AI ROI Tracking Lags Far Behind Enterprise Adoption in 2026

A Widening Gap Between Spending and Measurement

Enterprise AI adoption has reached a fever pitch heading into 2026, but a consistent thread runs through the latest industry data: organizations are pouring money into artificial intelligence far faster than they are learning to measure what it actually delivers. Global AI spending is projected to hit $2.59 trillion in 2026, a 47% year-on-year jump, underscoring just how aggressively companies across sectors are betting on the technology 5. Yet across marketing, supply chain, and wealth management, leaders are conspicuously unable to say whether that spending is paying off.

Marketing's Measurement Blind Spot

Nowhere is the disconnect more stark than in marketing departments, where 94% of teams have adopted AI tools but only 19% have established formal ROI tracking 1. That gap means the vast majority of marketing organizations are essentially operating on faith, layering AI into campaigns, content generation, and personalization workflows without a clear accounting of whether those tools are improving efficiency or wasting budget. The scale of adoption suggests AI has become table stakes in marketing, but the absence of measurement infrastructure means many teams may be unable to defend their AI budgets when finance leaders start asking harder questions.

The Same Story in Supply Chains and Wealth Management

The pattern repeats well beyond marketing. Gartner research found that AI now makes up 67% of digital investment in supply chain operations, yet 55% of supply chain chiefs remain unclear on the actual returns generated by that spending 3. In wealth management, the picture is similarly lopsided: financial firms are sharply increasing AI budgets, but many still lack formal frameworks to quantify tangible returns on those investments 4. Together, these cases suggest the ROI measurement gap is not confined to one industry or function but reflects a broader structural weakness in how enterprises govern AI spending.

What Organizations Can Do

Industry guidance increasingly points toward building disciplined measurement practices before, not after, deployment — setting clear success metrics, tying AI initiatives to specific business outcomes, and regularly auditing performance against those benchmarks 5. Without such frameworks, companies risk repeating the same mistake across departments: scaling adoption while flying blind on value.

Governance Risks Extend Beyond ROI

Measurement is not the only oversight issue emerging as AI tools proliferate inside workplaces. Separately, AI meeting-transcription vendor Granola is facing a privacy lawsuit over its recording and transcription practices, following a similar case against competitor Otter 2. While distinct from the ROI question, the lawsuits reinforce a broader theme: as AI tools spread rapidly through enterprises, governance, accountability, and oversight structures are consistently lagging behind adoption, whether the concern is financial return or data privacy.

The Bottom Line

The throughline across marketing, supply chain, and wealth management is unmistakable: enterprises are adopting AI far faster than they are building the tools to evaluate it. As spending accelerates toward trillions of dollars globally, the pressure to close that measurement gap will only intensify.