The research capabilities of AI tools just got a serious upgrade. If you’ve been following recent developments, you might have heard about OpenAI’s new Deep Research tool. Unlike earlier AI models that could only access information they were trained on (with cutoff dates that left them in the past), these new research tools can search the web, find relevant information, and synthesize it into comprehensive reports.

What makes this particularly exciting is how these tools work. Rather than just doing a single search and generating a response based on whatever comes up first, Deep Research iteratively builds knowledge – much like a human researcher would.

It searches, reads what it finds, learns from it, then searches again with better understanding. This process allows it to “go down rabbit holes” and discover information that wouldn’t appear in initial search results.

Tasks that previously required hours or even days of research can now be completed in minutes. A retired architect quoted in a recent article estimated that a building code checklist generated by Deep Research in 28 minutes would have taken 6-8 hours to prepare manually. An antitrust lawyer said an 8,000-word report would have taken 15-20 hours for a human researcher.

So how can you start using these powerful research tools in your own business? Here’s a practical guide to get you started.

Getting the Most From AI Research Tools

The quality of results you get from any AI tool depends significantly on how you ask your questions. With research-capable AI, this becomes even more important since you’re essentially directing its search and synthesis process.

When formulating your research request, be specific about:

1. The exact information you need
2. The depth of research required
3. The format you want for the final output
4. Any specific sources you want it to consult (or avoid)

For example, instead of asking “Tell me about email marketing,” try something like:

“Research current best practices for email marketing automation for small e-commerce businesses in 2025, including recommended tools under $100/month. Include specific open rate benchmarks by industry. Format as a structured report with recommendations.“

The more specific guidance you provide, the more targeted and useful the research will be.

Validating AI Research

While these tools are impressive, they’re not infallible. It’s still important to verify critical information, especially for consequential business decisions. Here are some practical approaches to validation:

• Ask the AI to include its sources in the report. Deep Research can provide links to the pages it consulted, making it easier for you to verify key points.
• For especially important facts or claims, ask follow-up questions like: “Can you provide additional sources to verify the claim that [specific point]?”
• Cross-check surprising or counterintuitive findings with a second research request framed slightly differently.

Ethan Mollick, a well-known expert in AI and innovation, offers an essential caution when using AI research tools:

“When using an AI Deep Research tool for the first time, you need to review the output with a critical eye: follow every link to make sure things are really cited, read every line for hallucination, etc.

You aren’t going to keep that attention to detail long, so get an idea right away. You need to do the work, and you need to decide how much to trust the tool at the start. I see people taken in by good-looking output all the time. Start by being skeptical, then you can embrace it.

Don’t trust the assessment of anyone else – AI can be great in one area and bad in another.

And for goodness’ sake, start by testing it in areas where you are an expert. Don’t ask it to predict the future (it isn’t made for that) but ask it for something analytical and research-driven that you know, where you can judge its opinion and the quality of the sources it found.”

The key takeaway? AI is an incredible research assistant, but it requires human oversight. Start by using it in domains where you have expertise so you can judge its reliability before relying on it for critical decisions.

Practical Applications Across Industries

These new research capabilities open up possibilities across virtually every industry. Here are just a few examples of how businesses are using advanced AI research:

• Service-based businesses are creating comprehensive competitive analyses of their local markets to identify underserved niches and opportunities for differentiation.
• E-commerce stores are researching product trends, analyzing competing products, and generating detailed product descriptions backed by current market research.
• Consultants and coaches are quickly generating industry-specific research reports that would previously have taken days to compile, allowing them to provide more value to clients with less preparation time.
• Content creators are using AI research to develop deep, well-researched content without spending hours on background research.
• Real estate professionals are generating comprehensive market analyses and property reports enriched with current local data.

Imagine using Deep Research to prepare for a sales call with a prospect in an industry you aren’t familiar with. In just 10 minutes, you get a comprehensive overview of the industry’s current challenges, typical organizational structure, common pain points, and relevant case studies. Your prospect will be impressed by your seemingly deep knowledge of their specific business context, all thanks to targeted AI research.

Integrating AI Research Into Your Workflow

To make the most of these tools, consider establishing a consistent workflow:

1. Identify research tasks that are currently taking up significant time in your business. These are prime candidates for AI assistance.
2. Draft clear, specific research requests for these tasks, being as detailed as possible about what you need.
3. Review and validate the AI’s output, focusing on accuracy and completeness rather than rewriting everything.
4. Incorporate the verified research into your work product, adding your unique insights and expertise.

Many professionals worry that AI will make their expertise obsolete, but the reality is quite different. These tools don’t replace your judgment, creativity, or personal experience – they simply give you a research department that works at superhuman speed.

The professionals who will thrive in this new environment are those who learn to collaborate effectively with AI research tools, directing them intelligently and applying critical thinking to their output.

The difference between an amateur and an expert prompt engineer is often just practice. Start small, experiment with different types of research requests, and you’ll quickly develop an intuitive sense for how to get the results you need.

AI research tools are still evolving rapidly. The current iterations already demonstrate extraordinary capabilities, but they’re just the beginning. As models improve and search capabilities become more sophisticated, we can expect even more powerful research assistance in the near future.