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TechRadar
Craig Hale

New data claims small businesses haven't expanded their use of AI in the past few years

A person typing on a laptop and using a tablet. Only their upper torso, arms and hands are visible. Text superimposed on the image shows AI .
  • The number of companies using AI is up, but the number of tools per company hasn't grown much
  • ONS data also confirms that AI doesn't seem to be having a major impact on headcount
  • SMBs could be lacking a clear business use case for AI

New ONS data has claimed that while AI adoption has expanded rapidly across UK businesses, the actual depth of adoption is still limited, with most firms using just a small number of tools to improve existing processes rather than totally overhauling their businesses.

Among the businesses surveyed that had adopted AI, the number of tools they used only increased marginally from 1.4 to 1.6 in the three years leading up to 2026.

The ONS also revealed that, while 60% of larger businesses have used AI to improve business operations, no negative impacts on headcount have been observed.

AI adoption might be growing, but that's not the full story

The data mostly implies experimentation rather than widespread transformation, with just 17% of SMBs (0-9 employees) reporting extensive AI use, however the report warns that many small businesses are merely trying general-purpose AI tools rather than making substantial investments into tech that can truly unlock new levels of productivity.

What's less clear is why SMBs are falling behind their enterprise counterparts, because while 7-14% of all business sizes cited cost as a barrier, 41% of SMBs with 10+ employees said they didn't have any major barrier concerns.

Rather than being held back by costs, infrastructure or capability, the ONS data actually implies that SMBs might actually be lacking compelling business cases to use AI altogether.

All in all, the most recent data confirms that while SMBs haven't exactly ignored AI, they're still stuck in the experimentation stage. The readiness to use automation tech is very much there, but ability to identify a genuine use case is potentially holding back deeper deployment.

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