Did AI training change how the work gets done?
A workshop can leave people enthusiastic without changing the way work gets done. To assess its value, look at a task the team performs afterwards. The useful question is whether people can complete that work more effectively and recognise when the output needs attention.
Editorial responsibility: Nardus Oelofse, Chief Consultant.
By Chase & Marshal
Choose a task before evaluating the training
Identify the work the training was intended to improve. Preparing a customer summary, drafting an internal document and analysing feedback require different skills. Define what an acceptable output contains and what must be checked.
Use that task as the thread between the workshop and the workplace. Attendance and confidence surveys can explain the experience, but they cannot show whether someone can apply the learning to a real brief with imperfect information and normal time constraints.
Compare performance with a clear standard
Observe the whole task: selecting information, giving instructions, evaluating the output, making corrections and handing the work over. Record where participants can work independently and where they still need help.
In an illustrative customer-service team, participants might draft responses from approved policy information. Assess accuracy, tone, missing conditions and editing time. A confident user who accepts an unsupported answer needs a different follow-up from someone who understands the limitations but struggles to structure the request.
Look for the conditions around the learner
If people stop using the approach, examine the environment as well as the training. They may lack approved tools, access to current information, manager support or time to practise. A workflow may be too complicated for the benefit it produces.
Ask managers what changed after the workshop and what still gets in the way. This can reveal a systems or ownership problem that another introductory session would leave untouched. Training should connect to the way the business expects the work to be performed.
Use a short cycle of practice and feedback
Give the team a small set of suitable tasks to practise and a place to discuss difficult examples. Review outputs against the same quality standard, then refine the guidance. Keep examples that help people recognise recurring mistakes.
Decide what evidence would justify further training, a workflow change or broader rollout. The aim is dependable capability in the team. A useful training review explains which skills have improved, what support is still needed and where the business should focus next.
Questions answered
Is confidence a useful training measure?
It can help reveal how people feel about using a tool. Pair it with observed task performance, quality checks and the ability to explain when human review is needed.
Related
Sources
Sources supporting the factual statements in this article. Findings should be read within their stated dates, geography and scope.
- Helping Kiwi businesses succeed - Business.govt.nz. www.business.govt.nz. Publication date not supplied.
- Office of the Privacy Commissioner | Homedown arrow. www.privacy.org.nz. Publication date not supplied.
Find the gap between training and use
If your team has attended AI training but adoption remains uneven, we can help connect the next session to real tasks and a practical way to assess learning.
