Artificial Intelligence is Reshaping the Role of Financial Advisors. The Advisor Value Prop Has Never Been Stronger

Across pretty much every facet of the white-collar economy there is a twinge of existential angst about how AI is poised to disrupt professions left and right.

Like every technology revolution before it, we will settle into a new normal where some roles have changed, and some roles have been eliminated entirely.

For financial advisors, this change is inevitable. 

Kishore Nair, Head of Technology for Morningstar’s Advisor Software, has a front row seat to the ways in which top advisors are harnessing AI in their day-to-day. Strap in for his unfiltered view below in this Q&A below.

Q: The business of financial advice is built on a few things. Relationships. Trust. Financial expertise. At a high level, where do you see AI fitting into that equation?

A: AI doesn’t replace any of those — it amplifies the advisor’s capacity to deliver on all three. Today an advisor spends hours on portfolio reviews, data lookups, and meeting prep before they ever sit down with a client. Our AI assistant handles that operational load so the advisor can spend more time on the relationship and the advice. The trust and expertise still come from the advisor; the AI just removes the friction between knowing what to do and getting it done inside the platform.

Q: AI has clearly made its way into some of the support tasks an advisor does. Notetaking, scheduling, meeting prep, etc. But how do you think about AI for some of the higher-consequence facets of advisor work such as planning and investments? 

A: We treat those differently by design. The assistant can screen investments, analyze scenarios, and draft investment plans, but it always operates within the advisor’s guardrails — their selected models, their client’s risk profile, their compliance constraints. It doesn’t make autonomous investment decisions. It surfaces data, runs the analysis, and presents options. The advisor makes the call. For planning, the AI helps structure the conversation — pulling in portfolio data, risk scores, and talking points — but the advisor owns the client relationship and the recommendation.

Q: Is the role of the advisor safe? And how can (and should) advisors continue to demonstrate their value?

A: The role isn’t just safe — it becomes more valuable. Morningstar’s own research has quantified that good financial planning decisions — asset allocation based on total wealth, dynamic withdrawal strategies, tax-efficient asset location — increase retirement income by 29%, equivalent to 1.82% per year of additional returns. That’s value created by advisor expertise, not by picking stocks. An advisor charging 1% is already net-positive for the client before you count the intangible benefits. AI doesn’t erode that — it strengthens it. The AI assistant handles the operational work (screening, scenario modeling, report generation) so advisors can spend more time on the planning decisions where the Gamma actually lives: getting the withdrawal strategy right, optimizing asset location, adjusting allocations as circumstances change. The advisor’s role isn’t threatened by AI; it’s the reason the AI exists.

Q: Morningstar has talked about creating an “AI-first advisor experience.” What does that vision look like, and how does Morningstar’s AI assistant create that?

A: It means the AI is the primary interface for getting work done inside Direct Advisory Suite — not a sidebar feature. Today, the assistant connects to more than a dozen specialized capabilities: portfolio management, investment screening, scenario analysis, report generation, client management, and more. An advisor can type a natural language request — “show me large-cap ESG funds with a 4-star rating” or “prep a meeting brief for the Johnson account” — and the system routes it to the right tools, pulls the data, and delivers a result. The goal is that anything you can do by clicking through the software, you can do faster by asking. Going forward, Morningstar is likely to introduce new features first on the AI Canvas, followed by software. 

Q: This new system replaces Morningstar’s generative AI chatbot “Mo.” What did you learn from Mo, and how did that shape this new AI assistant?

A: Mo taught us a lot, on how advisors use Morningstar research and how they interact with the software because it doubled as a support center as well. The questions on Mo also made it clear that that advisors want to do real work through conversation, not just ask general knowledge questions. The usage patterns showed demand for portfolio-specific actions, client lookups, and investment screening — tasks that require live platform data, not just an LLM generating text. That drove the architecture of the new assistant: instead of one general-purpose chatbot, we built a supervisor that routes to a dozen specialized agents, each connected to actual Morningstar APIs, and MCP tool servers. Every answer is grounded in the advisor’s real data — their clients, their portfolios, their entitlements, and of course in Morningstar data and research. Mo was a chatbot. AI Assistant is an active assistant.

Q: Can you walk us through a real-world example of how an advisor might use the assistant?

A: Say an advisor has a quarterly review with a client tomorrow. They type “prep a meeting brief for Sarah Chen.” The assistant pulls Sarah’s portfolio holdings, recent performance, risk profile, any rating changes on her holdings, and generates a Word document with talking points — all in one request. During the meeting, the client asks about adding ESG exposure. The advisor asks the assistant to screen for ESG-rated funds that fit Sarah’s risk profile and model allocation. It returns a filtered list with Morningstar ratings and analyst research. The advisor picks a fund, asks the assistant to run a scenario analysis showing the impact of adding it to the portfolio. Three requests, maybe five minutes, and the advisor never left the conversation.

Learn more about the AI assistant in Direct Advisory Suite.