UK firms using AI assistants but multi-agent workflows lag

Slalom has published UK and Ireland survey data showing that 69% of businesses use AI assistants, while 31% use multi-agent workflows. The findings suggest a gap between adopting AI tools and using more structured systems.

The survey covered 417 business leaders in the UK and Ireland at companies that had started or were already pursuing AI adoption. It found that 55% of organisations use large language model chat interfaces, 47% use AI-powered productivity tools, and 43% use agentic AI.

The figures suggest many companies have introduced AI as individual tools rather than embedding it into broader working processes. As a result, employees still handle much of the practical work, including writing prompts, checking responses, and correcting errors.

Slalom argues that this pattern is adding tasks rather than removing them. Workers are often managing AI outputs on top of existing duties instead of handing off routine work in a more systematic way.

The numbers also sit alongside earlier Slalom research showing that 42% of UK companies said AI was delivering consistently higher-quality outputs. Together, the findings point to a wider issue around reliability and the level of human oversight still needed after deployment.

Adoption Gap

The survey shows a clear stepped pattern: AI assistants and chat interfaces are relatively common, while multi-agent workflows remain far less established.

The distinction matters because multi-agent systems are designed to co-ordinate tasks across several AI processes with less direct staff intervention. Without that structure, organisations may still rely on employees to translate tasks into prompts, judge whether outputs are accurate, and decide how work moves from one stage to another.

That creates an extra layer of administrative work for staff who were told AI would reduce routine burdens. In businesses where the tools are not tied to a clear operating model, any gains can be offset by the time spent supervising them.

The research forms part of a wider global study of 2,000 executives, leaders, and subject matter experts across five countries. Nearly all respondents worked at companies with annual revenue above USD $500 million, indicating a sample weighted towards larger organisations with active investment in AI.

Workplace Strain

Slalom linked the findings to what researchers have termed "AI brain fry", a phrase used to describe fatigue caused by constant prompting, verification, and correction. The issue goes beyond technical performance to workforce design, as employees are asked to take on new responsibilities without shedding old ones.

This concern comes as employers face a tighter labour market and rising scrutiny over how technology affects jobs. The debate over AI in the workplace has increasingly shifted from access to tools to the quality of implementation, governance, and accountability.

According to Slalom, the challenge is not simply whether a company has introduced AI, but whether its people can tell when the system is producing weak or incorrect work. That places a premium on critical thinking and domain expertise, especially in functions where errors can have wider operational or commercial consequences.

"Most UK businesses have given their people AI tools without giving them a structured way of working with those tools. The result is that employees are spending more time prompting and checking AI than they're saving. That's not transformation, that's new admin. The real question leaders should be asking isn't 'have we deployed AI?', it's 'can our people tell when the AI is wrong?' Because if the answer is no, you haven't just got a burnout problem. You've got a judgement problem that no amount of tooling will fix," said Sonali Fenner, managing director at Slalom.

Fenner's remarks reflect a broader argument emerging across the consulting and software sectors: AI adoption is moving faster than organisational redesign. Companies can buy or build tools quickly, but changing decision rights, workflows, and oversight arrangements takes longer.

For larger businesses in particular, the survey suggests the next phase of AI use may depend less on adding more assistants and more on deciding which tasks can be automated safely, where human review is required, and how the two should interact. Only 31% of respondents said their organisations had reached the stage of using multi-agent workflows.