Course Objective & Scope
When employees co-produce reports, code, analysis and client communications with AI, legacy performance management breaks down: how much of the output reflects the employee's skill? If someone automates half their tasks and is rewarded only with heavier quotas, why would anyone share their AI workflows? And when an AI-assisted decision goes wrong, who is accountable?
This course helps leaders redesign OKRs/KPIs, performance rubrics, reward structures and accountability chains for AI-augmented work. Participants produce an updated Performance Management Guide and a Human–AI Accountability Matrix for their organisation.
Who Should Attend?
- HR directors, performance management and total rewards leaders
- Line managers setting goals and conducting performance reviews
- Risk, internal audit, compliance and employment-law/ethics representatives
- Operational excellence and transformation office managers
Key Takeaways
- Shift KPIs from raw output volume and hours spent to decision quality, outcome impact and workflow innovation
- Eliminate perverse incentives that drive employees to hide AI productivity gains (secret cyborg behaviour)
- Assess AI fluency, critical verification and peer workflow sharing fairly during performance reviews
- Enforce clear human accountability rules that prevent "the AI told me to" blame-shifting
- Create a manager review playbook and a Human–AI Decision Accountability Matrix (RACI)
Curriculum
The AI Performance Paradox and the "Secret Cyborg" Phenomenon
- The efficiency penalty: why punishing fast AI adopters with extra grunt work kills transparency
- Why employees hide their best AI workflows from management and how to reverse it
- Moving from volume-and-hours metrics to outcome quality and complex problem-solving
Redesigning OKRs, KPIs and Incentive Structures
- Setting outcome-based goals and quality floors rather than raw document or ticket counts
- Rewarding employees who build, document and share verified workflows that lift the whole team
- Explicit gain-sharing norms for how freed capacity is split between business output and growth
Fairness and New Rubrics in Performance Appraisals
- Distinguishing AI-generated surface polish from genuine domain mastery and judgment
- Adding AI orchestration, verification rigour and responsible use to performance rubrics
- Ensuring evaluation fairness across roles with different levels of AI tooling access
Decision Ownership and the Human Accountability Chain
- Core principle: AI can assist or draft, but sign-off accountability always rests with a named human
- Root-cause triage when errors occur: rubber-stamping negligence vs. model/data defect vs. flawed process design
- Translating EU AI Act human-oversight obligations into job descriptions and sign-off authorities
Workshop: Accountability Matrix and Manager Review Playbook
- Completing a Human–Agent–Manager–Platform RACI matrix for critical business decisions
- Role-playing modern performance and feedback conversations for AI-augmented staff
- Drafting the organisation's AI Performance and Accountability Principles charter
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