AI-Enabled Performance Management Masterclass

• Translate organisational priorities into measurable team and individual performance objectives.
• Use AI to draft and refine KPIs, then test them for relevance, fairness and unintended incentives.
• Build dashboards that highlight performance trends, exceptions and gaps requiring follow-up.
• Use verified evidence and AI-assisted preparation to conduct coaching and appraisal conversations.
• Design a performance improvement plan and establish safeguards for responsible AI use throughout the performance cycle.

1. Performance Management in the AI Era

• Map the performance cycle from goal setting and monitoring to feedback, appraisal and development.
• Identify where AI can save time or improve analysis, and define which decisions require managerial judgement and approval.

2. Aligning Strategy, Team Goals and Individual Objectives

• Translate organisational priorities into team deliverables and individual commitments with clear ownership and deadlines.
• Use AI to draft objectives, then refine them against job responsibilities, available resources and agreed service standards.

3. AI-Assisted KPI and Scorecard Design

• Build a scorecard with baselines, targets, data sources and measures of productivity, quality and timeliness.
• Test AI-suggested indicators for unintended incentives, such as rewarding cases closed while overlooking errors or repeat complaints.

4. Performance Data Quality and Dashboard Development

• Clean and validate performance records in Excel, checking for missing values, inconsistent definitions and incomplete reporting periods.
• Build a dashboard that compares targets with actual results and highlights trends, exceptions and actions due.

5. Analysing Performance Gaps and Root Causes

• Use AI to suggest explanations for performance patterns, then test them against workload, case complexity, process delays and available evidence.
• Distinguish gaps that an employee can address from barriers that require management action, such as system downtime or unclear procedures.

6. Continuous Feedback and Performance Coaching

• Use approved AI tools to turn verified observations into coaching questions, feedback notes and follow-up actions.
• Practise a coaching conversation that invites the employee’s perspective, agrees on support and sets a review date.

7. Appraisals, Rating Calibration and Fairness

• Draft appraisal narratives from verified evidence and check that each statement reflects the agreed standards and the full review period.
• Compare sample ratings across managers to identify inconsistent scoring, bias and unsupported conclusions before sign-off.

8. Managing Underperformance and Developing Talent

• Build a performance improvement plan with measurable expectations, agreed support, checkpoints and evidence of progress.
• Use review findings to identify development priorities, recognise strong performance and assign work that builds capability.

9. Responsible AI, Employee Privacy and Human Oversight

• Apply controls for approved tools, data access, anonymisation and retention of performance records.
• Review AI outputs for factual errors, bias and unsupported inferences, and document the human decisions behind ratings and actions.

10. Integrated Case Study and 90-Day Action Plan

• Analyse a simulated team that meets its processing target but has rising error rates and complaints; prepare a dashboard and conduct a review conversation.
• Develop a 90-day implementation plan with priority AI uses, safeguards, responsible officers and measures of success.