De-Risking Transformation in a Complex Enterprise
Client
A large enterprise organisation with multiple business units, legacy systems, and operations across several regions.
Challenge
The organisation was planning a major transformation program involving new technology platforms, process redesign, and changes to roles and responsibilities.
Past transformation efforts had struggled with:
- Change fatigue and low trust among staff
- Conflicting priorities between business units
- Patchy adoption of new tools and processes
- Hidden risks emerging late in the rollout
Leadership wanted to avoid another “big bang” project that looked good on slides but created disruption on the ground. They needed a way to test change approaches, anticipate resistance, and design a smoother path to adoption.
Approach
The organisation partnered with Ministry of Insights to use Change Lab, an AI supported environment for testing and refining change strategies before full-scale implementation.
Working with change, HR, and operational leaders, Ministry of Insights:
- Mapped key stakeholder groups, change impacts, and likely points of friction
- Modelled different rollout sequences, communication approaches, and support models
- Simulated how engagement, adoption, and productivity might shift under each scenario
- Identified high-risk combinations of timing, messaging, and workload
Instead of relying solely on intuition and past experience, Change Lab allowed the team to “pre-experience” the change through multiple lenses.
Outcomes
Using Change Lab, the organisation was able to:
- Redesign the rollout into smaller, better sequenced waves
- Target additional support and training to the most impacted teams
- Adjust communication timing to avoid peak operational periods
- Anticipate specific objections and prepare clear, evidence based responses
- Reduce the likelihood of critical operational disruptions during transition
The program team reported fewer surprises and more constructive engagement from frontline leaders.
Value Delivered
The Change Lab engagement delivered:
- A more realistic view of how people would experience the change
- Fewer last minute plan changes and emergency workarounds
- Stronger alignment between executives, program teams, and business units
- Higher adoption rates in the early phases of rollout
Most importantly, the transformation shifted from “pushing change onto people” to designing change with a clearer understanding of human and operational reality.
Why It Worked
This approach was effective because:
- Change was treated as something to be tested and iterated, not assumed
- AI was used to explore patterns and scenarios, not to dictate decisions
- Human insight, culture knowledge, and local context remained central
