The Five AI Enablers

A large retailer we worked with had an AI vision that survived three budget cycles.

It survived because it was specific rather than ambitious. The vision was simple: make everyone more valuable. It was anchored to a real constraint, growth targets that couldn't be met through headcount alone, and it connected directly to what the CFO was already tracking. Every AI investment could be evaluated against it. It didn't require translation.

That's what a working vision looks like: simple enough that anyone in the organisation can explain it, tied to a business priority the board cares about, with KPIs that prove whether it's working.

McKinsey's research found that 92% of organisations plan to increase AI investment in the next three years. Only 1% have reached what they define as AI maturity. The distance between those two numbers comes down to whether the vision is clear enough to drive decisions and survive a budget cycle.

McKinsey

The organisations that stall are usually the ones trying to "AI everything." The ones that accelerate have picked something specific and strategically important, built the metrics around it, and embedded it into every initiative. The vision doesn't need to be grand as long as it's useful.

The official technology picture almost always surprises the senior team.

There's what IT has sanctioned. There's what people are using on their own. And then, reliably, there's something someone built over a weekend that six colleagues have quietly adopted because it works better than anything on the approved list.

70% of the effort needed to close the AI gap is change management, not tools. Most organisations are underinvesting in one and overfocusing on the other.

BCG · WFA Global Marketer Week, Stockholm, April 2026

The Technology enabler gives three perspectives on tools for your team. First: enterprise tools, Microsoft, ChatGPT Enterprise, the platforms already paid for and approved. The productivity gains available here are consistently underestimated, and it's where scale usually starts. Second: custom functional tools, built for specific teams or workflows, often grown from a successful pilot. These need to be managed like products, with a named owner, an engaged user community, and usage metrics tracked week to week. Third: on demand or shadow IT.

The reflex is to treat shadow IT as a risk, but it's more useful when read as a signal. People using personal tools instead of approved ones are usually doing so because the approved options don't meet their needs. The colleagues experimenting on their own time are often the first to tell you what to ask for next. Encourage it as learning. Draw the line only where company data is going into unmanaged systems.

Deft helps CMOs and marketing leaders build the functional capability, across strategy, adoption and operations, to move at a different speed than the organisations around them. We work with your tech, across the full AI programme, and stay until the change sticks.

If your AI programme is taking longer than you'd like to map out, or it's up and running but failing to demonstrate the results you'd like, that's the ideal time, as a leader, to start a conversation with us. We believe that a human, open conversation is the easiest and best place to start.