Morningstar advances wealth advisory with Copilot and Copilot Studio

Today’s investors operate in constant noise, facing information overload, biased narratives dressed up as insight and volatility that can shift portfolios in a single news cycle. In this environment, making the right decision is profoundly difficult. Morningstar, a global investment research and data firm, set out to ease this reality, breaking down barriers to empower every investor for success.

As agentic AI begins reshaping financial workflows, the organization recognized a defining moment. This was not about layering AI on top of existing systems. It was about embedding it directly into the environments where decisions are made.

Using the Model Context Protocol (MCP) as the connective tissue between agents and enterprise systems, and deeply integrating with Microsoft’s ecosystem, Morningstar is creating a step-by-step blueprint for the agent economy.

Chapter 1: Elevating advisors to amplify investor outcomes

Investor success begins with advisors. They navigate financial planning, estate strategy, retirement readiness, tax considerations, and portfolio construction, transforming complexity into clarity.

“Advisors deliver peace of mind,” explains Thomas Aviles, Head of Advisor Software at Morningstar. “If you think about the biggest stresses people experience, it’s often financial or health-related. Advisors help people navigate those pressures and provide confidence, impacting tens of millions of lives.”

Yet the daily reality for advisors does not always fuel that mission. Administrative and operational tasks consume a significant portion of their valuable time.

“Our research shows that about 20 to 30 percent of an advisor’s day is spent on operational tasks,” confirms Aviles. “If we can streamline that work, it makes a meaningful difference in the value advisors can deliver.”

The rapid progression of AI presented a clear opportunity to reimagine how work gets done. But Morningstar did not aim for incremental improvement. Instead of deploying AI as a generic layer for search, summarization, or drafting, the organization set out to build agentic solutions that live inside the systems advisors already trust, infusing intelligence seamlessly and within governed environments.

Chapter 2: Bringing trusted research into the flow of work

With Microsoft 365 Copilot as an entry point, Morningstar brings its independent, research-backed data directly into Copilot through Model Context Protocol (MCP).

“Copilot is often the first AI solution advisors and wealth firms adopt. With MCP, a standard that allows AI to retrieve data, use tools, and take action, we wanted firms leveraging Copilot to explain complex investment decisions clearly and communicate with clients, answering questions instantly, accurately, and compliantly.” says Aviles

For advisors, this eliminates what Adam Wheat, Head of Data and Research Solutions at Morningstar and CTO of the Direct Platform Business Unit, calls the “swivel-chair effect.”

“Often advisors are pulling information from one system, reviewing a document in another, cross-referencing somewhere else,” he explains. “Bringing that together – completing a workflow in a single pane of glass – that’s the power we’re unlocking.”

Two high-impact workflows demonstrate the shift:

Client meeting preparation. Getting ready for a client meeting traditionally means hours of collecting data across financial plans, portfolio systems, and tax documents.

“What used to take a couple of hours per client can now take a few well-crafted prompts. You gather materials, structure them in a templated format, refine the message, and tailor it. The advisor remains in control, but the heavy lifting is accelerated.” says Aviles.

Portfolio talking points. Portfolio reviews are central to every client conversation. Advisors pull data from multiple systems, validate sources, cross-check metrics, and shape the narrative for discussion. The process requires every conclusion to withstand scrutiny.

“If you’re pulling from broad internet sources, the information may be low quality or biased. Powered through Copilot and Morningstar data, advisors receive validated and high-quality insights. When they walk into a meeting prepared with talking points grounded in our data, they know the perspective is credible.” highlights Aviles.

Chapter 3: Enabling custom agents with Copilot Studio

“Microsoft Copilot and Copilot Studio create a powerful ecosystem. It’s plug-and-play access. You can connect tools that fuel the full advisor lifecycle with AI, creating agents to support discovery and research, portfolio construction, monitoring, and client communication, while grounding AI workflows in trusted data, research, and investment IP.” underscores Aviles.

Embedded intelligence is only the first step. Using Microsoft Copilot Studio, Morningstar connects its MCP server to proprietary research libraries to create governed agents that operate directly inside Copilot. These agents can be tailored to individual wealth platforms, automating alerts, combining multiple sources, and orchestrating complex workflows across systems.

This unlocks entirely new capabilities. For example, by combining Morningstar’s MCP server with PitchBook’s private market data, an agent can trace a company’s lifecycle – from early-stage funding to IPO to public market performance.

Wheat explains, “You could manually research 10 or 15 years of history across systems. The agent knew which MCP to call, gathered the information, and produced a structured narrative in minutes. That’s bigger than automation.”

For wealth firms operating in highly regulated environments, Copilot Studio also provides critical guardrails. Agents act only on approved data, follow defined workflows, and remain fully auditable.

Chapter 4: Making AI fluency universal inside Morningstar

Morningstar’s external innovation is matched internally. The company is investing in AI capability across its workforce to ensure AI is accessible to everyone.

Wheat describes the mindset, “AI adoption is strong at Morningstar. We push teams to try new things, use AI daily, hire an agent, give it a job, and see how it performs. As adoption grows, power users begin solving increasingly complex problems.”

To formalize that momentum, Morningstar launched an AI Academy. Hundreds of employees are already enrolled, with more underway.

“The AI Academy builds foundations,” says Wheat. “Prompt design. Tool use. Agent orchestration. Safety and governance. Even without a technical background, employees can feel confident engaging with AI, or building an agent themselves.”

Chapter 5: The rise of the agent economy

Morningstar’s true north remains constant: empowering investor success. But the scale of what’s possible is expanding rapidly.
“We are at the very start of this story,” reflects Wheat. “AI is inherently data-hungry. We’re seeing exponential adoption. In the future, there will be hundreds, millions, even billions of agents working alongside our systems and data.”

With MCP enabling structured AI workflows, possibilities multiply. “Given the right tools and the right data, AI can solve problems we didn’t even anticipate,” he continues. “A user brings a question. The AI routes across data sources, synthesizes research, returns a strong answer – sometimes delivering an experience we didn’t explicitly design. Those ‘aha’ moments are powerful.”

As Morningstar’s tool ecosystem grows more robust and use cases expand, the organization will continue working closely with Microsoft to unlock even greater value. Together, they are helping shape the emerging agent economy, where multi-agent systems orchestrate workflows across technical and organizational boundaries, securely and at scale, grounded in trusted data.

“Microsoft has been part of Morningstar’s AI story from the very beginning. Many of our advancements are natural extensions of where we started, with Microsoft as a key partner in our AI journey.” concludes Wheat.

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