As artificial intelligence continues to expand its influence across industries, one critical area remains largely untapped: structured data. Spreadsheets, databases, and business intelligence reports are integral to decision-making, yet they often require specialized skills to extract meaningful insights. For the untrained, navigating scattered data—whether in an Excel sheet, a CSV file, or a Power BI report—can be a frustrating exercise in futility. But what if those barriers could be lowered? Microsoft aims to answer that question with Analyst, an AI-powered reasoning agent embedded in M365 Copilot.

A New Approach to Analytical Reasoning

Traditional AI models often prioritize speed over depth, producing results that may lack adaptability when faced with complexity. Analyst is designed differently. Built upon OpenAI’s o3-mini model and fine-tuned for analytical tasks, it operates through a reasoning-driven, chain-of-thought (CoT) approach. Instead of rushing to a conclusion, it methodically evaluates each step—identifying patterns, testing hypotheses, and refining its answers as new information emerges.

This ability to iterate is critical. Analyst does not just retrieve data; it interprets and synthesizes it, making it particularly useful for business intelligence applications where nuanced insights can make all the difference.

Handling the Messiness of Real-World Data

Business data is rarely neat. Consider a common scenario: a project manager looking for quarterly performance insights across multiple files. Some figures are buried deep within an Excel sheet, while others are trapped in an inconsistent TSV file that uses commas instead of tabs. For most tools, this lack of uniformity would result in errors or incomplete reports. Analyst, however, is designed to navigate such obstacles with precision.

The agent’s problem-solving framework allows it to:

• Locate relevant data hidden in complex file structures.
• Detect and correct inconsistencies, such as misformatted delimiters.
• Generate and refine insights through an iterative hypothesis-testing process.

The outcome is not just a data summary but a coherent, meaningful interpretation of the information—delivered without requiring the user to be an expert in data science.

Image source: Microsoft

Built on Reinforcement Learning and Dynamic Code Execution

Analyst’s capabilities are rooted in reinforcement learning (RL), which allows it to continuously refine its approach. Post-trained on OpenAI’s o3-mini model, it utilizes RL techniques coupled with structured reasoning to navigate complex datasets. Unlike traditional AI systems that passively retrieve data, Analyst actively generates and executes code within a controlled environment, enabling it to dynamically adapt to new challenges.

Training data diversity has been a key focus in developing Analyst. Microsoft’s post-training process incorporated a wide range of structured data types—from Excel and JSON to SQL databases—to ensure the model can handle real-world business scenarios. By leveraging a robust reward system to encourage effective problem-solving, the model avoids common pitfalls such as superficial pattern recognition and overfitting to specific tasks.

Measuring Performance: Benchmarks and Real-World Applications

To assess its capabilities, Microsoft benchmarked Analyst against DABStep (Data Agent Benchmark for Multi-step Reasoning), a rigorous suite designed to evaluate AI-driven analytical tools. Analyst demonstrated leading performance across both simple and complex tasks, consistently outperforming other known models in real-world data analysis.

Beyond controlled benchmarks, its impact is measured through enterprise applications. Microsoft compared Analyst’s performance against standard M365 Copilot Chat features, evaluating accuracy in insight generation, data interpretation, and structured query execution. The results reinforced its strength in handling business intelligence workflows, particularly in complex analytical reasoning.

What’s Next for Analyst?

While the launch of Analyst marks a step forward, Microsoft acknowledges that work remains. Future developments aim to enhance its integration across M365 applications, refine its user interaction model, and broaden its analytic scope. The goal is clear: to democratize data analysis, making sophisticated insights accessible to a wider audience.

By embedding AI-driven reasoning into everyday workflows, Microsoft’s Analyst is not just improving business intelligence—it is reshaping how organizations interact with data, transforming challenges into opportunities for discovery.

Sources: Microsoft