AI research

Using AI tools to automate the gathering and summarisation of prospect, company, or market information for sales and marketing use.

What is AI research?

AI research refers to using AI tools to automate the gathering, synthesis, and summarisation of information about prospects, companies, competitors, or markets for sales and marketing purposes. The goal is to reduce the time a human spends on information gathering while improving the depth and consistency of the output.

In practice, AI research typically involves feeding a model a set of inputs, such as a company URL, LinkedIn profile, or domain name, and receiving a structured summary of relevant information. The model either pulls from its training knowledge or, more usefully, is connected to retrieval tools that pull live data from web searches, LinkedIn, news feeds, or company databases.

The quality of AI research depends heavily on the quality of the source data and the specificity of the instruction. A model asked to "research this company" will produce generic outputs. A model asked to "identify the top three operational challenges a Head of Operations at a 200-person logistics company would have based on recent news and the job postings on their website" will produce specific, actionable insights.

AI research is most valuable when it replaces a repetitive research task that a human performs consistently, such as pre-meeting briefings, weekly competitor monitoring, or account prioritisation updates. It is least valuable when it replaces the judgment-intensive part of research, where a human reads context and determines what matters. The summarisation is automatable; the interpretation often requires human review.

Accuracy is the critical constraint. AI models hallucinate. They may fabricate funding dates, misidentify leadership, or state facts about a company that are out of date or simply wrong. Any AI research workflow used in customer-facing materials or to make consequential sales decisions requires a verification step, particularly for specific facts like executive names, revenue figures, and recent events.

What separates a useful AI term from AI theater is whether it reduces manual work without creating new accuracy or compliance risk. The strongest teams define exactly where the model is allowed to help, what still needs human review, and which failure modes are unacceptable before they automate anything. It usually becomes more useful when it is defined alongside Knowledge base, RAG, and Guardrails.

AI research — example

An account executive team spends an average of 25 minutes per account preparing for discovery calls. The preparation includes reviewing recent company news, understanding the leadership team, checking for relevant job postings, and reading any prior CRM notes.

After deploying an AI research workflow, the team provides a company domain and the call date and receives a two-page structured brief covering recent news, inferred priorities from job postings, leadership names, and suggested discovery questions. Average preparation time drops to 8 minutes, mostly spent reviewing the brief rather than gathering information. Call quality improves because reps spend more time preparing their approach and less time on data gathering.

A revenue team pilots AI research in one part of the funnel where the output format is predictable. That gives them room to measure quality, refine prompts, and decide where human review should stay in the loop before more automation is added. They also make sure it connects cleanly to Knowledge base and RAG so the definition is not trapped inside one team.

Frequently asked questions

How do I prevent my AI research tool from presenting hallucinated facts as real?

Require citations. Any fact the AI includes should reference a specific source it retrieved, not knowledge from training. Instruct the model explicitly to mark any claim it cannot source with a confidence flag. For critical facts like leadership names and funding amounts, implement a human verification step before those facts appear in CRM records or customer communications.

What company data sources work best with AI research tools?

Web search for recent news, LinkedIn for role and company size data, company websites for stated priorities and job postings, and public funding databases like Crunchbase for investment history. Job postings are particularly underused as a signal. The types and volume of open roles tell you a great deal about a company's current focus and challenges.

Can I use AI research to monitor competitor activity?

Yes, and this is one of the more reliable applications. Define a set of triggers to monitor: new product announcements, leadership changes, pricing page updates, job postings in specific functions. Run AI research on a weekly cadence against each competitor and produce a structured change log. The model summarises what changed; a human decides what to act on.

How specific should my AI research prompts be?

Very specific. Research prompts that name the exact information you want, the reason you want it, and the format you need produce dramatically better results than open-ended prompts. 'Summarise this company' produces noise. 'List three signals from their website and recent job postings that suggest they are scaling their sales team' produces signal.

What is a realistic accuracy expectation for AI research at scale?

For basic factual data like company size, industry, and location sourced from structured databases, expect 90% to 95% accuracy. For inferred information like likely pain points, strategic priorities, or buying signals generated from unstructured text, expect 70% to 85% accuracy with review. Always audit a random sample of 10% of outputs weekly to catch systematic errors.