Responsible-AI Workforce Transition Board Adviser

The mandate

Claims and customer-service teams are deploying generative tools that draft correspondence, summarise evidence and recommend next actions. Productivity cases assume broad capacity release, but pilots do not measure correction work, judgment transfer or whether employees can contest automated direction. Job descriptions and reskilling promises lag the technology roadmap. The board repeatedly asks what responsible workforce transition means beyond generic principles.

The adviser will connect task evidence, service consequences and human accountability to workforce decisions without reviewing model safety or approving technology. Directors need to see which work disappears, changes or becomes more consequential; which roles require licensed or expert judgment; and whether redeployment pathways are real for affected populations. Advice should test both optimistic productivity and unmanaged work-intensification outcomes.

Three days monthly cover pilot evidence review, chair counsel and written challenges, with attendance at six people-and-technology committee meetings during ten months. Six in-person sessions will hear frontline staff, operational leaders and employee representatives. Urgent advice on a material job-impact decision will be returned within three working days when the chair supplies the decision paper and accountable management response.

The adviser has no line or executive authority, cannot select AI tools, direct employees, approve workforce reduction, negotiate collective arrangements, certify model compliance or make individual redeployment decisions. Executives and board committees retain those powers. The adviser may require the workforce case to show task and outcome evidence before endorsing its completeness, but cannot block deployment.

The term is ten months and ends after the board’s second portfolio review. Extension for a further two months requires committee approval tied to a named unresolved transition decision. Current work for competing insurers, AI vendors, labour organisations, claims outsourcers or workforce-technology investors must be disclosed. Any conflict affecting a pilot leads to restricted papers, recusal or termination as the chair determines.

Why the board wants this voice

Technology sponsors present automation value, risk teams assess controls and HR administers consequences, yet no one consistently connects task change to credible worker pathways. Directors want challenge grounded in real operating transitions rather than advocacy for or against AI. An independent workforce voice can expose hidden correction labour, accountability gaps and implausible reskilling assumptions before irreversible decisions.

What you will own

• Press the board to distinguish automated task volume, assisted judgment, exception handling and genuinely removed work.
• Test productivity claims against rework, escalation, customer outcome, control effort, learning time and demand rebound evidence.
• Challenge role redesign where accountability remains human but authority, information or staffing is materially reduced.
• Examine affected populations, adjacent skills, assessment routes, training capacity, placement supply and realistic redeployment timing.
• Probe workforce decisions for differential impact by location, working pattern, tenure, disability and protected demographic cohort.
• Shape governance linking pilot evidence, employee voice, job architecture, reskilling commitments and accountable approval bodies.
• Maintain board questions, management responses, unresolved worker risks and follow-up evidence across the automation portfolio.

Candidate qualifications

• Advised boards on workforce consequences of AI, automation or decision-support deployment in regulated service operations.
• Converted task-level operating evidence into defensible role redesign, capacity and redeployment challenge.
• Identified hidden rework, judgment concentration and work-intensification that weakened apparently strong productivity cases.
• Evaluated reskilling pathways through actual assessment, learning capacity and vacancy demand rather than course completion.
• Engaged employee representatives and frontline workers while respecting management, technology and legal decision boundaries.
• Maintained independence from AI vendors, outsourcing providers, workforce platforms and transformation-linked compensation.

Non-negotiables

• Can attend six Amsterdam, Rotterdam, Brussels or London evidence sessions across the ten-month appointment.
• Will disclose competing insurer, vendor, labour, outsourcer and workforce-technology interests before papers are shared.
• Brings board-level AI workforce transition experience; general digital transformation or responsible-AI policy is insufficient.
• Accepts no authority over tool selection, deployment, employee action, collective negotiation or model certification.

1. 49 words maximum. Describe an automation case where correction work materially reduced the claimed workforce benefit.
2. 49 words maximum. Which current technology, insurer or labour commitment could affect your independence here?
3. 49 words maximum. How would you test whether a promised redeployment path exists for the affected cohort?