Client story: B2B AI Healthtech Provider
Building a plan to protect at-risk revenue through a six-fold user expansion.
A four-week diagnostic traced rising at-risk revenue to fragmented, untrusted go-to-market data and risk signals that arrived too late to act on. Leadership was aligned on one plan, and within weeks the first early-warning signals were in build, a data team hire was under way, and an executive search was paused pending the new process.
The situation
A clinical AI provider serving hospital systems was about to grow its user base six-fold, and its one-to-one Customer Success motion would not scale.
Leadership asked for AI-enabled CS and read the problem as tooling and skills: the CEO felt execution was slow but wasn’t sure why, the CTO expected AI agents over existing data to be enough, and the President and COO wanted the team building now. In parallel, a Chief Customer Officer search was under way to take on account challenges.
The VP of Customer Success saw untapped value in the company’s user data, but was running into the limits of self-built tools: they were costly to maintain, went stale quickly, and were adopted unevenly across the team.
A four-week diagnostic was scoped to identify where AI investment would pay off most.
The diagnosis
Each function knew its own gaps but not the others’ – and every tool built on top inherited all of them.
Adoption risk surfaced at quarterly-review cadence while frustrated clinician users needed attention within days. Adoption had no shared definition across CS and Product. Knowledge of which numbers to trust lived in a few people’s heads: errors reached client-facing impact reports undetected, outdated reference data understated the ROI the product had delivered, and ~2 FTEs went to duplicated effort.
The new data warehouse served Product, not go-to-market, and no one owned go-to-market data; a Chief Customer Officer would have arrived needing account-health and ROI tracking the company could not produce.
The stakes, validated with Finance: 3–6% of ARR at risk in each of the next three years from a single half-year renewal cohort.
The plan
Leadership left with one diagnosis and a clear set of highest-leverage moves. The CEO and COO endorsed the recommendation, shifting effort from one-off tools that each introduce maintenance overhead to investments that drive compounding impact across the CS motion: a data strategy with a senior owner, improved data operations, and one consolidated warehouse in place of six disconnected tools.
On top of that foundation sits a new intervention process that turns data into action: leading indicators that flag risk early, a weekly triage of at-risk user cohorts ranked by renewal revenue exposed, and a record of each intervention’s impact so the team learns what works.
Within weeks, the first early-warning signals were being built on data the company already captures, a data engineer offer was extended with data leadership to follow, and the CCO search was paused until the new process shows which senior roles the team actually needs.