When you’re short on time and need reliable, in-depth insights for an article or client presentation, spending hours digging through pages of search results just isn’t practical.

But what if you had a team of dedicated research assistants available to trawl through reams of information for you? What if they extracted the key points and delivered a comprehensive, well-structured report full of citations so you could check their sources?

Welcome to the world of deep research AI.

Powered by advanced AI technologies—reasoners that think step-by-step, agents that pursue specific research goals, and large language models that understand context—that’s what these tools deliver. It’s something different from what came before: not just data, not just information, but actionable intelligence.

What Is Deep Research?

The defining feature of deep research tools is their ability to transform raw information into structured intelligence.

Unlike basic AI assistants that provide simple summaries and, perhaps, a few links in response to your questions, these tools can handle complex, multi-step research tasks that mirror what skilled human researchers do. They:

Work autonomously: These tools work independently to dig down into a subject and pull together information without you having to tell them what to do step by step.
Process large volumes of data: They can extract insights from diverse sources and formats, including text, images, and PDFs.
Deliver structured outputs: The AI organises its conclusions into clear and well-structured reports, often resembling a research paper or a detailed analysis.
Provide clear citations: Like contextual AI, these tools generate reports that reference where the information came from. This makes the reports more credible and easier to verify, addressing concerns about the accuracy of AI-generated content.

It’s like having a diligent research analyst working on demand and at high speed.

How Deep Research Tools Work

Deep research tools are trained using advanced reasoning methods, allowing them to consider your requirements, construct a research plan, and understand information from a wide variety of online sources without constant guidance.

These tools differ from the familiar generative AI chatbots. Traditional generative AI (like the basic version of ChatGPT) generates answers based on patterns in its training data. It might give a fluent answer, but it does not verify facts and, if it isn’t search enabled, it doesn’t fetch new information.

In contrast, deep research tools combine a generative AI’s language abilities with real-time information retrieval and step-by-step reasoning.

Under the hood, deep research tools combine several AI technologies:

Reasoners: While earlier AI models simply predicted text, “reasoner” models actually perform a chain of thought. They break a query into sub-tasks and think through problems step by step.
Agents: Deep research systems also act as agents. They can search the web autonomously, follow links, and decide what to read or ignore in pursuit of your answer. This allows them to handle multi-step research tasks on their own, instead of replying with whatever is immediately in memory.
Large Language Models (LLMs): This is the foundation that allows these tools to understand and organise information from various sources. Models like GPT-4, Gemini 1.5, and Claude 3 are trained on vast datasets and can understand and generate human-like text. They are used to summarise, answer questions, and synthesise information.
Retrieval Augmented Generation (RAG): This allows them to fetch real-time information rather than relying solely on what they were trained on.

The way they operate involves an iterative process, similar to how human researchers work by gradually asking questions, reading, and refining their research path based on the information they find.

When you ask a question or define a research topic, the typical workflow includes:

Query Analysis: Breaking down complex queries into manageable components.
Search Planning: Creating a structured plan for information gathering.
Autonomous Browsing: Conducting dozens of web searches across various sources.
Source Evaluation: Assessing the credibility and relevance of sources.
Synthesis: Combining and analysing findings using probabilistic reasoning models.
Report Generation: Creating a structured report with proper citations.

This yields more thorough and factual results. A deep research tool doesn’t just spit out a paragraph from memory. It takes time–often between 5 to 30 minutes–to process a request, really dig into a subject, and synthesise a full briefing for you.

Leading Deep Research Tools

As of March 2025, three major AI platforms offer deep research capabilities:

OpenAI Deep Research

OpenAI's deep research feature, powered by the upcoming o3 reasoning model, is regarded as the most advanced in reasoning and depth. OpenAI claims it synthesises the information it gathers into comprehensive research reports at an analyst level.

In testing, this tool performed exceptionally on hard questions—it scored 26.6% on a rigorous benchmark called Humanity’s Last Exam, far outpacing prior models.

What that means practically is it is better at complex, analytical tasks. If you ask a very complicated, nuanced question, ChatGPT’s deep research is likely to provide a more thorough and logically structured answer than others, potentially even drawing insightful conclusions (not just listing facts).

However, this power comes with some downsides. It can be slow—often taking between 5 to 30 minutes for a single query. You can watch its progress as it “thinks” through the task, but you need patience.

OpenAI offers varying levels of access to its deep research tool, depending on your subscription. Pro users on the $200 a month plan get 120 queries a month. Plus users get 10 queries included in their monthly $20 subscription.

Those limits mean you can’t use it for every little query unless you pay more for a higher tier. Also, because it’s so autonomous, you don’t get a say in how it conducts the research. You trust it to figure out the steps—which it usually does well, but if it misinterprets your intent, it might waste time on a tangent.

Perplexity Deep Research

Perplexity is an AI-powered answer engine that was built to integrate search with LLMs. Its normal mode already gives you concise answers with citations for every sentence, which many professionals love for quick Q&A. Its deep research mode goes further, producing a more in-depth report-style answer.

A big plus for Perplexity is speed and accessibility. It completes its multi-step research in 2 to 4 minutes, which is dramatically faster than ChatGPT’s deep research mode. It achieves this by doing a “flurry of searches” in parallel and synthesising rapidly.

Launched with an announcement that stated that “everyone should have access to powerful research tools,” Perplexity offers a freemium model. Anyone can try up to 5 deep research queries a day at no cost. The paid plan ($20/month) offers 500 queries a day, essentially removing limits for normal use. This makes it very attractive for users who need research help but don’t want an expensive subscription.

Because Perplexity prioritises speed, the analysis tends not to be as deep or reflective as ChatGPT’s. It will gather a broad base of facts and give you a straightforward summary, but is lacking with intricate analysis or creative inference. It won’t usually volunteer an opinion or hypothesis, whereas ChatGPT might give a slight analytical narrative.

However, for most purposes such as competitive intel, market trends, etc, Perplexity delivers fast, reliable answers with zero hassle. It’s effectively like a smarter, more accountable search engine that always shows its working.

Google Gemini Deep Research

Google’s entry into deep research leverages its greatest asset: the massive Google search index. Google’s deep research is excellent at ensuring comprehensive coverage—it will find even obscure references or up-to-the-minute news that others might miss. It’s like casting the widest net.

Another unique feature is the user-guided planning phase. Google’s tool shows you its proposed research plan before executing, giving you the opportunity to review and amend it before it begins.

Another strength is its integration with Google Workspace. You can save deep research results to Google Docs with one click, and they’re working on tying it into other Google Workspace apps.

However, our early results from Google’s Gemini deep research were less impressive than OpenAI’s. While it finds a lot of info, the breadth can lead to a surface-level synthesis. It might list 10 things but not deeply analyse them. Think of it as a super thorough researcher who sometimes doesn’t connect the dots as sharply.

Gemini deep research is currently free to use with limited access. For extended usage you will need to pay for a subscription. As Google packaged AI with the Business and Enterprise Workspace editions as standard, you might already have access to this tool. Other account types can purchase a Gemini Advanced add on.

Which Deep Research Tool Should You Choose?

In summary, OpenAI’s ChatGPT deep research is the go-to for depth and reasoning–if you have the time and budget. Perplexity is excellent for speed, ease, and transparency, and is often enough for most needs. Google Gemini is great for comprehensiveness and integration if you’re already in the Google world.

Many professionals will probably use a combination: for example, use Perplexity for quick daily questions, ChatGPT for the really thorny research tasks, and Gemini when you want to double-check coverage or leverage Google’s ecosystem. All three are developing rapidly and taking cues from each other, so the gap between them will probably narrow further in the coming year.

Why Deep Research Tools Matter for B2B Professional Services

In B2B professional services, information isn't just power—it's profit. Deep research tools transform how professionals gather intelligence, eliminating time wasted hunting for information and democratising access to insights once exclusive to firms with large research departments.

Here's how they deliver value:

Faster Insight Generation

B2B leaders thrive on insights—identifying emerging trends or spotting inefficiencies clients haven't noticed. Deep research AI compresses time-to-insight dramatically, sifting through noise to highlight findings in minutes that would take humans hours to discover. OpenAI claims its deep research agent "accomplishes in tens of minutes what would take a human many hours," and we've found a 5-30 minute AI research session can produce reports that would otherwise require a full day.

This speed enables truly iterative analysis—pose a broad strategic question in the morning, receive a detailed brief by lunch, and explore deeper implications that afternoon. Professionals can investigate "what if" scenarios, novel approaches, or multiple problem angles simultaneously without massive research budgets. Questions previously abandoned because of time constraints become viable explorations, while deadline-driven decisions gain depth previously impossible.

The result: hours formerly consumed by information gathering shift to analysis, creative thinking, and implementation—higher-value activities that leverage human judgment and expertise.

Competitive Intelligence on Tap

In marketing and consulting, keeping tabs on competitors and market shifts is critical—and deep research tools excel here. AI-powered research delivers richer competitive overviews faster, enabling quicker strategic responses.

Consider an accountancy firm exploring growth in a new region. A deep research tool can perform sector-specific competitor benchmarking with a simple prompt:

"Compare [Your Firm] with the top 5 accounting consultancies in Germany in terms of service offerings, client base, and unique market approaches. Highlight gaps or opportunities for [Your Firm]."

The AI compiles information from competitors' websites, press releases, interviews, and articles into a comprehensive comparison—perhaps creating a service offering table, noting competitor sector focus, and identifying white space opportunities (like sustainability consulting) that local firms aren't emphasising.

This kind of benchmarking provides strategy teams with actionable competitive insights and market opportunities. And because the AI cites sources, you can verify claims before acting on them.

Client-Specific Research and Personalisation

Deep research tools excel at gathering client-specific intelligence to help you understand a particular client’s industry or business quickly.

Imagine you’re about to pitch your consulting services to a prospective client in the retail industry. You have an initial meeting in 24 hours.

Instead of scrambling across the web, you ask a deep research tool for a briefing:

“Provide a briefing on [Client X]. Include their recent financial performance, their top competitors with key differentiators, customer pain points, any major challenges the company is facing, and notable news from the past year.”

The AI compiles a tailored report from recent articles, the client's website, analyst commentary, regulatory changes, and chatter from industry forums—synthesising everything into a comprehensive briefing.

Armed with this intelligence, you can tailor your pitch to address known frustrations and reference current events ("As you're undergoing supply chain reorganisation, we can assist by...").

This rapid research assistance gives you a flying start on pitch strategy while demonstrating impressive preparation to the client—all without requiring your team to work overnight on data gathering.

Audience Profiling and Message Refinement

In B2B marketing, understanding your target audience is key to crafting the right message. Deep research tools can help profile your audiences by aggregating information from industry surveys, forums, social media, and publications.

With a simple prompt like "What priorities and challenges are top of mind for CIOs in the healthcare sector in 2025?", the AI can pull together common themes (data security, interoperability, budget constraints) from conference reports, LinkedIn articles by healthcare CIOs, and relevant surveys. These insights allow you to align your marketing messages more precisely with your audience's actual concerns.

The tools can also critique and improve your draft messaging. Ask: "Here is our product pitch for hospital CIOs [insert text]. Based on current industry concerns, how could we improve this message?"

Having already processed substantial information about your target audience, the AI might recommend adding points about regulatory compliance or shifting to a less technical tone if the audience prioritises outcomes over technical details. This transforms the AI into a strategic sounding board for communication optimisation—ensuring your messages resonate with the audience's genuine needs and preferred language.

Thought Leadership and Content Brief Development

Professional services firms rely on thought leadership content for marketing and brand building. But, in our experience, the quality research that underpins it takes time that busy experts rarely have.

Deep research tools address this bottleneck by serving as your first-pass researcher. It can help you consider a thought leadership topic more thoroughly, suggesting angles you hadn’t considered, and follow-up questions to explore.

Suppose you are a marketing manager at a law firm and want to publish a thought leadership article on data privacy laws in different industries. You might prompt:

"Summarise recent developments in data privacy regulations for healthcare, finance, and tech sectors, with notable non-compliance penalty cases for each."

The AI compiles a structured sector-by-sector summary from legal databases, news sites, and industry blogs—complete with source citations and thematic insights across sectors.

This foundation speeds up your process from research to refinement, letting you focus on adding your firm's unique perspective rather than basic information gathering. The cited sources enable easy fact-checking, and the comprehensive brief supports outsourced content creation.

The outcome: thought leadership that would have taken days to research alone takes a fraction of the time to complete, keeping your content calendar on track and your firm's voice active in industry conversations.

Strategic Decision-Making Support

Deep research tools transform internal strategic decisions when exploring new markets or service offerings. Before committing to more expensive market research reports or assigning teams to investigate, AI can step in.

With a prompt like "What is the current landscape of [your market] in ASEAN countries? Include market size, key players, government incentives, and challenges for each major country," the AI produces a structured mini-report you can immediately share internally—essentially putting a "quick brief" on any strategic topic at your fingertips.

By rapidly highlighting knowns and unknowns of potential moves, deep research enables decision-makers to focus their attention more effectively. For consultants, you can share preliminary findings with clients and show faster progress. When providing the client with timely insights is crucial, AI gives you that speed.

Tips for Getting the Best Results from Deep Research Tools

Like any tool, how you use AI deep research systems will determine the quality of the outcome. Here are some practical tips to make the most of deep research tools:

Start with a Clear, Specific Brief

To write a strong, well-structured prompt for deep research, you need to combine clarity, specificity, and a clear outcome. Here’s a structure you can adapt:

Context: What the research is for and who it's for.
Objective: What you want to discover, analyse, compare, or synthesise.
Scope and Constraints: Time frame, preferred source types (e.g. peer-reviewed studies, industry reports), exclusions.
Output format: Desired form of results (e.g. executive summary, comparative table, presentation outline).
Tone and depth: Level of detail and target audience understanding.

For example:
“I'm a design consultant preparing for a strategic workshop with an FMCG client. They're repositioning their brand to appeal to Gen Z/Millennial consumers in the UK and Western Europe.

I want to understand current design and packaging trends in the FMCG space with a focus on sustainability, minimalism, and digital-native aesthetics. Please identify:

Please identify:

Key visual and structural packaging trends in 2024–2025 (materials, typography, colour palettes)
Consumer preferences regarding sustainable design and brand ethics, especially among Gen Z
Examples of UK/EU FMCG brands that successfully repositioned using design-led strategies in the past 2 years
Quantitative data from trusted sources (Mintel, McKinsey, WGSN, Nielsen, Ipsos, trade publications like The Grocer, Packaging Europe)

Exclude vague forecasts and marketing fluff. Prioritise credible, recent sources with citations.

Deliver findings as:

Bulleted summary of key trends
Brief discussion on consumer insights
Annotated list of 3–5 brand case studies
List of cited sources with publication dates”

If you’re not sure what to ask, start with a broad prompt and ask a standard chatbot’s advice.

Take Advantage of AI Clarification

The best deep research tools will often ask you clarifying questions, especially if your prompt is ambiguous.

Don’t skip this step. When the AI seeks clarification, it’s an opportunity to crystallise what you really need. Providing additional details when prompted leads to a more tailored report.

If the AI doesn't ask but you realise you could have been clearer, make a note on how you can re-prompt with more detail. Treat this as an ongoing dialogue where your goal is directing the AI accurately before it invests time researching.

Use Iterative Querying

After receiving an initial report, skim for gaps and ask targeted follow-ups like "Can you dig deeper into point X," "Compare these findings with last year's data," or "Expand on competitor B's strategy analysis."

All major tools allow follow-up questions in the same session while maintaining context. This approach—like peeling an onion—lets you guide the AI deeper where needed. Often, the most valuable insights emerge in these follow-up rounds as the AI focuses on specifics.

The takeaway: prod further. You won't offend the AI, and the second or third inquiry often yields a goldmine of detail.

Provide Examples or Context

If you have information that can seed the research, include it.

For example: "Our client operates in X industry; include findings specific to companies similar to [Client]." Or share a hypothesis: "Investigate evidence supporting that remote work increases productivity in software teams."

Setting context or direction upfront prevents the AI from wandering off-topic or returning generic information. Think of yourself as a coach—provide background so the AI knows precisely where to dig.

Verify Critical Facts

While deep research outputs include citations, maintain professional skepticism, especially for critical information. Always check source links to verify the AI interpreted them correctly and that they're reputable.

Despite accessing information, AI can misquote, take content out of context, or hallucinate answers. These tools reduce grunt work but don't eliminate the need for human judgment. Consider the AI your junior researcher—excellent 95% of the time, but you're still responsible for verifying key points before presenting them.

One advantage: the AI directs you to relevant sources. Read these to ensure accuracy, guaranteeing the information you deliver to clients or stakeholders is reliable.

Combine AI Research with Your Domain Knowledge

As we’ve seen, deep research AI can slot into various professional workflows. But it needs to act as a force-multiplier, not replacing expertise or judgment. The ideal workflow is AI + human, not AI alone.

Use deep research results as a foundation, then apply your domain knowledge to interpret significance. The AI might report a trend, but as the industry insider, you determine its importance or likely causes. You can then direct the AI to investigate those causes further or incorporate your insights into its follow-up research.

Be Mindful of Limits (and Work Around Them)

Deep research tools, advanced as they are, have their limitations. They might struggle with very recent events if not connected to a live search (though most are). Paywalled content or very niche data might stump them.

These tools typically have usage caps and require processing time. Plan strategically—don't waste your queries on trivial questions or start a 30 minute research session 5 minutes before a meeting. Save your limited deep research credits for complex, high-value questions, and use simpler queries for quick information needs.

The Future of Deep Research Tools and What It Means for Professionals

Deep research is an emerging AI technology. We’re only just seeing the tip of the iceberg of its capabilities. Looking ahead, there are several trends and developments that could shape how these tools evolve—and by extension, how marketers, consultants, and other B2B professionals will use them:

More Domain-Specific AI Researchers

We expect today's general-purpose research tools will soon give way to specialised models fine-tuned for specific industries and domains. Companies are already exploring this by fine-tuning large models on their industry data.

For professionals, this means partnering with AI that truly understands your industry context. These specialised assistants will provide deeper insights by understanding the nuances of your field—its terminology, data structures, and authoritative sources. The result could be faster learning curves for new team members (the AI becomes the institutional memory) and more refined outputs.

Deeper Integration with Enterprise Systems

In the future, we can expect these tools to connect not just to the public internet, but also seamlessly into private company data and knowledge bases. Major players like Microsoft are already weaving AI into enterprise software, while startups are building solutions that plug into workplace tools like Slack, project management platforms, and CRMs.

Imagine being able to query an AI from your desktop to pull data from the web and the firm’s internal archives. The output could blend external insights with internal best practices. When crafting strategies, you might ask "Has our firm done a similar project before? What were the outcomes, and what does the latest external research suggest about such projects now?" The AI could retrieve an internal case study and combine it with current market data to provide an insight rich answer.

Such integrations could reduce information silos dramatically. Instead of separately searching SharePoint, Google, and consulting colleagues, you might ask once and receive insights from all connected sources. This potential, of course, will depend on robust security measures to ensure business-sensitive and client-confidential information remains protected.

Even Smarter Reasoning

Right now, deep research tools largely compile what’s found. As these tools develop, they’ll likely get better at critical reasoning—cross-checking multiple sources and highlighting contradictions or uncertainties.

Future deep research AIs might say, “Source A says this, Source B disagrees, here’s why and my analysis of which is more credible.” This would be a big step towards an almost advisor-like role.

We may also see the emergence of continuous research agents—tools that monitor topics over time and proactively update you on developments. Picture an AI that conducts weekly deep research on your industry and flags meaningful changes—functioning like a hybrid of Google Alerts and a dedicated market analyst that works around the clock. This persistent monitoring could help prevent strategic surprises from emerging information and speed up your reaction times.

The Future of Work with AI Research Assistants

Deep research tools will become as fundamental to knowledge work as calculators are to engineering. They are going to change how professional intelligence is gathered and applied. What once required dedicated research time or expensive database subscriptions will be accomplished with AI-powered tools that work alongside human expertise.

As with any technological advance, the professionals who benefit most will be those who approach it pragmatically—neither overestimating the tools’ capabilities nor underestimating their potential to transform workflows. As these tools handle the labour of information gathering, knowledge workers must develop a new literacy—knowing when to trust AI insights, when to verify, and how to craft queries that deliver precise results.

The real advantage won’t come from having access. When everyone has access to the same information, competitive advantage comes from how you apply it–the bar for insight rises. The human edge remains irreplaceable. You cannot outsource critical thinking, ethical judgment, and relationship-building to AI.

Perhaps the most important question isn't what these tools can do, but what you'll do with the time and mental bandwidth they free up. Will you use that capacity to deepen client relationships, develop more creative solutions, or pursue the complex challenges that were previously beyond reach?

When everyone has research assistants, insight becomes the differentiator.

1827 Marketing helps professional services firms turn their insights into thought leadership that clients actually value. Get in touch to learn how to double the impact of AI with content that showcases your thinking, not just your tools.

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How Deep Research AI Can Support Actionable Insights for B2B Marketers