How to Use ChatGPT's Deep Research Feature for Business Analysis

Standard ChatGPT generates responses from its training data, with web browsing as an optional supplement for recent information. Deep Research conducts autonomous multi-step research: it generates a research plan, runs multiple web searches, reads multiple sources, synthesises the findings, and produces a cited report.

The difference is systematic thoroughness. A standard prompt asking about a market will produce a plausible overview from training data. Deep Research will search for recent market reports, read analyst commentary, find relevant case studies, and produce a synthesis that draws on current sources and cites them.

The output is a long, structured research report, typically 1,500-3,000 words with numbered citations. It is significantly more thorough than standard ChatGPT output and substantially faster to produce than commissioning equivalent research from a human analyst.

Deep Research delivers most value for complex background research where thoroughness matters more than speed.

Market entry analysis: 'Research the UK market for [product/service category]: market size, key competitors, regulatory environment, and the main challenges for a new entrant.' Deep Research will synthesise information from industry reports, regulatory publications, and market commentaries into a structured overview.

Regulatory landscape briefings: 'What are the current and forthcoming UK regulatory requirements for [regulated activity] that would affect a [type of organisation]?' A Deep Research report on a specific regulatory area can orient a senior leader before a deeper expert conversation.

Competitor strategy synthesis: 'Conduct a thorough analysis of [Competitor Company]'s strategy based on public sources: annual reports, management presentations, press releases, and industry commentary.'

M&A target backgrounds: 'Research [Target Company]: business model, financial performance, strategic direction, competitive position, and any publicly known risks or concerns.'

Deep Research reports require the same verification approach as other AI outputs, with additional considerations.

Check the citations: Deep Research produces numbered citations that you can review. Click through to the source and verify that the source says what the report claims it says. AI can mischaracterise sources, particularly when paraphrasing complex arguments.

Assess source quality: Deep Research draws on publicly available web content, which varies greatly in quality. Industry association reports, FCA publications, and FTSE 350 annual reports are high-quality sources. Vendor marketing content, unattributed blog posts, and anonymous forum posts are not. The report mixes these freely; it is your job to distinguish them.

Look for the date of sources: regulatory environments and market dynamics change. A source from 2021 may describe a regulatory landscape that has materially changed. Note source dates and flag where the currency of information is uncertain.

Deep Research is not a substitute for expert analysis or professional advice. It is a tool for building background understanding and identifying the questions worth asking of experts.

Deep Research cannot access paywalled sources, proprietary databases, or non-public information. Its synthesis of publicly available information will miss insights available only in specialist professional databases.

For decisions of material consequence (significant investments, regulatory compliance, market entry), Deep Research provides useful background orientation but should be supplemented by expert advice, primary research, and due diligence appropriate to the decision stakes.

The appropriate mental model: Deep Research produces the quality of background research that a well-briefed executive assistant might produce after a day of research. Useful for orientation and question generation; not sufficient for consequential decision-making without further verification and expert input.