IBM: Can Executives Turn AI Ambition Into Revenue By 2030?

Business leaders across the UK and Ireland face a paradox that could define corporate strategy for the remainder of the decade.

While the potential for AI to drive revenue growth appears increasingly clear, the mechanisms through which this value will materialise remain opaque to most organisations.

According to IBM's Institute for Business Value Enterprise 2030 Study, 77% of C-suite executives across the UK and Ireland expect AI to significantly contribute to their revenue by 2030. Yet only 27% of these same executives say they have a clear understanding of where that revenue will originate.

This gap between expectation and strategic clarity represents what IBM identifies as a fundamental leadership challenge, one that extends beyond technology implementation to questions of organisational transformation and competitive positioning.

Investment patterns reveal a fundamental shift

The disconnect between confidence and clarity has not dampened investment appetite. Executives predict that AI spending will surge approximately 149% between now and 2030, driven by ambitions to embed intelligence across operations and offerings.

This trajectory suggests that, for many organisations, the perceived cost of inaction outweighs concerns about strategic uncertainty.

More telling than the volume of investment could be its changing direction. While nearly half (47%) of current AI spend focuses on efficiency, businesses expect that by 2030, 64% will be dedicated to product, service and business model innovation.

This signals a decisive pivot from cost-cutting to value creation, though the study warns that 73% of executives fear their AI efforts could fail without proper integration into core business activities.

Rahul Kalia, Managing Partner for the UK and Ireland at IBM Consulting, says: "AI is no longer just a tool for efficiency – it's becoming a growth engine for the enterprise. With UK AI investment set to increase significantly in the next four years, success will hinge on integrating AI into core business strategies and reskilling the workforce.

"Organisations that act decisively, with the appropriate governance and controls in place for AI, will be the ones defining competitive advantage tomorrow."

The workforce and governance equation

The study suggests that technical infrastructure alone will not determine outcomes. More than half of global executives expect workforce skills to be fundamentally reshaped by 2030, positioning reskilling as a universal business priority rather than a regional challenge.

This human dimension intersects with governance concerns that executives increasingly view as inseparable from AI deployment.

IBM's research points to a widening gap between leaders and those slow to make progress in how effectively organisations apply AI.

Nearly half (48%) of UK executives believe their competitive edge will come from the sophistication of their AI models, yet only 29% have a clear understanding of which models they will need by 2030.

Most (81%) expect to rely on a mix of large-scale and smaller, specialised AI systems designed around their own business logic, indicating a shift toward more customised, multi-model ecosystems.

The convergence of AI with quantum computing adds another layer of complexity. Six in ten executives believe quantum-enabled AI will transform their industries, but only 37% are currently preparing to make their organisations 'quantum-safe'.

The challenge lies in balancing the drive for innovation with robust governance and risk management.

Macro implications extend beyond individual enterprises. The UK Government's AI Opportunities Action Plan projects that AI could expand the national economy by £400bn (US$551bn) by 2030 through enhanced productivity and innovation.

IBM's findings suggest that AI-driven productivity across the UK and Ireland could rise by 44%, with most gains realised before the decade's end.

Across the 33 countries surveyed, business leaders share similar ambitions and anxieties about turning AI into measurable growth. In mature markets across North America, Europe and Asia-Pacific, enterprises are moving beyond pilots to embed AI into products, workflows and decision-making structures.

Emerging economies may benefit from a leapfrogging effect, with fewer entrenched legacy systems allowing them to adopt AI-native models earlier, often in tandem with cloud and data infrastructure upgrades.

The study indicates that the path from AI-first ambition to tangible results depends on integration, reskilling and trust, with governance frameworks and transparency now as critical to success as technology itself.

For executives navigating this transition, the data suggests that strategic clarity around value creation may prove more decisive than the pace of technology adoption.