AI Boss · Whitepaper

Chapter 8 · The AI Boss Today

Performance Scores, Productivity Metrics and Dismissal by Algorithm

Key insights from Chapter 8 of "AI Boss"

Author Conceived and directed by Gunnar Nordqvist; text written by AI agents under his direction.
Published 6 November 2026
Build number 2026.11
algorithmic-management ai-governance architecture-as-code ai-agents co-determination
Figure 8.1: How a score travels from M2 Evaluation to M3 Consequence, and the automatic path the book excludes.
Figure 8.1: How a score travels from M2 Evaluation to M3 Consequence, and the automatic path the book excludes.

A performance score looks like an instrument of measurement but often works as an instrument of decision: it sets a bonus, triggers a warning, places a name on a restructuring list or, in the reported cases, ends a working relationship by email. This chapter examines how individual scores are computed, what they miss, what the courts found about dismissal and deactivation by algorithm, and what the GDPR, the AI Act and national dismissal law require when a score shapes a decision about a person. It concerns M2 Evaluation, the rating of performance and conduct, and M3 Consequence, the pay, discipline, dismissal and deactivation decisions that follow. Here the book's rule that every M3 Consequence decision has a named human decider, and that Level 4 Autonomous is never used for M3 Consequence, is first tested against practice, and the test is hard because a score rarely becomes a decision by anyone deciding that it should.

Every performance score is built in three layers: activity data such as scans per hour or documents edited; indicators computed over a period and compared with a target or peer group; and a composite score that weights indicators into one figure or band and often ranks people. Each layer is a choice, because the data are what the system can log rather than what the work consists of. The construction is M2 Evaluation performed by software, and Ajunwa's account of the quantified worker describes how much evaluation has moved from the manager's observation to the system's record (127, Ajunwa, 2023).

Beyond the rate scores of Chapter 6 and the platform ratings of Chapter 5, a newer family spreads quietly: office dashboards built from collaboration-suite logs and from monitoring software, which critics call bossware, that records application use and presence on workers' computers.

Primary topics explored

  1. The score
  2. What scores miss
  3. Dismissal by algorithm, reported
  4. The productivity-score backlash
  5. Trusting the system over the person
  6. Scores and Article 22

About AI Boss

AI Boss is Book 7 of The Transformed Architecture. It examines what happens when artificial intelligence takes the manager's chair: algorithmic management of direction, evaluation, consequence, development and care, the effects on people, the management of AI agents, Europe's legal answer, and how to design an accountable AI boss, for European enterprises and public authorities.

It builds on the conclusions of Architecture as Code, AI Governance, AI Platform and Security, AI Red Team, AI Threat and The Transformed Security Architect.

Who should read this

Executives, line managers, HR and people leaders, works councils and trade unions, public employers, architects and AI governance leads, data protection and legal officers, procurement and programme leaders, and risk, compliance and audit.

Portrait of Gunnar Nordqvist

Gunnar Nordqvist

Conceived, directed and published the books of The Transformed Architecture.

Continue with the full chapter

Read the full chapter online

Chapter 8: Performance Scores, Productivity Metrics and Dismissal by Algorithm in AI Boss gives the detailed explanations, practical examples and implementation patterns behind this summary.

This whitepaper belongs to “The AI Boss Today” and is one of 37 drawn from the manuscript.