Adoption
One definition of what meaningful adoption looks like for each use case, role and stage of the lifecycle. A dashboard counts activity and stops there. Your GTM system's memory keeps the behaviours you decided indicate success, and the evidence required before adoption is claimed.
The moments adoption gets judged, and what it is reading
Every team already believes it knows what good adoption looks like. What changes across these three columns is whether that belief is written down, shared, and tested against who actually renewed.
01 | The Current Way
02 | AI Added On
03 | AI-Native
A customer looks active
Activity read as success
Logins and events are up, so the account is called healthy. Nobody has said which behaviours were supposed to matter.
A usage summary, well written
The trend is now described fluently. It still describes activity, not whether this customer is getting what they bought.
Judged against expected behaviours
Usage is read against the behaviours you defined for this use case and these roles, so activity alone never counts as adoption.
Two CSMs read the same account
Two defensible answers
Each applies their own bar. Both can justify it, and the portfolio ends up with no consistent meaning.
Two summaries, the same gap
A usage-analytics tool describes activity the same way for both. The interpretation is still left to whoever happens to be looking.
One bar, applied the same way
Both read the same approved definition, so a difference in verdict has to trace back to a difference in evidence.
A new use case ships
Thresholds go stale quietly
The dashboard keeps reporting against behaviours that mattered for the old shape of the product.
Faster reporting on the wrong thing
Summaries update instantly. The definition of what good looks like does not, because nothing owns it.
The definition is versioned
Expected behaviours are updated as a new version, dated and approved, so every capability moves to the new bar at once.
An account renews on low usage
Filed as an anomaly
The exception is noticed, discussed once, and never reaches the logic that produced the wrong reading.
Explained away confidently
A plausible narrative for why the model was off. The model itself is left exactly as it was.
The exception becomes evidence
It is recorded against the threshold it contradicts, so a pattern of exceptions becomes a case for changing that threshold.
A quarter of renewals comes in
Adoption logic never learns
Retention data sits in one system and the adoption definition in another. Neither one corrects the other.
The same misreading, faster
An unexamined definition applied across the whole base repeats one error reliably instead of occasionally.
Outcomes retune the thresholds
Renewal, churn and expansion outcomes show which behaviours actually predicted success. The CS lead approves any revised definition before it becomes the new bar for judging adoption.
Capabilities that reason with this memory
Sales
Deal Health
Sales
Daily Brief | Sales
Prospecting
Daily Brief | Prospecting
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An advantage competitors cannot buy back: years of success and failure, codified.
One approved definition of what meaningful adoption looks like: the expected behaviours, the roles that should be active, the maturity stages, and the evidence required at each. Every capability that judges adoption reads that same definition rather than each applying its own.
Activity is what happened. Adoption is whether what happened means the customer is getting the outcome they bought. A login count is activity. Three teams using the workflow the account was sold on, with the right roles involved, is adoption, and only a stated definition tells them apart.
It should interpret usage against your definition, since a model can summarise events accurately and still hold no view on which behaviours indicate success for this product, this use case and this stage. The interpretation has to be codified by the people who know, then applied consistently.
That is what this memory is built to establish and keep current. Adoption readings carry the behaviours and thresholds behind them, so renewal and churn outcomes can show which ones actually predicted the result, and which were noise worth removing.
Adoption is evidence; health is the standing judgement that reads it. Adoption asks whether the product is being used in a way that means something to the customer. Customer health weighs that alongside value, relationship and support signals to say how the relationship as a whole is doing.