56% of CEOs See Zero Returns on AI: Is Your Organisation Burning Cash on Digital Theatre?

PwC surveyed 4,454 chief executives across 95 countries. The majority reported no financial benefit from their AI investments. Here is what project professionals can do about it.

The annual Davos gathering has become a reliable barometer for executive sentiment, and this year's delivered a sobering message about artificial intelligence. Despite billions poured into AI initiatives, 56% of chief executives reported neither increased revenue nor lower costs from their investments.  Only 12% achieved the holy grail of both revenue gains and cost reductions.

For project professionals, this statistic should feel familiar. We have watched organisations chase technology trends before, often with similar results. The pattern repeats: executive enthusiasm, rushed procurement, pilot programmes that never scale, and eventually, quiet abandonment. What makes AI different is the scale of investment and the intensity of expectation surrounding it.

The PwC findings align with broader industry research. MIT's Project NANDA study found that 95% of enterprise AI pilots deliver zero measurable return, despite $30 to $40 billion in corporate spending. The gap between AI promise and AI delivery is not closing.

Why Most AI Projects Fail

What separates the 12% achieving measurable returns from the majority seeing nothing? The answer lies in foundations, not features.

PwC's successful organisations embedded AI extensively across operations before expecting results. They established Responsible AI frameworks. Their technology environments enable enterprise-wide integration. They invested in the boring groundwork before chasing the shiny applications.

MIT's research identified a critical pattern: how companies adopt AI matters more than what they adopt. Purchasing AI tools from specialised vendors and building partnerships succeeds approximately 67% of the time, while internal builds succeed only one-third as often.

Organisations trying to build proprietary solutions are failing at twice the rate of those who partner strategically.

Gartner adds another dimension to this picture. The analyst firm predicts that through 2026, organisations will abandon 60% of AI projects unsupported by AI-ready data. Sixty-three per cent of organisations either do not have or are unsure if they have the right data management practices for AI.

Where Project Professionals Come In

These failure rates are not AI failures. They are implementation failures. And implementation is precisely what project professionals do.

We see a critical opportunity here for the profession. Successful AI adoption requires exactly the competencies project managers bring to the table: stakeholder alignment across resistant silos, phased delivery that builds capability incrementally, risk management frameworks that account for algorithmic uncertainty, and benefits realisation planning that ties technology investment to measurable outcomes.

The MIT study found that line managers, not central AI labs, drive successful adoption. Mid-market companies moved from pilot to full implementation in approximately 90 days.

Large enterprises took nine months or longer and converted the fewest pilots to production systems. Speed and decisiveness matter more than resources.

The PwC survey revealed that CEOs spend 47% of their time focused on issues with horizons of less than one year. Only 16% of their attention goes to decisions looking more than five years ahead.

This short-termism creates the conditions for AI failure.

Organisations need structured approaches that bridge the gap between quarterly pressure and the multi-year timeline required for genuine transformation.

The Real Question

CEO confidence in revenue growth has fallen to its lowest level in five years. Only 30% express confidence about the next twelve months, down from 38% in 2025 and 56% in 2022.

The pressure to show AI returns is intensifying precisely as most organisations struggle to achieve them.

Meanwhile, a shadow AI economy has emerged. MIT found that while only 40% of companies have official LLM subscriptions, over 90% of workers reported using personal AI tools like ChatGPT for work tasks.

Employees are crossing the AI divide individually even as their organisations remain stuck in pilot purgatory.

The question for project professionals is whether we position ourselves as essential to solving this problem or remain spectators watching another technology wave crash against organisational reality.

What Happens Next

The divide between AI leaders and the ones who are yet to gear up is widening.

PwC analysis shows companies applying AI widely to products, services and customer experiences achieved nearly four percentage points higher profit margins than those that did not.

That margin advantage will compound as successful organisations reinvest returns into further AI development.

MIT researchers found the biggest returns in back office automation, eliminating business process outsourcing, cutting external agency costs, and streamlining operations. Yet more than half of enterprise AI budgets flow to sales and marketing.

Organisations are investing in the areas that feel most visible rather than the ones delivering the strongest financial outcomes.

For project professionals, the message is clear. AI adoption without project discipline produces expensive disappointment.

The organisations succeeding are not there because they found better algorithms. They are there because they did the hard work of integration, governance and cultural change.

We have been exploring this territory for a while. Are you keen to ride the bandwagon?

Project Flux brings you the insights that matter for delivery professionals navigating the AI transformation. Subscribe now and join the community shaping how projects get delivered in the age of artificial intelligence.

All content reflects our personal views and is not intended as professional advice or to represent any organisation.