A professional services firm had dozens of informal AI experiments across teams, rising tool spend and no view of where the technology actually changed its economics.
ENGAGEMENT OVERVIEW
The situation
Partners were enthusiastic but divided: some teams used AI assistants daily, others distrusted them, and the firm had no policy on client data, no measure of benefit and a growing list of subscriptions.
What we did
We mapped the firm's workflows end to end, quantified where time and cost were concentrated, and tested AI use cases against three criteria: material economic impact, data and confidentiality risk, and the capability required to sustain them. We then designed the operating model — governance, data policy, tooling standards and a small central team — to scale what worked.
The outcome
The firm adopted a governed AI operating model with a prioritised portfolio of use cases, a client-data policy its risk committee endorsed, consolidated tooling and a quarterly review of benefits. Experiments became capabilities; capabilities became standard practice.
The most valuable use cases were rarely the most visible ones: document preparation, research synthesis and reporting delivered more than the client-facing ideas that had attracted attention.
Controls were designed with the firm's auditors and risk function from the start, which turned compliance from a blocker into an enabler of adoption.
Identifying details have been removed at the client's request.
WHAT IT TOOK
AI creates advantage only when it is tied to the economics of the work and governed like any other core capability.
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AI Operating Model for a Professional Services Firm
A firm that had been experimenting without direction gained a clear portfolio, a policy its partners trusted and a way to measure whether the investment was paying back.