── ── AI & agents
Human–AI Role Redesign
When an agent takes over part of a job, the job does not disappear — a bundle of tasks changes hands. If the role, KPIs, skills, and incentives stay as they were, the old scorecard keeps rewarding people for the work the agent now does. People then compete with the agent, route around it, or quietly rebuild the old process…
Run Human–AI Role Redesign on a real problem
Bring something you're actually deciding — free, in the browser.
How it works
Output: New role card (duties + KPIs + skills + incentives + communication plan)
1. Decompose into tasks. List every task in the role with its share of hours. Gate: shares sum to <90% → complete the list before continuing. 2. Assign each task a destination. From the delegation map: fully delegable (agent runs it, human spot-checks) → agent; partially delegable (agent drafts, human approves) → human–AI collaboration; not delegated → stays human. 3. Write the new human duties. Judgment (decisions outside the agent's boundary) · exception handling · customer and partner relationships · supervising the agent (sampling, correcting, feeding back rules). Gate: supervision has no named owner → stop. 4. Rewrite the KPIs. Move from throughput to outcome + quality + exception resolution time + agent-correction rate. Gate: an old volume KPI is still live → stop; it will fight the new incentives. 5. Close skill gaps and retrain. List the new skills (prompting and evaluating outputs, exception judgment, data checking); set format, duration, and a pass criterion. 6. Align incentives. State where freed hours go (higher-value work, more accounts), and set an explicit rule — or equivalent arrangement — that using the agent does not cut pay. 7. Communicate the change. Lead with why, what stays the same, and each person's path; run a trial period with a feedback channel. 8. Stop-rule: freed hours have no higher-value destination → escalate to the business level to decide headcount, then re-run this process.
Output template: role · task → destination (hours %) · new duty + owner · new KPIs · skill gap + training + pass criterion · incentive rule · communication date · review date
When to use it
- user says 'what does this role do once the agent takes over?', 'how should KPIs change after AI goes live?', 'what new skills do our people need?', 'how do we tell the team about AI replacing work?', 'rewrite the job description for AI', or an agent has already taken over part of a role's tasks
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Klarna's AI Assistant and the Return of Human Service (2024–2025)
Klarna is the most visible public case of an agent absorbing a large share of a role's tasks. Within fifteen months the company publicly changed course and redesigned the human role around what the agent could not do. The record is public on both ends, which makes it useful for running this skill backwards: what the Process would have demanded at launch, and what Klarna ended up building.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
Related mental models
When AI replaces human work, the customer needs fewer seats — so seat pricing moves in the opposite direction from the value delivered.
In the AI era, models and compute can be bought.
Answer engines — featured snippets, "People Also Ask" boxes, voice assistants, AI answer panels — lift one passage and present it as the answer.
Teams usually decide how much to hand to an agent by watching a demo.