How to Build a Team in AI Startups

GoBeyond.AI: E-commerce Magazine
12 min readJul 31, 2019


Creating an AI startup team structure is demanding in terms of time and resources needed for building a team. This post will cover 10 top tips on startup team building.

Artificial intelligence is a force for businesses to reckon with. It has enough potential to reshape the way businesses approach daily workflows and manage projects. As for consumer-facing AI applications, every existing industry will soon be introduced to projects that involve one or more applications of artificial intelligence.

As a result of the sprouting growth of AI, businesses should start accepting the technology and integrating it as a part of the organization.

According to the Harvard Business Review, adopting the technology in a bottom-up way is inefficient and unproductive. Rather than leaving AI integration to employees, CEOs and business managers should take the lead to incorporate artificial intelligence adoption.

What does building a great team takes? Can a startup create a team that can handle artificial intelligence projects successfully? This post will cover the top 10 things SME managers should take into account while building a high-performing AI team.

Discover Latest Jobs in Bots, NLP, AI, ML, NLU & More

1. Use various recruiting means

Hiring any tech specialist is a challenge these days. The unemployment rates worldwide are hitting rock bottom. American citizens, for one, haven’t been as busy at work since 1969, according to BBC.

Finding the right candidate in a field as rare and expensive as artificial intelligence is not an easy task for CEOs and HR managers. The talent pool is fairly narrow, and the bids are through the roof.

Under these circumstances, creativity is the key to recruiting AI specialists. HR directors and business owners are now forced to consider alternative recruiting means.

Here are a few of these:

- freelance marketplaces

For the last 10 years, the dominance of the freelance economy has become evident across any industry. Yet, it was in tech that it shone the brightest. According to the World Economic Forum, by 2027, the freelance economy is expected to employ over 50% of the American population.

In order to be one step ahead of the job market, AI team recruiters should take advantage of the emerging trends. Instead of hopelessly trying to fill an in-house opening, heading over to freelance marketplaces might a win-win move.

The most popular freelance marketplaces include:

- referral programs

Using referral programs is a go-to strategy when it comes to closing a challenging job opening. In case a company doesn’t have enough money to run advertising campaigns, it’s time to ask around the team.

According to LinkedIn, referral programs have become the most popular way for job-seekers to find out about new openings. It turns out, in fact, that companies can expand the talent pool by 10 times by starting a corporate referral program.

- local tech meetups

AI is a rapidly developing industry — recruitment managers should take advantage of that. There are dozens of conferences all over the world that aim to push the envelope of AI development.

Simply attending AI-related meetups will improve your company’s corporate image — you’ll come across as innovative and disruptive. Reach out to the hosts of the event — chances are, they will spread the word about your offer. After all, tech meetups are usually attended by active job-seekers — an event manager will most likely be happy to have recruiters and HR managers joining the community as well.

- outsourcing companies

Having a brand-new in-house team, fully dedicated to the company’s projects is highly attractive. However, do you really need a crowd of AI specialists that needs to be sustained and paid for?

For one-off projects, it’s better to find a team for AI startups that’s already well-oiled. While hiring a local company may exceed your budget tremendously, reaching out to outsourcing company might be a better fit.

In India, for instance, an average AI developer earns around $18,700 per year. For an American agency, that wouldn’t even be a junior’s salary.

While reaching out to outsourcing companies comes with its own drawbacks — the absence of full control over the project, time zones and language barriers — it also expands your AI talent pool.

- social media

Using social media is a way to establish a deeper connection with AI specialists. Keep in mind that a good job seeker in the AI industry is accustomed to offers — that’s why forming a deep bond with a candidate is extremely important.

Sharing posts, photos and videos of the most memorable moments of a team’s daily lives is a good way to ‘show, not tell’ that your offer is worth exploring.

If you’re going with platforms like Twitter, keeping track of AI-related news in the industry is a good way to create an image of an innovative hub among job-seekers.

2. Define corporate culture

While the lack of skill or experience is the most popular reasons for employee turnover, statistically, so is the fact that the candidate is simply not fit for the company’s corporate culture. Keep in mind that AI developers have seen a fair share of companies that chase artificial intelligence as a trend for the right to be called innovative.

If you truly have an ambitious project that involves artificial intelligence, make sure the business’ corporate culture reflects your personal innovative drive. There are three main components of a strong corporate culture:

  • Building values. Depending on the company’s niche, corporate values may differ. Several common examples include transparency, directness, community, understanding, and big-picture vision. If none of the above resonate with your business, create your own list.
  • Defining goals. While most companies focus on revenue-defining goals, it’s best to take several plains into account — financial sustainability, the impact your company has on each team member, social mission, and so on. Keep in mind that long-term goals are the main part of a successful company’s culture — don’t hesitate to create plans that stretch several years in the future.
  • Establishing practices. As a company upscales and the core members of the team change, corporate standards are at risk of collapsing. That’s why it’s important for startup managers to codify development and project management practices and share them with job candidates as well as new employees during onboarding.

Among other things, defining values, goals and practices will, above all else, help you define where AI stands in the grand scheme of things as well as the purpose of the new department.

3. Stay up to date on average salaries in the market

In order to ensure your company has a competitive advantage in the job market, it’s crucial to stay up to date regarding the salaries of AI specialists. This way, you will be capable of making reasonable offers to potential employees.

Median yearly salaries of junior, middle and senior AI professionals in the US are listed in the table below (data belongs to Payscale).

With a clear idea of the state of the AI market, you will be able to estimate the budget needed to sustain a development team and make informed decisions for AI startup team building.

4. Establish agile project management

Now that you know how to build a good team, it’s important to choose a project management methodology that would increase the speed and efficiency of project development.

When we’re talking about AI development and other tech projects, an Agile framework inevitably comes to mind. It’s the most optimal and balanced way for small, experimental teams to function. There are several pillars of Agile — let’s examine them closer:

  • Building projects as a series of long-term deliverables. Setting micro-goals and reaching them makes it possible for everyone involved in AI development to give feedback — when a team moves on to the next development stage, it means everybody is happy with what has been done thus far.
  • Rotation programs for all AI-related units. Moving employees around the team and allowing them to take on different roles will increase the overall expertise of the team as well as the dedication of each member. You will also be able to avoid inertia — a state when, by the force of habit, developers no longer notice small bugs and tech debt.
  • Frequent meeting with stakeholders. Agile presumes that there is a constant connection between the end client and a development team. When it comes to AI, there are dozens of technologies, technical specs that require explanation, and so on. By creating a regular schedule of team meetings and calls with the end customer, your team will be able to assess the project requirements and ensure all stakeholders are on the same page in terms of technological vision.
  • Creating departments for AI ethics. Tech giants like Amazon and Microsoft all have a dedicated AI ethics lab. While a startup might not be financially sustainable enough to be able to create a team for AI startups that would handle ethics, consider hiring one or more specialists that would instruct the team on how to stay privacy-compliant and avoid algorithmic bias when working on your next AI project.

5. Allow employees to work on their own terms

Micromanagement is highly beneficial when approached attentively. As there aren’t too many functional AI teams to take as reference points, a management team for AI startup will have to experiment. In case you’re not too experienced in employee management, ‘less is more’ is the answer.

Instead of pressuring the team to follow a strict schedule, give the experts you hire enough freedom to cross tasks off of their to-do lists without burnout.

The following are common ways to provide employees with enough freedom and space for building innovation.

а) flexible schedule

A few years ago, Harvard Business Review covered research that proved the importance of time flexibility within the workplace. Some employees are early birds, while others are night owls — employers should take the team members’ individual biorhythms into account.

Apart from team meetings and conference calls, don’t set any fixed time constraints for employees. Instead, let them work on projects in their own productive peaks.

b) allow working from home

Fully distributed teams (like Buffer) might not be an option for AI development considering high project complexity. However, allowing employees to work from home every once in a while is a positive practice.

One benefit is that there will not be a tight crowd at the office space. Rotating office and remote work will also lower the operating costs of your AI department.

c) don’t restrain employees in terms of tools and methods

As a nascent AI team, you are yet to create codified development standards and settle for one technology stack. Instead of trying to impose rules and methods on the entire team, allow employees to showcase their expertise by choosing development tools on their own.

The benefits of such an approach are self-evident.

  • You will be able to expand your own knowledge of AI-related technologies.
  • Seeing different approaches to project management will help you choose the most optimal one.
  • Employees will not be constrained, nor they will have to reskill and familiarize themselves with new tools — that’ll improve the overall development pace, increase employee retention rates and build a better team at work.

d) encourage perpetual learning

Artificial intelligence is developing with every passing day as research companies publish white papers and startups continue to create intriguing pilot projects. In other words, there are AI-related data volumes employees can use for learning and self-improvement.

  • Share news about AI-related events in the area;
  • Create a corporate chat where team members can share interesting AI-related reads;
  • Host workshops where your peers can exchange knowledge.

Encourage curiosity. Create an environment that supports learning and sustains it financially.

6. Structure your team

A strong AI architecture derives from choosing the right organization paradigm while building AI startup team management paradigms. To avoid miscommunication and bottlenecks, follow this team management tips:

  • Divide the team into smaller departments. This way, everyone can bring insights to the project, whereas in large departments, ideas are often overlooked or undervalued.
  • Create separate research and implementation units. Mixing the two will create chaos within the team and make it harder for project managers to track the progress in research and product development. Keep in mind, however, that these separate units should be able to connect with each other, exchange knowledge and have shared feedback loops.
  • Outline the team management architecture. The most popular choices are a top-to-bottom (vertical) AI startup team structure with a clearly defined hierarchy and reporting system or a horizontal team structure where all employees are on an equal plane in terms of decision-making. Examine the team management structure in the company’s other departments, evaluate its efficiency, and choose which of the two approaches fits your corporate culture better.
  • Ensure each person involved in development is updated on the processes within the team. This includes the status of the project, being able to take a look at the work done by his peers and being a part of the feedback circle. Integrating the entire department into decision-making is a way to increase transparency and trust within the team.

7. Start small-scale

While it’s tempting to build innovative, disruptive projects right off the bat, in order to test its current expertise and development architecture, a successful AI team is bound to start small.

By completing small, corporate-facing projects, you’ll be able to define a working project management methodology through trial and error.

  • Test pilot projects with small teams. Divide an AI department into small groups and assign small-scale projects to each of those. This way, you’ll have room for employee evaluation as well as the option to test working with different objectives, technology stacks, and industries.
  • Practice on designing corporate products instead of third-party solutions. Working with a client, you will be expected to provide a detailed workflow, a developed project management paradigm, and a tried-and-true technology stack. Designing for the team, on the other hand, helps you kill two birds with one stone. On one hand, you can reiterate until the project is fully functional without deadline-based pressure. On the other, you’ll be able to create proprietary solutions for workplace optimization.
  • Choose technologies that don’t require advanced knowledge to test out AI. By creating basic pilots, you’ll be able to ensure if the company really needs artificial intelligence to reach its objectives, and if so, which technologies are involved in product design.

8. Be open to international collaboration

With the talent pool of AI specialists being as narrow as it is, why don’t you remove location constraints? Instead, consider hiring remote international specialists.

By embracing international collaboration, a startup lowers operating price, brings diversity and a fresh perspective to the team.

Needless to say, international collaboration is no hurdle-free. Language barriers and time zone differences are all worth taking into account. Here are several tips on facilitating international project management:

  • Use collaboration tools to improve the quality of collaboration. The most popular picks include Google Suite, Jira, Basecamp, and Slack.
  • Make English the primary language of communication and documentation. This way, you’ll be able to onboard people from all over the world.
  • Run orientation sessions on international collaboration for project managers when you build your AI startup team.

9. Consider AI as a service

Finally, if your goal is to create a consumer-faced AI project, there are ways to do it rather than building an in-house architecture. For instance, you can use AI-as-a-Service (AIaaS) platforms.

These are premade tools that help businesses and developers improve their products by implementing artificial intelligence into them.

Using AIaaS tools is cheaper than developing a custom project from scratch. Also, you would be benefiting from a solution developed by modern tech giants — Google, Microsoft, Amazon, and others.

Here’s a short rundown on top AI-as-a-Service vendors:

10. Boost team expertise on a budget

Finally, to avoid recruitment problems and boost the AI expertise of the entire team, consider investing in online educational materials. While sending your employees off to college is borderline impossible, covering the cost of online courses, provided by some of the top schools in Canada and the US is a reasonable idea.

Here are a few AI-related courses to consider:

The courses listed above will not be enough to convert an employee from an AI-unrelated field into an expert — however, they would give your tech team a better understanding of all subsets of the technology.

Building a team is not easy due to narrow talent pools in all AI-related fields, a complex development process, and high operating costs. However, if a business owner adheres to traditional project management practices and starts testing a newly formed unit by taking on a straightforward, small-scale project, eventual progress will be self-evident.

If you’re looking forward to building a project that involves speech and face recognition, computer vision or AI-based video analysis, consider reaching out to Pro Vision Lab. As a team of experienced artificial intelligence developers and researchers, we help SMEs and big corporations build consumer-faced and proprietary solutions that involve intelligent image processing.

We use Java and OpenCV to harness the full potential of image processing. Take a look at our case studies to see what Pro Vision Lab specialists can accomplish. To discuss project details, be sure to contact us — we’ll get back to you right away.



GoBeyond.AI: E-commerce Magazine

We are a team of computer vision experts. We implement computer vision algorithms for facial processing, analysis, and recognition: