Many companies have not developed a way to determine whether they have received a good return on investment in the artificial intelligence (AI) tools they have deployed; Wedbush Securities Analysts said, according to Friday (June 26). a report By searching for alpha.
This is perhaps the most important finding to emerge from discussions at Wedbush Securities’ Disruptive Technology Conference earlier this week, with analysts led by… Dan Ives This was stated in an investor note on Friday, according to the report.
Analysts learned from executives at the event that companies have invested in AI experiments without a framework for measuring success, and that without this framework, they will likely have difficulties justifying the investment, identifying successful approaches, and building organizational confidence in AI-based decision-making.
“Many executives have noted that clients are feeling increasing pressure from boards and CFOs to demonstrate actual returns from AI, and the inability to answer this question represents a real barrier to additional investments in technology development over the long term,” Ives said, according to the report.
CEO of PYMNTS Karen Webster He wrote in September that PYMNTS INTELLIGENCE I found that most organizational executives have realistic expectations when they expect positive results recovery From their investments in generative artificial intelligence.
More than eight in 10 executives surveyed said it could take between three and 10 years.
“Executives at these organizations also realize that major transformation does not typically occur on a predictable timeline, nor with the expectation of an immediate or immediate payback in the millions,” Webster wrote.
Another intelligence report from PYMNTS,”The enterprise AI readiness gap: What company data reveals about the real barrier to scalingfound that when executives were asked whether organizational readiness or technological capabilities for AI was the biggest barrier to AI performance, 71% of them cited the readiness of their organization’s staff, processes, or data.
Executives cited an average of four to five regulatory barriers limiting AI performance, with the most common bottlenecks being data quality, budget constraints, and governance processes.
“With executives citing several barriers at once, solving problems piecemeal will not work,” the report said. “Improving data quality, clarifying accountability, addressing talent gaps, and rethinking budgets in parallel to take full advantage of cross-functional AI operating models.”
