From AI pilot to operational workflows in customer-facing teams
Integrating generative AI into service workflows with adoption-focused rollout and measurable engagement lift.
- 10%Customer activation improvement
- 3Practice areas integrated
The challenge
Leadership wanted generative AI to move beyond experiments into day-to-day service operations—without compromising quality, accountability, or team confidence. Success required more than a model deployment: workflows, guardrails, and adoption had to be designed together.
Frame, ship, govern
- Frame
Defined scope, success metrics, and human-in-the-loop checkpoints for AI-assisted customer engagement. Mapped existing service workflows and identified where automation would augment—not replace—team judgment.
- Ship
Led integration of generative AI models into service team tooling, coordinating product, engineering, and operations through phased rollout. Implemented operational frameworks for coordinators and delivery leads to apply PM discipline consistently.
- Govern
Tracked activation and engagement signals, reported program health to leadership, and applied organizational change management so teams adopted new AI-assisted workflows sustainably.
Outcomes
- Improved customer activation rates by approximately 10% through proactive AI-assisted engagement
- Established repeatable rollout pattern for AI features in operational teams
- Built leadership visibility into AI program health and adoption metrics
- Transferred operating knowledge to internal delivery leads
Practice areas
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