Enterprise AI adoption is rising fast across UK workplaces. Yet new research shows a widening gap between usage and real business value.
The Work AI Index from Glean surveyed 6,000 digital workers. It covered the US, UK, and Australia, including 1,500 UK respondents.
The findings matter for telecom and IT teams. Many operators now use AI for support, network operations, sales, and reporting. But the study shows automation can also create new manual work.
In the UK, 90% of digital workers use AI at work. That compares with 84% in the US. Also, 77% say AI makes them more productive. Workers report saving about 12 hours each week through automation.
However, only 18% say AI has clearly improved organisational performance. This creates what the report calls a British AI productivity paradox.
A key issue is “botsitting”. This means workers must guide, check, correct, and rerun AI outputs. UK workers spend 6.3 hours each week doing this extra work.
For telecom teams, this will sound familiar. AI tools can draft incident summaries or customer responses quickly. Yet engineers still need to validate facts, context, and risk.
The study also highlights “botshitting”. This means sending AI-generated work without full checking or understanding. In the UK, 70% of AI users admit to at least one such behaviour.
That raises clear quality concerns. Around 40% say they sometimes deliver AI-assisted work they could not explain. In regulated sectors, including telecoms, this creates operational and compliance risk.
Tool sprawl adds another challenge. Some 80% of UK AI users juggle several AI tools weekly. Meanwhile, 39% use four or more. Many workers rerun the same prompt across tools.
This turns staff into the integration layer. Instead of simplifying work, fragmented AI can increase effort. Half of UK workers also say AI tools lack key workplace information.
“Too many companies are treating AI adoption like a vanity metric: more seats, more prompts, more usage,” said Dr. Rebecca Hinds, Head of the Work AI Institute at Glean. “But the UK data shows why that is not enough. British organisations have moved quickly to put structure around AI, and that is a real advantage. The next step is making sure AI is grounded in the right enterprise context, governed in the flow of work, and measured by whether it actually improves outcomes.”
The message is timely for communications providers. AI can improve service quality and reduce repetitive work. But weak data access and poor governance can erode those gains.
“The UK data shows a workforce that is not hesitant about AI – in many ways, workers are already ahead of their organisations,” added Jen Rhymer, Professor of Business Strategy, University College London.
Her point is important. AI success depends on workflows, verification habits, and human judgement. Without those foundations, adoption may look impressive while cleanup quietly grows.
