Change management for AI: the underrated success factor
Models do not transform organisations. People using models well, inside a system that supports them, transform organisations.
The under-discussed bottleneck
Model capability is the conversation everyone wants to have. Change management is the work that actually determines whether the capability gets used. The teams compounding AI value have a disproportionate investment in the second.
What good AI change management actually includes
Tiered enablement by role and risk exposure. Workflow integration so the AI shows up where work is already happening. Prompting patterns documented and shared. Manager rituals that inspect AI-assisted work alongside everything else. Recognition systems that reward AI leverage, not just AI usage.
The 60-day adoption window
If you do not have 70%+ weekly active usage by day 60, the problem is rarely the model. It is workflow integration, enablement quality, or manager reinforcement. Diagnose along those axes before blaming the technology.
Avoid 'AI tools' as a separate category
Treating AI as a separate toolset, surfaced through separate UI, with separate training, almost always under-performs treating AI as a layer inside existing workflows. Adoption follows the path of least friction.
Key takeaways
- Change management is usually the leading constraint, not model capability.
- Invest in tiered enablement, workflow integration, prompting patterns, and manager rituals.
- If usage is below 70% by day 60, the issue is workflow, not the model.
- Avoid surfacing AI as a separate tool category — integrate it into existing workflows.
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