The boardroom conversation has shifted. Not to whether AI matters. That debate is over.

The new question is what to actually do with it. And the 2025 data suggests most organisations are answering it badly.

BCG’s research found that 60% of companies globally are generating no material value from AI despite significant investment. McKinsey’s 2025 State of AI survey found that nearly 80% of organisations are regularly using generative AI in at least one function, yet only around 5% are seeing substantial financial returns. The gap between AI as a boardroom announcement and AI as a functioning operational capability is the defining management challenge.

 

The pressure is real. The direction is not.

Executives are asking about AI. Vendors are selling it. Competitors are announcing it. Boards are expecting it.

And in the middle of all that noise, organisations are launching AI initiatives without defining what success looks like. That is not ambition. It is drift with a budget.

The fear of being left behind is understandable. But speed without clarity produces activity, not results. And activity without outcomes is expensive.

RAND Corporation’s 2025 research into AI project failure found that 80.3% of initiatives failed to deliver their intended business value. A third were abandoned before reaching production. A further 28% reached completion and still failed to deliver. The problem is not a lack of investment or effort. It is a lack of clarity about what the investment is actually for.

 

Most organisations are starting in the wrong place

The most common mistake in AI adoption is starting with the tool rather than the problem.

The conversation usually begins with: “What AI platform should we procure?” It should begin with: “What is broken and where?”

AI is not a business objective. It is a capability. And capabilities only create value when they are pointed at something real.

BCG’s research found something that cuts against the instinct of most organisations. The organisations generating real value from AI average 3.5 use cases. Those generating no value average 6.1. More is producing less.

The organisations getting results are not asking which platform to buy. They are asking where their people are losing time to work that should not exist. Where good decisions are being slowed down by fragmented information. Where a process that should take hours takes weeks because nobody has questioned whether it needed to be that way.

That is the right starting point. Not a procurement conversation. A diagnostic one.

 

AI exposes what you were already avoiding

Many leaders believe AI will fix their inefficiency problem. It will not. It will make it harder to ignore.

Unclear processes do not get fixed by AI. They get amplified by it. Poor data quality does not improve because a model has been deployed on top of it. Weak governance does not disappear. Siloed departments do not start collaborating because there is an AI tool in the mix.

The evidence on data readiness is striking. Cloudera and Harvard Business Review Analytic Services, surveying enterprise organisations in early 2026, found that only 7% said their data was completely ready for AI. More than a quarter said it was not ready at all. Gartner estimates that 60% of AI projects unsupported by AI-ready data will be abandoned through 2026.

The obstacle in most AI projects is not the technology. It is the organisation the technology is being asked to work inside. Not the model. Not the platform. Not the vendor. The organisation itself.

 

Fewer tools. More focus. Better results.

Buying advanced technology does not automatically create change. It creates the expectation of it.

Many organisations are deploying AI on top of environments defined by disconnected systems, inconsistent processes and poor data. They are not implementing AI. They are automating the same dysfunction at scale, with a larger budget attached to it.

The organisations getting real value are not the ones running the most pilots. They have chosen a small number of problems that matter, built the right foundations to address them, and stayed focused long enough to see results. They do not announce it. They build it.

They also understand something most organisations have not yet accepted. AI should work in service of a clear business direction. If that direction is unclear, AI will not provide it.

 

Stop performing AI and start implementing it

A significant portion of what organisations call an AI strategy is AI theatre. Presentations. Innovation labs. Pilot programmes. Strategy papers. Announcements. And very little operational change.

IDC’s 2025 research found that for every 33 AI proofs of concept an enterprise starts, only four reach production. A March 2026 survey of 650 enterprise technology leaders found that 78% of enterprises have AI agent pilots running but fewer than 15% have reached production deployment. The pilots do not fail because the technology is immature. They fail because the hard work of creating the conditions for success was never done.

That hard work is not glamorous. It is redesigning how work flows. Making difficult calls about what to stop doing. Getting data into a state a model can actually use. Bringing people along rather than announcing change at them. None of it photographs well for a board update.

But that is what transformation actually looks like. The rest is performance.

If your AI strategy looks like a series of well-designed slides with no corresponding change in how work gets done, you do not have an AI strategy. You have a communication exercise.

 

The question every organisation eventually has to answer

Every organisation reaches the same moment. The excitement fades. The pressure increases. The questions get harder. And someone in that room finally asks: “What exactly is this improving?”

The organisations with a clear answer will accelerate. The ones without one will keep funding a future they never properly defined.

The question is not whether your organisation is using AI. Most are. The question is whether you can name, in a single sentence, the operational problem your AI investment is solving, and how you will know when it is solved.

If you cannot, the technology is not the problem.