How to create a highly effective AI team

We heard from Errol Koolmeister, Founder, The AI Framework, on how to create an effective AI team.
We heard from Errol Koolmeister, Founder, The AI Framework, on how to create an effective AI team.

At the Data Innovation Summit 2021, Errol Koolmeister, the Founder of the AI Framework, shared his insight into creating an effective AI team. Koolmeister leveraged his five years of experience during the talk. He shared that he had hired over 350 software developers, AI specialists, ML engineers and business analytics and built large teams globally.

Koolmeister supports an AI-driven transformation, which focuses on organizing value, AI and data. He emphasizes that AI is no longer a competitive edge but a “hygiene factor” that is now a necessity in the evolution of businesses. He emphasizes that change is inevitable, and businesses need to adopt this flexibility. 

The characteristics of a high-performing team

The first factor to consider when building an AI team is identifying what an organization will do with AI. This is why it is essential to ensure that an AI team is strong enough to plan around value and strong enough to say if a solution isn’t right. For example, if an organization needs to adopt a rule-based product as opposed to AI. A team that can evaluate time-to-value and know when to reject AI is key in ensuring that an organization isn’t just adopting further AI principles simply for its sake. 

According to Google, creating a team means making a shared sense of purpose that strives for continuous learning. Furthermore, a team needs to harbour mutual respect for one another, fostering open communication and sharing leadership. In addition to this, effective working procedures built on differences, flexibility, and adaptability are the building blocks of a successful, strong team. Koolmeister says that he used to measure a good candidate by their credentials and previous experience in the past, but has since learned that an assertive attitude is just as valuable.

Knowing the problem that needs solving 

Identifying the right problem is as important as building the right team. Koolmeister refers to the three-maxima model for enterprise innovation. Businesses often are not aware of the issue they are trying to solve, and so often follow the first maxima: business optimization. This means increasing the volume of current operations, which is easy to implement, as business leaders are easier to convenience as it is familiar. 

The other method is product-market method innovation, which involves breaking into new markets. The third method is business model innovation, which is entirely new. Unfortunately, in Koolmeister’s experience, the teams he has spoken with have often been unable to differentiate between building on current processes and developing something new. These conflicting agendas will be detrimental to any business transformation, which is why the problems must be identified first. 

Following on from the three-maxima model for enterprise innovation, organizations can identify problems in three categories: the core problems that may relate to regulatory issues; the adjacent problem, where an organization knows the problem, but not the solution; and lastly, the transformative, where an organization does not know the problem or the solution. Koolmeister often sees organizations rush to adopt AI, SAFe and Agile, but businesses are failing to organize and separate these issues instead of expecting agile to compensate for this. Though agile has its place in remaining reactive to market shifts, this means little if an organization is not aware of either the problems and solutions.

Lean startups 

Koolmeister suggests that if an organization is unsure what problem it is solving, it should operate as a lean startup. A lean startup pivots around problems and does not focus on one for too long. He emphasizes that “If you are working on the wrong problem, then you will never get the right solution.” He references the hierarchy of needs, noting that the solution needs to be the last thing an organization focuses on, without building all of the pipelines around the problem and gathering tools first. 

Enabling best practice analytics 

These are four components of ensuring that a team is highly effective:

  1. Create a team that supports one another. Koolmeister shares a favourite quote of his: “culture eats strategy for breakfast.” Knowing that a team will support one another in the event of sickness is the key foundation to running a successful team. 
  2. Operating on agile practice methods supports the team in reacting to changes. However, he says this should not be mandatory, as teams need to discover their rhythm and way of working. 
  3. Teams need to use tools that support them in communicating efficiently and ensuring productivity. 
  4. Lastly, a workspace needs to be optimized to enable a team to share knowledge with other teams and ensure maximum productivity for the team itself. There needs to be a clear infrastructure for the team to obtain the necessary tools to carry out their work. 
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Koolmeister concludes by reminding IT leaders building teams that the team’s success needs to be measured on the value it generates for an organization, and its return on investment, not on structure. The way to achieve this is by ensuring deliverables are clear and that the team is proving the solution’s real value by delivering this value. As the value is delivered, an organization can then expand the team to match the delivery requirement. Finally, by returning autonomy to a team, making them responsible for their output, Koolmeister enthuses that this will increase productivity from that team and value the organization.

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AI team, Business, How to create a highly effective AI team

Amber Donovan-Stevens

Amber is a Content Editor at Top Business Tech

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