Executive AI Skills Gap Stalls ROI on Crucial AI Technology Investments

The barrier to achieving AI success is not the technology itself, but a profound disconnect in the AI skills gap among executives. This gap is creating a strategic vacuum between the leaders who fund AI projects and the teams expected to implement it, leaving crucial projects stuck in pilot mode or that remain unfinished, according to Coupa’s Coupa Clarity AI Impact Report, done in partnership with Incisiv Research.

"This research is a wake-up call. The days of funding AI based on hypothetical results or unproven potential are over," says Dennis Bruder, chief product officer of AI at Coupa. "The bar for ROI on AI investments has been substantially raised, and decision-makers are looking for technology to drive meaningful value. To achieve the aggressive projections–69% of organizations anticipate substantial AI-powered automation by 2030 and 83% by 2035–executive leaders must move past theoretical commitment and focus on the strategic platforms that provide both the necessary technical infrastructure and the embedded governance to enable workforce adoption. Execution now depends on platform selection more than many other factors."

Key takeaways:

• Only 5% of executive decision-makers use AI on a daily basis, compared to 57% of their technical teams. This massive fluency gap means the people funding multi-million-dollar AI initiatives often lack the hands-on understanding of AI to set realistic goals and strategies.
• A staggering 86% of companies recognize AI as essential for survival, yet just 29% of companies have a clear, company-wide strategy for implementation, proving that enterprise AI ambition currently far outpaces execution.
• 72% of AI initiatives and projects remain stuck in pilot mode, yet 47% of executives still expect meaningful business payback within 6–12 months. 77% of organizations cite data quality and system integration (including legacy IT) as the primary barriers to real results.
• 80% of companies now favor purchasing AI from external solutions like unified platforms rather than building it internally (only 10% choose custom internal builds), indicating a decisive shift.
• Only 2% of AI investment is currently dedicated to orchestration, even though 77% of leaders prioritize simple task automation. This gap highlights a critical failure to move beyond isolated AI tasks or tools to achieve exponential enterprise-wide AI value.
• While 65% of executives prefer "human-in-the-loop" oversight to manage fear of critical errors, 56% of leaders are unsure if their company even has an AI governance policy. Without strict governance frameworks, this necessary human safeguard threatens to become a major bottleneck, slowing down automation.