Wide-ranging research: can AI cover it without missing parts?

#The wider the scope, the thinner the later items

"Summarize e-payment regulation trends in three Southeast Asian countries and where the major competitors stand in each market." Whoever gets this request spends several days on it. They dig up material for each country, collect presentations and news coverage for each competitor, check it all against internal data, and pull it together into a report. The wider the scope, the longer it takes, and with a deadline closing in, the later items get shallower research.

Handing it to AI changes little if the AI looks things up one item at a time. OwlAI's deep research splits the job across a team instead. A lead AI drafts a research plan, assigns a researcher AI to each item so they all work at once, and then gathers their results into a single report.

#An AI that works through items one by one is a lone researcher

A typical AI chat can search the web and read what it finds too. But it goes one thing at a time: the first country, then the second, then the competitors.

That breaks down as the item count grows. Everything read earlier piles up and crowds what the AI can handle at once, and the time grows with every item added. It is the same wall a single person hits when researching alone.

#Research starts only after you approve the plan

In deep research, each researcher digs deep into its own item and reports back, and the lead gathers those reports into the final report.

The sequence is plan, research, report writing, and polishing. The plan shows up on screen first, and research starts only once you confirm it. If an item is missing or you want a different scope, type it into the input box and send it. The lead revises the plan, shows it again, and marks which items are new and which changed. This step keeps you from discovering a wrong turn only after a long stretch of research.

Once research is underway, you can see at a glance which items are in progress, which are waiting on an earlier item's results, and which are done. An item that depends on an earlier one picks up as soon as that result arrives. The detail view shows which steps each researcher went through.

#No email goes out in the middle of research

Researchers look beyond the web, into the company databases and documents you have permission to use. That is how one report can combine market data with your internal performance figures.

Researchers are limited to tools that need no approval. Actions that require a person's approval, such as sending email, do not run during research.

#One stuck item does not stop the report

When research finishes, the lead writes the report, polishes the text, and delivers it as the answer. The material behind the report is linked under Sources below the answer, so you can click through to the original right away. How to check those sources is covered in “Numbers for a report: can you trust the sources AI cites?”.

Even if one item gets stuck, the report still comes out. The stuck item is flagged on the progress screen, so you can have just that part researched again with a narrower scope. Once the report arrives, you can keep asking follow-up questions as in a normal chat.

#Which research to hand to deep research

For market research, split the items by region or customer segment and research them at once. For competitor analysis, assign a researcher to each company and have them build a comparison table on the same criteria. For regulatory trends, split the work by country and by law to summarize recent changes. In every case, your people concentrate on reviewing the plan and judging the results, while the AI research team divides up the work of finding and gathering material. Without internet access, the team researches internal databases and documents instead of the web.

Deep research takes more time and usage than a normal question. Use normal chat for quick fact checks, and save deep research for investigations that have to range across many threads.