An executive AI baseline that grew firm-wide
How a national healthcare consulting firm moved from individual AI experiments to shared ways of working
Our client is a national healthcare consulting firm with several hundred people across multiple practices, and its clients expect judgment as well as data. Its leaders knew AI would matter to the business. They needed an evidence-based way to see where their people stood, and a plan for moving from individual curiosity to a capability the whole firm shared.
Strong instincts, manual habits
The firm’s leadership team could describe in detail how AI would reshape client work, pricing, and the consulting model. Day-to-day work was still mostly manual and depended on whoever was motivated enough to experiment. Four pressures made the timing urgent.
Understanding was ahead of practice.
Leaders’ sense of where AI was headed had not yet turned into shared, repeatable ways of working.
The advisory core was changing.
Benchmarking, survey interpretation, and analysis are the core of the firm’s client work, and AI speeds up exactly those tasks. Senior people needed real fluency before clients started asking about it.
Access was already in place.
The firm had already rolled out its AI platform widely. To turn access into adoption, it needed a shared vocabulary, a governance approach leadership could stand behind, and a starting point that matched where people were.
Leaders had to use AI visibly.
People inside the firm would believe in an AI strategy only if they saw the people advocating for it using AI themselves.
The firm needed a partner who would start by measuring where people stood, and build a program the executive team could stand behind before anyone else in the firm was asked to follow.
Started with executives, then grew on request
We ran the work through our four phases (Align, Equip, Apply, and Sustain), starting narrow and proving each step before taking the next.
- 01Align
A private baseline for every leader
AI Maturity DiagnosticEvery member of the firm’s executive leadership team completed a private, one-on-one consultation and was scored across four dimensions: Mindset, Skills and Fluency, Critical Judgment, and Strategic Integration. One on one, leaders were candid about gaps they would not have raised in a group session, and that candor became the baseline for everything that followed.
- 02Align
From individual views to a shared roadmap
Leadership AI Alignment ProgramIndividual prep sessions drew out each leader’s honest position before group discussion could sway it. A facilitated session then brought the team to a shared, one-page roadmap of prioritized big bets. Ongoing office hours and an optional first tool build kept the team working from that roadmap after the session.
- 03Equip and Sustain
A credible peer in every function
AI Champion ProgramOnce the executive team had real fluency, the firm’s leadership asked us to extend the same approach to the rest of the firm. We set up an internal network of practice leaders and managers, with a defined role standard, readiness segments scored against the same diagnostic, a shared value framework, and a tier and triage model. Manager enablement started only after the champions were active, so each function had a credible peer in place before expectations were set.
- 04Equip
Fluency for the client-facing middle
AI Fluency 101We then took the same evidence-based approach to the firm’s client-facing managers and directors. In our experience, this layer does more than any other to decide whether a rollout takes hold.
- 05Apply
Practice-level build scoping
ScopingIn parallel, individual practices brought us in to scope work of their own, including an early assessment of a benchmarking application and a roadmap for a pricing capability. This scoping is the first step toward a Custom Agent and Workflow Build or a Compliance-Grade Data Analysis Build.
Four lessons from the engagement
Leaders’ ideas hadn’t reached daily work.
Leaders could describe exactly how AI would change pricing, workflow, and client value, but most of that thinking stayed in individual conversations and one-off experiments. Saying so directly was the most useful finding of the engagement, because a strong point of view is easy to mistake for changed behavior.
Act on interest quickly.
When leaders came back within hours asking whether their own teams could get the same experience, the firm acted on it right away. Interest that shows up unprompted is the cheapest adoption a firm can build on, and it fades if nothing follows.
Define terms before writing AI policy.
People were using words like “deploy” to mean different things, from a single project to a firm-wide rollout, which made ordinary progress look like duplication. Defining terms before writing any formal policy removed a surprising amount of confusion and let leadership support a bottom-up approach without heavy approval steps.
The middle layer was the least likely to build fluency.
Firm-wide programs reach the top, and a champion network reaches the front line. The client-facing leaders in between are closest to revenue and the least likely to build fluency on their own. Seeing that changed the order of the program.
Leaders using AI first, with the firm following
The firm’s leadership team now works from a measured baseline and a shared, one-page roadmap of big bets. A common vocabulary lets leadership back bottom-up experimentation without heavy approval steps.
Within twelve hours of the first session, three executives asked to bring the program to their own practices. Practice champions now give every function a credible peer, client-facing managers and directors are building fluency, and individual practices are scoping AI builds for their own client-facing tools.
Leaders often expect AI to change how their firm delivers value well before it changes how the work gets done.
That gap can be closed. The firms that move fastest start with an honest, evidence-based look at where their people stand, instead of a generic rollout that assumes everyone starts in the same place.
More examples of our work
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