I was being asked to review around 50 pieces of agent generated work a day. I was spending too much time rereading work because I was not always clear what needed a full review, what could be approved quickly and when I actually needed to step in.

Maya helped me understand what I was accountable for, what I needed to check and what the agent could handle without me. I had a much clearer way to review and approve work without slowing everything down.

Results: Agent output moved through review and approval with much clearer boundaries. I spent less time repeating work the agent had already completed and could focus my attention on the decisions that genuinely required human judgement.

Metrics impacted: Saved 11 hours of productivity per week, and 38% less manager admin time.

— Daniel W., Operations Lead who navigated a role change during an AI transformation programme

We had a finance professional whose role was being significantly changed as more work moved into agent enabled workflows. He was capable, but unclear about where his experience would fit in the new operating model and how his role should work alongside AI agents.

Maya helped us map his strengths against the new operating model, identify where human judgement was still required, and focus his development on the capabilities the new role needed. That gave both the employee and the organisation a clearer view of where his experience could continue to create value as the work changed.

Results: He was successfully redeployed into a Data and Insights role within six weeks. There was no external hire and no replacement cost. He entered the new team with a clear understanding of how to work alongside agents, which responsibilities remained his, and where he remained accountable.

Metrics impacted: 60% reduction in time-to-fill internal roles, and 2.8x increase in internal mobility.

— HR Director, Financial Services firm, 1,200-employee digital transformation programme

Our team had introduced more AI into the workflow, but the handoffs between people and agents were not working as clearly as they needed to. Work was being reviewed more than once, ownership was not always obvious, and employees were spending time checking output that did not necessarily require their attention.

Maya helped make the workflow explicit. We could see what the agent owned, what the employee owned, where human review was required, and when the work needed to move to someone else. Instead of relying on people to work out those boundaries as the workflow moved, the responsibilities were clear from the start.

Results: Within three weeks, the workflow was running with clearer ownership, fewer unnecessary handoffs and less duplicated effort. Employees could spend more time on the parts of the work that needed their judgement rather than repeatedly checking work that had already been completed.

Metrics impacted: 10x faster across reporting, workflows, and admin, and 40% reduction in time spent building forms and workflows.

— Sarah M., Product Manager navigating an AI-driven team restructure

Emma was flagged as an attrition risk three months into our AI workflow rollout. Her design function had changed substantially and she was disengaged. We had no systematic way of knowing whether the new version of her role actually suited her, or whether we needed to find her an alternative pathway within the organisation.

Maya gave us a clearer picture of how her strengths mapped against the changing role and gave her a structured way to work through the decision rather than making it by default.

Results: She moved into a different role within the organisation that was a better fit for her strengths and experience. Twelve months on, she is still with us. The alternative was a resignation and a £40,000+ replacement process.

Metrics impacted: 35% improvement in workforce planning and 100% matching accuracy.

— People & Culture Lead, Technology company, post-AI restructure cohort of 340 employees