AI enters its ROI era - copy

The pulse of global C-suite leaders signals this specific shift in transformation and AI across organisations worldwide, where Board and investor demand, accountability measures and competitive advantage have accelerated expectations on its returns. Finally, AI has entered its return-on-investment (ROI) era, where the pressure to demonstrate value from specific returns can be prioritised and proven. The context for this shift is telling at a time when optimism for growth remains steady and high in an uncertain economic environment and markets are still rated favourably with the majority of leaders worldwide.

Technology transformation overall is clearly still a top strategic priority. Yet, beneath this rare certainty in today’s climate lies a renewed focus when it comes to AI investment. From these latest mid-year findings, we can see that diversification is pertinent – especially in transformation strategies. While the proportion of organisations increasing investments has fallen slightly over the past six months, boosts to AI, emerging technologies and transformation are still planned over the course of the rest of the year. Financial commitments are simply being recalibrated – where we once saw executives prioritising investment in sourcing new talent and brand strategy to stay ahead of competition, managing suppliers and supply chain now has a greater need. The one constant – AI implementation remains the top priority for boosted investment (alongside acquiring new customers and IT systems/digitisation).

Rest assured, leaders are not (and should not be) stepping back from AI, they are just becoming more selective, redirecting resources towards areas that can demonstrate tangible impact. This is where the difference between the start of the year and now is most visible. Earlier in 2026, AI investment was driven primarily by ambition – better decision-making, operational advantage and competitive differentiation. By mid-year, those motivations have evolved into measurable outcomes. Productivity now stands as the dominant metric for AI success, cited by 59% of leaders, followed by customer satisfaction (49%) and employee satisfaction (45%). The emphasis has shifted from adoption to accountability.

Crucially, this reassessment reflects real-world results. Most organisations are already seeing productivity gains from AI and, while at a modest level for this majority, it’s nothing to be sniffed at. Nearly two-thirds (63%) report returns of up to 10%, while 19% have exceeded that threshold. These figures mark an inflection point: AI is now clearly delivering but is it at the scale many expect. The answer will be different depending on the size and level of investment, to name a few, and we can see this in varying returns across sectors – unsurprisingly, TMT is the sector with the most businesses claiming the highest returns from AI investment of up to 20%. Regardless, the implication and next phase of returns for the C-suite is clear – future investment must be more targeted, more diversified and more tightly linked to outcomes.

This diversification is a defining theme across our mid-year insights and more critically against the current backdrop. Across the board, leaders are rebalancing portfolios to move investment away from broad experimentation and towards a mix of initiatives that combine productivity and efficiency gains, customer impact, employee satisfaction and new revenue generation. This is reinforced by a wider strategic response to uncertainty: diversifying resources is now the number one reaction from C-suite leaders in response to global events. AI investment is following the same logic. Rather than placing a single, high stakes bet, organisations are spreading risk and return across multiple use cases.

Customer impact, in particular, has gained prominence. While internal adoption rates have become less of a focus, customer-facing outcomes (such as satisfaction with AI-enabled products and services) meanwhile are rising in importance. This recognises that AI and its ROI has moved on from the adoption phase and is now being defined externally. In terms of this next era of transformation, ROI and growth no longer comes from how widely AI is deployed, but from how effectively it enhances customer experience and unlocks new value streams.

At the same time, operational agility has moved up the agenda, reinforcing the link between AI and measurable business performance at a time when businesses need it most. Leaders are restructuring teams, with 80% having done so in the past two years to support AI adoption, ensuring that capabilities are aligned to delivery rather than experimentation. The focus is shifting from building AI capacity to embedding AI capability – integrating it into workflows where value can be tracked and scaled.

The overall outcome will be a more mature, more disciplined AI transformation strategy. Investment is no longer a question of how much – we can see that in the slight dip from the beginning of the year – but the where and to what end is the difference in mindset.

The most effective organisations are aligning AI deployment to specific metrics. For the C-suite, the implications are significant. The ROI era demands a different approach from leadership to ensure that ambition is balanced with rigour. It requires clear metrics, sharper prioritisation, and a willingness to reallocate investment based on performance. It also requires a diversified portfolio of AI initiatives that balance short-term gains with longer-term strategic value.

There’s no question of whether AI has lost its transformative potential, but it’s finally being tested against it. Organisations that succeed in this next phase will be those that treat AI as a disciplined investment programme rather than a standalone strategy – one that continuously measures, refines and redirects resources towards proven impact.

Practical solutions leaders can start applying today that will help ensure effective AI deployment

• Anchor AI use cases in financial outcomes: prioritise initiatives that directly impact profits and losses, whether through cost reduction, revenue growth or margin improvement. If value can’t be measured, it shouldn’t be scaled.
• Set clear “scale or stop” thresholds upfront: define, at the outset, what success looks like and when to exit. This avoids pilots drifting without decisions and ensures resources follow proven results.
• Strengthen oversight of the AI portfolio: treat AI as a managed investment portfolio. Prioritise, track performance, manage risk and rebalance regularly rather than allowing fragmented, uncoordinated initiatives.
• Align AI with broader transformation priorities: position AI as an enabler of structural efficiency. Link investments to core transformation programmes (cost base, operating model and productivity) to ensure impact across the transformation agenda overall.

AI’s ROI era is a step change. The shift from experimentation to measurement is what will ultimately unlock scale and turn these early gains we’re seeing now into sustained competitive advantage.