CIO Summit: Four top tips to build a future-fit AI team


Keynote speaker Kosta Kontos advises CIOs to start with the recruitment process - and avoid brilliant jerks who will cost the company more in the long run.

, who is a lecturer of the Data Science Leadership course at the Graduate School of Business, University of Cape Town, and managing director of Kontos Databases (KDB), gave the keynote address at the first-ever CIO South Africa Cape Town Summit held at the 180 Lounger.

He provided a comprehensive overview of overview of what organisations need to consider when building a future-fit AI team.

In his introduction, Kosta highlighted the key roles required for such a team, including statisticians, data analysts and machine learning engineers. The most central of them all, he said, is a data scientist. “Even if we talk about putting together an AI team, we’ll still rely on data science.”

He did, however, warn that finding a great data scientist may be a challenge. “Supply and demand of data scientists is working against us locally, but having a great data scientist is an integral part of building a successful AI team for the future. The data science lead must be on board to oversee the entire process and team management.”

Four top tips for building a high performance team

Speaking to top CIOs from the Mother City and beyond, Kosta’s pointers were:

  • Consider the recruitment process. “The issue of hiring a team has created some of the most heated debate in the room. It all depends on the size of your organisation. If you want to build an AI team for the future, you have to look where you are right now. If you are already a highly sophisticated team with processes in place then you can afford to take the risk of building a team internally by retaining and upscaling talent. But if you are a smaller organisation looking for highly specialised knowledge, then it makes more sense to hire a consultant,” he says.

In his opinion, the best approach would be a hybrid of the two, where the organisation can maximise the advantages and minimise the disadvantages.

  • Be transparent in the recruitment process. “It's important to be very clear on the skills you want to recruit, and the tools you want to use. As much as it is a grey area, maybe include salary there, because you don't want to attract awesome candidates and once you are so far down in the process, realise another company has offered that individual more. That will just waste everyone’s time.”

  • Create an impressive and attractive culture. “Young data scientists in particular, love to work for organisations where they can learn. Moreover offer unlimited study opportunities within reason. Once the studies are completed, the information will be shared with the rest of the team and this will foster a culture of learning, growing and sharing.”

  • Avoid brilliant jerks. These are individuals who are very smart but cannot be integrated or connect with the rest of their team members, and in doing so, cost the company a lot more in the long run. “The best way to get rid of them is to create other opportunities for them elsewhere. That way they are no longer your problem,” he concluded.

Related articles