Data sourced from Dakota Marketplace, the global LP and GP intelligence platform trusted by thousands of investment professionals. Learn More | Book a Demo
Ninety-five percent of fund managers now use generative AI in their work, up from 86% in 2023 (AIMA, "Front-office Gen AI adoption shifts from 'if' to 'when' for leading fund managers," September 2025). Adoption isn't the question anymore. Whether the answers AI gives you about allocators are actually correct is.
They often aren't, at least not by default. Ninety-three percent of finance professionals have concerns about the accuracy of AI-generated outputs, pointing to hallucinations, inaccuracies, and incomplete datasets as the main drivers (ACCA and Chartered Accountants Australia and New Zealand, 2026 survey of 1,600 finance professionals). For institutional investor research specifically, that gap has a cost: an AUM figure that's two years stale, a decision-maker who's already left the role, or a fund that closed months ago can cost you an introduction.
This post covers how to use AI to research allocators well: what it's good at, where general-purpose tools break down on institutional data, and how Dakota Marketplace's own AI connectors close that gap.
Fund Managers Have Already Moved Past Experimentation
Fifty-eight percent of fund managers expect increased Gen AI use in the investment process over the next year, up from just 20% in 2023 (AIMA, September 2025). At the firm level, 70% of buy-side firms now use AI to support the front office, up from roughly 10% the year before, according to a survey of 200 executives at asset managers, pension funds, and insurers with $10bn or more in AUM (SimCorp/WBR Insights 2026 InvestOps Report, January 2026).
That adoption hasn't come with matching confidence. Beyond the accuracy concerns already noted, 29% of finance professionals cite data security as the top barrier to firm-wide AI deployment, and 21% point to integration challenges with existing systems, based on a survey of 529 finance professionals (Hebbia, "AI Trends in Financial Services," December 2025).
Adoption outpaced governance. Most firms started using AI at the individual level before building the verification habits or data connections needed to make it reliable for anything customer-facing, allocator outreach included.
Where General AI Tools Get Institutional Data Wrong
General-purpose AI models, used on their own, are trained on public information available up to a fixed point in time. They have no live connection to a firm's current AUM, its most recent close, or who holds a given title today. Ask a general model who leads a specific allocation team and it will answer confidently, whether or not that person still holds the role.
This isn't a flaw specific to one AI tool. It's a structural fact of how large language models work: without a live, verified data source behind them, they can only draw on what's public and how recent their training happened to be. Allocator data doesn't sit still. People change roles, funds close and reopen, AUM shifts quarter to quarter.
How to Prompt AI for Allocator Research
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Be specific about the segment, not just the allocator type. Ask for family offices with $500M+ in AUM investing in private credit in the Southeast, not "family offices that invest in credit."
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Ask for the source and date behind any figure. If the AI can't tell you where an AUM number or contact detail came from, treat it as unverified.
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Use AI to synthesize, not to originate. It's strong at summarizing recent fundraising news, drafting outreach, and prepping talking points from information you supply. It's weak as the sole source of a hard number.
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Re-verify names and titles separately. Personnel changes are the fastest-moving data point in allocator research and the most likely to be stale in a general-purpose tool.
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Treat "I don't have current data on that" as the correct answer, not a failure. A tool that admits a gap is more useful than one that fills it with a guess.
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Ground your queries in a live, licensed data source whenever the research will inform outreach. General web-trained models are a starting point. A connection to continuously updated data closes the gap.
How Dakota Marketplace Connects AI to Verified Data
Dakota Marketplace connects directly to both ChatGPT and Claude, so the habits above happen automatically instead of needing a manual verification step. Both connectors pull only from Dakota's records, current AUM, current contacts, current fund status, and say so when something isn't available rather than filling the gap with a guess. Each user's access is scoped to their own Marketplace subscription, so the answers reflect exactly the data they're entitled to see.
Ask for family offices by AUM and geography, and the connector filters Marketplace records directly. Ask who the current decision-maker is at a specific plan, and the answer comes from data updated daily, not a model's training cutoff. Ask for recent fundraising activity ahead of a call, and it's sourced from Marketplace's tracked commitments, not the open web.
Can ChatGPT Access Institutional Investor Data? covers how the ChatGPT connector works in more detail, and Ask Claude to Find Your Next Allocator: What the Dakota Marketplace + Claude Integration Actually Does walks through the same workflow inside Claude. Both connect using Model Context Protocol, the standard that lets AI assistants query a live data source directly instead of relying on training data alone.
Setup takes about five minutes on either platform, and works across web, desktop, and mobile.
AI can speed up allocator research. It can't replace verification, unless it's connected to a source that's already verified. Dakota Marketplace's ChatGPT and Claude connectors pull directly from data updated daily, so the research you get back doesn't need a second pass. Book a demo to see it inside your own workflow.
