Customer Success
/Adoption
Adoption
Adoption continuously compares product usage and stakeholder behaviour against the company's own Adoption and Customer Outcomes logic, this customer's goals and lifecycle stage, and explains where expected behaviour is missing and what intervention it calls for.
Customer Success
Is this customer actually adopting the product, or only logging in?
What changes across the three columns isn't whether someone is watching the usage dashboard, it's whether a dip or a lull gets read against this customer's own goals and lifecycle stage, or gets treated the same as every other account's.
01 | The Current Way
02 | AI Added On
03 | AI-Native
Continuously, day to day
Dashboards, read alone
The CSM opens the usage dashboard and has to work out alone whether a change in the numbers means anything for this account.
A trend, no meaning
It can summarise that usage rose or fell, but a trend line still doesn't say whether that's the behaviour this customer needs to succeed.
Gaps named by use case
Usage and stakeholder behaviour are compared to what this customer's goals and use case actually require, so a real gap is named.
During onboarding, when adoption first starts
Too early to tell
Early usage looks thin by nature, so the CSM has no reliable way to separate a normal ramp from a customer already falling behind.
Same summary, wrong stage
A usage summary written the same way at week one as at month six misses that adoption means something different this early in the relationship.
Read against this stage
Usage is judged against what this lifecycle stage requires, so on-track early adoption reads differently from early adoption that's already slipping.
The QBR
Rebuilt for the meeting
Before every QBR the CSM manually pulls dashboards and account notes together to reconstruct the adoption story from a standing start.
Faster deck, same guesswork
It can draft the usage slide faster, but the CSM still has to work out unaided whether that usage is the adoption this customer actually needed.
Assembled before the meeting
The adoption picture by use case and stakeholder already exists before the QBR, with the gaps and their likely cause already named.
The risk review, when usage dips
Drop noticed, meaning guessed
A usage dip shows up on the dashboard, and the CSM has to guess whether it's a real risk or a familiar seasonal lull.
Flagged the same, every time
A bolted-on alert treats every usage drop the same way, whether it's this customer's known quiet quarter or a stakeholder actually disengaging.
Weighed against this customer
The dip is read against this customer's own adoption pattern and goals, so a real disengagement stands out from a familiar lull.
The renewal review, when the outcome is known
Judgement never checked
Once the renewal is decided, nobody goes back to see whether the adoption picture read months earlier actually predicted it.
Same behaviours count, regardless
A bolted-on trend keeps treating the same usage behaviours as meaningful adoption, whether the customers showing them renewed or churned.
What counted as adoption updates
Renewal and value outcomes show which behaviours actually predicted retention, and RevOps configures whether that evidence updates what counts as meaningful adoption automatically or after their review.
It reads usage and stakeholder behaviour against everything known about the customer, applying the company's own Adoption, Customer Outcomes and Segmentation logic, and returns where adoption is missing by use case. The gap is delivered to the CSM, with the reasoning behind it traceable.
It raises retention and makes better use of CSM time, because effort goes to the customers whose adoption is actually falling short instead of being spread evenly across every account by default.
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Frequently Asked Questions
AI-native GTM Systems didn't exist two years ago - here are the questions everyone wants answered.
Talk to Us→Adoption reads usage against this customer's own goals, lifecycle stage and adoption history, so a dip that would be normal at this stage isn't flagged as a gap by default. Anything ambiguous stays a human call, and the CSM decides whether it becomes a customer-facing intervention.
Adoption is not the same as Onboarding or Value Realisation: Onboarding coordinates the path to first successful use, Adoption judges whether ongoing usage represents meaningful adoption for this customer's use case, and Value Realisation assesses whether the outcomes promised in Sales are actually being evidenced. All three draw on the same customer picture but answer a different question.
Adoption's picture reflects usage and stakeholder behaviour as it happens. It's built from everything known about the customer, so a stakeholder going quiet or a feature suddenly picking up shows up in the read as it happens, whether or not someone opens a dashboard that day.
Adoption's definition does change, because retention and value outcomes feed back into which behaviours actually count as meaningful adoption. When customers showing a certain usage pattern keep renewing, or a behaviour the team assumed mattered turns out not to predict retention, RevOps configures whether that evidence updates the logic automatically or waits for their approval.
AI analyses product adoption by comparing usage and stakeholder behaviour to what this specific customer's use case and lifecycle stage require. Adoption applies the company's own Adoption and Customer Outcomes logic to everything known about the account, so it explains where expected behaviour is missing and why, instead of reporting a number with nothing behind it.
Which signals predict retention isn't fixed inside Adoption, it's whatever the company's own outcomes prove. Adoption compares current usage against lifecycle stage and what has counted as meaningful adoption so far, then tests that read against what actually happens: when a behaviour that looked meaningful stops predicting renewal, RevOps configures whether that evidence updates the logic automatically or only once they sign off on the change.