A manufacturer embarking on a three-year, plant-by-plant AI rollout had a change management playbook built for single, discrete software rollouts — not for a multi-year program that would need to sustain momentum and trust across dozens of separate go-lives.
Redesigning the Change Management Playbook for a Multi-Year AI Rollout
Service Provided
Capabilities Utilized
Change Management, Communications Strategy, Adoption Measurement
Client challenge
The existing playbook assumed a single kickoff, a single training push, and a single go-live — fine for a one-time system change, but poorly suited to a rollout where the first plant’s experience would shape how skeptical every subsequent plant was going into its own go-live. Early missteps risked compounding across the whole program.
Our solution
We rebuilt the playbook around a “first plant as proof point” strategy: the first three plants were treated as deliberate pilots with extra support and measurement, generating specific, credible results that later plants’ communications could point to rather than relying on abstract promises. We built a standing feedback loop where each plant’s lessons learned were incorporated into the next plant’s rollout plan before go-live.
Communications were tailored plant by plant rather than broadcast company-wide, referencing the specific concerns raised by that plant’s workforce during pre-rollout listening sessions rather than generic messaging.
Results
By the twelfth plant go-live, average time-to-productivity for frontline staff had dropped from 6 weeks (plant one) to 2.5 weeks, as each rollout incorporated lessons from the ones before it. Employee sentiment surveys showed the plants going live in year two and three reported meaningfully higher pre-rollout confidence in the technology than year-one plants had, directly attributable to being able to point to specific results from earlier plants.
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