go when it comes to improving DEI metrics. Part of the problem of why problematic practices like racial or gender pay gaps exist, however, is low visibility into DEI data. Data analytics, combined with AI, can help collect and track this information in real-time. 

For instance, Diversio uses data from existing HR platforms to evaluate the company’s DEI efforts and benchmark its performance against industry peers. The in-built recommendation engine, powered by 1,200 vetted DEI programs and policies, also advises which adjustments are worth making to improve the inclusion scores. 

Toronto-based The Drake Hotel used the Diversio platform to better understand its workforce and improve DEI metrics. The analytics platform helped the hotel better investigate gaps in racial equality, LGBTQIA+ employee inclusion, and promotion of good mental health. After implementing the suggested policies, The Drake Hotel got four points ahead of its industry when it comes to women’s inclusion, six points ahead for representation of LGBTQIA+ team members, and 20 points ahead for representation of disability and people living with mental health conditions. 

AI in DEI can also help bring other positive transformations, namely around new employee onboarding, talent pipeline management, and overall employee experience at the workplace. So it’s a promising area worth addressing with tech-enabled ESG solutions. 

5. De-Biased Recruitment 

AI algorithms can help eliminate biases at different stages of recruitment by auto-redacting data about the candidate’s race, gender, or age, adjusting the job description language, and fine-tuning corporate communication. 

Textio trained AI algorithms to flag problematic language from job posts, sourcing mail, and employer brand content. Similar to Grammarly, it also provides contextual prompts for writers to use more inclusive language. US telecom T-Mobile rolled out Textio to its team of 125 recruiters, employer branding specialists, and DEI consultants, plus some 9,000 hiring managers. Apart from reporting a positive usage experience, the HR team also saw a 17% increase in female applicants and a 5-day reduction in average time-to-fill for open roles. 

Likewise, AI can help job-seekers discover better work opportunities by recommending jobs they may have not considered. For example, the new generation of AI recruiting apps assesses candidates’ skills and interests and then recommends suitable job openings. Arya by Leoforce uses AI to provide recruiters with the best candidate profiles, and sources from 70+ different platforms. Arya generates a shortlist of candidates based on the skills listed in the job description and helps pre-screen candidates using 7 multi-dimensions and 300 different attributes. In this way, AI can increase talent pool diversity and help improve D&I by introducing you to a broader selection of applicants. 

6. Better Financial Inclusion

AI in FinTech and finance can improve access to banking services for unbanked and underbanked consumers, as well as traditionally marginalized population groups. 

According to PwC, improvements in financial inclusion can add over $5 trillion to the US gross domestic product over the next five years. Moreover, easier access to lending and investing can also help consumers build better money habits, leading to a wider scope of positive changes. 

By combining AI and data analytics, credit and lending products can be made available even to people with no credit history, formal employment, or digital financial track record — and without extra risks for the banks. Take it from Chinese digital banks that issue over 10 million loans annually, while maintaining a non-performing loan ratio of 1% on average, thanks to machine learning. 

In the US, SoLo Funds chose a similar path of using alternative credit data and custom credit scoring algorithms to evaluate each lender’s capacity. The company only uses the provided bank account information to make short-term lending decisions at an affordable rate.

7. Risk Intelligence 

Businesses today face a “conveyor belt” of risks — climate, economic, geopolitical, social, and supply-related. Algorithms can help measure the company’s risk exposure to multiple factors and provide early warnings to promote better governance. 

Datamaran, for example, created a prolific ESG risk analysis platform, powered by AI. It helps businesses conduct in-depth materiality assessments to identify critical and emerging ESG issues, as well as collect data for necessary ESG disclosures. 

AI can also facilitate greater transparency through analysis of business performance, operations, and compliance data. S&P Global, for example, relies on a machine learning model for predicting the probability of defaults to inform its investing efforts.  

8allocate has also helped a major logistics company de-risk its operations through better container tracking. Our dedicated development team implemented an AI-powered mobile application for automatic container recognition and subsequent streamlined monitoring. With the ability to track shipments in real-time, the company substantially minimizes operational risks and delivers higher customer satisfaction. 

8. Automated Compliance 

AI can be also deployed to automate an array of compliance tasks: from regulatory findings analysis to automated audits. 

For example, Google launched Checks this year — an AI-powered platform that helps companies with Google Play and iOS mobile apps quickly discover, communicate, and fix privacy compliance issues. 

JP Morgan, together with Cleareye.ai,  is working on an AI-driven compliance module for the identification of high-risk financial trade transactions. It uses optical character recognition (OCR) and natural language processing (NLP) technologies to extract relevant information from corporate documents and map data directly to the bank’s back-office system. This increases operational efficiency and the accuracy of sanctions screening.

Effectively, AI can be applied to a wide variety of auditing tasks, helping analysts procure, check, and report on ESG data. 

Challenges of Using AI to Harness ESG Improvements 

AI has immense potential to improve companies’ ESG performance. But AI implementation also comes with a host of challenges. 

Limited Data Accessibility 

Data may be ubiquitous, but access to it is often constrained due to interoperability issues, poor data management practices, or privacy regulations.  Yet, to provide accurate insights, AI systems require large, variable datasets. 

According to an EY survey, 60% of companies have ESG information stashed across a patchwork of software applications, and 55% of respondents still store their ESG data in spreadsheets. 

In other words: Organizations will still need to get the basics of data management right: 

• Create appropriate data architectures 
• Centralize ESG data storage 
• Implement data quality controls 
• Set up metadata management 

…And only then look into evaluating different machine learning methods for ESG data analysis or other use cases. 

Systemic Biases 

When AI systems are trained on small datasets (e.g., such that underrepresent a particular gender or ethnic group), they can deliver biased results. Some AI-powered predictive policing systems were trained on historical arrest data mostly from poorer neighborhoods, and can thus reinforce existing patterns of racial profiling. 

A new NIST paper also points out that AI systems often inherit human and systemic biases of their creators:  

When human, systemic, and computational biases combine, they can form a pernicious mixture — especially when explicit guidance is lacking for addressing the risks associated with using AI systems.

When asked to create images of people in specialized professions, Gen AI app Midjourney had been heavily criticized for generating ageist, sexist, and classist images. Google’s online advertising system, in turn, was found to show higher-paying positions to males more often than to women. 

Substantial Environmental Footprint 

The use of AI comes with a “carbon tax”. During the training stage, one AI model can produce over 626,000 pounds of carbon dioxide — the equivalent of driving 62.6 gasoline-powered passenger vehicles for a year. 

To train an AI model like GPT-3 with 175 billion parameters, organizations will need 1287 MWh of electricity, resulting in carbon emissions of 502 metric tons — an equivalent to driving 112 gasoline-powered cars for a year.

Many advanced AI use cases demand substantial computing powers and if you’re not properly offsetting these, your ESG metrics will not move in the right direction. Plan how you’ll offset the carbon costs of AI model development when working on your software requirements specifications. 

Conclusion

As ESG gains the central stage in corporate operations, AI becomes a crucial ally, not just for compliance but for real, impactful transformations in corporate governance, workforce management, and operational sustainability. 

And the examples of AI for ESG that we see today are only the beginning. Using algorithms to analyze more data will yield even deeper insights, accelerating sustainable practices and better governance. 

That said, AI implementation for ESG comes with a host of technological and ethical complexities. These must be addressed during the discovery stage of product development and factored into the product maturation strategy. 8allocate team is available to consult you on the optimal implementation strategy of AI for ESG initiatives. Contact us! 

Frequently Asked Questions

Quick Guide to Common Questions

How does AI contribute to ESG initiatives?

AI enhances ESG initiatives by optimizing energy management, monitoring air quality, measuring carbon footprints, improving diversity and inclusion efforts, reducing recruitment bias, strengthening financial inclusion, providing risk intelligence, and automating compliance reporting. AI-driven analytics offer real-time insights that help organizations meet sustainability goals while improving operational efficiency.

Can AI help reduce carbon footprints and energy consumption?

Yes, AI-powered energy management systems analyze real-time data from IoT sensors to optimize energy use, predict consumption trends, and reduce waste. Machine learning models can also assess carbon emissions across a company’s operations, helping businesses track and report sustainability metrics more accurately.

How does AI support diversity, equity, and inclusion (DEI)?

AI can analyze HR and workforce data to highlight gaps in DEI initiatives. Platforms like Diversio benchmark DEI performance against industry standards and suggest actionable improvements. AI can also help identify biases in job descriptions, recruitment processes, and pay structures to promote fairer hiring practices and workplace diversity.

What role does AI play in financial inclusion?

AI-powered alternative credit scoring models help expand financial services to underserved communities. By analyzing behavioral data and transaction patterns, AI enables banks and FinTech firms to offer fairer lending opportunities, making banking services more accessible to individuals without traditional credit histories.

How does AI improve ESG risk management?

AI models process vast amounts of ESG-related data to assess corporate risk exposure. Companies use AI-driven platforms like Datamaran to conduct materiality assessments and monitor regulatory compliance. AI also enhances transparency by identifying fraud risks and governance weaknesses in financial reporting.

Can AI automate ESG compliance reporting?

Yes, AI streamlines compliance efforts by analyzing regulatory requirements, automating data collection, and generating ESG reports. AI-powered tools, such as Google’s Checks, help businesses identify privacy compliance gaps, while AI-driven financial platforms improve risk screening for trade compliance.

How can companies ensure AI-powered ESG solutions are ethical and effective?

Organizations should adopt robust data governance frameworks, perform bias audits on AI models, and implement sustainable AI practices. This includes using renewable energy for AI computing, improving model transparency, and integrating human oversight in automated ESG decision-making.

When you have a great product idea or a strong internal driver, it’s tempting to get straight down to action — coding that is. But when you haven’t fully thought through how your application will function, how will you know what features to develop and how will you manage the users’ expectations? 

Failure to account for specific user preferences can lead to poor product adoption. And incomplete technical requirements can extend project timelines and budgets. 

Software requirements specification describes what the new product should do and which characteristics it must have to be considered successful. 

In this post, we explain how to collect, write, and validate requirements for software. We cover functional, non-functional, and technical requirements gathering and documentation, plus provide some best practices for organizing the writing process. 

What is a Software Requirements Specification (SRS)?

A software requirements specification (SRS) is a document that outlines the behavior, features, and performance criteria of new software. It acts as a blueprint for initial project scoping, estimation, and timeline planning. 

By investing time in writing detailed software requirements, you can avoid costly re-dones and omissions at the later stages of the development cycle. A software requirements specification document also provides a solid basis for estimating the project costs and schedules.  

Finally, a software requirements document helps coordinate the development work. It establishes the common “reference baseline” for the products’ capabilities and helps circulate this knowledge among your in-house engineering talent or an external software development team.

What to Include in a Software Specification Requirements Document?

An Software Requirements Specification document should describe: 

• The product’s purpose and include justifications for its creation 
• All user and project stakeholder requirements
• Features or functions of your future software

Depending on the complexity of your product idea, your software requirements specification document may be just under one page or span over a hundred. For more complex software engineering projects, it makes sense to group all the software requirements specifications into two categories: 

High-level requirements: 

• Business requirements (e.g. business goals, objectives, and needs)
• User requirements (what the user needs the product to do)
• System requirements (functional and non-functional ones)

Lower-level requirements: 

• Stakeholder requirements
• UI requirements
• Performance requirements 
• Security requirements 
• Market requirements
• Implementation requirements

This structure helps ensure that all requirements are well-documented and can be easily cross-referenced when needed. Here’s how the above SRS format looks in practice: 

Sample Software Requirements Specification (SRS) Document Format 

Introduction:

• Overview of the software project 
• Business goals 
• Project objectives 

Functional Requirements:

• Use cases or user stories
• User input and output specifications
• System behavior in response to specific inputs

Non-Functional Requirements:

• Product performance requirements
• Security and authentication requirements
• Scalability and reliability criteria
• Usability and user interface design guidelines
• Regulatory and compliance requirements

Reference System Architecture

• High-level system architecture diagrams
• Key software components and modules

Data Requirements:

• Data models, schemas, and data flow diagrams 
• Data storage and processing methods 
• Reference data architecture 

Integration Requirements: 

• Types of internal system integration required 
• Types of third-party system integration required
• Authentication methods used for each system 

User Requirements:

• User roles and permissions
• Standard user workflows 
• Reference user interfaces 

Project Constraints and Assumptions:

• Constraints and limitations of the software
• Assumptions made during requirement gathering

Appendices:

• Extra information such as glossary, abbreviations, or additional diagrams.

Business Requirements 

Business requirements define high-level commercial objectives, needs, and constraints for the project to be considered valuable for the company. 

A business requirements section includes: 

• Reasons for initiating the project 
• Market and customer research 
• Goals and objectives to be met 

Examples of business requirements for a new FinTech platform may include: 

• Revenue diversification: “The new robo-investing functionality will attract extra users to the product and help generate more transactional revenues.”

• Customer retention: “A new chatbot interface will help users discover more product features and resolve common queries through self-service. It also provides new opportunities for in-app engagement”. 

• Competitive advantage: “With a new data processing architecture, we can deploy self-service analytics tools for financial advisors such as next-best-action models to differentiate better over competition”. 

TL:DR: The business requirements section summarizes the reasons for initiating the project and documents the expected commercial benefits.

Functional Requirements

Functional requirements provide a clear, coherent, and consistent description of the products’ features, capabilities, and expected behaviors. They are mostly about how the system will cater to different user actions such as account registration, data input, payment processing, etc. 

High-level functional requirements describe the general product feature or standard user flows.  For example:

“A user must have the option to create a free account using single-sign-on. After the general information is provided, the system must offer to start a free 7-day trial or select a premium plan”. 

On the lower level, functional requirements document the exact system response to a particular user action. For example:

“The system automatically sends a confirmation email to validate new account creation.” 

“The option to start a free trial is available only to people with previously unused emails and full name combinations.” 

To better convey your idea, you can document functional requirements as a combination of flow charts/diagrams and step-by-step feature descriptions as shown in the example below: 

Source: Academia

How to Write Functional Requirements for Software 

The standard approach to documenting functional requirements is by describing the set of product use cases at a high level and associated user stories at a lower level. 

Use Cases

A use case specification describes a sample product usage scenario for a specific actor (type of user) and details a sequence of events within this scenario.  

For example, a use case specification for a login flow can look like this: 

Source: ResearchGate 

As you can see from the above example, the use case specification includes: 

• Use case name and description 
• Pre- and post- interaction condition
• Basic interaction path
• An alternative path

An extra way of documenting use cases is via diagrams. Use case diagrams provide a sample user flow, visualizing how your application interacts with the user, organization, or external services. 

Source: Lucidcharts 

Similar to text-based use case specifications, use case diagrams help document the goals users (actors) try to achieve. Diagrams are handy add-ons to text descriptions as they help showcase the scope of the system and visualize different goals of system-user interactions.

By describing your system through different use cases, you have a better chance to ensure the completeness and non-redundancy of requirements. Use cases, combined with business requirements, also help the software development teams determine the optimal technical characteristics for the system later on. 

User Stories

User stories are a popular Agile technique for documenting functional requirements. As the name suggests, it’s a short software description, created from the perspective of the end user. 

The standard user story format is: 

As a < type of user >, I want < some goal > so that < some reason >.

User stories help better capture the users’ goals and needs. They also explain the rationale behind certain actions, highlighting which features must be included in the software. 

Each user story also includes a set of acceptance criteria — a formal list of specific, measurable conditions or requirements that must be met to mark a user story as complete. User stories can be engineered in different ways. Acceptance criteria narrow down the scope of possibilities. 

For example, the acceptance criteria for a login feature might be as follows:

• “User can use an email or a mobile phone number to log in”
• “The lengths of the password should be between 6 and 20 symbols”
• “Symbols in a password may include letters, numbers, and special signs”.

The common types of acceptance criteria for user stories include:

• Given-When-Then scenarios, which validate if the user achieves the desired outcome(s). 

• Rule-based acceptance criteria, based on specific functional or behavioral parameters

Without clear acceptance criteria for user stories, you’ll struggle to validate the end product against the initial requirements at the user acceptance testing stage. In addition, acceptance criteria also help set clear expectations for the development team, facilitate feature prioritization, and help with scope control.  

Non-Functional Requirements 

Non-functional requirements define how your system should perform in terms of reliability, security, usability, and other quality criteria. Effectively, they document how the software must function, rather than what it should do. 

The common examples of non-functional requirements include: 

Performance 

Performance requirements describe how well a system must operate under certain conditions (e.g., peak traffic, limited bandwidth, or in normal conditions). 

A standard software project specification typically includes the following performance requirements:

• Max/min satisfactory response time for different types of user interactions 
• Typical throughput time under normal conditions 
• Size and timing of maximum-throughput periods
• Min/max storage capacity per system 

Many of the system performance characteristics are driven by existing or expected customer service level (SLA) agreements. For example, Google SLAs state that its App Engine Service will provide a monthly customer uptime of at least 99.95%. Therefore, any new SRS document for this product would likely reference an equivalent performance requirement. 

Pro tip: Consider system dependencies when deciding on appropriate performance requirements. For example, relational NoSQL databases allow faster processing speeds, while SQL ones offer higher data integrity. Therefore, two different data analytics projects, built atop these systems will inherit the technologies’ respective strengths and shortcomings.   

Scalability 

Scalability requirement defines how the system will respond to increased usage and which mechanisms are in place to accommodate higher loads (e.g., more users, more data, etc). Scalability can be defined both on hardware and software levels. 

For example, you can document requirements for: 

• Data compression
• Extra on-premises CPU usage 
• Automatic cloud instance scaling 
• Proper disk space allocation 

Consult with technical specialists such as Solution Architects and IT infrastructure engineers to correctly capture and outline all the scalability requirements for software development. 

Security 

In the past, many engineering teams treated software security as a “bolt-on” — something you do after the first release when the product is already in production. Such an approach, however, proved to be unsustainable. According to the Veracode State of Software Security 2023 report, over 30% of applications have security flaws at the first scan, and 70% contain security vulnerabilities after five years in production. 

Because a company’s trustworthiness and security are interrelated, you should always consider security upfront. 

Security requirements define different policies, practices, and system design methods for the prevention of unauthorized access and usage. Mostly, these requirements document:

• Desired data security methods (e.g., data encryption, permission access, etc). 
• Identity and access management (IAM) controls 
• Authentication methods for users and other subsystems/modules 
• Intrusion detection and prevention mechanisms 
• Data backup and recovery methods 

This section may also include more specific security requirements, mandated by regulations such as PCI DSS, HIPAA, and GDPR among others.  

Usability 

User experience (UX) has become a core differentiator for software products. Design-driven companies grow 2X faster compared to industry peers.  

Usability requirements document the system’s ability to provide an intuitive, efficient, and delightful experience for end-users. Typically, this set of non-functional requirements will describe:

• Product navigation 
• Interface design 
• Accessibility 
• Error prevention 
• UI personalization 

On top of that, you may also want to quantify some of the above criteria. For example, prove navigation design effectiveness by establishing a minimum number of attempts a user needs to complete one use story. 

Interoperability 

Few software development projects are created in a vacuum. In most cases, new solutions are designed to fit into a wider tech ecosystem. 

Interoperability requirements specify how the new software will be designed to interact with other assets in your technical portfolio (e.g., perform write or read operations, automatically synchronize data records, etc). 

The common interoperability requirements for software include:

• Cross-platform compatibility (e.g., across browsers and operating systems)
• Database compatibility requirements 
• Third-party software compatibility 
• Compliance with specific industry standards (e.g., EDI)
• Integration with popular cloud service providers (CSPs) 

Systems with high interoperability reduce vendor lock-in risks, are easier to scale, and have fewer maintenance overheads. Moreover,  they also help cultivate a data-driven culture by diminishing data silos and data redundancy. 

Data Requirements

Data analytics has become one of the key technologies for fast, commercial growth. Over 80% of businesses recognize the strong potential of using big data analytics for improving business efficiency and performance. 

Data requirements describe how the software system will retrieve, exchange,  manage, and store data. Data requirements typically cover the new applications’ database design and integrations with other elements of data management strategy. 

Examples of data requirements for software include:

• Data standards (schema, ranges, allowed values, etc). 
• Data quality and validation rules 
• Metadata management practices 
• Data storage and retrieval mechanisms 

Clear data requirements help establish better data quality, clear data lineage, and greater system interoperability. 

How to Write Specifications For a Software Development Project: Best Practices 

Now that you have a structure for your software specifications document, let’s get down to the deets. Here’s how to write software requirements that get read, understood, and effectively implemented! 

Approach the Right Stakeholders

Great software specifications are centered around user needs — and user knowledge rests with multiple stakeholders. 

To answer the fundamental question of “who this product is for?” and “why it is needed”, you should engage the following stakeholders: 

• Direct users: People who will interact with the new product the most. These can be both internal users and/or external participants, recruited to participate in user research. 
• Indirect stakeholders: People who’d be impacted by the developed software. For example, a new payment processing app will impact the process flows across the Financial, Compliance, and Security departments. 
• Beneficiaries: Any other people who will derive benefits from the new software. In the case of a payment processing app, that could be Sales specialists, customer support staff, etc. 
• Project team: Product owner and senior engineering talent, who’d be able to “translate” the business requirements into functional and non-functional characteristics, plus advice on the optimal tech stack. 

To keep the requirements-gathering process streamlined, you can collect some inputs via a questionnaire and invite a smaller group of people to requirements-gathering workshops. Workshops are typically led by business analysts (BAs), who are trained to elicit and clarify requirements in a structural manner. Then organize them into a coherent SRS document. 

That said, requirements gathering and formalization can be an intensive project. Especially, when you lack customer knowledge, struggle with assessing the projects’ technical feasibility, and looking to enter a new market. 

At 8allocate, we guide clients through the above challenges as part of our product discovery service. By combining interactive workshops with a wider market, customer, and competitor research, we help companies create comprehensive software specifications and transform these into viable digital products.  

Apply the Least Work Principle

When writing specifications for software, always keep this principle in mind: 

Do not do any work that costs you more than it is worth to you.

What we mean by this is that you don’t have to create an exhaustive, one-hundred-page-long SRS document for every software engineering project. 

An SRS document makes sense for:

• Large applications: (Multiple interfaces, 20+ features, integrations with other systems). 

• Critical business applications: (Core banking system, online reservation module for a hotel chain, etc). 

If you’re considering a proof of concept (PoC) or minimal viable product (MVP), a shorter requirements document, listing key business, functional, and non-fuctional specifications would do. Or even a collection of prioritized use cases and user stories. 

Use Accessible, Implementation-Neutral Language  

An SRS document will be read by multiple people — ranging from beneficiaries and secondary stakeholders to software development team members. Ambiguous, superfluous, or overly complex language means that some important details will be misunderstood or overlooked. 

Good quality software specifications must be: 

• Explicit: Don’t make things sound more complex than they should. Avoid terminology and unnecessary acronyms. Use diagrams, models, and schemes to break down more complex ideas. 

• Measurable: Create clear boundaries between different tasks. Include quantifiable metrics where possible. Without clear definitions of done (DoD), the team will struggle to validate and verify the end product against the original specifications. 

• Flexible: State what the system should do, rather than how it should do it. This way you give the development team more space to come up with the optimal tech solutions to the problem, rather than blindly following an instruction. 

• Viable: Verify that all the system requirements can be fulfilled within the outlined budget and timeline. Make sure there are no contradictory requirements or those with technical implementation constraints. 

Finally, make sure all the information is accurate, complete, and easily verifiable. 

Refine and Decompose More Complex Requirements

Work from the top to bottom. Spell out all high-level functional requirements at first. Analyze that these are feasible and aligned with the limitations of the current program (e.g., budget or human resources). 

Then use functional or object-oriented analysis to formalize lower-level requirements for each of the system’s components. You can group the system requirements by initiatives, epics, and stories or create another taxonomy category (e.g., by product subsystem). 

In either case, each lower-level requirement must: 

• Be written as a single definitive statement
• Have a reference number for tracking purposes 
• Be traceable to a higher-level requirement 

Also, ensure that all requirements also have acceptance criteria. Check that the set requirements are testable. 

Check For Consistency 

Once you’re done with writing the bulk of the software specifications, switch to the editing mode. 

Check for any: 

• Redundancies 
• Contradictions 
• Compatibility issues 

The SRS document must not have any conflicting statements in terms of required quality or performance characteristics. If there are some conflicts, consider the trade-offs between different requirements and select the better option.

Finally, verify the completeness of the document. Did you describe all high-level requirements as lower-level ones? Are there any orphaned specs? If all looks good, your SRS is ready for sign-off. 

Conclusions 

Detailed software requirements help establish the scope of work so that the project manager can accurately estimate the project timelines, prioritize backlog, and create effective Sprint plans. 

The SRS document also acts as a “reality-check” for all the done work. It helps ensure that the developed product meets both the business goals and the identified user needs. Changes made to software in the last stages are both costly and difficult to implement. SRS document helps prevent costly reworks and helps ship software faster. 
And you can kick off your software development event faster by partnering with an experienced software development vendor. At 8allocate, we help global teams launch products that hit all the user requirements and generate proven ROI for the business.

Contact us to learn more about our product discovery and software engineering services.

Frequently Asked Questions

Quick Guide to Common Questions

What is a Software Requirements Specification (SRS), and why is it important?

An SRS is a document that defines a software system’s features, behavior, and performance expectations. It acts as a blueprint for development, ensuring clear communication between stakeholders, preventing costly reworks, and setting measurable project goals.

How do I structure a Software Requirements Specification (SRS) document?

A standard SRS document includes business requirements, functional and non-functional requirements, data requirements, system architecture, integration specifications, and constraints. It should be organized for easy cross-referencing and scalability.

What’s the difference between functional and non-functional requirements in an SRS?

• Functional requirements define what the system must do (e.g., user authentication, payment processing).
• Non-functional requirements specify the system’s performance (e.g., scalability, security, performance under load).

How do I ensure my SRS document is clear and effective?

• Use simple, unambiguous language and avoid technical jargon.
• Define measurable acceptance criteria for each requirement.
• Ensure consistency and check for redundancies or contradictions.
• Include diagrams and structured formats to enhance clarity.

What are common mistakes to avoid when writing an SRS?

• Vague or incomplete requirements that lead to misinterpretation.
• Ignoring scalability and security concerns during early planning.
• Overloading the document with unnecessary technical details.
• Lack of stakeholder input, leading to missing user expectations.

How do user stories and use cases fit into an SRS?

User stories provide a user-centric view of system functionality, focusing on goals and expected outcomes. Use cases outline specific interaction flows between users and the system, detailing step-by-step actions. Both approaches ensure requirements align with real-world user needs.

How do I validate software requirements before development?

• Conduct stakeholder reviews and feedback loops.
• Use prototyping and wireframes to visualize key functionalities.
• Perform requirement traceability analysis to ensure all business needs are met.

When should I use a detailed SRS versus a lightweight version?

A full SRS is essential for large-scale, critical systems with complex integrations. A lighter version with key functional and non-functional requirements is sufficient for MVPs, proofs of concept, or agile projects.

What role does an SRS play in Agile development?

While Agile emphasizes iterative development, an SRS still serves as a living document to align stakeholders, define system scope, and guide sprint planning while allowing flexibility for changes.

How does 8allocate support teams in writing an effective SRS?

8allocate provides product discovery, requirement gathering, and software engineering expertise to ensure your SRS aligns with business objectives, technical feasibility, and scalability needs.

The insurance sector is undergoing a profound transformation through technology integration, commonly referred to as Insurtech. By combining advanced technologies with traditional insurance practices, Insurtech addresses the challenges faced by traditional insurance companies, such as inefficiencies due to manual processes, legacy systems, and data silos. This leads to a more expedient and user-centric experience in obtaining insurance coverage. This article explores the current trends driving this evolution and the impacts on both insurers and consumers.

Key Takeaways

• Insurtech is revolutionizing the insurance industry by utilizing technologies like artificial intelligence (AI), big data analytics, and blockchain to enhance operational efficiency, provide ultra-customized policies, and improve customer satisfaction.
• On-demand and usage-based insurance models are reshaping how auto insurance is bought and used, offering flexibility and personalized premiums based on real-time data and individual behaviors, which cater specifically to modern lifestyles. Insurtech companies are leveraging telematics and smartphone technology to provide more personalized car insurance experiences based on driving behavior.
• Despite the benefits, traditional insurers face challenges in integrating Insurtech solutions due to legacy systems, regulatory compliance, and resistance to change. However, collaborations with Insurtech startups can drive product innovation and operational efficiency.

Understanding Insurtech

The convergence of insurance and technology, called Insurtech, is reshaping the insurance sector by leveraging novel technologies to create efficient solutions while unlocking potential savings. Insurtech signifies a departure from legacy insurance models toward more customer-centric methods that are streamlined and adaptive. Traditional insurance companies face significant challenges due to manual processes, legacy systems, and data silos, which lead to inefficiencies and complexities in system upgrades and integration. Insurtech addresses these issues by automating processes and utilizing modern infrastructure. Companies within the Insurtech space lead this evolution with tech-driven initiatives aimed at:

• Increasing operational efficiency: By automating processes and reducing manual intervention.
• Reducing fixed costs: Through digital platforms and optimized workflows.
• Delivering customizable insurance experiences: Offering policies tailored to individual needs.

This technological wave is redefining traditional norms in the industry, offering both insurers and consumers unprecedented avenues for growth. For example, a study by Accenture found that insurers utilizing AI and automation reported a 20% increase in operational efficiency and a 30% reduction in claims processing time. Imagine securing coverage perfectly suited for your unique requirements—whether it’s transient or long-term protection. Insurtech pushes forward faster claim resolutions and user-friendly interfaces, all part of delivering finely tailored products that break away from standard offerings in mainstream insurance.

Early Development and Impact on Traditional Insurance

The early 2000s marked the beginning of the Insurtech journey, addressing long-standing pain points in the insurance industry, such as lengthy paperwork and outdated customer experiences. Traditional insurance companies faced significant challenges due to manual processes, legacy systems, and data silos. Initial efforts focused on making insurance processes more efficient and user-friendly, streamlining operations through technology. This early phase saw a significant transformation as traditional insurers began to realize the potential of integrating digital solutions into their business models.

Traditional insurers grappled with multiple challenges, such as protracted underwriting procedures and obsolete customer experiences, which Insurtech endeavored to address. The rapid growth of Insurtech attracted significant investment and talent, driving its expansion and forcing established insurers to innovate and adapt to stay competitive. According to Deloitte, 64% of insurers are now collaborating with Insurtech firms to accelerate their digital transformation initiatives.

The ascent of Insurtech has spurred the creation of novel insurance needs and products, like tech E&O policies or OSP policies, as emerging technologies foster unique insurance necessities. This shift transformed the financial industry and set new standards for efficiency and customer satisfaction in the insurance market.

Key Technologies Driving Insurtech Innovation

The transformation of the insurance industry is being driven by several key technologies that are reshaping its various facets. At the heart of this revolution, artificial intelligence (AI) and machine learning play a critical role by streamlining customer interactions, enhancing the efficiency of underwriting processes, and accelerating claims handling procedures. Incorporating these innovative approaches heightens operational performance and leads to more precise and tailored service offerings.

Artificial Intelligence and Machine Learning

The introduction of artificial intelligence and machine learning into the insurance sector is revolutionizing numerous operations, streamlining them to be more efficient. Through AI-driven chatbots available round the clock, customer interactions are enhanced by providing immediate responses and support, which greatly boosts the customer experience. With the application of AI in underwriting processes, tasks become faster and accuracy improves significantly due to deep learning algorithms that can assess risk profiles and establish appropriate premiums based on past data.

Machine learning plays a pivotal role in spotting fraud as well as refining claims processing procedures. For instance, Shift Technology utilizes AI to identify fraudulent activities across various types of insurance like health and travel, an essential step toward risk reduction during claims handling. Similarly, Wisedocs offers an innovative platform powered by AI designed for medical document analysis, thereby expediting claim evaluation’s speed while elevating precision.

87% of insurers are investing in AI and machine learning technologies to improve operational efficiency. For example, Shift Technology’s AI fraud detection system reduced fraudulent claims by 30% for one of its clients within the first year of implementation.

Big Data Analytics

Big data analytics stand as another cornerstone technology within Insurtech, enabling insurers to sift through vast datasets concerning clients to forge tailor-made insurance products while ensuring accurate risk assessment. By strategically harnessing this data, companies can pinpoint potential risks with greater accuracy, offer competitive pricing models tailored specifically to individuals’ needs, and craft customized products efficiently.

Within the insurance industry, deploying big digital datasets yields multiple advantages such as:

• Offering an extensive perspective on probable risks and client tendencies: Enhances underwriting procedures.
• Streamlining decision-making processes: Greater accuracy.
• Providing personalized product offerings: Competitive rates aligned with customers’ unique demands.
• Increasing satisfaction levels among consumers: Through a focus on their needs.

IBM reports that 81% of insurance executives believe big data analytics is critical to their business strategy.

Blockchain and Smart Contracts

The insurance industry is being transformed by blockchain technology and smart contracts, which provide secure and transparent transactional processes. The establishment of unalterable, decentralized records through blockchain guarantees that transactions are both secure and intact. Smart contracts on a blockchain stay inactive until triggered by predefined conditions, leading to the automatic initiation of claim validation or dispersal of insurance funds. Such automated mechanisms diminish human involvement requirements, thus optimizing operations and minimizing chances for mistakes.

Insurtech companies are adopting these technologies—blockchain coupled with smart contracts—to bolster reliability as well as streamline operational efficiency within the realm of insurance.

The Role of IoT in Insurtech

The Insurtech industry heavily relies on the Internet of Things (IoT) to deliver instantaneous data crucial for assessing risks, preempting potential losses, and streamlining claims processing. Connected devices like smart home sensors and vehicle telematics enable insurance companies to observe and evaluate risk with greater precision. Commercial properties equipped with IoT sensors can monitor environmental factors, aiding in preventing incidents such as fires or mold growth. This forward-thinking strategy curtails financial loss and bolsters customer satisfaction through its proactive nature.

Through real-time tracking provided by IoT technology, insurers can promptly identify issues like water leaks or potential fire dangers within insured assets, enabling them to prevent mishaps and more effectively administer their risk portfolios. Insurtech firms such as Avinew are capitalizing on this advancement by tailoring premiums that reflect driving behaviors and vehicular usage patterns—this illustrates a shift towards adjustable pricing schemes within the insurance market based upon precise information regarding actual use states of insured entities.

According to IoT Analytics, the global number of IoT devices is expected to surpass 30 billion by 2025, significantly impacting the insurance industry.

On-Demand and Usage-Based Insurance Models

Insurtech companies are transforming the traditional ways in which customers purchase and utilize insurance through on-demand and usage-based models. With on-demand insurance, consumers gain access to flexible and cost-effective coverage for short durations, from mere hours up to several days—appealing particularly to millennials and those involved in the gig economy due to its digital convenience.

Usage-Based Insurance (UBI) revolutionizes premium determination by employing telematics technology that monitors driving patterns instead of relying solely on estimated risks. This advanced strategy results in more equitable pricing tailored specifically to policyholder behavior. These cutting-edge methods respond directly to changing consumer desires by crafting personalized insurance products that fit their unique way of life. Insurtech firms thus can offer precise premiums tied closely to actual use and conduct, leading toward fairer rates and encouraging safer practices among drivers while enhancing efforts at risk mitigation.

The expected significant expansion within the global UBI market space reflects growing consumer enthusiasm for these adaptable, bespoke insurance solutions.

Benefits of Insurtech for Traditional Insurers

Insurtech brings a plethora of benefits to traditional insurers, including:

• Boosting operational efficiency: Automating underwriting, claims processing, and policy administration to streamline processes and reduce operational costs.
• Propelling product innovation: Digitizing historical customer data to enhance customer service and offer personalized services.
• Improving customer experiences: Providing faster and more accurate claims resolutions and user-friendly interfaces.
• Reaching new demographics: Offering flexible and personalized insurance solutions appealing to modern consumers.

Collaborations with Insurtech startups can amplify customer experiences, smooth out claims processes, reach new customer demographics, expedite the adoption of new technologies, and drive product innovation. These collaborations benefit both Insurtech firms and established insurers, signifying a new norm of cooperation and a necessity in the digital age. According to Capgemini, 56% of traditional insurers have partnered with Insurtech firms to enhance their digital capabilities.

Challenges in Integrating Insurtech Solutions

The incorporation of Insurtech solutions poses multiple challenges for traditional insurers, such as legacy systems, regulatory compliance, and cultural reluctance to change. Legacy systems restrict scalability and risk monitoring, making digital transformation essential for staying competitive. Insurers are updating these systems and migrating data and infrastructure to the cloud to amplify their technological prowess.

Regulatory compliance and data privacy also pose considerable challenges. Insurers must navigate complex regulatory landscapes and ensure their use of AI and other technologies does not result in unfair discrimination or data breaches. Compliance requirements, licensing procedures, and data privacy regulations can be hurdles to innovation, but standardizing processes and ensuring data security is critical to overcoming these challenges. By addressing these issues, traditional insurers can successfully integrate Insurtech solutions and reap the benefits of digital transformation.

Future Outlook: Emerging Trends and Technologies

The Insurtech field is poised for an exciting future, with emerging trends and technological advancements set to revolutionize the insurance industry even further. For example, Generative AI promises a significant boost in productivity across various applications within the sector, potentially contributing trillions of dollars to economic value each year. There’s a growing interest in decentralized frameworks and ecosystems that could reshape how insurance products are managed and delivered more effectively.

Explorations into immersive internet technologies for corporate usage are underway, creating new avenues for engaging customers and conducting risk assessments. The realms of predictive analytics along with artificial intelligence (AI) and machine learning remain at the forefront of technology trends that lead substantial shifts within the insurance domain. Notable is parametric insurance—an innovative model based on preset triggers—that uses advanced modeling of risks coupled with refined data collection processes to facilitate payouts that are both accurate and prompt.

As these innovations progress, we can expect ongoing creativity from the Insurtech community as it continues its role in charting out novel pathways for what lies ahead in insurers’ offerings.

Summary

The advent of Insurtech is drastically transforming the insurance sector by employing cutting-edge technologies such as artificial intelligence, big data, blockchain technology, and the Internet of Things to develop solutions that are both more efficient and focused on delivering a personalized experience for customers. Starting from initially addressing challenges within traditional insurance models to now introducing demand-driven and usage-based offerings, Insurtech has persistently been at the forefront of fostering innovation and enhancing customer experiences. The advantages for established insurers include increased operational efficiencies, innovations in product development, and expanded access to novel consumer markets through collaborations with innovative Insurtech startups.

As we look forward to future developments within this space, nascent trends like generative AI models, decentralized system structures, and parametric forms of insurance are set to revolutionize the field. Traditional insurers who adopt these new technological advancements will be well-positioned to remain competitive in an evolving marketplace where meeting ever-changing customer needs is crucial. With bright prospects ahead for its ongoing progression, it’s clear that Insurtech will continue generating transformative breakthroughs—opening doors to new possibilities across all realms of the industry ecosystem.

Are you ready to harness the power of Insurtech to transform your insurance business? Contact us today to explore how our innovative solutions can drive operational efficiency, enhance customer satisfaction, and keep you ahead of the competition.

One thing remains constant in the current unpredictable business climate: The need for more tech talent. Cloud architects, data engineers, AI developers — companies face talent shortages in almost every tech role. 

The solution? Expanding your search to new geographies. 

Ukraine, Poland, and Romania have emerged as promising tech hotspots over the years, offering a dynamic landscape of new opportunities for talent sourcing, R&D center establishment, and sometimes even strategic company acquisition. 

Learn why Ukraine, Poland, and Romania are the best countries for hiring software engineers from our deep-level analysis. 

Tech Talent Landscape in Central and Eastern Europe (CEE) 

A strong focus on STEM education, cultural proximity to Western markets, and competitive salaries are among the reasons why global companies choose to hire software engineers in Central and Eastern Europe (CEE).

Three countries, in particular, stand out as attractive hiring destinations — Ukraine, Romania, and Poland. 

Combined, Ukraine, Romania, and Poland are home to over 870,000 tech specialists, and more talents are joining the workforce each year. 

School students in CEE countries (including Poland, Ukraine, and Romania) have higher maximum scores for mathematics, reading, and science, compared to their peers from the Big 5 Countries (France, Germany, Italy, Spain, and the United Kingdom). In other words: they enter universities with a stronger background. Poland has around 70,000 students enrolled in various information and communication (ICT) programs. Ukraine has 16.8 thousand bachelor graduates in IT each year. 

A solid educational system results in an ample, proficient workforce, who are also growing more productive year-on-year. McKinsey found that labor productivity in CEE increased by 4.7% between 2010-2019, outpacing the Big 5 countries. 

The [CEE] talent is quite strong not just in software as a service, but in other applications of technology. When we think about these leapfrogging technologies, and we see the capabilities of early-stage fund managers to detect good founders, to partner with good research, to match with universities, it creates a unique opportunity […] to develop out of these markets.

William Sonneborn, Global Director, IFC – International Finance Corporation

Global tech leaders are already well aware of the region’s high-tech talent potential. Google, IBM, and Microsoft among others operate R&D centers in these countries. While the likes of Amazon and Snapchat completed successful team and product acquisitions, namely Ukrainian-founded Ring and LOOKSERY. 

The growing number of successful startups and scale-ups has earned CEE the nickname of the next Silicon Valley. Poland has 10 unicorn companies, Ukraine — 9, and Romania — 3, according to a joint report by Dealroom and Google Atomic. Collectively, established digital companies in these three countries now have an estimated ecosystem value of $68.2 billion, growing at rapid speeds. 

Source: Dealroom

Local governments are also making targeted efforts to increase the levels of foreign direct investment (FDI) into the countries and improve the ease of doing business. Favorable tax regimes for foreign corporations, government services digitization, modernized legislation — each country has made steady progress in improving the business climate. Over the past year, Ukraine has moved from the lower half of the Ease of Doing Business Index to the 64th position, while Poland sits on the 40th  and Romania – on the 55th. 

Due to improved bureaucracy, higher government integrity, and better infrastructure, Central and Eastern Europe offers better business conditions than Asia Pacific and Latin America. CEE countries also have more competitive labor costs, compared to Western Europe or North America, with almost as high graduate education attainment rates and technology proficiency levels. 

Ukraine

Ukraine has a strong IT sector, which has grown by nearly 40 times over the last 15 years, according to the local Ministry for Development of Economy, Trade and Agriculture. The busy tech hubs of Kyiv, Lviv, Kharkiv, and Dnipro are home to over 20 IT clusters — strategic coalitions of local IT service companies, focused on developing the regional technology industry together with representatives of government and education. The total number of IT companies in Ukraine is over 5,000. 

Since the mid-1990s, Ukraine’s IT sector has been growing at double digits almost every year, reaching 58% annually at its peak. Outstaffing has driven much of this growth in the early days, but the value proposition has then shifted. 

Today, Ukrainian IT companies sit higher up the value chain, offering comprehensive value-added services, ranging from technology consulting and cloud system design to proof-of-concept (POS) solution development, using emerging technologies (ML, DL, AI). Assuming the role of strategic technology advisors, Ukrainian IT services providers are helping global leaders become more competitive in the digital world. 

As an example, the 8allocate team helped a UK-based provider of financial analytics services deploy high-performing machine learning algorithms on its data intelligence platform. We also helped Dopay modernize its payment platform architecture to support 24/7 payment processing and self-service features for users, which led to them landing McDonald’s and SWVL as their customers. 

Overall, Ukrainian IT companies serve a variety of industries, with data management, telecom, cloud, gaming, and ecommerce being among the top service sectors, according to the local Ministry for Development of Economy. 

High levels of proficiency, operational resilience, and unwavering commitment to results, have also helped Ukrainian IT companies to sail through the turbulent times. Despite the global pandemic, IT exports grew by 20.4% in 2020 and remained in the double digits until late 2021. 

Similarly, Ukraine’s IT industry continued to show strong performance after the full-scale invasion began. In 2022, the IT sector generated $7.34 billion in foreign exchange earnings for the Ukrainian economy. 

Ukraine IT workers have shown remarkable levels of personal resilience in the face of ongoing calamities. Reporters on the ground from Wired, BBC, and The New York Times were deeply inspired by the tech sectors’ commitment to maintaining business continuity and thriving against all odds. 

During the 12 months following the start of Russia’s full-scale invasion, Ukraine’s tech sector had generated $7.34 billion in export revenues, 5% more than the previous year, and maintained jobs for the 270,000+ specialists employed in the industry. This is a remarkable reflection of Ukrainian talent, resilience, and innovation.

The U.S. Agency for International Development (USAID)

In anticipation of the inevitable, Ukrainian business leaders have successfully introduced business continuity programs (BCP), aimed at ensuring undisrupted operations. New security policies have been introduced to ensure the safety of the staff and corporate data, with many leaders activating secondary cloud data centers, testing disaster recovery (DR) practices, and ensuring undisrupted access to communication with Startlinks. After the first few challenging months, Ukrainian IT companies found a new cadence of delivering the same levels of services as in the usual time,  showing high commitment to maintaining customer SLAs. 

Ukrainian product companies and startups also continued business as almost usual throughout 2022-2023. In fact, seven new Ukrainian FinTech companies were launched in 2023, bringing the total number of FinTechs operating in the country to 246, of which 33% operate in the international market. An end-of-year report by Startupbridge further found that 64% of all Ukrainian startups that still operate in the country successfully base their business on the global market. 

Ukrainians continue to demonstrate extraordinary resilience, cooperation, and back-of-the-office creativity to power through the turbulent market. Strong work ethic, global outlook, and top-notch technical expertise make Ukraine one of the most attractive sectors for hiring tech workers. 

Ukrainian Software Development Companies’ Specializations

IT companies in Ukraine boast a wide range of competencies, ranging from emerging technologies (like AI development or IoT) to more general service lines like web application development, application testing, and legacy software modernization. 

Thus, making Ukraine an ideal destination for businesses in high-value-added industries such as DeepTech, e-commerce, and data science.

Average Software Engineer Salary in Ukraine 

A high price-to-value ratio is one of the main reasons why businesses choose to hire Ukrainian software engineers. 

Despite having high proficiency levels in multiple programming languages and modern software engineering principles, Ukrainian tech talents can still be hired for competitive rates. 

Sample median salaries of Ukrainian software engineers (gross/monthly):

• Software Architect – $6,700
• Team Lead – $5,800
• Senior Software Engineer – $4,900
• Middle Software Engineer – $3,300
• Junior Software Engineer – $1,000

Poland

Poland has also maintained a positive growth track since the 1990s, with its domestic economy rapidly expanding in the first decade of the 21st century. The country has a strong business services sector (BSS), with a host of in-house shared service centers and outsourcing business processing centers. Poland now houses over 70% of all BSS centers in the CEE region. 

IT services providers are a major part of Poland’s booming BSS sector. The country has over 60,000 registered IT companies, which include external services providers, research and development (R&D) centers from multinationals, as well as a growing cohort of startups and scale-ups. The top 50 largest IT companies, operating in Poland, include Capgemini, Amazon, Sii, Accenture, IBM, Comarch, Epam, and Atos among others.

The Polish IT market also further expanded in early 2022 as Ukrainian IT companies relocated some of their operations to the country and established new development centers. In 2022, Ukrainians accounted for 45% of all new foreign companies, according to the Polish Economic Institute. Of these over 50% were registered to continue operating the business established in Ukraine. 

According to the Polish Agency for Enterprise Development, the total value of Polish IT services exports increased by almost 90% since 2021, reaching €9.6 billion (approx $10,3 billion) in 2021 (the latest year measured at the time of publishing). Poland has also a substantial market of ITC equipment manufacturers, growing year to year by 7.31%, according to the same report. The largest categories of exported products include machines and automatic data processing devices, plus telecommunication equipment, primarily sold to Germany, Sweden, Netherlands, and Italy. 

Much of the ITC market growth comes from Poland’s strong workforce. Poland has a tight network of higher-educational institutions, with 40 state-owned universities and 20 public institutes of technology that produce 20,000 IT graduates per year. About 43% of Poles aged 25-34 hold a bachelor’s degree or higher in technology or software development. 

A well-educated, young population and attractive cost of doing business have made Poland an attractive destination for multinationals. Global players such as  Google, Amazon, and Microsoft have regional offices and plan to expand their investment in technical infrastructure in the region. Poland expects to become the leading data center hub in the CEE region by 2026 as new facilities will be opened. 

Poland also has a dynamic digital economy, driven by the gaming market. The local video game and esports market stood at $664 million in 2019 and is expected to reach $850 million by 2024. Combined, the top 5% of Polish startups from various sectors have created $2.7 billion in value since 2013 and continue to grow rapidly. These include HealthTech startup DocPlanner, educational platform Brainly, and a beauty appointment booking platform Booksy. 

Generally, Poland’s excellence in software engineering is reflected in its startup industry segmentation. Compared to Europe in general, Poland has more startups in Enterprise Software, Marketing, FinTech, and HealthTech. 

With a 13th place on the Global Services Location Index (GSLI) index by AT Kearney and a sizable tech workforce, Poland is an appealing destination for tech services acquisition. With Microsoft’s first regional data center, arriving in the country, the market is expected to see further growth. 

Microsoft’s investment in Poland will accelerate our country’s transformation into a technology hub for the Central and Eastern European region. Thanks to the development of the Polish Digital Valley, hundreds of engineers and developers will be able to play a key role in the global economy without leaving the country.

Mateusz Morawiecki, the Prime Minister of Poland

The flipside of Poland’s rapid digital market growth, however, is emerging tech talent shortages. With multinationals expanding their presence in the market, hiring efforts become more constrained. And salary growth increases. Over 40% of local companies find it hard to fill in IT vacancies as Poland’s share of IT professionals in the workforce remains slightly under the ideal workforce threshold of 3.65% of the workforce. An extra 25,000 IT professionals are needed to fill in all the market demands. 

Polish Software Development Companies’ Specializations

Polish has a thriving tech ecosystem, fueled by knowledge sharing, industry collaboration, and global partnerships. The local IT vendors have a range of general tech competencies (web and mobile development, cloud consulting, application testing), as well as more specialized tech skills. 

Average Software Engineer Salary in Poland 

Despite the country’s rapid economic growth, Poland still maintains a relatively affordable cost of living. Although well-remunerated, local tech talent remains on the more affordable side, with the average ICT sector employee salary being 2,148 euros as of 2021.

Sample median salaries of Polish software engineers (gross/monthly):

• Software Architect – $9,500
• Team Lead – $8,400
• Senior Software Engineer – $6,600
• Middle Software Engineer – $4,400
• Junior Software Engineer – $1,800

Romania

Romania is another CEE country that has shown remarkable levels of economic transformation at the beginning of the 21st century. After joining the EU in 2007, Romania implemented progressive reforms to improve its business climate, digitize government services, and modernize its educational system. 

The country’s ITC sector emerged in the early 2000s and since then maintained a positive growth streak. The IT services market volume stood at a “modest” €4.6 billion in 2015, but doubled by 2022, to €9 billion (approx $9.4 billion). 

Today, Romania houses R&D centers of multinational tech companies (Amazon, HP, IBM, Microsoft, and Oracle among others), with 50 of the largest tech employers in the market quadrupling the size of their operations in the country over the past several years. Overall, Romania has over 7,900 registered IT companies. 

The Romanian IT&C industry has also been recently credited with bringing Romania’s GDP “close to pre-cris is level” in Q1 of 2021.

The US International Trade Organization

Over 200,000 specialists are employed in the ICT sector, with more talents joining the industry each year. In 2021, over 40,000 students were enrolled in STEM programs across Romania with 9,300 graduates entering the workforce. Romania ranks 6th in the EU in terms of total number of ITC graduates per year. A refreshing 24% of ITC graduates are female, allowing the country to maintain gender-balanced workplaces. 

Romania has a strong cyber-security services market, with Bucharest being considered one of the main European cyber centers. The country’s universities offer over 15 prolific programs in cybersecurity, with others offering courses on cryptography, digital investigations, and network security. 

Romania presently ranks first in the European Cybersecurity Challenge (ECSC) and has been hosting the Cybercrime Programme Office of the Council of Europe (C-PROC). The Office works on strengthening legislation on cybercrime and data protection standards, as well as operates specialized cybercrime and forensic units. On the commercial side, the Romanian cybersecurity market is expected to bring $166.50 million in revenue by the end of 2023.

Romania also has an actively growing startup ecosystem, which expanded by 6.8X over the past five years. Robotic process automation (RPA) platform UiPath became the country’s first unicorn company to successfully go public in 2022. UiPath is now valued at $20 billion. Ecommerce marketplace eMag and cryptocurrency startup Elrond also boast billion-dollar valuations, with FinTech company FintechOS, cybersecurity vendor Bitdefender, and preventive maintenance software platform MaintainX aiming to join the club next.

Overall, Romania has an actively developing ITC market, focused on both IT services exports and supply for locally established businesses. However, it’s still a relatively small talent pool, mostly concentrated in the four hub cities. The workforce is also getting quite saturated as more multinationals enter the region. 

Romanian Software Development Companies’ Specializations

Romania is a popular IT outsourcing destination for businesses seeking developers with experience in multiple programming languages (Java, Python, JavaScript) and front-end development frameworks (React, Angular, etc.). Local IT vendors are also well-known for offering high-quality managed IT services, especially in cybersecurity, infrastructure monitoring, and technical support among others. 

Average Software Engineer Salary in Romania 

Known for high-tech qualifications, multi-language skills, and dedication, Romanian tech talent has a strong reputation. Yet, labor costs remain on the more affordable side. The average gross salary in the ICT sector is $2,141, which is substantially higher than median salaries in other industries. 

Sample median salaries of Romanian software engineers (gross/monthly):

• Software Architect – $8,700
• Team Lead – $7,600
• Senior Software Engineer – $5,800
• Middle Software Engineer – $3,900
• Junior Software Engineer – $1,500

Reasons To Work with Central and Eastern European Software Development Companies 

Eastern Europe offers a cost-to-value ratio, with a highly competent, productive, and motivated IT task force. 

Here are four reasons why startups, scale-ups, and enterprises alike commission IT services from this region. 

Strong Cultural Fit

Eastern Europeans share similar values to their Western European and North American counterparts. Strong work ethic, professionalism, and transparent communication are among the key qualities of local talent. The progressive economic and political integration with the European Union also encourages similar economic practices and legal frameworks. 

CEE tech sector workers are predominantly young (under 35 years on average), meaning they have lived most of their lives in post-Soviet societies, enjoying free access to foreign travel and international cultural exchanges. Most of your team members can quote “Friends” and share banter on the latest pop-culture trends. 

A good command of the English language, paired with time-zone proximity, also helps Eastern European software engineers easily integrate into globally distributed teams. 

High Talent Availability 

Globally, the demand for tech talent will continue to outweigh the supply until at least 2026, according to Gartner. Central and Eastern Europe remains one of the few regions, shielded from talent shortages.

East Europe remains untapped for tech talent, as the region has fewer multinationals than Western Europe. The startup ecosystems are still young, with many professionals choosing to join IT services companies instead for better job stability prospects and higher pay. 

The local labor market also gains a steady influx of new minds every year. The share of STEM graduate students in Romania stands at 29%, which is above the EU average rates.  Ukraine expects the total number of tech students graduating yearly in Ukraine to increase by 23% in the next few years. 

Careers in the tech sector are well-paid and thus deemed prestigious by many youngsters in Ukraine, Poland, and Romania. Moreover, local IT companies are actively partnering with higher educational institutions to create better curriculums, launch new programs, and recruit more students into the ITC field. 

Learn how to scale teams with global tech talents. 

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Optimal Cost-to-Value 

Despite the current economic conditions, rates in Eastern and Central Europe have remained steady. Although the labor costs won’t be as low as in South-East Asia, the returns on investments are much higher. 

As Debi Hamill, CEO of the International Association of Outsourcing Professionals (IAOP), noted: “Unlike other leading delivery destinations such as India and the Philippines, the emerging European countries have typically been awarded contracts on a smaller scale but consisting of higher value transactional work”.

Eastern European businesses are supporting digital projects for global leaders, ranging from Alphabet and Amazon to JP Morgan Chase and Visa Inc. The most innovative companies are also sizing up expansion into the region as it boasts a high concentration of AI, data engineering, and blockchain talent.

OpenAI CEO recently broached the subject of opening a regional office in Poland, while Samsung and Rakuten continue to operate AI R&D centers in Ukraine. A recent public data analysis found that Ukraine and Poland have a higher number of AI service providers, than Western European countries. On LinkedIn, over 5,300 software engineers from these countries have “AI” listed among their skills. 

High experience with various technologies, coupled with competitive salaries and lower tax burden, is another strong reason to partner with software development vendors in CEE. 

Flexible Partnership Models 

With over three decades in the making, the regional IT export services market is in its prime shape. Regional software development vendors have already established effective frameworks for international collaboration, both on the processes and legislative levels. 

While South-East Asia is mostly a destination for outstaffing, Eastern European vendors offer more synergetic partnership models, aimed at establishing ongoing strategic collaboration. For example, through a dedicated software development team model — a service offering, which allows businesses to assemble and operate a flexible, external IT team, hired to support a specific business initiative. 

CEE IT partners can support other businesses with:

• End-to-end product new engineering 
• Externalization of various IT functions 
• Ad-hoc IT strategy and technology consulting 
• Joint R&D center establishment 

In other words: They propose an outcome-oriented service delivery model, where the external partner takes greater ownership of the project and commits to delivering specific results. 

Eastern European software development partners can not only help complete complex engineering projects, but also deliver positive changes upstream by improving organizational processes, IT portfolio management practices, and overall business strategy. 

Get the Best Tech Talent with 8allocate 

Since 2015, 8allocate has been at the forefront of the software development service market. Our mission is to help global businesses with technology at the core of their operations. With development centers located in Poland, Ukraine, and Romania, we can help you partner with multi-skilled and experienced engineering teams. 

If you’re looking to fill specific software engineering roles or looking for a cross-functional team to spreadhead a new project, we’d be delighted to help you. Contact us for a personalized presentation on our service lines and areas of expertise.  

Frequently Asked Questions

Quick Guide to Common Questions

Why do companies choose Ukraine, Poland, and Romania for hiring software engineers?

These countries offer top-tier technical expertise, strong engineering education, and competitive costs. With a combined tech workforce of over 870,000 specialists, businesses can access skilled engineers in AI, cloud, cybersecurity, and enterprise software development at a better cost-to-value ratio than Western Europe or the U.S.

What are the key advantages of hiring developers from Central and Eastern Europe (CEE)?

• High technical skills: Strong STEM education and expertise in AI, cloud, and advanced software development.
• Cultural alignment: Similar work ethic, Agile adoption, and fluent English communication.
• Time zone overlap: Ideal for real-time collaboration with European and North American teams.
• Cost efficiency: Competitive salaries compared to Western Europe while maintaining high quality.

How does 8allocate help companies leverage AI engineering talent from Central and Eastern Europe?

8allocate specializes in AI solutions development, providing dedicated AI engineering teams, team augmentation, and full-cycle AI project delivery. We manage hiring, contracts, security compliance, and operational oversight, ensuring seamless collaboration. Our approach enables:

• Access to top-tier AI engineers with deep expertise in ML, NLP, and data-driven solutions.
• Seamless integration with your existing engineering teams and workflows.
• Focus on AI innovation—delivering high-impact, scalable AI-driven solutions.