Many enterprises have not developed a way to determine whether they have gained a good return on investment on the artificial intelligence (AI) tools they have deployed, Wedbush Securities analysts said, according to a Friday (June 26) report by Seeking Alpha.
That is perhaps the most important finding that came out of discussions at Wedbush Securities’ Disruptive Technology Conference earlier this week, the analysts led by Dan Ives said in a Friday investor note, per the report.
The analysts learned from executives at the event that enterprises have invested in AI pilots without a framework for gauging success and that without such a framework, they are likely to encounter difficulties in justifying the investment, identifying approaches that are working, and building organizational confidence in AI-driven decision.
“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, per the report.
PYMNTS CEO Karen Webster wrote in September that PYMNTS Intelligence found that most enterprise executives have realistic expectations for when they expect positive payback from their investments in generative AI.
More than eight in 10 of the executives surveyed said it could take between three and 10 years.
“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,’” Webster wrote.
Another PYMNTS Intelligence report, “The Enterprise AI Readiness Gap: What Company Data Reveals About the Real Barrier to Scale,” found that when executives were asked whether organizational readiness or AI technology capabilities are the greater constraint on AI performance, 71% pointed to their organization’s people, processes or data readiness.
Executives cited an average of four to five organizational barriers limiting AI performance, with the most common bottlenecks being data quality, budget limitations and governance processes.
“With executives citing several barriers simultaneously, piecemeal problem-solving won’t work,” the report said. “Improve data quality, clarify responsibility, address talent gaps and rethink budgets in parallel to take full advantage of cross-functional AI operating models.”
