A Framework for Understanding AI at Work

What AI is made of → How you use it → How we make good use repeatable across the team
HUMAN OVERSIGHT AT EVERY STEP
HUMAN OVERSIGHT AT EVERY STEP
PART 1 - The Anatomy of AI
AI isn't one thing. It's a stack of three layers, and what's possible depends on all three working together. Click any block to explore.
Most people think AI = the model. In practice, the app and harness determine what AI can actually do in your workflow.
Pause & reflect

Think about the AI tool you use most at work. What's the model, the app, and the harness? Which layer is doing the most work for you - and which might you be underusing?

what you do with it →
PART 2 - The Practice of AI
AI use can look very different depending on three factors: how much context you give it, how repeatable your use is, and how much independent action you allow it. Explore the four modes below.
- not a hierarchy, but a map
How each mode scales
Single use
(one person, one task)
Persistent
(one person, ongoing)
Shared
(the whole team)
ConversationFresh each time--
ContextUpload a fileClaude ProjectShared Projects
Packaged ExpertisePaste a promptSaved SkillTeam-wide Skill
Supervised AgencyOne-off taskRefined workflowStandard agentic workflow
Pause & reflect

Think of one work task you regularly use AI for. How much context do you give it, how repeatable is your approach, and how independently does the AI act? Could someone else on the team pick up your approach and start using it right away?

individual → institutional
PART 3 - From Individual to Collective
Strong AI practices exist on this team, but they're siloed inside individuals and sub-teams. Here is one path to turn individual fluency into institutional capability - six steps that form a continuous improvement loop.
How do we turn individual fluency into institutional capability?
The human-oversight principle from the opening applies here with the most weight. When a practice is used by one person, the stakes of a quality failure are contained. When it's used by the whole team, the stakes multiply.
Pause & reflect

Above is one proposal to build shared AI practices across the team. What do you agree with, and would you do anything differently from what's proposed? Try to be as concrete as possible.

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