You Don’t Have an AI Talent Gap: You Have an AI Leadership Gap |

Companies want to digitally transform themselves today using AI and ML. However, most companies do not have an AI leader with the ability and authority to implement the significant changes necessary to achieve AI-driven outcomes. Kjell Carlsson, head of data science strategy & evangelism at Domino Data Lab, shares how companies can find an AI leader.

It is rare to find a company that does not want to transform itself with artificial intelligence (AI) and machine learning (ML), and for good reason. These technologies provide the biggest opportunities available to increase growth and operational efficiency. Outsourcing and offshoring have shown their limits, customer experience improvement is incremental, and real digital transformation today isAI-drivendigital transformation. In Domino Data Lab’s 2022Revelate survey, nearly half of leaders said they expect double-digit growth from their investments in AI and ML — double the number from the previous year. 

The expectations of AI are very high. But ask yourself: how many AI leaders do you see in the upper echelons of companies? Not how many leaders are talking about AI — how manyactuallyhave the ability and authority to implement the significant changes necessary to achieve AI-driven outcomes?

Unfortunately, it is very few.  

These individuals do exist and are incredibly valuable. They have the ability to identify areas where data science can have the greatest impact, oversee the development of AI solutions, push them into production and drive ongoing adoption. We have seen AI leaders take small teams with a handful of models and scale them to multiple data science teams across the organization running hundreds of projects.

For example, in pharma, we have seen a chief data science officer leading the charge to accelerate and expand their company’s R&D pipeline — everything from targeted cancer therapies to vaccines. In insurance, we have seen a chief analytics officer build a data science and AI center of excellence into the core of the company’s business, enabling real-time, model-based decision-making across the enterprise.

These trailblazers have the trifecta of characteristics — understanding, responsibility and authority — to significantly move the needle with AI and ML. They have the necessary experience and expertise to own the organization’s AI strategy and outcomes, from running data science teams to being data science practitioners themselves. And they have the power and influence to drive alignment between stakeholders, acquire the support they need from IT and the business, and ensure adoption and continuous improvement. 

Every organization needs AI leaders like this. So, how do you get one? 

The answer: grow one, catch one, or become one. 

Most organizations have plenty of raw materials to create successful AI leaders. They are the individuals who are leading increasingly large data science teams and driving the mostly incremental AI-driven outcomes we see today.