Enterprises Struggle to Measure AI ROI, Harming Future Deployments, Report Says

Enterprises have not developed a way to determine whether they have gained a good return on investment (ROI) on the artificial intelligence (AI) tools they have deployed, according to a June 26 report from Wedbush Securities analysts, citing insights from the Disruptive Technology Conference. The lack of frameworks for gauging success is hindering further investment, with executives reporting difficulty justifying costs and demonstrating value to stakeholders.

Enterprise AI ROI Challenges

Wedbush Securities analysts, led by Dan Ives, highlighted that enterprises have invested in AI pilots without a framework for gauging success. This gap, they said, creates barriers to scaling AI initiatives and building organizational confidence. “Many executives noted that customers are feeling increased pressure from their boards and CFOs to demonstrate actual returns from AI, and the inability to answer this question presents a real barrier to additional investments in long-term technological buildouts,” Ives said in a June 26 investor note, per the report.

A separate PYMNTS Intelligence report from September revealed that more than eight in 10 of the executives surveyed said it could take between three and 10 years for positive payback from their investments in generative AI. “These enterprise executives also understand that big-‘T’ transformation doesn’t usually happen on a predictable timetable, nor with the expectation of an immediate or direct payback ‘in the millions,’” wrote PYMNTS CEO Karen Webster.

PYMNTS Intelligence Insights

A PYMNTS Intelligence study, “The Enterprise AI Readiness Gap: What Company Data Reveals About the Real Barrier to Scale,” found that 71% of executives pointed to their organization’s people, processes or data readiness as the greater constraint on AI performance. Common barriers included data quality, budget limitations, and governance processes, with executives citing an average of four to five organizational barriers limiting AI performance.

The report advised companies to address these issues in parallel: “Improve data quality, clarify responsibility, address talent gaps and rethink budgets in parallel to take full advantage of cross-functional AI operating models,” it stated.

Implications for AI Investment

The findings align with broader concerns about AI adoption. “With executives citing several barriers simultaneously, piecemeal problem-solving won’t work,” the report said.

As AI becomes increasingly integral to business strategies, the absence of clear metrics could delay adoption. Companies that fail to establish robust evaluation systems may struggle to compete.