Microsoft and Databricks Push AI Deeper Into Semantic Model Operations
Copilots are moving into the governed layer of analytics, where they can create and update the semantic assets that power reporting, access, and production data products.
What is this trend?
Microsoft and Databricks are moving AI from analysis into governed creation and maintenance of semantic-model assets, making data products faster to build but harder to manage without strong policy and validation.
- Copilots now create and update semantic-model objects, not just answer questions.
- Natural-language prompts are reaching charts, measures, dataflows, pipelines, and RLS roles.
- Databricks is enforcing Unity Catalog permissions on AI requests by default.
- The bottleneck is shifting from dashboard work to semantic design and governance.
- Teams need precise intent, auditability, and validation for AI-generated artifacts.
What’s the latest?
Microsoft pushed Copilot from analysis into execution by letting users generate charts, measures, dataflows, and pipelines from natural-language prompts across Excel, Power BI, Teams, and Fabric.
How it developed
Go deeper
Curated long-form picks on this trend — podcasts, videos, and analysis, by seniority.
If you're an individual contributor
Designing and Implementing AI Workflows for Repeated Tasks
How-to Substack guide on governed, multi-step AI workflows—building a language-learning pipeline with targeted feedback.
Wonder Tools · Substack
Read →
Applying the 5 AI Mastery Stages to Automate Weekly Reports
Summary tutorial on Substack mapping AI mastery stages to building a governed autonomous agent for weekly reports.
Sabrina Ramonov 🍄 · Substack
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Governance, Access Control, and Reuse: Foundations for Production AI Agents
Explainer podcast interview with John Capobianco on governing AI agents—role access, audits, and avoiding sprawl.
GO AI Podcast · Podcast
Listen from 3:46 →If you manage a team
Five‑Step Framework for Enterprise AI Governance
How-to podcast interview on governed enterprise AI execution: a 5-step model for policies, gaps, fixes, monitoring.
Analytics Insight · Podcast
Listen from 18:47 →
Testing and Scaling a Fee‑Proposal AI Agent in a Law Firm
Podcast case study interview with Sandokan and Matthias on governed AI readiness—evaluating a fee-proposal agent plan.
ILTA Voices · Podcast
Listen from 0:00 →
Developing AI Skills and MCP Servers for Agentic Power BI
How-to podcast with Mike Aronson on agentic Power BI semantic ops using MCP tools and git governance.
Explicit Measures Podcast · Podcast
Listen from 33:03 →If you lead the organization
Controlling agents a joint priority for data, AI governance teams | TechTarget
News analysis interview with Siddarth Jain and Johnna Till Johnson on governed agent execution as Copilot differentiator.
TechTarget · News
Read →Semantic Layer Ownership Splits Data Team Roles in AI Era
Analysis interview on semantic-layer ownership and ops, aligning data definitions for AI agents in the data stack.
DataCamp · YouTube
New Harness Report Reveals Enterprise Confidence in AI Agents Isn't Backed by Real Controls
Research & data report: survey shows AI-agent confidence outpaces testing, security, inventory, rollback controls.
PR Newswire - Business Technology · News
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