The Execution Gap: Why Enterprise Digital Transformation Keeps Stalling, and What Closes It

The organisation's that are succeeding simply treat strategy and delivery as one conversation instead of two.

Enterprise digital and AI transformation present a significant commercial opportunity, but the conversations I have, and so many headlines, state that organisation's are failing to capture it. Not because they lack ambition, but because engagement and alignment were never central to how early strategic decisions were made.

Microsoft has put a number to it. Their 2026 Work Trend Index, drawn from a 20,000-person survey across 10 countries, found that only 19% of AI users have reached what Microsoft calls the Frontier; the zone where individual capability and organisational readiness reinforce each other. Around half are stuck in the emergent middle, investing effort without generating traction.

Businesses are investing time and energy but are seeing poor adoption rates. Overarching transformation ambition falls short. That is not a technology failure. Every organisation in that research had access to AI tools. Most had invested in training. The failure is elsewhere.

Boston Consulting Group's 2024 'Where's the Value in AI?' research also highlights the scale of the problem; "only 22% of companies have advanced beyond the proof-of-concept stage to generate some value, and only 4% are creating substantial value".

The pace is faster. The stakes are higher.

AI is making the pace of transformation seem urgent. Leaders arrive with solutions. Consultants arrive with frameworks. Technology vendors arrive with demos. Companies undertake discovery, develop a proof of concept, build.

It’s the single most consistent mistake I come across. The people who will use the system, whose daily workflows, professional identities, and working relationships are most affected, are consulted late, or too often simply have something new thrown their way with a hope that they will catch up.

The result is a programme that delivers technically and fails humanly.

What integration actually looks like

Prosci's ADKAR model names five conditions for genuine adoption: Awareness, Desire, Knowledge, Ability, and Reinforcement. Most programmes address three of these. Awareness and Knowledge, communicating the change, and Ability, training people on the new system.

Desire and Reinforcement are treated as soft, secondary concerns, and that is exactly why adoption unravels months after go-live, long after the project team has moved on and nobody is watching the metrics anymore.

Previously, I’ve written about a live AI transformation programme where I integrated this ADKAR model directly into Agile delivery; not theoretically, but operationally, sprint by sprint. Stand-ups became an early warning system for Awareness and Desire risks. Sprint Reviews were redesigned as Knowledge and Ability accelerators. Retrospectives became the engine for Reinforcement; the stage most programmes abandon long before the change has embedded.

The fix is not a change workstream bolted onto delivery. It is integration, mapped to the mechanics teams already use.

The same discipline pays off earlier than most programmes realise!

Stakeholder mapping happens before a backlog exists, so Awareness is built into the brief and early discovery rather than added afterwards.

Early engagement and user testing catch resistance while it is still cheap to address, rather than after a full rollout has already been built around assumptions nobody tested.

Sprint 0 becomes the moment end users voice their own pain points, so Desire is generated before a line of code is written.

Knowledge gaps surface inside sprint planning rather than a separate communications plan.

Sprint Reviews become adoption checkpoints as well as product demos, increasing knowledge and ability, catching hesitation before it hardens into resistance.

The Definition of Done expands to include a human readiness condition, so understanding and ability are a condition of delivery, not an afterthought.

Reinforcement, the stage almost every programme cuts first when budgets tighten, gets tracked through retrospectives and post-go-live metrics running alongside delivery metrics.

The programme does not close until the change is visible in how people work with the new systems and new strategies.

What closes the gap

The organisation's that are succeeding simply treat strategy and delivery as one conversation instead of two.

Whether I am defining a new enterprise strategy, shaping a Target Operating Model, or modernising digital infrastructure, this is the approach I follow to help organisation's resolve strategic uncertainty and transition from ambition to action in all areas of transformation.

Evaluate and engage, understand the data and evidence, align cross-organisational priorities short-medium and long term, define an action plan and roadmap. Leadership aligns before the programme starts, so disagreement gets resolved at the table rather than played out during delivery.

Transformation is designed around outcomes. Strategy is embedded into governance, incentives, daily rhythms and future ways of working, not left behind in a slide deck after launch.

The result is not just successful delivery. It’s adoption that is earned rather than mandated, with the full confidence of the people using the system.

That is what turns ambition into something a team can deliver, instead of another initiative that quietly fades by year end.

If you want a practical way to start that conversation inside your own organisation, I've put together a free resource that walks through it in more depth.

Download the free guide here.