Feedback Loops for Better AI Workflows
Oct 3, 2025
Feedback Loops for Better AI Workflows
AI adoption isn’t a one-off rollout—it’s a continuous process of refinement. The first version of a workflow is rarely the best, and even the strongest AI models need adjustment once they meet the messy reality of everyday work.
That’s why feedback loops are critical. They help organisations capture what’s working, fix what isn’t, and ensure AI keeps delivering real value over time.
Why Feedback Loops Matter
AI adoption succeeds when workflows evolve with the people using them. Without structured feedback, teams risk poor adoption and wasted investment.
What happens without feedback
Errors or biases go unnoticed
Workflows stay clunky and underused
Employees lose trust in AI outputs
Leaders struggle to measure impact
How Champions Enable Feedback Loops
AI Champions are perfectly placed to gather and act on team insights. They work close enough to everyday tasks to notice friction, while also connecting back to leadership and governance.
Roles Champions play
Observers: Spot where workflows slow people down
Listeners: Collect user concerns and suggestions
Translators: Turn feedback into actionable improvements
Amplifiers: Share success stories and proven practices
Building Effective Feedback Loops
A good feedback loop is simple, consistent, and visible. It encourages input without adding unnecessary burden.
Tactics that work
Dedicated channels: Create a chat group or email for quick AI feedback
Regular check-ins: Short team discussions to share wins and pain points
Usage tracking: Measure which workflows are adopted and which are ignored
Iterative updates: Share improved versions so teams see feedback in action
Encouraging Honest Input
For feedback loops to work, employees must feel safe to share concerns. Champions should make it clear that feedback is not criticism, but collaboration.
Ways to encourage openness
Ask for specific examples rather than general opinions
Celebrate improvements that came from team input
Share lessons learned from failed experiments
Keep discussions blame-free and solution-focused
Closing the Loop
Feedback is only valuable if it leads to action. Champions must close the loop by showing how input shapes improvements.
How to close the loop
Update the playbook with refined workflows
Communicate changes clearly to teams
Show before-and-after impact (e.g. time saved, error rates reduced)
Recognise contributors who provided valuable feedback
Final Thought
AI adoption is not a straight line—it’s a cycle of testing, learning, and improving. Feedback loops make this cycle productive, ensuring workflows evolve alongside team needs.
With AI Champions leading the process, feedback becomes more than commentary—it becomes the engine that drives adoption forward.
