Factory digital programs fail when adoption is treated as training only
In industrial environments, change management is often reduced to a final training session before go-live. That model is outdated. Adoption must be designed as an operational system: role clarity, feedback loops, escalation paths, and measurable behavior change.
Why this matters now
The operating context is shifting fast. AI and digital capabilities are improving rapidly, and organizations are scaling usage. Stanford's AI Index reports that 78% of organizations used AI in 2024, up from 55% in 2023. If frontline teams cannot absorb process change quickly, even strong technology investments underperform.
A practical framework for change in factories
1) Define role-level outcomes before tools
For each role (planner, supervisor, quality engineer, shift lead), define: decisions owned, required data, expected response times, and escalation rules.
2) Pilot with real shifts, not demos
Use live operational scenarios and real exceptions. Validate handoffs across teams, especially during high-load windows.
3) Instrument adoption
- Daily active users by role
- Exception backlog and closure time
- Manual workaround frequency
- Time-to-decision for recurring issues
4) Run a biweekly friction review
Track where users still bypass workflows. Fix root causes in process design, permissions, or data quality.
Leadership behaviors that increase adoption
- Translate transformation goals into team-level operational targets.
- Reward behavior change, not just system login counts.
- Create transparent ownership for unresolved exceptions.
- Keep one prioritized backlog for process and tool improvements.
Change management is an execution asset
When adoption is managed operationally, digital programs move from isolated tools to sustained performance improvement. This is the difference between a short-term rollout and a long-term execution system.
Book a change-management workshop
Sources
- Stanford HAI AI Index 2025 - organizational AI usage and workforce context - https://hai.stanford.edu/ai-index/2025-ai-index-report
- Our World in Data - Technological Change overview - https://ourworldindata.org/technological-change