When an organization adopts AI, a request for training often arrives before anyone has defined the work that will change. That order can produce confident users without a workable operating process.
When training is appropriate
Training helps when employees need to understand a tool’s limits, practice a changed task, exercise judgment, protect information or handle exceptions. The need should be role-specific and tested through realistic work, not inferred from a generic AI literacy survey.
When workflow redesign comes first
Redesign is the priority when a process has unnecessary handoffs, unclear ownership, missing data, conflicting approvals or work that no longer needs to be performed in the same way. Training employees to operate an inefficient process more quickly can preserve the underlying problem.
When both are required
Introducing AI may change task boundaries, decision rights, review responsibilities and escalation paths. In that case, redesign the workflow and then develop the human capability needed to operate it safely. Test the combined intervention before scaling it.
A practical decision sequence
- State the business outcome and current baseline.
- Map the work, including decisions and exceptions.
- Identify constraints involving capability, process, technology and governance.
- Choose the minimum set of interventions that addresses those constraints.
- Assign ownership and define evidence of improved performance.
The same principle applies outside AI: a course is only valuable when it removes a real performance constraint. For related resources, read AI workforce readiness assessment, Performance diagnosis before training and CATALYST advisory services.
If you are evaluating a workforce capability or learning investment decision, describe the decision in a consultation enquiry.
By Ravinder Tulsiani, DBA.
