Do you really need a chief AI officer?

Stage 1 

Your organization is in the early stages of working with AI. You are aware of the technology but are doing little with it. This is Stage 1, when hiring a CAIO is unlikely to benefit you – and may even do more harm than good through lack of integration across departments and functions, and duplication of effort. 

Stage 2 

You are actively experimenting with AI, but in a fragmented manner with multiple initiatives spread across the organization. As these experiments become more numerous and mature, gaps and overlaps should become apparent and the need for a CAIO grows, but it should primarily be to capture and map AI activities to ensure visibility around what is being done.  

Stage 3  

The periods in which a CAIO can provide the most benefit are stages 3 and 4. In Stage 3, they can guide the process whereby AI initiatives move from experiments to selective implementation. Critically, the CAIO needs to prepare the groundwork for widespread integration of AI across the organization.  

Stage 4 

In Stage 4, as integration starts to scale throughout the organization, the CAIO role is still potentially beneficial; not least because scaling AI initiatives inevitably means canceling many local projects and replacing them with enterprise-wide AI solutions. And, because these changes will surely encounter local resistance, the political skills of the CAIO will become as important as their operational role.  

Stage 5 

Once AI has become an accepted way of working across much of the organization, the need for a CAIO diminishes – indeed, it may even be counterproductive to maintain the role. For this reason, the role should not become a permanent position. Rather, it should be a fixed-term appointment with a specific brief to build a set of enterprise AI capabilities that will ultimately be handed over to the business and/or technology organizations.