Artificial intelligence investment continues to accelerate across global businesses despite mounting evidence that many organisations are struggling to generate the returns they expected.
New research from IDC, commissioned by Expereo, suggests that enthusiasm for AI is increasingly being driven by competitive pressure rather than proven business outcomes. While 70 per cent of organisations are now investing heavily in AI, only 19 per cent report that their projects have exceeded expectations.
The findings point to a growing disconnect between corporate ambition and operational reality. Organisations are committing larger budgets to AI technologies even as concerns persist around data quality, governance, infrastructure readiness and workforce capability.
The trend highlights one of the defining characteristics of the current AI market. Unlike previous waves of enterprise technology adoption, many organisations appear unwilling to slow investment despite uncertain returns, fearing that delaying adoption could leave them at a competitive disadvantage.
The pressure to keep investing
The latest findings suggest that AI has moved beyond the traditional technology investment cycle, where spending is often closely tied to measurable performance improvements.
Instead, many businesses now view AI as a strategic necessity regardless of whether clear returns have yet been realised. The result is a market where investment continues to rise while questions remain about how effectively organisations are deploying the technology.
According to the research, more than half of organisations cite poor-quality training data as a significant barrier to AI performance. The findings also suggest that many businesses lack the governance structures and infrastructure maturity required to deploy AI effectively at scale.
Richard Bovey, Chief for Data at AND Digital, believes the problem is becoming more acute as organisations experiment with increasingly autonomous AI systems.
“AI investment is accelerating fast, especially with the rise of agentic AI platforms,” he said. “However, without strong data governance, business leaders are effectively flying blind and risk losing oversight of critical value streams.”
Bovey pointed to research indicating that 58 per cent of organisations describe their data environment as “chaos”, warning that poor-quality data could become a systemic risk as AI takes on more independent tasks.
Skills remain a missing ingredient
The findings also highlight a growing gap between AI adoption and workforce readiness.
While organisations continue to invest heavily in AI technologies, many appear to be lagging behind in preparing employees to work effectively alongside them.
Sheila Flavell, Chief Operating Officer of FDM Group, said the demand for AI skills is now extending across almost every level of business.
According to FDM’s research, 54 per cent of organisations expect AI skills to be required in all early-career roles. However, only six per cent of teams currently report high levels of AI proficiency.
Flavell argued that organisations risk undermining their AI investments if workforce development does not keep pace with technology adoption.
“AI is not a replacement for human expertise, it amplifies it,” she said. “By embedding practical AI training into professional development, organisations can equip employees to critically assess, guide, and collaborate with AI tools, rather than outsourcing thinking to technology.”
Moving beyond the hype cycle
The challenge facing many businesses may not be whether to adopt AI, but where to apply it.
Jason Kurtz, Chief Executive of Basware, suggested that disappointing returns often stem from organisations pursuing ambitious projects without clearly defined use cases.
He argued that many AI initiatives fail because they are driven by expectations surrounding the technology rather than specific business problems that need solving.
Kurtz believes organisations should focus on highly standardised and data-rich processes where automation can deliver measurable benefits more quickly. Areas such as invoice processing and payment workflows, he said, can provide a clearer path to return on investment while helping businesses develop the experience needed for wider deployment.
The findings suggest that the next phase of AI adoption may be defined less by model performance and more by organisational discipline. As businesses move from experimentation to implementation, success is likely to depend on data quality, governance, workforce capability and careful selection of use cases rather than the technology alone.
For many organisations, the question is no longer whether to invest in AI. It is whether they can build the foundations necessary to turn that investment into sustainable business value.
