── ── AI & agents
AI Disruption Risk Audit
AI does not hit a company evenly. Inside one business, some activities see their price collapse (they are commoditized) while others become scarcer and more valuable (they are reinforced). Research on AI's effect on work finds the same unevenness at task level: capability is a "jagged frontier," improving some tasks and worsening others of similar apparent difficulty (Dell'Acqua et al.…
Run AI Disruption Risk Audit on a real problem
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How it works
Output: AI impact heat map + hold / attack / retreat action table
1. Break down the value chain. 5–12 activities, each tagged with its share of revenue or gross-margin contribution. Gate: an activity that maps to no revenue or cost → merge it; no empty rows. 2. Score substitutability (1–5). Is the output standardized, digitizable, verifiable? Can today's general-purpose AI produce it at a quality the customer accepts — tested on real tasks, not imagined? 3. Score cost collapse (1–5). How far does AI cut the cost of the activity against today's cost? Record the basis. Gate: a score with no test result or price quote behind it → mark "unverified"; it does not enter the conclusion. 4. Score customer bypass risk. Can customers do it themselves with AI? Check switching costs, trust and liability requirements, data access, regulation — and the distribution channel that brings customers to you. Gate: high substitutability + low switching cost → red zone. 5. Find where advantage migrates. When execution gets cheap, scarcity moves to: proprietary data, distribution and customer relationships, liability-bearing and compliance, integration into workflows, judgment and taste. For each, ask whether you actually own it — and whether AI can reproduce it too. 6. Decide the action. Red zone: retreat (re-price and bundle, or hand the activity to AI) or attack (rebuild it on AI yourself first and take share at the lower price). Reinforced activities: hold and invest more. Each row gets an owner, a metric and a 90-day milestone. 7. Stop-rule: most scores are "unverified" → run a 2-week test (AI on real tasks) before returning to Step 2.
Output template: activity · revenue share · substitutability · cost collapse · customer bypass risk · zone (red / amber / green / reinforced) · advantage migration point · action (hold / attack / retreat) · owner · 90-day metric
When to use it
- user says 'will AI disrupt us?', 'which parts of our business will AI replace?', 'is our moat still there in the AI era?', 'run an AI impact assessment', or customers have started using AI tools to do work they used to pay us for, or an AI-native competitor has entered at a much lower price
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Chegg and Generative AI (2023–2025)
A worked example of AI disruption arriving at two different activities of the same business, two years apart — documented in Chegg's own earnings releases and annual report, and in CNBC's market coverage. The audit below is a retrospective reading of the public record through the skill's steps; Chegg did not publish such an audit, and the scores are illustrative of the method, not Chegg's internal figures.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
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