Guides
Guide: Usage-to-Outcome Map
A practical guide to connect AI usage to what actually changed in the work and the outcomes. Every AI usage metric needs an outcome sitting beside it.
Guides
A practical guide to connect AI usage to what actually changed in the work and the outcomes. Every AI usage metric needs an outcome sitting beside it.
Signals
Prompt counts are useful, but they need an outcome sitting beside them. Before deciding on prompt usage dashboards, consider also measuring the why.
Guides
A practical check for deciding what deserves to exist when AI can build more things faster. All buildings deserve a strong foundation, would you let AI build your foundation?
Signals
When builders move faster, judgement becomes the bottleneck to determine which design will be useful in the real world.
Guides
Responsible use needs examples, not just a poster. Allowed AI Use Rules Starter helps turn the policy intent into practical examples people can follow.
Signals
A Policy is not generally an operating guide. People need rules they can use in real work, not just the transformation or compliance announcements.
Guides
Before every meeting gets an AI secretary, ask whether the meeting still deserves to be on the calendar. Before adding AI notes to everything, ask what the meeting is actually doing.
Signals
AI can make unnecessary work easier to preserve. Better notes are useful, but they do not automatically make the meeting necessary. Pattern is ensuring not more work is being added reviewing AI transcripts.
The Chaos
Discovery: The Blue Blob found an entry-level desk with a senior-sized chair, the role title stayed small but the checklist did not. Where will this discovery lead us?
Guides
This guide helps managers, HR teams, universities, early-career workers and team leads check whether a junior role is still designed for learning because AI may have changed the task mix.
Signals
Entry-Level job role requirement is now expected to supervise AI outputs. The first job is still called entry-level, but the role checklist is starting to look very experienced senior level.
Guides
AI Workload Waste Audit: a practical check for extra review tax, rework, reruns and the hidden labour around AI outputs. This guide helps teams compare the visible AI benefit with the hidden labour sitting around it: