P2P

P E E R T O P E E R M A G A Z I N E · S U M M E R 2 0 2 5 15 3. Measure What Matters: Trust, Capability, and Traditional Metrics Frontier firms require expanded measurement frameworks that go beyond productivity and quality to capture the human- AI relationship dynamics that determine long-term success. KPI framework: Productivity metrics: Speed gains, task completion rates, cost reduction. Quality metrics: AI output accuracy, human review findings, client satisfaction scores. Capability metrics: AI self- assessment accuracy (how often AI correctly predicts its performance), human skill development, task complexity growth over time. Trust indicators: Human compliance with AI recommendations, escalation accuracy, user satisfaction with AI delegation decisions. Trust-building mechanisms: Research shows that demonstrating AI performance is more effective than explaining AI functioning. Design workflows where humans can observe AI competence over time through low-stakes tasks before delegating higher-value work. Create "trust calibration" periods where humans and AI work side by side on similar tasks, allowing humans to gauge when to rely on AI judgment. Human-centric communication: AI delegation interfaces must adapt to individual human characteristics, including technical expertise, role seniority, and attitude toward automation. When an AI escalates a complex legal issue, a senior partner needs different information than a junior associate. Human attitude assessments: Algorithm aversion scores, perceived control levels, confidence in AI decision- making. Risk and governance metrics: Track "delegation failures," instances where AI incorrectly assessed its capabilities or humans inappropriately rejected sound AI guidance, beyond bias scores and audit findings. Monitor the "separation of accountability," ensuring legal responsibility remains assigned to humans even as AI takes operational control. Dynamic calibration: Unlike traditional performance management, AI delegation requires continuous recalibration as both AI capabilities and human attitudes evolve. By month twelve, what humans trust AI to handle will expand dramatically from what they trusted in month one, requiring flexible measurement frameworks that can track this progression. This approach recognizes that successful AI delegation is not just about task allocation. It focuses on rethinking how we organize work when both humans and machines can make decisions and execute tasks. It requires new frameworks for responsibility, oversight, and coordination that account for this fundamental shift in who is responsible for what.

• Cover