Here’s a use case that nearly every organization like yours can relate to: making sense of customer feedback and support notes. While these case notes hold valuable insights, manually reviewing them is often slow, inconsistent, and costly. That’s where Agentic Process Automation (APA) and AI skills can transform your process.

The Problem with Manual Classification

Think about a typical customer case you might handle. A product is delivered, a customer encounters an issue, logs a complaint, an engineer adds notes, and surveys or logs may follow. Traditionally, you or someone on your team needs to read through all of this—from initial customer comments to field engineer notes—to classify what happened and decide on next steps.

This approach is:

• Time-consuming: Reviewing each note is tedious, especially at scale.

• Inconsistent: Two people on your team may interpret the same notes differently.

• Challenged by language: If you operate globally, multilingual input can complicate classification.

Without accurate classification, you can’t effectively resolve issues or identify patterns to prevent them in the future.

Applying APA and AI Skills

To solve this, you can apply APA along with an AI skill built on Azure OpenAI. The solution automates your entire flow:

Data Gathering – Pulling customer comments, engineer notes, and logs from different systems you use.

Data Privacy – Using AI Guardrails to mask personal identifiable information (PII) before sending it to the model.

Classification – Passing structured prompts through the AI skill, which classifies cases into primary, secondary, and tertiary categories, and provides a reason for the classification.

Recommended Action – Going beyond classification, the AI also suggests actions—for example, adjusting engineer scheduling if delays are flagged as a recurring issue.

Visualization – The results flow into dashboards, giving your teams insights in near real time.

The Power of Structured Prompting

The accuracy of this process hinges on how you design prompts. You can adopt the COSTAR framework—Context, Objective, Style, Tone, Audience, and Response—which brings structure and reliability to classification. Instead of generic instructions, your prompts are tailored with precision, reducing hallucinations and increasing trust in the results.

In fact, when comparing AI-generated classifications against your pre-tagged business data, the AI skill often proves more accurate than manual classification, spotting errors and inconsistencies introduced by human reviewers.

Results and Key Takeaways

What should excite you most about this approach is how quickly it delivers value—without needing massive training datasets. Where other tools required tens of thousands of examples and still hovered at 60–70% accuracy, APA achieves around 95% accuracy with zero training data, thanks to structured prompting and LLM capabilities.

The key outcomes for you are:

• Higher accuracy than human review.

• No dependency on large training datasets.

• Faster resolution and better efficiency.

• Actionable insights that help you proactively improve customer experience.

Looking Ahead

This is just the beginning. The next phase is to expand recommendations beyond classification—helping your teams not just understand issues but act on them faster. Combined with built-in AI Guardrails for privacy and system prompts for greater control, APA provides you a powerful way to scale automation securely.

If you’re struggling with overwhelming case notes or customer feedback, I encourage you to explore how APA and structured AI prompting can help.