The Future of Primary Research: Why AI Agents Are Replacing Manual Expert Interviews

The expert network industry has grown into a $4 billion market. But the way institutional investors conduct primary research is about to change fundamentally.

The Scale of Expert-Driven Research

Expert networks have become essential infrastructure for investment decision-making. The global market reached approximately $4.19 billion in 2025 and is projected to grow at a 16% CAGR through 2034. In the U.S. alone, expert networks represent a $1.8 billion industry, accounting for 55% of worldwide revenue.

The numbers tell a clear story: 65% of corporations now rely on expert networks for strategic decision-making, with over 45 million expert consultations conducted globally in 2025. For hedge funds and asset managers, primary research through expert calls has become non-negotiable.

Yet despite this growth, the underlying model hasn't changed in decades.

The Hidden Costs of Manual Expert Calls

Traditional expert network workflows create friction at every step. Analysts spend hours scheduling calls across time zones, preparing questions, conducting 60-minute conversations, and synthesizing notes into actionable insights. Multiply this by the dozens of calls required for thorough channel checks or competitive landscape analysis, and the operational burden becomes significant.

The challenges compound:

• Scheduling overhead: Coordinating availability between busy analysts and harder-to-reach experts
• Inconsistent quality: Call outcomes vary based on interviewer skill and expert engagement
• Knowledge silos: Insights trapped in individual notes rather than searchable institutional knowledge
• Compliance complexity: Tracking expert interactions, managing conflicts, maintaining audit trails

These aren't new problems. But the solution set has expanded dramatically.

AI Adoption Is Accelerating Across Finance

The investment industry's relationship with AI has shifted from experimentation to implementation. According to AIMA research, 95% of fund managers now use generative AI in their work—up from 86% in 2023. Over 70% of global hedge funds employ machine learning models somewhere in their trading pipeline.

This isn't limited to quantitative strategies. 91% of asset managers are currently using or planning to use AI within investment research. The question has moved from "if" to "when"—and increasingly, "how fast."

Perhaps most telling: 35% of new fund launches in 2025 branded themselves as AI-driven or AI-enhanced. The competitive landscape is shifting.

Where AI Agents Create Value in Primary Research

AI agents—autonomous systems that can conduct structured interviews, synthesize responses, and surface insights—address the core inefficiencies of traditional expert research:

Scalability without proportional cost: An AI agent can conduct dozens of expert interviews simultaneously. For channel checks requiring broad coverage across customers, suppliers, or competitors, this changes the economics entirely.

Consistency and structure: Every interview follows the same methodology, asks follow-up questions based on responses, and captures information in standardized formats. No more variance based on which analyst happened to take the call.

Institutional memory: Insights flow directly into searchable knowledge bases. When a new analyst needs background on an industry vertical, prior expert conversations are immediately accessible.

Compliance by design: Every interaction is automatically transcribed, timestamped, and logged. Audit trails are built-in rather than bolted on.

What's Working Today

The technology has matured beyond proof-of-concept. AI agents can now:

• Conduct natural voice conversations with industry experts over phone or web
• Adapt questioning based on expert responses and areas of expertise
• Generate structured summaries with key takeaways and notable quotes
• Flag contradictions or consensus patterns across multiple expert inputs
• Integrate findings with existing research workflows and CRM systems

The 35-85% productivity gains reported in adjacent use cases like due diligence are now achievable in primary research workflows.

What's Still Developing

Intellectual honesty about limitations matters. AI agents excel at structured information gathering but aren't replacing judgment-intensive work:

• Relationship development: Building long-term expert relationships that yield differentiated insights over time
• Reading between the lines: Detecting hesitation, emphasis, or what's left unsaid during sensitive conversations
• Creative exploration: Pursuing unexpected threads that emerge from serendipitous conversation

The most effective implementations augment human researchers rather than replace them—handling the volume work so analysts can focus on the highest-value conversations and synthesis.

The Competitive Landscape

Adoption patterns are emerging along familiar lines:

Large asset managers are building internal capabilities, integrating AI agents into existing research platforms and compliance infrastructure.

Mid-market funds are partnering with specialized providers, seeking faster time-to-value without the build cost.

Emerging managers see an opportunity to punch above their weight—accessing research depth previously available only to larger competitors.

The playing field is leveling. Primary research coverage that once required teams of analysts can now be achieved with smaller, AI-augmented teams.

Looking Forward

The trajectory is clear. As AI agents become more capable at nuanced conversation and insight synthesis, the volume of expert interactions any investment team can process will increase by an order of magnitude.

This doesn't diminish the value of human expertise—it amplifies it. Analysts freed from scheduling and note-taking can focus on building conviction, identifying patterns across dozens of expert inputs, and making better investment decisions.

The future of primary research isn't AI replacing experts or analysts. It's AI enabling both to operate at unprecedented scale.

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InsightAgent helps investment teams conduct and analyze expert interviews with AI-powered agents. Learn more about our platform.