Even greater challenges in accessing AI talent (part 6)

By Tim Beswick

We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts, Anderson MacGyver shares her point of view on this topic. For those who want to start from the beginning, you can read:

• Part 1: How do we become an AI-driven enterprise?
• Part 2: Data-to-AI-to-Value journey
• Part 3: theme 1: The generative / general-purpose AI model buzz
• Part 4: theme 2: Business process redesign requiring even more attention for people change
• Part 5: theme 3: Additional risks and different measures

Now, let’s dive into the last part: the fourth underestimated theme.   

4. Even greater challenges in accessing AI talent 

Theme four of the four underestimated themes

It is widely acknowledged that demand for data talent is higher than supply. This imbalance increases when including specific AI capabilities in the equation. 

AI relies on talent in domains that are most scarce. It concerns domains such as software engineering, data science, machine learning engineering, NLP engineering, robotics engineering, data engineering and multidisciplinary agile development. It is important to take this into account and include the following in your journey to becoming an AI-driven enterprise. 

1. Focus; do not run after abstract visions but work with the business on defining and prioritizing tangible Data-to-AI-to-Value opportunities. Direct your scarce talent towards these highest priority opportunities. 

2. Retention; Do not fall into the trap of promising the most advanced AI applications in your organization to attract talent. You will probably disappoint and quickly lose anyone who was driven by this after a while. Throwing away your recruitment investment, creating inflated costs through constant delay and handovers. Instead, be honest and clearly articulate what truly makes your organization attractive; your societal role, your working atmosphere, your maturity stage and associated opportunity to be part of something new, etcetera. Attract talent that is driven by your organization’s true characteristics and stand a higher chance of being an attractive environment for your AI talent for a longer period. 
3. Strategic sourcing; Pay attention to defining a sourcing strategy. Utilize all sourcing options to your benefit. Carefully consider where to vest your inhouse talents. Assess which external suppliers can be leveraged for which scope. Investigate options to collaborate in your eco-system if there are potential synergies and there is no commercial value in differentiation in your eco-system.