Bringing AI to Your Team: Vibe Querying in Microsoft Teams with Copilot Studio

Business teams use CData Connect AI with Microsoft Copilot Studio to query multi-source data, automate workflows, and get instant insights – all without leaving Microsoft Teams. The tenth episode of our Vibe Querying series explores how business professionals across support, sales, and customer success can transform Teams into an AI-powered data hub. New hosts Stan and Cam demonstrate how to deploy conversational AI directly into the communication platform your team already uses daily, making enterprise data accessible through natural language conversation.

Watch Now: Vibe Querying with MCP - Episode #10

Introducing MCP, CData Connect AI, and vibe querying

Model Context Protocol (MCP) is a standard protocol designed to connect AI models to external data sources, tools, and workflows securely and efficiently. MCP enables AI models to access real-time data and interact naturally with that information through conversational AI or agent-driven actions.

CData Connect AI provides a remote MCP server capability that connects over hundreds of data sources to AI platforms like Microsoft Copilot Studio. This combination gives AI agents access to live business data through a standardized SQL interface, enabling natural language queries across all your enterprise systems.

This brings us to the concept of "vibe querying" – much like vibe coding, it's a conversational approach to data exploration. You no longer need intimate knowledge of data schemas or prebuilt pipelines. You simply ask questions in natural language through Microsoft Teams, and the AI agent accesses live data to provide answers and take actions.

Today's focus: AI agents in Microsoft Teams

For this episode, we explore a use case that highlights the exciting potential of agentic AI: giving everyone in your organization – from support teams to sales reps to customer success managers – the ability to query business data and automate workflows without leaving their primary communication platform.

Stan and Cam demonstrate how to build and deploy an AI agent using Microsoft Copilot Studio that connects to Salesforce and Zendesk data through CData Connect AI's remote MCP server. The result? A conversational AI teammate available directly in Microsoft Teams that can answer questions, provide insights, and take actions on your behalf.

The setup: Building your AI agent in Copilot Studio

Before diving into queries, Cam walked through the surprisingly simple setup process for connecting Microsoft Copilot Studio to CData Connect AI:

Step 1: Create an MCP tool in Copilot Studio

Navigate to the agent builder in Copilot Studio, add a new tool and select MCP (Model Context Protocol), name your server (e.g., "ConnectAI"), and provide the Connect AI MCP endpoint URL.

Step 2: Configure authentication

Use API key authentication method, set up the authorization header, and authenticate with your Connect AI credentials (email plus Personal Access Token).

Step 3: Enhance agent instructions

Provide the agent with context about its capabilities, list available tools and data sources, and define the agent's role as an expert MCP client.

Step 4: Deploy to Microsoft Teams

Navigate to the channels section in Copilot Studio, deploy your agent to Microsoft Teams, and share with your team members.

This entire setup takes just minutes, and once deployed, anyone in your organization can start having conversations with your business data directly in Teams.

The queries and insights

Note: Any numbers and customer names presented below have been obfuscated for privacy.

Query 1: Multi-source account intelligence

The question: "Show me my top five Salesforce accounts with the most currently open Zendesk support tickets."

The AI agent automatically executed sophisticated cross-platform analysis by querying Salesforce to retrieve account information, querying Zendesk to count open support tickets, joining data across both systems, and ranking accounts by ticket volume.

This single query demonstrated the power of multi-source data integration. Without any manual report building, data exports, or complex joins, the agent provided immediate visibility into which high-value accounts are experiencing support challenges. For customer success teams, this insight allows proactive outreach to accounts that might be at risk due to unresolved support issues.

Query 2: Conversational context and opportunity analysis

The question: "Which of those five accounts also have open Salesforce opportunities?"

Here's where the conversational intelligence really shines. The agent maintained context from the previous query, remembered the five account IDs without needing them repeated, queried the Salesforce opportunities table, and filtered for open opportunities linked to those specific accounts.

The ability to maintain conversational context transforms how teams work with data. Instead of starting fresh with each query or manually copying IDs between queries, users can have natural, flowing conversations with their data. This particular insight – knowing which accounts have both support tickets AND open opportunities – is critical for sales teams, signaling potential deal risk and creating opportunities for cross-functional collaboration.

Query 3: Account name resolution

The question: "What are the account names for those five account IDs?"

The agent seamlessly retrieved human-readable account names for the IDs it had been working with, making the data more actionable and shareable with team members. This demonstrates the agent's flexibility in presentation – technical IDs are useful for system operations, but business users need actual names.

Query 4: Deep insight generation with ticket summarization

The question: "Can you summarize what types of issues these customers are experiencing?"

This is where the AI moves beyond data recall into genuine intelligence. The agent analyzed ticket descriptions and categories across all identified accounts, categorized issues by type (questions, tasks, incidents, problems), identified severity patterns across different accounts, and provided actionable intelligence about which accounts need escalation.

The agent revealed critical distinctions between low-risk accounts like Green Mountain Medical Center (only questions and routine tasks) and high-risk accounts like Pacific Trust Financial (active incidents and problems requiring immediate support attention). This type of analysis represents a fundamental shift from data retrieval to business intelligence, enabling intelligent triage for support managers, early warning signals for customer success teams, and contextualizing renewal conversations for sales teams.

Query 5: Task automation with email-to-CRM workflow

The scenario: Cam demonstrated receiving an email from Badger State University reporting that their Snowflake connection is failing and needs urgent help.

The question: "Create a Salesforce task for the support team to respond to Badger State University's Snowflake connection issue."

The AI agent executed a sophisticated multi-step workflow automatically: querying Salesforce to find the account, retrieving account details including revenue and open opportunities, creating a new task linked to the account, adding context about the account's importance and issue diagnosis, and providing detailed confirmation of the completed action.

This capability transforms Microsoft Teams from a communication tool into an action center. Users can go directly from receiving an email to creating tracked, prioritized tasks in their CRM – all through natural language conversation. As Cam noted: "That's the power of AI. It's not just doing one thing after another and specific tasks. It's using intuition. It's being smart. It's doing things on the move in an agile way, which that's the reality of business."

Technical excellence: Observability and error handling

One of the standout features demonstrated in this episode was the observability provided by Microsoft Copilot Studio's activity log. After executing queries, Cam showed how you can review exactly what happened behind the scenes.

The activity log reveals every MCP tool call made to Connect AI, the exact SQL queries generated, error handling and recovery (including automatic correction of incorrect schema names), query iterations and refinements, and the complete execution flow from prompt to response.

The log showed a particularly impressive moment where the agent attempted a query with an incorrect schema name, received an error from the data source, automatically queried for the correct schema name, and reformulated and executed the corrected query successfully. This self-correction capability demonstrates sophisticated agent behavior—rather than failing and requiring user intervention, the agent uses available tools to diagnose and fix its own errors.

The business impact: From communication tool to intelligence hub

The transformation in team efficiency is dramatic. Traditional approaches to the queries demonstrated require logging into Salesforce separately, logging into Zendesk separately, exporting data from both systems, joining data in Excel or another tool, creating manual reports, copying data back to create tasks, and switching between multiple tools constantly.

With an MCP-powered agent that resides directly in Microsoft Teams, users can ask questions in natural language, get answers from live data across multiple systems, receive intelligent insights and summaries, take actions directly through conversation, and stay in Teams where your team already works.

As Stan summarized: "At this point, it becomes like your teammate. It becomes a coworker that you can offload tasks to and save yourself time."

What this means for enterprise teams

This episode showcases how AI-powered Microsoft Teams can transform business operations through accessibility (no technical skills required), efficiency (compress hours of manual work into seconds), context maintenance (natural conversations that build on previous answers), multi-source intelligence (seamlessly query across all connected systems), actionability (move from insights to action without switching tools), and observability (full visibility into what the AI is doing).

While Cam demonstrated support and sales use cases, this capability applies across your entire organization. Support teams can identify accounts with multiple open tickets and prioritize responses. Sales teams can monitor deal health across CRM and support systems. Customer success can proactively identify at-risk accounts. Marketing can analyze campaign performance. Project management can track tasks across multiple tools.

Getting started with your own Teams AI agent

If you want to unlock similar capabilities for your Microsoft Teams, sign up for Connect AI at www.cdata.com/ai/ for a free trial, access Copilot Studio using your Microsoft 365 subscription, connect your data sources (Salesforce, Zendesk, or any of hundreds of supported sources) to Connect AI, build your agent following Cam's simple setup process, deploy to Teams, and start conversing with your data.

For inspiration and proven queries, visit the CData prompt library to see examples across different business functions.

Beyond chat: The shift to conversational business intelligence

This episode demonstrates that we're moving beyond traditional business intelligence tools and into conversational intelligence platforms. When AI can access, analyze, and act on your business data through natural language – and do it all within the communication platform your team already uses – the transformation is profound.

The bottleneck shifts from technical capability to strategic thinking. Teams can now ask the questions they've always wanted to ask, test hypotheses instantly, and act on insights immediately – all without leaving Microsoft Teams.

What makes this episode particularly compelling is the emphasis on meeting users where they already are. Rather than requiring teams to learn new tools or switch between multiple applications, CData Connect AI and Microsoft Copilot Studio bring intelligence directly into Microsoft Teams. This "embedded AI" approach delivers reduced context switching, collaborative intelligence, lower adoption barriers, enhanced productivity, and team alignment.

A milestone episode and your next steps

This tenth episode of Vibe Querying marks a significant milestone in our journey exploring conversational AI for business intelligence. From sales management to product development, from marketing analytics to project management – and now bringing it all into Microsoft Teams – we’ve demonstrated how MCP technology is transforming every aspect of business operations.

The age of vibe querying has arrived, and it's more accessible than ever. With solutions like CData Connect AI and Microsoft Copilot Studio, any organization can deploy conversational AI agents that make data accessible, actionable, and intelligent.

Ready to bring AI to your team? Visit www.cdata.com/ai/ to start your free trial of Connect AI and begin building your own Microsoft Teams AI agent today. Join our CData community and Vibe Querying subreddit to share prompts, demos, and experiments with other practitioners. Until next time, stay curious and keep vibing with your data.