9Q / How to prioritise AI use cases
How to prioritise AI use cases
To prioritise AI use cases, list them, score each for value and feasibility, then place it on the 9Q Grid: how much the AI acts (Assist, Augment or Adapt) and what kind of work it is (structured, dynamic or complex). Fund the ideas whose quadrant matches the trust and governance you already have, and stage the rest.
The 9Q Grid is Suhit Anantula's map of AI agency and type of work, from The Co-Intelligent Organisation. Read the 9Q Grid.
Assist / Augment / Adapt × structured / dynamic / complex
Why value × feasibility isn't enough on its own
Two ideas can score the same and still ask completely different things of people and governance. A Q1 drafting assistant and a Q9 agent system are different bets.
Most AI failures happen when organisations try to buy a Q9 outcome with a Q1 trust budget.
Step 1: build the inventory
Gather ideas from the room, tools already in use and vendor offers. One line each: what the job is, who owns it, and what friction it addresses. Start from friction in real workflows, not from tools. Keep the list short enough to discuss in one sitting.
Step 2: first-pass score
Score each idea for value, feasibility and risk. This is the standard method — useful, and incomplete. It tells you what looks attractive; it does not tell you what the AI will do or how much trust that requires.
Step 3: place each idea in a 9Q quadrant
For every idea, ask two questions: how much will the AI act (Assist, Augment or Adapt)? What kind of work is it (structured, dynamic or complex)? The answers land the idea in one of nine quadrants.
A worked, illustrative example — not a client story. Three made-up ideas:
- A policy-search assistant (Assist × structured) → Q1 Operator: people stay at the helm; the AI retrieves and drafts.
- An invoice-matching automation (Adapt × structured) → Q3 Automator: the machine runs the workflow; humans govern rules and exceptions.
- A multi-agent procurement assistant (Adapt × complex) → Q9 Conductor: agents coordinate across systems; humans govern aims and escalation.
Same value score, three different trust and governance asks.
Step 4: read the portfolio shape
Use the book's "Before We Move On" questions:
- Which quadrant is each idea actually in?
- Which quadrants are overcrowded?
- Where are we now, and where should we be next?
- Which moves are coherent, and which are fantasy?
- Which ideas were sold as one thing but are actually another (mislabelling — for example Q3 automation sold as Q9)?
Step 5: fund by trust
Fund ideas whose agency you can already govern, and stage the higher-agency ones behind readiness. The trust gradient is the sequencing rule: the higher the agency, the more trust must already be earned. Position is not permission — see the AAA Licence chapter.
Template
Use this table as an AI use case template, then download the 9Q canvas PDF.
| Idea | Value | Feasibility | Risk | AI agency | Type of work | Quadrant | Trust needed | Decision |
|---|---|---|---|---|---|---|---|---|
| Policy-search assistant | H | H | L | Assist | Structured | Q1 | Low | Fund |
| Invoice-matching automation | H | M | M | Adapt | Structured | Q3 | Medium | Fund |
| Multi-agent procurement | H | L | H | Adapt | Complex | Q9 | High | Stage |
Decision values: fund / stage / stop / delete first.
FAQ
What is an AI use case?
A specific job where AI does or supports work, with a user, an input, an output and an owner.
What is an AI use case prioritisation framework?
A repeatable way to rank AI ideas. Most score value and feasibility; the 9Q Grid adds the AI's role and the trust it needs.
How do you identify AI use cases?
Start from friction in real workflows, not from tools. Delete steps that shouldn't exist before adding AI — Delete Before Digital thinking.
Is there an AI use case template?
Yes: the table on this page and the 9Q canvas PDF.
How many AI use cases should we start with?
Plot your top ten. That is enough to see the portfolio shape without drowning in inventory.
What's the difference between an AI use case inventory and a register?
An inventory lists ideas and current use; a register adds owner, risk and status for governance.
Helix work in this area
With the City of Mitcham, Helix narrowed 51 use cases to 3 pilots and ran a six-week AI Pod. Helix case studies ↗
Related questions
Continue the work
Turn your map into a next move.
Use the 5Ps to explore responses to what the map reveals, test a move and carry the learning into the next loop.
For AI software options, explore Agent Watch ↗.