What a Readiness Score Actually Measures.
PH—002
The readiness score is the first number a Terrein install produces. It is not a grade. It measures the distance between an organization’s operating reality and its operating ambition. Here is what the number contains, how it moves, and why eighty-seven out of a hundred means something.
I The Score Is Not a Grade
The first time a leadership team sees their readiness score, the room goes quiet. Not because the number is low. Because the number is specific.
A grade implies a curve. An audit implies a finding. The readiness score does neither. It is a measurement of distance. Where you are sits at one end. Where the 2026 ontology says you could be sits at the other. The score is the gap, expressed as a number between zero and one hundred.
Most enterprises have never been measured this way. They have been benchmarked, surveyed, and reviewed. None of those exercises produce a number that moves.
II What the Scorer Reads
The readiness score is composed from four inputs, weighted against the industry filter:
- Role competency. Every role scored against the 2026 competency ontology. Not whether the person can do the job. Whether the job itself has evolved faster than the role description.
- Process adjacency. How close existing workflows sit to workflows that already ship AI in production. Zero means the work is entirely manual. One hundred means a competent agent could run it tomorrow.
- Data reach. What the organization can see. HRIS, LMS, project history, tool logs. The score rewards breadth of signal, not volume.
- Decision cadence. How often the organization’s decisions get revisited. A quarterly budget scores differently than a weekly ops review.
III How the Number Moves
The score updates continuously after cutover. We have seen it move as much as four points in seven days when a new data source comes online. We have seen it move half a point over a month when the underlying work is heavy.
The direction of travel is more meaningful than the absolute number. An eighty-seven trending upward is a different story than an eighty-seven trending down. The trend gets its own line on the dashboard for a reason.
IV What Scores Look Like in Practice
From the thirty installs, the distribution has been flatter than expected.
- Public innovation organizations: mid to high eighties at baseline. Civic data discipline pays off.
- Mid-market regulated: low to mid seventies. The compliance filter is working, but it reveals more adjacent gaps than leadership expected.
- PE portfolio operations: wide spread, sixty-one to ninety-two. Depends entirely on whether the portco has an ops function or outsourced one.
- Home services: surprisingly high, averaging high seventies. Simpler operational surface area.
V When the Score Is Wrong
The score has been wrong twice in thirty installs. Both times it was high when it should have been middling. Both times the cause was the same: data reach looked generous because tool logs were routing to a central store, but the logs were recording the wrong events. The ontology caught it within two weeks.
Scoring is an act of honesty under load. If the number is wrong and the organization acts on it, the install will surface the problem before cutover. That is the point of simulation.
The score is not the product. The movement of the score is the product.