Missing ROI Benchmarks Block Enterprise AI Success – Wedbush Warns” (Alternative options if preferred:) “Why Enterprises Struggle to Prove AI ROI – A Major Deployment Hurdle” “No Clear Metrics? AI Investments Risk Failing Without ROI Proof

Wedbush Securities analysts reported that many enterprises lack a framework to measure the return on investment (ROI) from artificial intelligence (AI) tools, a challenge that could hinder further adoption of the technology, according to a June 26 report by Seeking Alpha. The finding emerged from discussions at the firm’s Disruptive Technology Conference, where executives highlighted gaps in evaluating AI initiatives.

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Dan Ives noted that enterprises have invested in AI pilots without clear metrics to gauge success. This absence of a framework creates obstacles in justifying investments, identifying effective strategies, and building organizational confidence in AI-driven decisions. “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 Friday investor note.

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PYMNTS Intelligence data underscores the scale of the challenge. A September report by PYMNTS CEO Karen Webster revealed that 80% of enterprise executives expect generative AI investments to take between three and 10 years to yield positive payback. “These 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,’” Webster wrote.

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Another PYMNTS Intelligence study, “The Enterprise AI Readiness Gap: What Company Data Reveals About the Real Barrier to Scale,” identified organizational readiness as the primary constraint on AI performance. Seventy-one percent of executives surveyed cited people, processes, or data readiness as the greater limitation compared to AI technology capabilities. The report highlighted that executives typically face four to five barriers simultaneously, with data quality, budget constraints, and governance processes ranking as the most common bottlenecks.

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“The inability to address these issues in parallel undermines the potential of cross-functional AI operating models,” the report stated. It recommended improving data quality, clarifying responsibilities, addressing talent gaps, and rethinking budgets to unlock AI’s full value.

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The findings align with broader concerns about AI deployment. Enterprises are increasingly under pressure to demonstrate tangible returns, yet many lack the tools to quantify success. This gap could slow the pace of AI integration, particularly as boards and financial leaders demand accountability.

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Executives at the Wedbush conference emphasized that without clear ROI metrics, AI projects risk being perceived as experimental rather than strategic. “Organizations that fail to establish measurable outcomes may struggle to secure ongoing funding or support,” Ives said.

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The PYMNTS data also reflects the long-term nature of AI transformation. While some companies aim for quick wins, the majority recognize that AI adoption requires sustained investment. “Big-‘T’ transformation doesn’t usually happen on a predictable timetable,” Webster noted, highlighting the need for patience and realistic expectations.

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The challenges extend beyond technical hurdles. Organizational barriers, such as fragmented data systems and unclear governance, often impede AI performance. Companies must address these issues holistically, according to the PYMNTS report. “Piecemeal problem-solving won’t work,” the study warned. “Improve data quality, clarify responsibility, address talent gaps, and rethink budgets in parallel.”

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Industry experts suggest that enterprises should prioritize ROI measurement from the outset. This includes defining key performance indicators (KPIs), aligning AI initiatives with business goals, and fostering cross-departmental collaboration. Without such steps, the promise of AI may remain unfulfilled.

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The Wedbush and PYMNTS reports highlight a critical juncture for AI adoption. As enterprises navigate the complexities of implementation, the ability to prove value will determine the technology’s long-term impact.

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“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.”
SourceDan Ives, Wedbush Securities

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“With executives citing several barriers simultaneously, piecemeal problem-solving won’t work. Improve data quality, clarify responsibility, address talent gaps, and rethink budgets in parallel to take full advantage of cross-functional AI operating models.”
Source“The Enterprise AI Readiness Gap: What Company Data Reveals About the Real Barrier to Scale,” PYMNTS Intelligence