The only thing that rivals AI as a topic in business seems to be surveys of how businesses are using AI.  ChatGPT, Claude or your favourite search engine can summarise this for you well enough.  My team and I wanted to get behind these numbers; to listen more and try to make sense of the challenges so we can offer some guidance on the paths ahead. 

So we ran over 200 workshops with enterprises across Europe and the Middle East.  These sessions gave us a unique insight into the internal dynamics of businesses grappling with AI as well as the different approaches to integrate the technology. The results, unsurprisingly, gave a contrasting picture of those still to get started and those already part of the way there. Yet, it was notable that even top performers had yet to reach full potential and AI impact remained inconsistent even among mature adopters.

Our conversations taught us that there are essentially two characteristics that determined where an organisation was on their journey: how ambitious their mindset is and the if their execution methodology is fit for purpose. Measuring on these two axes, we concluded that most organisations fit into four broad categories or archetypes.

Four AI Archetypes

First, we have the Sleepers. Guilty of low ambition and limited execution, their procrastination is either due to a 'perfectionist' mindset or having no understanding of the potential rewards. Serious productivity gains are within reach, even if they're blissfully unaware of the AI capabilities already embedded in their systems.

Stallers have real enthusiasm for AI but lack a plan to get it live. They tend to over-engineer strategy documents, build endless proof-of-concepts, and fail to link AI to real business outcomes. In contrast, Movers are good at executing AI, even if it is frequently in isolation. They rely heavily on out-of-the-box tools, but lack the vision to truly accelerate their efforts or build a cohesive strategy.

Leading the pack are the Maximisers who rank highest in terms of ambition and execution. These organisations have mature AI strategies that are delivering measurable value. However, making further gains will take a concerted effort. A solid foundation does not make them impervious to future complacency.

The Path to Enablement

Understanding which archetype best describes your organisation's current situation can be an important step toward progress. But evolution requires more than awareness, you need a way forward too.

Sleepers and Stallers may have the most ground to make up in terms of getting value from AI, but making relatively small changes will rapidly reap rewards. Firstly, it's about starting small and measuring outcomes. Remember, not every step needs to be scaled, just those that prove impact.

Begin with highly targeted, incremental rollouts with performance tracking baked in. Focus on business functions, such as finance or HR, where AI tools are readily available and can quickly make a demonstrable impact. Learn as you go and then iterate to scale AI workflows across different areas of your business.

A great example of this in action is Wood Group, a British multinational engineering and consulting firm. It faced a challenge filling open roles, taking on average 45 days from publishing a job advert to start date. By turning on AI agent capabilities that were already embedded in their HR system, they are now able to hire candidates in around 21 days. This was a no-cost, minimal effort move that reduced time-to-hire by 53 percent.

For Movers and Maximisers, a lot of this foundational micro-AI is already in place. To unlock the next level of productivity, they will likely need to shift some of the balance towards more ambitious AI projects with transformational potential. However, it's vital that they avoid what we call Pilot Purgatory.

This makes it essential to have a structured roadmap from Day 1 to bring effective AI pilots through to production. A pragmatic execution plan is central to this. It should simultaneously foster a culture of experimentation while feeding a funnel of enterprise-wide value creation. Successful adoption will hinge on detailed planning, incorporating timelines, training, and change management. Based on data collected from our workshops, we estimate by embedding AI into everyday tasks, a typical organisation of 10,000 fulltime employees could achieve a 4 percent reduction in their total cost base.

Of course, these models are frameworks rather than prescriptions to help companies navigate finding value from AI.  The thing that unites successful adopters is being astutely aware of the business value they are attempting to deliver and working with colleagues with that focus of purpose.