AI sales agents are getting smarter. The data they rely on? Not always.

People.ai today announced a Model Context Protocol (MCP) integration for its SalesAI Platform, aiming to solve one of revenue AI’s biggest problems: incomplete and inaccurate data. The integration allows revenue teams to connect AI agents—including Claude, Microsoft Copilot, and ChatGPT—directly to People.ai’s Answer Platform, which unifies structured CRM data and the unstructured reality of sales activity.

In plain terms, sales teams can now ask pipeline questions inside the AI tools they already use—and get answers grounded in both CRM records and what’s actually happening in emails, meetings, and calls.

The AI Revenue Problem: Garbage In, Confident Answer Out

Enterprise AI adoption is accelerating. Gartner predicts that 33% of enterprise software will include agentic AI by 2028. Revenue teams are already using AI agents to forecast pipelines, identify risks, and prioritize opportunities.

But there’s a catch.

Research suggests 80% of CRM data is inaccurate. Reps forget to log calls. Opportunity stages lag reality. Buying committees evolve without updates. When AI models analyze that incomplete data, they can produce answers that sound authoritative—but aren’t.

For sales leaders asking high-stakes questions like:

• Where is risk building in my pipeline?

• Which deals are stalling?

• Who actually has buying power?

A wrong answer doesn’t just skew a dashboard. It can cost deals.

People.ai’s new MCP integration is designed to address that foundational flaw by expanding what AI agents can “see.”

What the MCP Integration Actually Does

Through its Answer Platform, People.ai automatically collects and connects:

• Emails

• Meetings

• Chats

• LinkedIn interactions

• Call transcripts

• CRM opportunity data (stage, close date, deal size)

Its patented matching technology links unstructured activity data to the correct CRM accounts, contacts, and opportunities. NLP-based filtering removes sensitive content while preserving business context.

With MCP, that unified data layer can now be accessed directly from external AI tools. Instead of exporting reports or toggling between systems, revenue teams can query their preferred AI assistant and receive responses enriched with full activity intelligence.

This is less about adding another dashboard and more about embedding revenue intelligence into existing AI workflows.

From Activity Capture to Composable AI Infrastructure

Many activity capture tools rely on basic email or domain matching. That approach can create data duplication or incorrect associations—poisoning the AI models downstream.

People.ai is differentiating on data fidelity. Its platform enriches structured CRM records with persona data, buying power insights, and historical win rates. That enables AI agents to evaluate not only who is in the deal—but what they’re actually saying.

Jason Ambrose, CEO of People.ai, framed it succinctly: revenue teams don’t need more dashboards; they need complete answers at decision time.

Andrew Brown, Chief Revenue Officer at Red Hat, tied the announcement to a broader enterprise AI shift. Red Hat is orchestrating a company-wide move toward becoming an AI-enabled enterprise, and Brown highlighted the value of open architecture and MCP in building composable AI infrastructure. According to him, the approach has helped improve win rates by more than 50 percent.

That comment underscores a key trend: enterprises are moving away from siloed AI tools toward interoperable systems where AI agents can reason across unified data layers.

Why Model Context Protocol Matters

The Model Context Protocol (MCP) is gaining traction as a way to standardize how AI models access external systems. Rather than simply passing static datasets, MCP enables dynamic exchange of context between tools.

In this case, People.ai’s AI model doesn’t just send raw records to Claude or Copilot. It exchanges structured intelligence, enabling deeper reasoning instead of data dumps.

That distinction matters. Modern AI agents thrive on context-rich inputs. By providing both structured CRM fields and conversational insights, People.ai is aiming to give those agents a more complete understanding of pipeline health.

Competitive Context: Revenue Intelligence Gets Agentic

The revenue intelligence space has evolved from basic activity tracking to predictive analytics. Now it’s entering an agentic phase, where AI agents autonomously surface risks, suggest next actions, and answer complex business questions.

But as AI tools proliferate, integration becomes the bottleneck.

Rather than forcing teams into a proprietary interface, People.ai is leaning into accessibility:

• No additional logins

• No context switching

• AI queries within existing tools

• Answers enriched with complete activity data

For enterprises standardizing on Copilot, ChatGPT, Slack bots, or internal AI agents, that flexibility could be a strategic advantage.

The Bottom Line

AI in revenue operations is only as strong as the data foundation beneath it. And that foundation has historically been shaky.

With its MCP integration, People.ai is positioning itself not as another AI layer—but as the intelligence substrate powering enterprise sales agents. By bridging structured CRM data with the messy, unstructured reality of customer engagement, the company is attempting to close a critical gap in agentic revenue workflows.

As AI becomes embedded in more enterprise decision-making, the winners won’t just be the tools that answer questions fastest. They’ll be the ones that answer them correctly.

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