The world's top CEOs are racing to capitalize on AI disruption, but a stark divide is emerging between those winning and those stuck in endless pilots. A comprehensive survey of 415 chief executives representing roughly 10% of global market capitalization reveals that while 65% of CEOs view today's volatility as an offensive opportunity to outmaneuver competitors, the majority of their companies remain mired in planning and experimentation . Those who have scaled AI across two or more use cases report roughly twice the return on investment compared to laggards, yet 53% of companies say it's too early to assess any ROI at all .

The disconnect between CEO ambition and organizational capability is creating what researchers call a "great divergence." Companies that execute across multiple strategic vectors simultaneously,combining AI deployment, targeted mergers and acquisitions, talent restructuring, and compressed planning horizons,are beginning to pull ahead of their peers. But most enterprises are paralyzed by a fundamental problem: they lack a clear understanding of how to translate AI investments into measurable business value.

Why Are Most Companies Still Stuck in AI Pilots?

Despite widespread AI adoption efforts, two-thirds of companies remain in planning or pilot mode, according to the CEO survey . The gap between experimentation and scaled deployment has actually widened since 2025, when 41% of companies said it was too early to assess ROI. Now that figure has jumped to 53% . This stagnation isn't due to lack of investment or executive commitment; rather, it reflects a misalignment between how companies approach AI and what actually drives business results.

Research from Forrester reveals the core issue: enterprises are "paralyzed by a lack of understanding" of AI technology itself . Many organizations treat AI as an internal productivity tool, focusing narrowly on cost reduction or efficiency gains. But companies achieving measurable success take a fundamentally different approach. They are deliberate about creating customer value first, then extracting internal benefits as a secondary outcome.

"Despite experimentation, few organizations have translated early AI innovation into meaningful business impact. Productivity gains are incremental. Trust remains fragile. Customer experience, particularly in North America, is at an all-time low. For many executives, AI feels both urgent and elusive at the same time," explained Sharyn Leaver, chief research officer at Forrester.

Sharyn Leaver, Chief Research Officer at Forrester

The data supports this observation. Among high-performing AI adopters, 25% named their CEO as the primary driver of AI business strategy, compared to lower adoption rates at companies without clear executive ownership . This suggests that successful AI transformation requires not just technology investment, but deliberate leadership alignment around customer-centric outcomes.

How to Build an AI Strategy That Actually Delivers Results

• Lead with Customer Value: Successful companies resist the temptation to confine AI to internal use cases. Instead, they prioritize creating value for users, improving user experience, and boosting marketing effectiveness. This customer-first approach builds trust and long-term competitive advantage.
• Invest in Data Governance and Infrastructure Early: High-performing adopters made foundational investments in governance, infrastructure, and shared platforms before scaling AI. Many accelerated progress by working with external partners to modernize data and platforms faster than they could build alone.
• Align Talent Strategy with AI Ambitions: Companies recording success with AI are 47% more likely to specify AI skill requirements in job descriptions compared to 33% at lagging companies. They're also more likely to require applicants to demonstrate those skills, at 54% versus 29%.
• Secure Executive Sponsorship: CEO-driven AI strategies correlate with higher adoption rates. Business leaders have a narrow window during early adoption phases to shift organizational narrative and prioritize AI as a core strategic lever, not an experiment.

What's Driving the ROI Gap Between Leaders and Laggards?

Company size plays a critical role in AI outcomes. Nearly five times as many mega-size companies (those with annual revenue exceeding $10 billion) report AI-driven cost savings above 10% compared with their midsize peers . This advantage stems partly from scale, but also from the ability to deploy AI across multiple use cases simultaneously. Large enterprises can afford to experiment across operational efficiency, customer service, and revenue-generating applications at once, while smaller companies often must choose.

Geographic variation also shapes outcomes. In the Asia-Pacific region, 47% of companies qualify as AI deployment leaders, far ahead of Europe's 30% and North America's 37% . This regional difference reflects varying approaches to workforce restructuring and M&A strategy. European CEOs, facing consolidation pressure, are pursuing geographic expansion through deals more aggressively than their North American counterparts, while also shrinking workforces at higher rates.

The workforce transformation itself reveals how seriously leading companies are taking AI. The share of companies reducing junior-level roles has spiked to 43% from just 17% in a single year, while 33% are shifting the workforce toward midlevel roles . This "diamond-shaped" workforce, heavier in the middle than traditional pyramids, reflects the reality that AI is automating routine junior work while creating demand for experienced professionals who can manage AI systems and interpret their outputs.

Are CEOs Actually Seeing Measurable Returns on AI Investment?

The honest answer is complicated. Among companies scaling AI across two or more use cases, ROI is measurable and significant. These deployment leaders report roughly twice the return compared to companies still in pilots, measured in both cost savings and revenue gains . However, the majority of companies haven't reached that threshold yet.

A separate survey from PwC found that just one in eight CEOs report AI has delivered both cost and revenue benefits simultaneously . This suggests that while some companies are capturing value, most are still struggling to move beyond incremental productivity gains. The challenge isn't that AI lacks potential; it's that realizing that potential requires sustained commitment to governance, infrastructure, and customer-centric strategy.

Time horizons are compressing as boards demand faster results. CEOs now devote half of all planning efforts to horizons of less than one year, up from 43% in 2025, as 96% report increased board involvement in at least one area . This pressure is pushing companies to make faster decisions about AI investment, but it's also creating risk. Companies rushing to deploy AI without proper governance or customer focus may achieve short-term cost savings while missing larger revenue opportunities.

What Does the CEO Agenda Look Like in 2026?

The priorities are clear: growth, M&A, and AI deployment, executed in parallel. Two-thirds of CEOs chose a growth lever such as revenue uplift or organic investment as their top objective, while 58% ranked cost management in their top three . Self-funded growth is the dominant strategy, meaning companies are using AI-driven cost savings to finance expansion and M&A rather than relying on external capital.

Mergers and acquisitions are on almost everyone's agenda. Ninety-four percent of CEOs plan deals in the next year or two, with 65% pursuing industry consolidation, up six points from the previous year . But the nature of M&A is shifting. Forty-two percent plan to acquire new capabilities and intellectual capital, rising to 54% among the largest companies and above 55% in sectors where technological change is moving fastest . In other words, CEOs are buying speed and AI capabilities rather than just pursuing traditional consolidation.

The companies that execute across all these vectors simultaneously are starting to pull ahead. Those that balance disruption and speed with operational stability will shape what comes next. For the majority still in pilots, the window to catch up is narrowing. The divergence between AI leaders and laggards isn't inevitable; it's the result of deliberate choices about strategy, governance, and customer focus. The question now is whether the remaining two-thirds of companies can make those choices fast enough to compete.

" }