A recent EY report reveals that 50% of UK CEOs consider artificial intelligence a leading factor in improving productivity, outpacing traditional operational changes. However, only a small fraction have full visibility into AI spending, creating challenges in assessing real returns on investment.
- 50% of UK CEOs cite AI as a top productivity contributor.
- Only 14% have real-time visibility on AI-related costs.
- Skills shortages and legacy systems remain major hurdles.
What happened
According to a new EY report, AI is increasingly recognized by UK CEOs as a significant driver of productivity gains over the past year, ranking above initiatives like process redesign and workforce adjustments. Half of the surveyed CEOs identified AI as one of their top three influencers for enhancing operational performance.
Despite this optimism, many executives admit to lacking full real-time knowledge of their AI expenditures, with only 14% reporting clear visibility into associated costs. This opacity makes determining the return on AI investments challenging, as nearly one-third of leaders feel unable to properly measure its financial effectiveness.
Why it matters
The findings highlight a critical gap between the perceived benefits of AI and the ability of organizations to quantify its financial impact. This uncertainty risks undermining decision-making around AI adoption and scaling, especially as CEOs increasingly demand evidence of measurable business value from new technologies.
Moreover, persistent challenges—such as outdated legacy technology infrastructure, complex organizational structures, and worsening skills shortages—continue to impede effective AI integration. With three-quarters of CEOs expecting talent gaps to deteriorate further, the capacity to leverage AI fully remains constrained.
What to watch next
Businesses will likely intensify efforts to enhance transparency around AI costs and performance metrics to justify further investments. Tracking ROI closely will become a focal point for aligning AI initiatives with broader productivity and revenue goals.
Simultaneously, organizations must address long-standing blockers by modernizing legacy systems and developing workforce skills to support AI adoption. How companies balance these factors will shape the extent and speed of AI’s productive impact across sectors in the near term.