Most AI training teaches tools. The bottleneck for organisations adopting AI at scale is not which tools your team uses; it is the judgement skills underneath them. Wharton professor Ethan Mollick's research on AI adoption identifies the same pattern: success belongs to practitioners who hold a defined set of judgement skills, not to those who have memorised the latest prompt tricks. The 201 layer names six of these skills and treats them as the curriculum that survives every model upgrade.
| Skill | What it is | Why it matters |
|---|---|---|
| Context Assembly | Knowing which background, constraints, format, and examples to provide AI for useful output | The single biggest predictor of whether an AI output is usable in real work |
| Quality Judgement | Knowing when to trust AI output and when to verify, based on stakes and task type | What separates polished nonsense from sound work |
| Task Decomposition | Breaking work into AI-suitable chunks instead of throwing everything in at once | The foundation of agentic governance |
| Iterative Refinement | Moving from a 70% first draft to 90%+ through structured passes, not accepting raw output | What turns one good prompt into a reusable working method |
| Workflow Integration | Embedding AI into how work actually gets done, not treating it as a side activity | What separates a tool subscription from a productivity gain |
| Frontier Recognition | Knowing where AI excels and where it fails for your specific domain | The skill that prevents confident, plausible errors from reaching the client |
These six are deliberately not prompt engineering, not tool tricks, not technical implementation. Those matter, but they do not explain adoption success at scale. Organisations that buy tools and train people on buttons land predictably on the wrong side of the adoption curve. The 201 skills are the layer that gets crossed instead of skipped.
For the framework that ties these six skills into a single discipline, see AIMED. For the engagement options, return to the home page.