Advisor on AI Operating Accountability and Workforce Design

Examine the work before redesigning roles

Observe the tasks being supported or automated and identify the judgement that is currently exercised informally. Determine which tasks disappear, which move to exception handling and which new work is created by monitoring and correction. Avoid assuming that faster task completion translates directly into removable capacity.

Assess how employees recognise an unreliable output and whether they have access to the information needed to challenge it. Review the incentives created by productivity targets and the risk that users will bypass checks to meet them. Consider the effect on learning when junior staff no longer perform tasks that previously developed professional judgement.

Design choices to put before management

Recommend role boundaries, escalation routes and capability development. Define where human review adds value and where a different process or narrower use case would be safer and more effective. Evaluate the staffing and cost implications of exception volumes, including periods when the model or its inputs change.

The advisor should propose an adoption test using real work, with measures of quality, effort and decision reliability. Findings must distinguish observed performance from assumptions about future improvement. Management retains authority over deployment, role changes and employment matters, with appropriate consultation and specialist advice where required.

Selection evidence

Candidates should combine operating-model or workforce-design experience with practical understanding of AI deployment. They must describe a project where automation changed work in an unexpected way and how the operating design was corrected. The ability to engage employees and technical teams without treating either as an obstacle is important.

A usable recommendation

The final output should explain the proposed allocation of work, the controls it depends on and the conditions for expansion. Success means leaders can connect the technology investment to a credible human operating model. The advice should remain useful when performance varies, helping management identify whether a problem requires better training, a process change or reconsideration of the automation itself.