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AI Investment Is Surging, but Businesses Are Struggling to Prove the Value, HTEC Global Study Finds
Research among 1,500 C-suite executives finds 77% believe people and processes now constrain AI value more than technology
PALO ALTO, Calif., October 15, 2026–(BUSINESS WIRE)–HTEC, a global product engineering and AI transformation company, today launches The HTEC AI Value Realization Report 2026, revealing a new challenge emerging as organizations move beyond AI experimentation: investment is accelerating faster than many companies’ ability to measure, govern and consistently realize its value.
The independent study finds that 79% of organizations have increased AI investment over the past year, with spending rising by 31% on average. Yet 77% of executives now believe AI value is constrained more by people and processes than by technology.
The findings point to a significant shift in the enterprise AI debate. The question is becoming less about whether organizations can deploy AI and more about whether they can turn it into sustained and measurable business performance.
“The AI conversation has moved on. The challenge is no longer simply getting access to AI or finding places to deploy it. It is proving that investment is translating into meaningful business outcomes,” said Alex Rumble, Chief Marketing Officer at HTEC.
“What stands out from this research is that the biggest barriers are increasingly organizational rather than technological. Companies need to be as deliberate about adoption, operating models, measurement and governance as they have been about the technology itself.”
AI value is moving beyond cost reduction
The research suggests the definition of AI value itself is changing. Just 18% of executives rank cost reduction among their most important AI outcomes. Improved decision-making, productivity, customer experience, innovation and faster time-to-market now rank more prominently.
But as expectations broaden, proving value becomes harder.
Only 27% of organizations have clearly communicated AI’s purpose and expected outcomes across the business, while organizations remain divided on how AI value should be measured. Strategic outcomes, financial returns, operational performance, employee measures and customer outcomes all feature prominently, with no single measurement model emerging as the standard.
At the same time, 71.5% of organizations lack comprehensive enterprise-wide visibility into AI adoption, usage patterns and outcomes, relying instead on partial reporting, function-level tracking, anecdotal feedback or having no visibility at all.
That visibility gap is beginning to have consequences. Around a quarter of executives report higher AI usage costs resulting from insufficient tracking, while 26% cite increased reliance on external partners as an unexpected consequence of adoption.
