Deep Research: When AI spends 20 minutes working for you
Most professionals use generative AI reflexively: a question, an answer in ten seconds, then they move on. This mode suits the vast majority of uses. But it overlooks a completely different kind of capability: Deep Research. Here, AI doesn't answer, it investigates. For five to thirty minutes, it consults dozens of sources, cross-references them, revisits its hypotheses, and delivers a structured and sourced report. This is the difference between asking a colleague a question in the hallway and entrusting a research project to an analyst.
What happens during those twenty minutes
Unlike a typical, all-out response, in-depth research works iteratively. AI begins by breaking down the question into sub-questions, runs searches, analyzes the results, identifies contradictions and gaps, and then runs new searches to fill them in. It proceeds much like a skilled analyst: broaden first, verify second, and synthesize finally.
The deliverable is also completely different: several structured pages, with cited and accessible sources. This sourcing changes the document's status. A chat response should be used with caution; a report where every statement refers to its source is verified, discussed, and shared.
When it's worth it, when it's not worth it
In-depth research comes at a cost: waiting time and, for most services, a monthly quota. It is justified when three conditions are met: the question is open, the answer depends on scattered and recent information, and the issue warrants more than a hunch.
The best use cases we see with our clients include: analyzing a market or competitor before an investment decision, reviewing the state of the art on evolving regulations, providing an overview of solutions before choosing a tool, preparing a request for proposals, and conducting structured monitoring on a strategic topic. In all these cases, the real alternative isn't a quick chat response; it's a full day's work for a consultant or research analyst.
Conversely, there's no point in spending thirty minutes researching a definition, a reformulation, or a question whose answer is already established and known. And be wary of topics where online sources are scarce or biased: thorough research synthesizes what already exists; it doesn't create information where none exists.
The art of launching a successful mission
The quality of the report depends on the brief, just as with a human analyst. Three elements make the difference.
The perimeter. "Studying the AI training market in France" will produce a general overview. "Comparing AI training offerings for French industrial SMEs, focusing on formats, public prices, and quality guarantees, with a preference for sources less than a year old" will produce a usable document.
The intended use. Stating the purpose of the report (preparing a committee, informing a purchasing decision, providing material for an article) guides the level of detail and structure.
Exclusions. Specifying what we already know and what we don't want to see addressed avoids paying twenty minutes for ten pages, half of which is already known.
Getting started: your first mission, step by step
The feature now exists in all major assistants under similar names: Deep Research in Claude and Gemini, Deep Research in ChatGPT and Mistral, and Researcher in Copilot. It is activated with a single click when sending the request, and each offer comes with a monthly quota: check yours before making it a team habit.
For your first attempt, deliberately choose a topic you already know very well. It's counterintuitive, but it's the best calibration exercise: by reviewing the report, you'll immediately see what the tool finds, what it misses, and where it simplifies. You'll then know how much confidence to place in reports dealing with topics you're not familiar with.
Before launching, have the AI itself draft the brief: explain your needs in a normal conversation, ask it what details it needs to conduct a thorough search, answer its questions, and then have it rewrite the entire brief. Two minutes that improve the mission more than any wording trick. A complete brief is five lines long:
- the precise question and its scope: sector, geography, period;
- the use of the report and its recipient;
- the expected format: length, comparative table, final recommendation or not;
- the types of sources to favor and to avoid;
- something you already know and don't want to see retired.
One last habit to adopt from the very first assignment: require a final section on "limitations and areas of uncertainty." The tools readily generate it when requested, never spontaneously, and it's often the most useful part for critical review.
Reviewing an AI report without being overwhelmed by it
The main risk in deep research is not error, but overconfidence. An eight-page report, well-written and sourced, inspires a level of credibility that its content doesn't always warrant. Therefore, review must be active, and it has a method: first, verify the three or four claims on which the conclusion rests by opening their sources; check the date and nature of these sources (a press release is not a study); look for what's missing, because omission is more common than error; and compare everything with what you know from the field.
This review takes fifteen minutes. It's the price to pay to transform a plausible document into a reliable one, and it's exactly the same supervisory work we described regarding agents: AI shifts the effort, it doesn't eliminate it.
A new distribution of intellectual labor
When used effectively, in-depth research redistributes roles. Data collection and initial synthesis, time-consuming and offering little differentiation, are automated. Framing the research question, critiquing the results, and making the final decision remain human tasks, saving the time freed up by automated data collection. The professionals who benefit most are not those who ask the most questions of AI; they are those who have learned to delegate tasks and review findings like research directors.
To learn more: discover how skills and the context engineering complement this new way of working with AI.