// FEATURE // nalysts and reporters are
A increasingly sharing research about the slow nature of productivity gains that can be attributed to newly implemented AI technologies. This mirrors the conversations I have with SME leaders – they are experimenting and implementing, but they can’ t say for sure that any productivity gains are consistently turning into revenue.
Don’ t just take my word for it. British Chambers of Commerce data shows adoption is accelerating, with 54 % of firms now actively using AI, up from 35 % in 2025. But according to DSIT research, 75 % of AI adopters report workforce productivity gains, but 77 % have not yet seen revenue impact.
There’ s evidence that many SMEs are showing enthusiasm though they are delaying real adoption by using it without ownership, data discipline or the right commercial measurement. They are accidental digital adoption and value-recognition delayers, without even realising.
Start with the business problem, not the tool
This is not the fault of the average business. The tech industry has overwhelmed many with hype, promises, ambiguity and serious FOMO. That’ s not a great environment for making the best business decisions.
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THE TECH INDUSTRY HAS OVERWHELMED MANY WITH HYPE, PROMISES, AMBIGUITY AND SERIOUS FOMO.
AI investment should begin with a specific bottleneck and problem. Let’ s take one common business challenge as an example. The Sales team faces slow lead follow-up, poor forecasting, low conversion between sales stages and weak customer retention. What might a business leader do? Well, after exposure to advertising and then doing their own research of vendor claims, here’ s what not to do: avoid buying any tools because competitors are doing so or because staff are already experimenting with something informally.
The smartest right first step is to go back to basics and question which workflow,
Intelligent SME. tech
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