Your research body is the true asset. The AI layer is interchangeable. VerityRMS GM Will Keuper shares perspective on applying AI to proprietary research.
A recent survey of 200 buy-side operations leaders found that 55% now cite competitive differentiation as their top strategic priority for AI in 2026, overtaking operational efficiency, which has held the crown for the previous three years. That’s the right instinct. But differentiation through AI only works if your AI is working with something differentiated. And that means your firm’s own research. Everyone else is using the same tools, models, and data.
Over the past few years, buy-side firms have had no shortage of distractions when it comes to AI: which tools to buy, which workflows to automate, which vendors to consolidate. These are all reasonable things to think about. But there’s a question that tends to get skipped in the rush.
What’s the highest-impact thing we can feed it?
For a fundamental investor, market data is shared; every firm has the same feeds. Third-party research is shared too; your competitors are reading the same sell-side. The input that is genuinely yours is the research your own team produces every day: thesis notes, earnings analysis, model versions, conviction calls, and the reasoning behind them. The judgment and wisdom your analysts have built up over years covering the companies in your portfolio. An intelligent research body makes your analysts sharper and your PMs better informed. It’s also the foundation that turns generically capable AI into your differentiator.
Most firms have the top input covered. The bottom one, firm context, is often what’s missing. Without it, every firm’s AI is working from the same picture.
Research accumulates and scatters simultaneously, more importantly it stays inert. Notes get written and never thought of again. Model data is static, not compared across coverage or versions. A meeting that impacts the thesis sits in a shared notebook. The knowledge exists, but it can’t be put to work by your team or your AI.
Some firms may think pointing AI at drives and notebooks solves this. Those tools hold documents. They don’t hold the reasoning behind decisions, the moment thinking shifted, or the connective tissue between today’s note and last year’s decision. They have only a distant connection to the portfolios the research is supposed to inform. And nothing prompts capture at the right moments: no earnings alert, no sentiment shift, no notification that a name you own is under-researched compared to its weight.
When research isn’t captured properly, the result is a record of what your firm wrote, with little your firm can use. There’s also the question of trust: knowing that AI is querying the right research, with the right context, with the right permissions. Not everything that ever landed in a shared folder. AI grounded on a mediocre corpus is worse than no AI. This problem is solved at capture, well before retrieval.
One line has come up repeatedly in conversations with CTOs and heads of research, something we explored in detail last December:
“We know AI won’t replace our research process, but our research systems can’t limit us from staying ahead.”
The synthesis those systems can’t yet deliver is the kind that tells you everything your team has believed about a company over three years, how conviction has shifted, how a current position connects to a thesis written two years ago. That requires research intelligence, captured properly at creation, structured, and connected to the securities and portfolios it relates to.
Some people hear “RMS” and think “filing cabinet.” Most of the time, they are right. VerityRMS is the exception: research that compounds, connects, and feeds every tool your team runs.
When the research foundation is in place, the nature of what your team can do changes, with or without AI. A PM preparing for an earnings call gets a synthesis of everything her team has written on that company over three years, her own firm’s united view in place of this morning’s sell-side. A coverage gap surfaces before it becomes a problem. When a position is being reconsidered, the conviction trail is there: what the team believed, when, and why it shifted. New analysts get up to speed faster because the firm’s thinking is actually discoverable. The research that was always being produced starts doing work it never could before.
Add AI to that foundation and the value multiplies. Queries return answers grounded in your firm’s own thinking. Patterns surface across years of research that no individual analyst could hold in their head. The research body that made your team better now makes your AI better too. That’s where the differentiation becomes durable.
The research body also needs to be connectable: accessible to the AI tools and workflows your team is already using, and portable beyond any single platform. We built MCP support into VerityRMS for this very reason. So your knowledge context can be integrated with any MCP-compatible agent, including the ones you don’t use yet. Your research stays yours, stays intelligent, and it stays portable as your stack evolves.
None of this requires a multi-year transformation. It requires one decision: research that compounds at the point of creation.
Larger firms usually assume they’ll build their own AI infrastructure, and many already are. The firms doing this well are building on a research foundation. The model is interchangeable. The research foundation underneath it lasts. Your research body is the true asset. The AI layer is interchangeable.
The firms that will look back on 2025 and 2026 as the years they built a genuine AI advantage are the ones that recognized something simple: They built the foundation around their firm’s edge, and the differentiation followed.
All firms accumulate research. The best fully activate it. VerityRMS captures everything your analysts produce, activates it as a live asset that makes the next decision better, and connects it to the workflows, data, and AI tools your firm already runs.
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