The rise of the Chief AI Officer: Linking AI & Culture

Recent industry and media reports about the impact of AI tend to either declare that it’s coming directly for your job, or that most workers are fine as long as they adopt GenAI. In the last few weeks this got even more interesting when a few tech CEO’s put their own thoughts on record: 

• Shopify CEO Tobi Lutke shared his previously leaked internal memo which included the guidance that “before asking for more Headcount and resources, teams must demonstrate why they cannot get what they want done using AI”. 

• Fiverr CEO Micha Kaufman wrote in an internal email that “It does not matter if you are a programmer, designer, product manager, data scientist, lawyer, customer support rep, salesperson, or a finance person — AI is coming for you” 

Then early May we heard from Klarna, one of the early voices on AI-first recruitment, that they were hiring human workers again to ensure that customers always have a human presence to talk to, if needed. It is hence not as simple as saying that AI can now fix it, this needs to be thought through carefully (surprise surprise). In this context it is then logical to see a significant rise in the number of Chief AI Officer appointments: 

Where the story gets more interesting is when we look at what backgrounds these Chief AI Officers are being hired from i.e. 67% technical (data science or engineering / technology). That would make perfect sense if the problem that most organisations are trying to solve is developing the right technical solutions for their well understand and AI-suitable business problems. Why then do most AI pilots fail?! 

Recent previous enterprise technology trends (i.e. RPA, Low Code) were not difficult for IT and business users to understand because they replicated what people already did, the way they did it. The education needed to see how these technologies might fit into a given operation or process was simple, and people quickly learnt how to spot good use cases. Unfortunately, the same is NOT true of GenAI and AI Agents. 

GenAI and AI Agents work very differently (non-deterministic), and present different risks as well as different capabilities for automation and employee enablement. The level of understanding in most organisations is still very low, and trust can also be in short supply. The path to improved efficiency and effectiveness using GenAI and Agents is not a technical one but a human one. It’s about change management, cultural adoption and broad training, none of which are usually the strengths of data scientists and software engineers (with all due respect). 

At Kainos we’ve seen this time and again across different sectors and in organisations of all sizes. We’ve hence developed approaches designed to help you define your AI strategy, identify impactful use cases, build a compelling business case, assess your cultural readiness and support people through change management initiatives. We’d love to help you too.