Only 1 in 4 European businesses have fully defined outcomes for AI initiatives, Rebura research reveals
Boardroom pressure driving AI adoption before business outcomes are defined
LONDON, UK – 22 September, 2026 – Organisations across Europe are rushing to embrace artificial intelligence without first defining what success looks like, according to new research by Rebura, a Westcon-Comstor company and award-winning AWS (Amazon Web Services) specialist consultancy and solution provider.
The study, which surveyed 300 business leaders across the UK, France, Germany, Italy and Spain, found that just a quarter (25%) have fully defined business outcomes for their AI initiatives. At the same time, more than a third (37%) say their AI activity is being driven primarily by executive mandates, suggesting many organisations are launching projects before establishing clear objectives and measures of success.
The findings highlight a growing gap between organisations’ AI ambitions and their ability to translate investment into measurable business outcomes.
According to the report, called The AI Reality Check: Why Most AI Initiatives Never Make It, only 27% of AI initiatives successfully reach production environments.
Meanwhile, more than half (58%) of respondents describe their approach to AI delivery as ad-hoc experimentation, and fewer than a third (29%) follow a structured AI lifecycle.
Almost one in five organisations (19%) say they have no clearly defined outcomes for their AI initiatives at all.
The findings suggest that the barriers to AI success often extend beyond technology itself. Many organisations continue to struggle with governance, planning, data readiness and operational processes, raising questions about whether businesses are focusing too heavily on AI tools and not enough on the foundations required to deliver measurable outcomes.
The research also revealed a significant readiness gap between smaller organisations and large enterprises. Just 14% of SMBs consider their data AI-ready, compared with 36% of enterprises. SMBs are also disproportionately affected by other barriers to AI success, with 58% citing security and compliance as a problem and 46% struggling with poor data quality.
Regardless of company size, security and compliance emerged as the most commonly cited challenge both during AI deployment and when organisations attempt to scale initiatives after launch, indicating that governance remains a long-term operational consideration rather than simply an implementation hurdle.
“The conversation around AI often focuses on the technology itself, but our research suggests the bigger challenge lies elsewhere,” said Aaron Rees, CEO at Rebura. “Organisations are under increasing pressure to demonstrate progress on AI, yet many are doing so before they have clearly defined the outcomes they’re trying to achieve. The real value comes from combining technology with the right governance, data foundations and operational discipline. Businesses that treat AI as a business transformation initiative rather than a standalone technology project will be far better placed to move beyond experimentation and achieve lasting results.”
- View the Rebura report: The AI Reality Check: Why Most AI Initiatives Never Make It
About the research
The research was conducted by Coleman Parkes on behalf of Rebura in May 2026. It surveyed 300 small, mid-market and enterprise organisations actively pursuing AI initiatives, based across France, Germany, Italy, Spain and the UK. Respondents operated across 11 sectors, including technology, professional services, financial services, manufacturing and healthcare, and on average had 2,494 employees and annual revenue of £3.27 billion. Respondent job titles included Head of IT Operations, Head of Digital Transformation and Head of IT Infrastructure.
About Rebura
Rebura, a Westcon-Comstor company, is an AWS Premier Partner specialising in cloud, data and AI transformation. Rebura helps organisations build secure cloud foundations, modernise applications and develop the governance, data and operational frameworks needed to turn AI investment into measurable business outcomes.
