Around 78% of UK businesses claim to use AI in some way. For medium-sized organizations (100-249 employees), this percentage rises to 85%, the highest of any group. According to research by Studio Graphene, a further 14% are considering or plan to implement AI options in 2026, while 8% are not using AI and have no plans to use it.
However, the study found that less than a third (31%) of companies using AI see a positive ROI from their technology investments. Almost a fifth (18%) said their AI projects were not having the impact they expected, and 16% said it was too early to tell.
Surprisingly, less than half (41%) of AI users have a clear understanding of what “success” looks like when implementing an AI solution.
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Businesses cannot define “success” for AI
Among the leading AI adopters, midsize companies, only a minority (46%) say they can define success.
Ritam Gandhi, Director and Founder of Studio Graphene, said, “Many organizations are at a critical juncture in their AI journey. The past year has seen a surge in adoption, especially among mid-market companies, and our research clearly shows how much progress is needed to make AI projects successful.”
“Amid massive hype and a proliferation of new tools, there is a rush to adopt AI. This is certainly true for private equity-backed mid-sized companies that are turning to AI for automation, scalability, and improved competitiveness.
But the problem arises when AI is introduced without first defining where it will be placed in the workflow, the decisions it will inform, the processes it will support, and the criteria by which success will be measured. Teams often don’t agree on whether the purpose of AI is to save time, improve decision quality, reduce risk, support growth, or all of the above.
“These are critical issues that threaten progress. Without these definitions, it will be difficult to build the long-term business case for AI and realize its value. At board level, the lack of clarity on how and why AI will be used and to what effect will increase frustration. This highlights the need for careful planning of AI transformation projects, not only in selecting the right tools, but also in defining broader strategy, implementation, and success criteria.”
