For two years, the AI industry operated on faith. Companies poured billions into infrastructure, hired machine learning teams, and launched pilot after pilot. Markets rewarded spending over results. Boards asked how fast capital was being deployed, not what it was producing. That era is over. As Venky Ganesan of Menlo Ventures put it bluntly: "2026 is the show me the money year for AI."
The numbers tell the story. According to BCG's AI Radar survey of nearly 2,400 executives, companies plan to double their AI spending in 2026, from 0.8% to about 1.7% of revenues. Hyperscalers alone are projected to spend $527 billion on AI infrastructure this year, per Goldman Sachs estimates. Gartner expects AI software spending to nearly triple to $270 billion. The money is flowing. The question hanging over every boardroom is whether any of it will come back.
At Fusion AI, we've watched this tension build across our client engagements. The enthusiasm hasn't faded, but the questions have sharpened. Executives who once asked "How do we get started with AI?" now ask "What exactly did last year's investment produce?" That shift in framing changes everything.
The Accountability Crisis
Here's the uncomfortable truth: while 88% of surveyed companies now use AI in at least one business function, only 23% actively measure their return on investment. That disconnect has created what analysts call the AI accountability crisis. Billions invested, minimal visibility into actual business impact.
The gap between expectations and reality has grown wide enough that Forrester predicts a market correction, with enterprises deferring 25% of planned 2026 AI spending into 2027. The signal is hard to ignore. According to Kyndryl's Readiness Report, 61% of senior business leaders feel more pressure to prove AI ROI than they did a year ago.
James Brundage, who leads EY's Global Technology Sector practice, captured the mood shift: "Boards will stop counting tokens and pilots and start counting dollars." After years of accepting experimentation as progress, leadership teams want receipts.
Why Most Companies Are Not Ready
McKinsey's latest State of AI report found that meaningful enterprise-wide bottom-line impact from AI remains rare. Only about 6% of respondents attribute EBIT impact of 5% or more to AI use. These high performers share common traits: they redesign workflows rather than just adding AI to existing processes, they scale faster, and they invest more. Three-quarters of them have already scaled AI broadly, compared to just one-third of other organizations.
The implication is stark. For most companies, AI has been additive rather than transformative. They've bolted chatbots onto customer service, added copilots to developer workflows, run some document processing. Useful improvements, perhaps. Strategic differentiation, rarely.
From Fusion AI's experience working with enterprises across sectors, the gap typically comes down to integration depth. Surface-level AI deployments generate surface-level returns. Companies that achieve meaningful ROI have usually done the harder work of rethinking processes, retraining teams, and rebuilding data pipelines. That work doesn't happen in a quarter.
The Agent Question
Much of the 2026 ROI conversation centers on AI agents. Nearly 90% of CEOs surveyed by BCG believe AI agents will produce measurable returns this year. The technology has matured rapidly, moving from research demos to production deployments.
But there's a warning embedded in the optimism. Ryan Gavin, CMO of Slack at Salesforce, predicts that 2026 will be "the year of the lonely agent," with companies spinning out hundreds of agents per employee, most of which will sit idle like unused software licenses. PwC observed that many agentic deployments in 2025 didn't deliver much value because organizations weren't using agents in ways that actually mattered.
The pattern is familiar. New technology arrives, companies rush to adopt it, most deployments underperform because the organizational change lags the technical capability. Agents are powerful, but only if they're given meaningful work and integrated into real workflows.
What the Winners Do Differently
The 6% achieving substantial returns aren't working with better models or bigger budgets. They're executing differently. McKinsey found that high performers commit more than 20% of their digital budgets to AI, but more importantly, they push for transformative rather than incremental applications.
This means targeting processes where AI can fundamentally change economics, not just marginally improve them. It means accepting that ROI timelines for AI are measured in years, not quarters. The 383% average return that successful implementations achieve doesn't arrive on day one. It requires patient capital and sustained organizational commitment.
Half of CEOs now believe their job depends on getting AI right, according to BCG. That personal accountability is driving a shift in governance. Nearly three-quarters of CEOs say they're now their organization's primary decision-maker on AI, double the share from last year. When the CEO owns the outcome, execution tends to improve.
What Comes Next
The 2026-2030 period represents AI's commercial proving ground. By the end of this window, today's massive infrastructure investments will be largely operational, creating pressure to monetize these assets. The companies that built intelligently will see returns. Those that bought technology without strategy will write off their investments.
At Fusion AI, we advise clients to approach this moment with clear eyes. The pressure to prove ROI is real, but panic-driven metrics games won't help. What matters is selecting high-impact use cases, integrating AI deeply into operations, and measuring outcomes honestly. Ninety-four percent of companies plan to keep investing even without immediate returns, according to BCG. That patience only makes sense if it's coupled with rigorous learning.
The show-me-the-money moment has arrived. For some companies, 2026 will vindicate years of investment. For others, it will expose how much was spent on technology that never found its purpose. The difference won't be determined by which models they use or how much they spend. It will come down to whether they built AI into their business or just bought AI for their business. That distinction, as it turns out, is worth billions.