- AI adoption rises in the UK workforce, Accenture finds.
- UK firms blame limited AI returns on skills and system gaps.
A report by Accenture, titled Generating Impact , based on surveys of more than 450 UK executives and 1,800 employees and analysis of around 30 million job postings, indicates uneven progress in AI adoption in the economy.
Matt Prebble, CEO of Accenture in the UK and Ireland, states in the report that the UK’s challenge lies in converting AI ability into impact at scale, noting that organisations often apply AI to isolated tasks not end-to-end transformation.
Employee use of generative AI tools has increased, with 18% of workers reporting daily use compared with 2024 levels. Less than half of employees use AI tools provided by their employer, while a portion continue to source tools independently.
The report identifies agentic AI as a recent development. Here, companies’ abilities have expanded, with the share of UK working hours that AI could support rising from 47% to 82%. Employees report using AI for tasks covering around one-fifth of their working time.
But organisational adoption has not kept pace. Around a quarter of employees say a major team process has been redesigned around AI in the past year. Prebble states that organisations achieving greater progress are moving beyond pilot programmes and are redesigning processes and workflows, not treating AI as a standalone initiative. Some organisations have embedded AI into core operations, though implementation varies in functions.
Skills demand changes shape
Job market data shows declining demand for routine cognitive skills. AI-related skills are among the fastest-growing categories. Demand is also increasing for roles involving judgement and quality control, as well as domain-specific expertise.
Employers also seek skills in communication and change management. Around 31% of employees expect their jobs to change or disappear by the end of the decade. Most employees in this group expect to re-skill or move into different roles, while organisational support remains limited.
Only a minority of organisations have conducted skills assessments. Fewer have implemented large-scale training or re-skilling programmes.
Executive expectations around employment have shifted. Nearly half (49%) expect AI to reduce national employment over the next decade, compared with one-third in 2024. Expectations for entry-level roles have declined, with fewer executives anticipating increased demand and more expecting reductions.
Consumer behaviour diverges from executive expectations
More than 25% of UK consumers report using AI to search or purchase products. A further 29% say they would delegate purchasing decisions to AI systems if reliability improves. In contrast, 21% of executives expect AI shopping agents to have a influence on consumer decisions over the next three to five years.
Financial impact on business
Around 46% of executives report little to no positive impact on profit and loss caused by agentic AI, thanks to skills and systems gaps. Nearly one-third (31%) say stopping AI use would have no material effect on operations. A share of organisations report inefficiencies in AI spending or limited visibility into its effectiveness.
At the national level, the UK government has committed to accelerating AI adoption, with productivity assumptions incorporated into fiscal projections.
Constraints and maturity gaps
The report identifies certain constraints affecting AI deployment: delivery and trust. More than 58% of executives say their organisations are not ready to integrate AI agents with core enterprise systems.
Prebble states that barriers to adoption are increasingly organisational, including how work is structured, how decisions are made, and how employees are prepared for new roles.
Only 7% of executives consider their workforce fully prepared for agentic AI. At the same time, 54% of employees expect to re-skill in response to AI adoption. Executives cite data security and user acceptance as important barriers, while many employees report discomfort with AI systems making higher-impact decisions. Technical constraints include error accumulation in multi-step processes, challenges in specifying objectives, and coordination complexity in multi-agent systems.
Organisations are at different stages of AI maturity. Around 27% are classified as experimenters, 48% as integrators, 16% as adopters, and 10% as ‘scalers’ with AI embedded in core operations. The report links higher levels of AI deployment to broader changes in workflows and workforce preparation.
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