Stage 1
Tool experimentation
Teams try AI tools in isolated pockets without a shared operating model.
What this stage looks like
- AI usage depends on individual champions, not company standards.
- Teams use different tools for similar tasks with little coordination.
- Wins are visible but difficult to repeat across the organization.
Where it breaks
- Tool sprawl increases software cost without consistent return.
- Knowledge stays local to teams and disappears when people change roles.
- Leadership lacks reliable visibility into true business impact.
Next move
- Select 2-3 priority workflows where AI can produce measurable business value.
- Assign one executive owner to define success and accountability.
- Set baseline metrics before scaling any tooling decisions.
What to track
- Time saved on high-frequency workflows
- Adoption consistency across teams
- Cost per pilot versus realized business outcome
Governance focus
- Create minimum rules for approved tools, data handling, and review cadence.
- Document basic usage boundaries for sensitive information.