Opening context
LawCo is a global commercial law firm ranked in the AmLaw 100, with offices across North America, Europe, and Asia Pacific, serving technology, life sciences, and financial services clients across jurisdictions and time zones.
2,400 professionals were invited to participate in a firm-wide baseline — attorneys, legal assistants, business services, and operational staff. Over 1,000 responded, a 44% participation rate, contributing more than 3,400 verbatim comments.
The firm had recently appointed a new CIO with a mandate to advance AI adoption and modernize the firm’s digital platform. Before accelerating that agenda, firm leadership needed an independent view of how technology was actually landing across offices and practice groups.
The question was not whether the platform was running. It was whether it was ready.
The situation
Operational metrics showed availability and ticket performance. Strategic roadmaps described cloud migration and AI initiatives. Neither revealed how reliably existing technology performed in the daily work of a billable-hour firm — where friction does not just accumulate quietly, but converts directly into cost.
When AI investment drives the agenda, foundational technology — Knowledge Management, document systems, intranet — can fall behind. Budget and attention follow the new platform. The tools lawyers depend on every day receive less governance. By the time the gap becomes visible, it is partners who are absorbing it most.
In a partnership model, that gap becomes political before it becomes operational. Inconsistent performance for one office or practice group is not an IT problem in isolation — it is a conversation the CIO will be asked to have with partners who bill by the hour and whose confidence in firm-wide initiatives shapes whether those initiatives succeed.
The CIO needed to know where the platform was stable, where it was not, and which gaps represented genuine risk to the AI agenda she had been brought in to deliver.
“Before you can build on a foundation, you need to know its actual condition. The independent record gave us that. Some of what it showed confirmed what we expected. Some of it didn’t.”
— CIO, LawCo
What the independent record showed
The baseline segmented results by office, role, and service domain — connecting scores with influence ratings to identify where friction intersected with client-critical work.
Three findings stood out.
Knowledge Management was the most critical service and the most broken.
Firm-wide, KM satisfaction was 22%. Among partners — the population the entire technology agenda was designed to serve — it was 5%. KM carried an influence score of 8.2 out of 10, placing it among the highest-consequence services in the stack. The service most critical to how attorneys find, organize, and apply the firm’s accumulated knowledge was the most broken part of the platform.
When KM fails, AI inherits the problem.
McKinsey Legal’s global KM lead has described a sound KM infrastructure as “absolutely vital in the gen-AI era” — because AI output is only as reliable as the curated data sets it draws from. At LawCo, the independent record showed what that meant in practice. An enterprise-grade AI tool was already in use and in demand across the firm. But the KM foundation those tools would depend on had a 5% satisfaction rate among the partners it was designed to serve. The sequencing risk was not theoretical. It was already present in the data — and invisible in every internal report.
Partners and attorneys were the most dissatisfied population in the firm.
The people whose productivity the entire technology agenda was designed to support recorded satisfaction scores of 22% across the firm. Non-attorney staff scored above 50%. The gap was not marginal, and it appeared in no operational report.
By contrast, IT Deskside Support scored highest across the firm. The team was performing. The tools were not.
“The partner satisfaction finding was the number that mattered most. The people whose productivity this is all designed to support had the lowest scores in the firm. The record made that impossible to miss.”
— CIO, LawCo
What changed
Three findings. One sequencing decision. The record made the case that existing reporting had not.
Innovation priorities were reordered against operational reality rather than roadmap assumptions. The firm could now identify where foundational reliability required attention before AI-driven capabilities were layered on top — and defend that sequencing with evidence rather than instinct.
The partner satisfaction finding gave the CIO something ticket data and availability metrics could not: a defensible, independent picture of where the platform was failing the people who mattered most, sourced directly from those people — and ungameable.
The outcome
By establishing independent ground truth across more than 2,400 professionals before acting on its AI mandate, LawCo ensured that its innovation agenda was grounded in the actual condition of its platform.
The record showed where the foundation was stable, where it was not, and what the AI deployment would land on if the sequence was not corrected first.
The AI agenda is intact. The sequence is evidence-led.
