Artificial intelligence (AI) now seems to permeate just about every industry, from manufacturing to Babytech. Over the last four years, AI adoption rate has increased by 270%, and, according to Gartner, 37% of all businesses globally are now using some kind of AI. This doesn’t mean they have entirely replaced their human staff with self-aware machines. It rather means that they are using AI-driven technologies like robotic process automation (RPA), machine learning (ML), predictive analytics, and NLP to enhance their workflows and ensure business continuity. If you’re not using AI yet, there’s a high probability that your competitors already are.
The trend is nowadays towards a full-fledged AI-first approach with tech giants leading the pack. Last year, Google CEO Sundar Picha declared that Google is shifting from being mobile-first to AI-first company, and later Microsoft and Apple came up with similar announcements. AI-first means putting primary emphasis on AI solutions development, even if it entails sacrificing short-term benefits.
Such readiness to meet the challenge of adopting potentially disruptive (read: difficult to implement) technology, means tech-giants believe in its highly beneficial transformative potential. The future lies in AI; for SMB companies and enterprises, though, it all starts with building AI solutions that will help eliminate their current bottlenecks and target primary business goals.
Challenges In AI Application Development
Becoming AI-driven is not as easy as it may sound. Organizations willing to become AI-driven are facing many bumps on the road. Deploying AI involves meeting numerous challenges, namely:
1. Strategic planning
It’s not just about jumping on the bandwagon and blindly following the trend. Analyzing your current situation, pinpointing urgent problems, allocating costs, and launching a pilot project require careful evaluation and planning. Before organizations start to build teams for AI and machine learning projects, they need to undergo a thorough and well-calculated preparation process.
From laying foundations to creating a future vision, businesses need a step-by-step AI implementation plan they can rely on. Starting small, learning from mistakes, and gradually gaining expertise will ultimately result in future benefits.
2. Focus on business value
AI adoption may be crushed in the bud and even deemed potentially unnecessary by failing to focus on business value. Organizations may waste time and effort on implementing AI projects that deliver little or no tangible ROI, which consequently results in putting off the idea of AI implementation until undeterminable future. Communicating the business value of your solution internally to employees and externally to clients is also vital to success.
3. Lack of buy-in from senior management
The Big Four giants declaring AI-first approach boast massive buy-in from their executive teams. The support of senior management helps companied propel AI projects and infuse the entire organizations with ideas and values driving digital transformation.
4. Lack of AI talent
Operating in a well-developed economy is a double-edged sword: on the one hand, it enables companies to envision long-term effects of digital transformation, while on the other – it puts them in front of a talent scarcity challenge. Organizations that have tried to develop their own AI solutions know what it takes to assemble an AI team. Good AI developers are rare, command large salaries, are, most of the time, already employed, and need solid reasons to leave their current jobs. Building AI software development teams in-house vs. outsourced AI teams is an important decision that businesses often feel compelled to make.
How To Build An AI Development Team
So how do you go about assembling a top-notch AI development team? Below are some insightful tips you can act on.
In-house vs. outsourced
A lot of C-level executives are struggling with a controversy that feels hard to resolve: implementing AI projects requires in-depth expertise, but the talent suited for these project resides overseas. With rising security concerns, it’s often difficult to make the right decision. It’s either lack of money that questions the idea of building an AI solution (which is to bring profit), or inevitable shortcomings, potentially threatening business continuity and reputation.
The good news is that they don’t have to choose. There’s an alternative solution indeed – staff augmentation. This collaboration model enables companies to augment their team with high-caliber experts from abroad, thus filling the skills gap for completing the project. In the mid- and long-term perspective, it helps reduce the time it’d typically take you to attract and hire employees onshore (in North America, the average time to hire a software developer exceeds 50 days, according to Workable), and eliminate additional expenses and overheads. For example, if you choose to outsource your AI R&D to countries like Ukraine, the outsourcing provider will most likely take care of all the HR, IT and administrative aspects, while you’ll be involved in interviewing candidates and choosing a development methodology you believe will work best for your solution.
Typical roles on AI development team
Successful AI software development requires multiple roles and versatile skill sets. Moreover, the AI team you assemble should work in tight cooperation with other departments in your organization to deliver a solution that would meet your business objectives. The major roles on a team typically revolve around working with data: you will need to hire data analysts and data modelers, deep learning specialists, and data engineers. When it comes to AI programming, R and Python are the most widely used languages, although, in some cases, knowledge of more complex languages like C++ would be necessary.
In essence, your AI team roles would depend on the project and the functionality you want to deliver to end-users. In most cases, you don’t need to hire AI developers with Ph.D. degrees. If the features you aim to build are relatively simple, it may suffice to hire developers with relevant backgrounds. Hiring three people who collectively possess the optimal skillset instead of one expensive AI guru could, ultimately, deliver more value.
When it comes to robust artifical intelligence solution development, you’ll need the following roles on your team:
- Product owner / Project manager
- Software engineers (front-end and back-end)
- Data engineer
- Data analyst and/or data scientist
- Business analyst
- Social scientist (aka qualitative expert)
- Applied machine learning engineer
- Researchers/data collection specialists
- UX / graphic designer
- QA specialist/tester
Data and infrastructure
AI solutions are usually data and resource-intensive. Before you plunge headlong into AI software development, make sure you have the necessary data for the project. Keeping it in disparate databases is not enough – you have to retrieve it and store it in an easily readable and processable format. Moreover, the data should be relevant to a solution you are trying to build. AI algorithms feed on data they process and numbers they crunch. If there’s no relevant data available, the meticulous task of collecting it could be a considerable part of your AI project.
Second, what definitely matters is infrastructure. Do you have the hardware capacities for running your AI project? If the project involves processing great volumes of unstructured and high-velocity data, double-check if your available company infrastructure can handle it without disrupting the core processes. If not, you may have to acquire additional storage, computing, and network capacities to give your project a solid foundation to run on. Consider shifting to the cloud if your budget doesn’t allow you to buy additional hardware. Or go for a hybrid cloud model and move at least part of your operations to the cloud.
Attracting, hiring and retaining AI talent
While recruiting and headhunting are time-consuming, chances are your current employees could already possess some of the relevant skills and experience for AI development. In this case, paying for their training in ML or data science could be more justified from the financial standpoint. This approach is potentially beneficial because it enables companies to retain expertise in-house and to foster specialists with in-depth knowledge of the company ecosystem and business specifics.
Another approach is partnering with educational institutions to tap into the pool of prospective fresh graduates. Taking part in conferences, bootcamps, and hackathons help build awareness about your brand and your AI project. This will help attract potential candidates to your company and build rapport with them.
Retaining highly valuable employees is yet another challenge. Successful retention strategies usually target their core values – financial or career perspectives, making an impact in a local or global community – anything that will maximize employees’ engagement and increase their job satisfaction levels.
Alternatively, think about outsourcing your AI solutions development to locations like Ukraine. Leveraging the staff augmentation model will grant you access to robust 3rd party expertise and AI R&D capabilities, which will eventually give you an impression of building your AI solution in-house. This will be achieved through careful integration of your augmented AI dev talent with your corporate culture and work ethics.
Staff augmentation is a way to go as it delivers cost efficiency and enables you to tap into vast pools of pre-vetted AI talent. While the ITO provider takes care of all administrative issues like salary, taxes and motivation and rids you of legal and HR headache, you get to enhance your AI team by hiring stellar performers with international expertise.