Case Study

PE-backed banking operations platform case study.

75 shadow AI cases, 20 unauthorized MCP connections, and an Engineering/GTM leakage-risk map.

The operating question was simple: which AI tools were actually in use, which ones created risk, and which decisions should the owners fund next?

WorkstreamAdoption + Governance

ScenarioPE-backed banking operations platform

Next moveAI Audit first, then adoption and governance workstreams.

Evidence snapshot

Shadow AI cases

Found across a 300-person org, with usage tied back to owner, tool, and data sensitivity.

Unauthorized MCP connections

MCP-shaped grants and endpoint findings moved from anecdote to remediation queue.

#1 / #2

Engineering, then GTM

Engineering carried the largest data-leakage risk. GTM was second, because customer and prospect data moved through unsanctioned tools.

Starting point.

Built through M&A across six acquisitions in two years, heterogeneous tooling, two equal PE owners asking for one view of AI value and risk.

Multiple AI tools doing overlapping work across inherited teams.
Different policy, access, and DLP assumptions by acquisition cohort.
No single quarterly view that could satisfy both operating leadership and PE ownership.

Shadow-AI inventory and baseline DLP view across priority workflows.
A consolidation roadmap that separated tool overlap from genuine workflow need.
Quarterly adoption and efficiency reporting template for the PE operating cadence.