Stop Pouring AI budget down the drain!

Most organisations are increasing their AI spend but are still struggling to turn that money into clear business value. The result - a lot of AI budget quietly leaking away.

The current AI spending reality

Analysts now talk about AI moving from hype to accountability, where CEOs and CFOs expect proof, not promises. At the same time investment continues to increase because leaders know AI will be a major driver of growth in the future.

McKinsey estimates that AI could generate trillions of dollars in annual economic value by 2040, but the distribution of this potential is massively skewed. A small group of “high performers” attribute more than 11% of their earnings to AI, whilst a very long tail of others are still stuck in pilot mode.

Why so much budget is wasted

Gartner highlights that only a minority of AI projects deliver meaningful return on investment, with most failing vague business cases and poor data foundations. Common issues include starting with technology first, undefined target outcomes, and no agreed metrics

Accenture’s research shows that organisations often scatter AI pilots across functions without a clear path to enterprise scale, leading to delays, cancelled projects and sunk costs. When programmes do not connect to P&L outcomes, finance teams become reluctant to approve the next wave of investment requests.

What leaders who win with AI do differently

Value is only uncovered when AI is tightly linked to specific opportunities within business activities, such as customer operations, marketing, software engineering and R&D. In these areas, leaders can point to measurable efficiencies, cost savings, incremental revenue or risk reduction rather than abstract "transformation" or "innovation".

Accenture describes a group of “Reinventors” that treat AI as part of a holistic reinvention strategy, not a set of silo'd experiments. These organisations have grown revenues faster and improved profit margins significantly more than those that take a more ad‑hoc approach.

A simple way to redirect your AI budget

A practical shift for executives is to move from AI-first thinking to outcome-first thinking. Start by defining 3-5 business outcomes you care about over the next 12-18 months (for example, reduce customer churn, improve process times, increase cross‑sell rates) and then ask where data and AI can directly influence those numbers.

Next, embed the same discipline you expect for any capital investment - clear baselines, target impact, time to value and an owner accountable for delivery. Finally, build a small portfolio of AI initiatives that can prove value quickly and scale the winners rather than betting big on unproven ideas. Afterall 20% of the ideas will drive ~80% of the value.

To explore how to stop pouring AI budget down the drain in 2026 and redirect it towards measurable and impactful outcomes for your organisation, contact us to continue the conversation.