Eqwitty AI ReadinessAI Use-Case Portfolio readiness
The use-case portfolio pillar measures what has actually shipped, not what has been discussed. It looks at how many AI capabilities are live, what the biggest single payoff was in the last year, and whether there is a repeatable way to move a pilot into production.
Most organizations score lower here than they expect. Pilots are easy to start and hard to finish, and the gap between "we ran a pilot" and "we run AI in production" is where the majority of AI value is lost.
What this pillar measures
The portfolio of AI in production today — breadth, ROI, and pipeline discipline.
AI Use-Case Portfolio is weighted at 14% of the overall AI Readiness score, which makes it the 2nd heaviest of the nine pillars. 7 scored questions feed its maturity score, and 1 more inform other parts of your result. See how the weights combine.
What we ask
Every scored answer describes a recognizable state of the world rather than an agree/disagree rating. These are the questions behind this pillar.
- How many AI tools are actually live in your business today — used by staff or customers in real work, not just being tested?Count anything in everyday use. Leave out trials, demos, and proof-of-concepts.
- How well do you track what your live AI tools are actually worth to the business (money saved or made)?Whether you can put a real number on what each live AI tool saves or earns.
- In the last year, what was the biggest single payoff from one of your AI tools — in money terms?Your best estimate. It could be extra revenue, money saved, or losses avoided — whichever is clearest.
- How do you decide which AI ideas get money and which don't?How you choose which AI ideas to fund when you can't do them all.
- Where has AI actually moved the needle for you? Tick anywhere you can point to a real, measurable result.'Measurable' means a number you could defend in a board meeting.Feeds capability breadth, not the score
- How much of your AI spending can you tie back to a specific business result?Be honest — most teams are at the low end here.
- When did you last stop an AI project because it wasn't paying off?Stopping projects is a sign of maturity, not failure — it means you're measuring.
- Think of the customer problem you'd most love AI to fix. Roughly what is that problem costing each year?This forces the money question. A small problem means a small AI prize. A big, unsolved problem is usually your best shot.
What each level looks like
Each answer maps to a maturity score from 0 to 4. Reading down a column shows what an organization at that level typically says.
| 1Awareness | None yet — only trials or experiments |
| 2Active | 1–5 |
| 3Operational | 6–15 |
| 4Systemic | 16–30 |
| 5Transformational | More than 30, across several parts of the business |
| 1Awareness | We don't track it |
| 2Active | Only informally — gut feel and anecdotes |
| 3Operational | We track real numbers for some of them |
| 4Systemic | We track real numbers for every live AI tool |
| 5Transformational | Live dashboards, updated as the value lands |
| 1Awareness | We don't have one live yet |
| 2Active | Under $100K |
| 3Operational | $100K – $1M |
| 4Systemic | $1M – $10M |
| 5Transformational | $10M or more |
What a strong score unlocks
A strong score means value is measurable and the path from pilot to scale is routine. Each new use case costs less than the last, and the portfolio is managed against return rather than enthusiasm.
Other pillars
All nine pillars, their weights and the scoring math are on the methodology page.
Find out where you stand
A 15-minute diagnostic, then your nine-pillar score, peer benchmarks and a 90-day plan you can act on Monday. One-time payment of CA$19.80 per report.