Client Outcomes

Anonymized results from recent engagements

Every quarter an enterprise runs AI pilots without a production operating model, it accumulates structural debt. These organizations built the operating model.

Decision ClarityManufacturingEUR 1.8B revenue

European Industrial Group

Situation

A European industrial group had 7 AI pilots running without executive oversight or a path to production. Each used different infrastructure, different model providers, and different evaluation criteria. No one had mapped which pilots shared data dependencies. The CTO could list the tools but not the owners.

Intervention

Within 6 weeks of a Decision Clarity engagement, the board approved a consolidated AI investment strategy. A named owner map connected each pilot to a production path with explicit go/no-go criteria. Infrastructure consolidation reduced redundant spend.

Outcome

The board retired 4 redundant pilots and doubled the budget for the 3 with measurable ROI. A single investment view replaced five separate pilot updates.

6 weeks
Time to board decision
57%
Pilot portfolio reduction
2x
Budget for viable pilots
Production CommitmentFinancial ServicesEUR 3.2B revenue

Regulated Financial Services Firm

Situation

A regulated financial services firm had spent 18 months in AI pilot mode with no production deployment. Business-line sponsors wanted productivity gains quickly, but control functions needed traceability, escalation, and human accountability. The governance layer was slowing deployment without improving outcomes.

Intervention

A Production Commitment engagement restructured the AI operating model: manager accountability was redesigned, infrastructure was consolidated, and the first AI workflow reached production. Named workflow owners were assigned with explicit KPIs.

Outcome

First production deployment in 14 weeks. The pilot-to-production cycle compressed from 18 months to 3 months. The internal team owned the result after engagement end.

14 weeks
First production deployment
18 to 3 months
Cycle reduction
Compounding ReturnsEnergy & Utilities4,200 employees

Mid-Cap Energy Company

Situation

A mid-cap energy company deployed AI across operations but saw adoption plateau at 12%. Managers were never given redesigned KPIs or authority to enforce the new process. Training was delivered as a one-time event. No quarterly review existed to track whether the system was producing value.

Intervention

A Compounding Returns engagement installed a governance framework with quarterly executive reviews, a constraint library, and manager-level adoption KPIs. Governance became operational, not documentary. Adoption was measured at workflow level.

Outcome

Within two quarters, measured adoption rose from 12% to 41%. The constraint library became the operating backbone. Manager roles were redesigned with AI-specific KPIs.

12% to 41%
AI adoption rate
2 quarters
Time to measurable ROI

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