The Mandate Without a Map

Ask a Fortune 500 CEO or Chief AI Officer about their AI strategy and you’ll get a confident answer. Ask the SVP running supply chain planning or the VP of FP&A what AI means for how their team works next quarter — and the answer gets a lot less clear. 

The most common breakdown I’m seeing right now isn’t resistance, and it isn’t lack of investment. It’s that AI strategy has been built at the wrong altitude. It’s designed for the C-suite conversation and never translated down to the people who need to change how they work. The functional leader sitting between that strategy and their team is left to bridge a gap no one planned for. 

The result is a familiar pattern: high ambition at the top, real confusion in the middle, and a widening gap between the enterprise roadmap and what’s changing on the ground. 

The navigation layer that’s missing  

Enterprise AI strategies are useful for what they’re designed to do: align leadership, secure investment, and set directional priorities. What they’re not designed to do is answer the operational questions that functional leaders are stuck with. 

A VP leading a 200-person Medical Affairs organization doesn’t need a platform selection framework. They need to know which of their team’s workflows are changing, which roles will look different, and what a realistic path to measurable productivity improvement looks like — inside the constraints of their existing systems, headcount, and workload. 

That kind of planning has to be built inside the function, around how the work really gets done. And in most organizations, it hasn’t happened.   

How it shows up 

The signals are easy to miss one at a time:  

• AI usage is concentrated among the most tech-forward people on the team. Everyone else is waiting for clarity about what’s expected of them. 

• Training has happened, but it wasn’t tied to specific workflows. People completed it and went back to working the way they always had. 

• Managers are fielding questions from their teams about what AI means for their roles — and don’t have good answers. 

• Pilots that showed real promise in a controlled setting quietly stalled when someone tried to scale them. No one had redesigned the broader workflow around them. 

• The functional leader is tracking the enterprise AI roadmap closely but can’t point to a single concrete change in how their department operates. 

Taken individually, each of these reads like an adoption issue or a change management problem. Taken together, they’re a planning problem. The function doesn’t have a vision of its own future state.    

When the vision exists, everything changes 

I’ve seen this play out clearly in recent engagements. AI tools were already deployed. Executive sponsorship was strong. Leadership was genuinely committed. And adoption was flat. 

When we got into the function-level work — mapping the actual decision flows, identifying where manual effort was concentrated, defining what the team’s operating model needed to look like once AI was embedded — the dynamic shifted within weeks. The functional leader had something concrete to bring to their team: a clear picture of what was changing, what wasn’t, and what was expected of each role.  

Adoption accelerated. Not because the technology changed, but because the people doing the work finally understood what they were being asked to do differently. 

That’s the unlock. Not more executive communication. Not another training module. A function-level plan specific enough to act on. 

What functional leaders who close this gap actually do  

For functional leaders who recognize this gap in their own organizations, the path forward is more practical than it might appear. 

1. Map the work before you touch the technology. Before selecting the AI tools to deploy, get specific about where the function’s biggest productivity constraints live. Which decisions are slow? Where does work pile up waiting for input or approval? Which outputs require the most manual effort for the least strategic value? That diagnostic tells you where AI creates real leverage — and makes it much easier to sequence what comes first. 

2. Get specific about what changes for your team. The ambiguity that stalls adoption at the functional level isn’t usually about the technology. It’s about roles and expectations. People want to know: Is my job changing? Am I being measured differently? Do I need new skills? Functional leaders who answer those questions clearly — in writing, for their specific team — consistently see faster adoption than those who leave the implications implicit. 

3. Anchor the roadmap to early, visible wins. A comprehensive 12-month AI implementation plan sounds rigorous. In practice, it almost always stalls before it gets traction. A better approach: identify two or three specific workflows where AI can deliver a visible improvement quickly, treat those as proof points, and build outward from there. Early wins create the organizational confidence that makes the harder changes possible later. 

The Bottom Line 

The enterprise AI strategy is necessary. It’s just not sufficient. The functional leaders who close the gap between AI ambition and actual adoption aren’t waiting for clearer direction from the top. They’re doing the function-level planning work themselves — and giving their teams something concrete enough to act on. That’s what a map does that a strategy can’t. 

Andrea Schnepf