AI adoption starts and stalls in the boardroom

Boards across the UK are urging their organisations to “do something with AI”. Yet, this pressure fuels a cycle of activity without progress, and pilots that look impressive but fail to scale.

The root cause isn’t always technology. Sometimes AI initiatives are rolled out in isolation without first aligning them to the broader business strategy. Without that alignment, activity generates noise rather than meaningful progress.

If boards are serious about advancing beyond pilots, AI can no longer be treated as a side project. Leaders must take an active role as conductors, orchestrating the tempo and conditions for AI to generate lasting business value.

The good news is that these challenges aren’t impossible to overcome. Boardroom blind spots are costly missteps that waste time, drain budgets, and let competitors race ahead. Yet, they can be addressed with straightforward solutions.

Boardroom blind spots and the antidotes

Again and again, boards encounter the same problems that stall momentum before AI can deliver real value. Worse still, these problems can often go unnoticed until it’s too late. So, what are the common blind spots boards should look out for – and how can they be fixed?

1. The AI strategy trap

A fear of missing out often drives boards to make one of the most common mistakes: pursuing an “AI strategy.” It sounds sensible, but it frames AI as the goal rather than the enabler, which fundamentally is AI for AI’s sake. This focus on spectacle over substance produces ambitious roadmaps misaligned with business goals.

The starting point should be the business strategy.

Re-frame the question around the implementation of AI. Instead of asking, “What’s our AI strategy?” boards should ask, “How can AI help accelerate our business goals?” This anchors every project to measurable outcomes and directs attention to friction points holding the business back.

Only once the business problem or opportunity has been defined can boards start thinking about how it can be solved or leveraged using AI.

2. Delegation without oversight

Another frequent mistake is when boards issue an intent for AI and then step back. The intention is often pragmatic – let the experts handle it – but the effect is that alignment evaporates.

AI projects need oversight from people who understand how the technology works, but hiring full-time senior AI talent that also understands business is expensive and slow.

Hiring a permanent AI executive to oversee projects is no longer the only option; fractional leadership now offers a flexible alternative, giving organisations immediate access to experienced leaders who offer the right expertise at the right time.

This keeps costs aligned with value delivered, accelerates adoption and keeps projects relevant and strategically anchored, while avoiding the trap of over-investing in permanent hires before the business is ready to support them.

3. The FTE fallacy

Most boards in this market are looking at how to save costs and sometimes that means finding less expensive alternatives to reduce FTE (full time equivalent) costs. This is never an attractive strategy but is, unfortunately, a reality in a lot of businesses. For those businesses that are looking at shifting capacity to higher-order tasks, the challenge is manageable but for those looking at reducing headcount, there is a less-than-obvious trap.

AI can execute tasks at scale, but it cannot set objectives, weigh trade-offs, or take responsibility when things go wrong. This leads to hours being saved horizontally (in small amounts across various functions) rather than vertically (enough savings in one role to remove it). Everyone in a 50-person business unit may have two extra hours in their day - that’s 100 hours total - but that doesn’t translate into a 12-person reduction. Having a plan on how to redirect that additional time is critical otherwise it will be absorbed as a personal benefit by individuals.

4. Accountability and risk

On the other side of the coin, there is a persistent belief that “agents” can scale indefinitely and, with it, increase productivity without restraint (barring the cost of compute, of course). But the reality is there are hidden constraints, and “accountability” is a common one.

AI cannot be held accountable. Material decisions and intent should be reserved for humans. But even that comes at a cost. A leader may be willing to take on accountability (and the risk) for a ten-person team. But, given that agents can err as humans do, are they willing to take on the risk of 100 agents?

There is a fuzzy cap on how much AI can scale under an individual, and it differs from person to person.

Simply folding this thinking into planning around AI could prevent a lot of missed commitments.

5. Weak data foundations

AI systems are only as good as the data that underpins them. Poor-quality, fragmented, or biased data doesn’t just limit performance, it amplifies flaws and undermines trust. Too often, boards treat data as a technical matter to be solved by IT, rather than recognising it as the foundation of strategic decision-making.

To correct this, boards must treat data as a core business asset, defining a clear data framework that ensures strong governance, robust infrastructure and continuous monitoring and validation.

Equally important is context. High-quality data only creates value when projects are designed with cross-functional input. When engineers, risk specialists, and frontline employees create AI systems together, they ensure data is interpreted correctly, and AI systems are built for real-world use. As a result, adoption sticks, feedback loops remain active and projects have the optimal conditions needed to scale.

Leadership, not technology, will decide

Unless boards confront these blind spots, AI will continue to generate more noise than value. By re-framing strategy, maintaining meaningful oversight, and strengthening data foundations, boards can break free of stalled pilots and create scalable, sustainable projects that deliver real business impact.

AI adoption will not be won in labs or pilots, but in boardrooms. Boards that act decisively will create the conditions for adoption to stick and scale with confidence. Those who hesitate will be left with clever but irrelevant tools, and watch competitors pull ahead.