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 is AI-driven digital transformation. In Domino Data Lab’s 2022 Revelate surveyOpens a new window , 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 many actually have 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. 

Grow an AI Leader

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. 

However, they are rarely sufficiently empowered to do their current jobs, as they lack the authority to secure the alignment and support needed from stakeholders. They must achieve what they can through influence, tenacity, luck and sheer force of will. 

These AI specialists lack a clear upward path to a position of power that would enable them to scale AI-driven outcomes, either because a more powerful role does not exist yet, or because the track that leads to that more powerful role is aligned differently. A typical organization will likely have existing career paths that lead from VP of Data or VP of Analytics to CDO/CDAO but no clear path from Head of Data Science to CDO/CDAO. Thus, there isn’t a track to create a true AI leader who; 

  • a) Actually understands and has experience with AI, and 
  • b) Has the power to implement it.

Companies must grow AI leaders by creating these powerful roles with responsibility for driving AI outcomes. They must promote data science leaders into these roles and ensure succession by creating a career pathway into these roles.

See More: The Power of AI to Revolutionize Talent Management

Catch an AI Leader

Hiring strategic AI leaders from outside the organization is always an option and may be necessary if you do not have enough in-house data science talent. You must seek individuals with:

    • A history of driving repeated, substantial outcomes with AI/ML
    • Leadership experience with data science/AI/ML teams  
    • Excellent change management skills
    • Extensive data science training (can be waived if the individual has a long history of managing data science teams)
    • Endorsements from leaders outside of data science (e.g., IT leaders and business stakeholders)

It is also critical to create the right AI leadership roles to place these individuals in. These roles must have support from the highest-level executive(s) and significant resources to make early success possible. They must also be aligned with other stakeholders, as previously discussed.

Become an AI Leader

Generally, you should avoid recruiting outside candidates who are data and analytics leaders but whose data science experience only goes as far as having been involved in AI/ML projects. These individuals are likely to focus on spinning up additional data and analytics projects vs. AI and ML projects and duplicate efforts that are already in flight. But what if you are an existing data and/or analytics leader in your organization? If so, you are in an excellent position to take the initiative and fill that gap in AI leadership yourself. 

Existing data and analytics leaders are particularly well positioned to become AI leaders because they fully understand and have power over the complementary assets important for driving impact with AI and ML. They can help drive alignment across stakeholders because they are already one of the most important stakeholders. By learning from data science teams and then supporting and taking ownership of more AI projects, they can eventually promulgate an organization-wide AI strategy.

The Key to AI-driven Transformation: AI Leadership

Driving sustained competitive advantage with AI and ML requires the scarcest commodity in AI right now: AI leadership. Every organization needs to build and cultivate AI leaders and create a leadership structure that can drive AI strategy for the enterprise. Create these roles, empower your promising leaders to grow into these roles, hire when necessary, or become that leader yourself to drive impact with AI and ML and ultimately transform the organization.

How are you finding an AI leader who can help your organization transform itself using AI and ML technologies? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .

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