AI Feedback LoopWhat is an AI Feedback Loop?
An AI feedback loop is a continuous improvement cycle where the outputs of an AI model are evaluated and used to refine future predictions. This approach is essential not only to improve model performance over time, but also to detect early signs of model drift which can indicate a degradation in precision, recall, or overall accuracy.
The loop typically involves the model generating results, receiving feedback, either from users or system signals, and then learning from that feedback to enhance its performance. A classic example is the “next best offer” in marketing: the AI suggests a product, the user’s interaction (or lack thereof) is tracked, and that behavior informs future recommendations.
What Are the Challenges?
In Power BI and Microsoft Fabric environments, AI models are often embedded into reports using tools like Azure Machine Learning or ML model in Fabric. While these integrations allow for powerful predictive insights, capturing feedback on model performance is still a challenge. Most feedback is inferred from indirect signals like click-through rates, filter selections, or downstream KPIs. These signals don’t always explain why a prediction worked or failed.
Human feedback is far more valuable but harder to collect. If users aren’t prompted to provide input directly in the report, and if it’s not part of their workflow, it often gets skipped. This limits the model’s ability to learn from real-world usage and adapt to changing business needs.
How can Write-Back Help you?
Write-Back for Power BI enables users to submit structured feedback directly within the report experience. Write-Back can be embedded alongside visuals and fully integrated on the report. You can configure forms to ask targeted questions about prediction accuracy, feature relevance, or business impact—tailored to each use case.
Write-Back can push feedback directly into a Lakehouse through Fabric SQL Database. This makes it easy to join user feedback with model outputs and retrain models using real-world insights. Because the feedback process is integrated into the same Power BI reports users already rely on, participation increases. This transforms feedback from a passive signal into an active, scalable input that helps your AI models learn faster and perform better.
Commenting
Forecasting
Managing Reference Data
Mark Annotation
Replacing Values
Root Cause AnalysisUse Cases / Commenting
Commenting
What is Commenting?
Qualitative analysis plays an important part in any analytics process. It is possible to give more context by providing additional information, going beyond what the numbers immediately state. Having specialists commenting on existing analysis and putting it into context for other users means that everyone will get more value from the analysis and be more effective in communication. By establishing a commenting procedure, you ensure more collaboration around the analytics solution and get more value out of it.
Challenges
On Power BI, while we can place comments on a dashboard, these cannot be associated with a particular filter. This means that, for instance, if you place a comment on a particular month, it will be displayed even when you move to the next one. This can sometimes be confusing and does not give you the full picture.
How can Write-Back help you
Write-Back is a great solution for commenting; you can have users providing their input and simply associate it across the board with any filters selected. You are free to choose the form fields and the association with filters meaning it will always be straightforward to fill it in and interpret it. The best thing is that it all gets stored on your database, and you can then use the information in any visualization. Besides this, you can even distinguish who is entitled to comment and users who can only see comments.
Commenting is key to providing more context on complex analyses; with Write-Back, you can take it to the next level and do everything from Tableau dashboards.