These items show how to prioritise training and support, which makes learning programs easier to scale.
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Prioritise support for high-impact tasks.
Example:
Identify tasks that create the most risk or business impact. Focus training and support on these tasks rather than treating all tasks equally.
Rationale:
Some tasks have a greater effect on performance than others. Prioritising them focuses training and support where improvement is most likely to reduce risk or improve results.
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Use data to personalise learning paths.
Example:
For a compliance refresher, consultants complete a pre-test. Those with excellent results complete a shorter version of the training.
Rationale:
Data shows who needs the full training and who does not. Shorter paths for people who already meet the standard free time and support for learners who need more help.
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Prioritise follow-up support for lower-performing teams.
Example:
After launch, review error logs and similar data. If certain branches have higher error rates, prioritise coaching for those sites.
Rationale:
Prioritising lower-performing teams avoids spreading coaching evenly across all teams, which makes support easier to scale.
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Provide extra support after launch for a set period.
Example:
For the first few weeks, provide extra support while learners start using the new process. After that, move ongoing questions to existing support channels.
Rationale:
Support demand is usually highest when people first apply a new skill. Extra support during this period manages the increase in questions and mistakes. Reducing support once demand drops keeps resources available for other needs.