How tacit knowledge, firsthand experience and expert interviews fill the gaps in AI-powered research.

AI has solved one of research's biggest problems: finding information. But it may be exposing another: what happens when the information you need doesn't exist?

AI can search, summarize, compare and analyze vast amounts of information in seconds. What once required hours of research can now happen in minutes. But a fast answer is not necessarily a complete one. And when the information needed to answer a question properly was never there in the first place, another search won't necessarily help.

Some insights simply aren’t in the data. They may be too new, too specialized or too specific to appear in published research or market information. The biggest risk, then, isn't necessarily that AI can't find the answer. It's that it can produce a convincing answer from incomplete information. Sometimes, the missing piece isn't another search. It's speaking to someone who knows.

What can AI not find? 

Not all knowledge is documented. The knowledge that matters most may be the knowledge that has never been written down. Some knowledge is created through experience. It comes from making decisions, solving problems, working in a particular market and seeing what happens in practice. It develops over time and often sits with the people who have lived through a situation rather than in a database or document. This is often referred to as tacit knowledge.

What is tacit knowledge?

Tacit knowledge is knowledge gained through personal experience that is difficult to fully capture in documents, databases or published research. It includes practical judgment, patterns, context and lessons learned from making decisions in real-world situations.

Consider a business trying to understand why customers are leaving a particular market. It may have survey results, customer data and competitor research pointing toward a likely explanation. AI can bring those sources together, identify patterns and summarize what appears to be happening. But what if the research reflects what customers say rather than what is actually driving their decisions? What if the market has changed since the research was conducted? Or what if the factor that would change the decision has never been captured at all?

The missing knowledge may exist only with someone who has experienced the market firsthand. Someone who has spent years working with those customers may recognize a pattern that is not yet visible in the data. They can reveal the reasons behind what we are seeing, not simply confirm that it is happening.

They may also recognize the early signs of a change because they have seen something similar before. That is knowledge that cannot simply be retrieved. Sometimes, you have to ask the person who knows.

Why does human expertise matter?

Human expertise is not a replacement for research or AI. Its value is in helping us understand what the information actually means and what it might be missing.

Return to the customer churn example. Imagine speaking with someone who has spent years operating in that market. They explain that the reason customers give for leaving is only part of the story. They point to another factor influencing decisions and explain why it has not appeared in the existing research.

The data has not changed. Your understanding of it has.

That conversation may challenge an assumption, provide context or reveal a question that was not obvious from the original research. This distinction matters because good decisions rarely depend on having more information alone. They depend on knowing which information matters, what is missing and how to interpret what is already there. Experience can provide that perspective.

When should you speak to an expert?

The harder question is becoming: Do we have the right knowledge to make this decision with confidence?

Sometimes, the answer will be yes. Existing research will be sufficient, and AI will help us get to it faster. But when the information is incomplete, outdated or simply does not exist, the next search may not be the answer. The answer may be a conversation with someone who has seen the situation firsthand.

AI can tell us what the world has already said. Human expertise can help us understand what it hasn't. And in a world full of answers, the most valuable insight may be knowing what everyone else has missed.

Frequently asked questions:

1. How can expert networks help fill research gaps?
   Expert networks can help organizations find and speak with people who have relevant firsthand experience. Rather than relying solely on published information, researchers can use expert interviews to test assumptions, explore emerging developments and understand the context behind the data. This makes expert research complementary to AI and desk research rather than a replacement for either. AI can help identify what is already known. Experts can help investigate what is missing.
2. Can AI replace expert interviews?
   Not always. AI can efficiently analyze existing information, but expert interviews provide firsthand experience, contextual understanding and tacit knowledge that may not exist in published sources.
3. Why is human expertise important in market research?
   Human expertise can help explain why something is happening, challenge assumptions and identify information that existing research has overlooked.
4. What is an expert network?
   An expert network connects organizations with professionals who have relevant industry, functional or firsthand experience, allowing them to conduct interviews and gather primary insights.
5. When should you use an expert network?
   An expert network can be useful when research is incomplete, highly specialized, outdated or difficult to access or when a decision depends on firsthand knowledge from people with relevant experience.