Enterprise AI Digest #37 - PODCAST on Spotify and Apple
Topics:
ERP: D365 BC - Copilot Analysis Assist
AI: Microsoft AI Horizon-Based Approach
Data: Copilot for Real-Time Intelligence
D365 Business Central - Copilot Analysis Assist
Microsoft’s Analysis Assist is taking data analysis in Business Central to the next level, acting as a Copilot for users working in analysis mode on list pages. Instead of manually setting up aggregations, filters, pivots, and sorts, users can now describe what they want in natural language, and Copilot will generate a suggested layout in an analysis tab.
What Can You Do with Analysis Assist?
Use natural language prompts like "Sort quantity from smallest to largest" or "Show average cost per category".
Quickly transform raw data into structured insights without manual setup.
Work with visible and hidden fields, automatically adding useful ones to your analysis tab.
Adjust layouts dynamically—modify filters, remove columns, and fine-tune results manually or through Copilot. Refer more details.
Microsoft AI Horizon-Based Approach
Adopting AI across an organization requires serious investment—but measuring ROI in AI is different from traditional investments. AI models need upfront investment before their impact can be fully measured, and they evolve over time, making long-term costs and benefits hard to predict.
To navigate this, Microsoft uses a horizon-based framework to evaluate and prioritize AI investments. This framework breaks AI initiatives into three horizons, ranging from optimizing existing operations to creating new revenue streams.
Horizon 1 (H1) – Running: Optimize Core Business Functions
Goal: Use AI to improve efficiency and automate existing processes.
Risk: Low – These are proven, incremental improvements.
Example: AI-powered quality control – A manufacturer currently inspects 100 parts/hour manually. With AI-driven image recognition, they can inspect 1,000 parts/hour, improving efficiency 10x.
Horizon 2 (H2) – Growing: Strengthen Market Position
Goal: Leverage emerging opportunities to create new customer experiences or services.
Risk: Moderate – Requires new data models & AI applications.
Example: Predictive Maintenance with AI & IoT – A manufacturer integrates IoT sensors to collect operational data and AI to predict optimal maintenance schedules. This reduces downtime, improves reliability, and enhances customer satisfaction.
Horizon 3 (H3) – Transforming: Disrupt & Create New Revenue Streams
Goal: Innovate with AI to change market positioning, cross industry boundaries, or create new customer needs.
Risk: High – Requires business model shifts and market validation.
Example: Electronics-as-a-Service (EaaS) – Instead of selling electronic components, a manufacturer uses AI models to predict the best devices for a customer's needs and offers AI-powered subscription-based hardware services instead of one-time sales. Refer more details
Copilot for Real-Time Intelligence
Microsoft’s Copilot for Real-Time Intelligence is a game-changer for analysts and data scientists who work with Kusto Query Language (KQL). This AI-powered assistant helps users explore data and extract insights without deep KQL expertise, making real-time analytics more accessible and efficient.
What Does Copilot for Real-Time Intelligence Do?
Translates Natural Language into KQL – Users can ask questions about their data in everyday language, and Copilot converts them into KQL queries.
Speeds Up Query Creation – Even experienced KQL users can save time by letting Copilot generate and refine queries.
Supports Conversational Interactions – Users can adjust, refine, and extend queries without starting over.
Bridges the Gap for Citizen Data Scientists – Copilot removes barriers for those unfamiliar with KQL syntax, making real-time data analysis more intuitive. Refer more details.