I reviewed an AI training program last month that had been running for eight months. They’d trained over 400 people. They had no idea if it was working.

They’d collected satisfaction surveys (mostly positive) and completion rates (respectable). But actual impact? Whether people used AI in their work? Whether skills transferred? They couldn’t say.

This blind spot is common. We invest heavily in training delivery but minimally in understanding its impact. Without feedback loops, we can’t improve what isn’t working or double down on what is.

Let me share how to build feedback mechanisms that actually inform improvement.

Why Most Training Feedback Falls Short

Traditional training feedback has significant gaps:

End-of-Training Surveys Measure the Wrong Things

The typical feedback form asks:

• Was the trainer engaging?
• Was the content relevant?
• Would you recommend this training?

These measure satisfaction, not learning. Not application. Not impact.

Happy participants don’t mean effective training.

Timing Is Wrong

Feedback collected immediately after training captures initial impressions, not lasting impact.

The important questions can’t be answered until later:

• Did learning stick?
• Did behaviour change?
• Did work improve?

Immediate feedback misses what matters most.

Feedback Doesn’t Close the Loop

Many organisations collect feedback but don’t act on it:

• Data sits in reports
• Reports sit in folders
• Nothing changes

Feedback without action is waste.

Wrong Sources

Training satisfaction is one perspective. Other important perspectives:

• Manager observations of changed behaviour
• Quality assessment of AI-assisted work
• Actual tool usage data
• Business outcome measures

Limiting feedback to participant surveys limits insight.

A Multi-Level Feedback Framework

Effective feedback operates at multiple levels, aligned with the Kirkpatrick model:

Level 1: Reaction

Did participants find value in the training?

Methods:

• End-of-session surveys (brief, focused)
• Facilitator observations
• Participation patterns during training

Timing: Immediate

Use for: Quick adjustments to delivery, content quality signals

Limitation: Doesn’t predict learning or impact

Level 2: Learning

Did participants acquire knowledge and skills?

Methods:

• Knowledge assessments
• Skill demonstrations
• Before/after capability comparison
• Confidence self-assessment

Timing: End of training and 2-4 weeks after

Use for: Content effectiveness, curriculum gaps, facilitator quality

Limitation: Doesn’t predict application to work

Level 3: Behaviour

Did participants change how they work?

Methods:

• Manager observations and interviews
• Self-reported behaviour change
• Tool usage analytics
• Work output analysis

Timing: 4-8 weeks after training

Use for: Transfer effectiveness, support needs, environmental barriers

Limitation: Doesn’t confirm business impact

Level 4: Results

Did the training produce business outcomes?

Methods:

• Productivity metrics
• Quality measures
• Business outcome correlation
• ROI analysis

Timing: 3-6 months after training

Use for: Investment justification, program prioritisation, strategic alignment

Limitation: Difficult to isolate training contribution

Effective feedback systems include all four levels.

Designing Feedback Mechanisms

How to build these feedback loops:

Immediate Feedback (Level 1)

Keep it short. 3-5 questions maximum. Longer surveys get abandoned or rushed.

Focus on actionable items. Ask about things you can actually change:

• Was the pace appropriate?
• Were examples relevant?
• What would improve this session?

Enable qualitative input. Open-ended questions reveal issues surveys miss.

Close the loop. Tell participants what you learned and what you’re changing.

Learning Assessment (Level 2)

Pre and post assessment. Compare capability before and after training to measure actual learning.

Skill demonstration. Have participants complete tasks using AI, not just answer questions about AI.

Delayed assessment. Reassess 2-4 weeks later to measure retention, not just immediate recall.

Self-efficacy tracking. Confidence matters—track how confident participants feel applying skills.

Behaviour Observation (Level 3)

Manager check-ins. Brief conversations with managers 4-6 weeks post-training:

• Have you observed your team using AI differently?
• What behaviours have changed?
• What barriers remain?

Participant pulse surveys. Quick checks with participants:

• How often are you using AI tools?
• What’s working?
• What challenges remain?

Tool analytics. If available, track actual usage data:

• Login frequency
• Features used
• Usage trends over time

Work sampling. Review samples of work product:

• Is AI being used appropriately?
• What quality is produced?
• What skills are evident?

Results Measurement (Level 4)

Outcome correlation. Connect training participation to business metrics where possible:

• Productivity metrics for trained vs. untrained groups
• Quality measures before and after
• Business outcomes in areas with high training concentration

Qualitative impact stories. Collect specific examples:

• What did someone do differently because of training?
• What outcome did that produce?
• What’s the estimated value?

Attribution acknowledgment. Be honest about attribution limitations. Many factors affect business outcomes; training is one of many.

Building Continuous Feedback Loops

Feedback should be continuous, not episodic:

Real-Time During Training

Facilitators gather feedback during sessions:

• Check-ins on pace and clarity
• Observation of struggles and successes
• Immediate adjustment based on what’s working

Immediate Post-Training

Quick feedback on experience:

• What worked?
• What didn’t?
• Suggestions for improvement?

Act on feedback quickly—before the next cohort.

Short-Term Follow-Up (2-4 weeks)

Check on learning retention and initial application:

• What have you tried?
• What’s working?
• What support do you need?

Medium-Term Follow-Up (6-8 weeks)

Assess sustained behaviour change:

• How has your work changed?
• What’s the regular pattern of AI use?
• What continued barriers exist?

Longer-Term Assessment (3-6 months)

Evaluate impact and outcomes:

• What results have you achieved?
• What capabilities have developed?
• What further development is needed?

Each checkpoint provides different insight.

Acting on Feedback

Feedback is only valuable if it drives action:

Rapid Response Mechanism

Create process for quick response to feedback:

• Who reviews feedback?
• What triggers action?
• How quickly are changes made?
• How are changes communicated?

Fast response demonstrates feedback matters.

Systematic Improvement Process

Regularly review accumulated feedback:

• What patterns emerge?
• What systematic changes are needed?
• What’s working that should be expanded?
• What’s not working that should stop?

Periodic systematic review catches what rapid response misses.

Closing the Loop

Tell stakeholders what you learned and changed:

• Participants: “Based on your feedback, we’re changing X”
• Managers: “Your input led to Y adjustment”
• Leadership: “Feedback showed Z, so we’re doing W”

Closing the loop encourages continued feedback.

Common Feedback Mistakes

Avoid these pitfalls:

Over-surveying. Too many requests for feedback lead to survey fatigue and poor data quality.

Under-analysing. Collecting feedback without thorough analysis wastes the effort.

Focusing only on Level 1. Satisfaction tells you little about effectiveness.

Ignoring negative feedback. Critical feedback is often most valuable.

Delayed action. Acting months later on feedback loses relevance and credibility.

No follow-through communication. People stop giving feedback if they never see it used.

Building Feedback Infrastructure

Sustainable feedback requires infrastructure:

Roles and Responsibilities

• Who designs feedback mechanisms?
• Who analyses feedback data?
• Who makes decisions based on feedback?
• Who communicates changes?

Clear accountability ensures feedback gets processed.

Technology Support

Tools that help:

• Survey platforms with analytics
• LMS reporting on completion and assessment
• Usage analytics from AI tools
• Dashboard for tracking across feedback sources

Technology enables scale.

Feedback Calendar

Schedule regular feedback activities:

• Continuous: Facilitator observation
• After each cohort: Satisfaction and learning surveys
• Monthly: Manager check-ins on behaviour change
• Quarterly: Impact assessment
• Annually: Comprehensive program review

Scheduled activities don’t get forgotten.

The Investment Case

Building robust feedback loops requires investment:

• Survey design and deployment
• Analytics and reporting
• Time for analysis and action
• Manager involvement in observation

But the return:

• Better programs that actually build capability
• Efficient resource allocation to what works
• Credibility with stakeholders through demonstrated impact
• Continuous improvement over time

Organisations that invest in feedback build better programs.

Those that don’t are flying blind—spending without knowing if they’re succeeding.

Which would you rather be?

Build the feedback loops. Watch your programs improve.