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OpenAI’s Deep Research: Unlocking Rapid ROI for SMB Owners and PE Rollups

The next level of AI-powered research is here, turning hours of PhD-level work into actionable insights instantly.
 
How could on-demand, expert-level analysis accelerate your business decisions?
 
Read on to learn how Deep Research streamlines intelligence gathering and unlocks rapid ROI—or skip the read and book time with Michael Weinberger for a quick, tailored discussion.

From Insights to Action: How Agentic AI is Transforming Business Automation in 2025

OpenAI’s new Deep Research is an AI-powered research assistant poised to transform how businesses gather intelligence.

Launched in February 2025, this tool conducts multi-step web investigations and delivers a report with the thoroughness of a PhD-wielding consultant within minutes – accomplishing what could take a human analyst many hours (OpenAI launches new AI tool to facilitate research tasks -February 02, 2025 at 07:19 pm EST | MarketScreener) .

For small and mid-sized business (SMB) owners and private equity (PE) professionals executing rollup strategies, Deep Research is a strategic game-changer.

This article explores what Deep Research is and how it works. We dive into practical ways SMBs and rollup-focused PE firms can leverage this “AI research analyst” to accelerate due diligence, market analysis, and decision-making.

The goal: demonstrate how adopting such autonomous AI agents can yield rapid ROI and tangible business value.

In the following sections, we’ll cover the debut of OpenAI’s Deep Research, unpack its inner workings in relatable terms, and outline actionable use cases that will make it feel like all you can do is win at work.

With the help of your AI research assistant, you can literally work while you sleep.

The Bottom Line

Deep Research and agentic AI can empower lean teams to scale their research capabilities effortlessly, turning hours of labor into minutes of output, and giving early adopters a serious competitive edge. Let’s examine how.

Introduction

We are entering an era where artificial intelligence isn’t just answering trivia or drafting emails – it’s fundamentally changing how we research and make business decisions.

For many SMB owners and PE professionals, especially those involved in rollups (acquiring and merging multiple companies into one), success hinges on information: market trends, competitor analysis, due diligence findings, and strategic insights. Traditionally, gathering this intelligence is time-consuming and costly, often requiring teams of analysts or pricey consulting reports. But recent AI advancements are leveling the playing field.

Imagine having a tireless digital researcher on staff, one that can comb through countless websites, reports, and databases, and then distill key insights into a concise briefing for you. This isn’t science fiction – it’s the promise of OpenAI’s Deep Research. Unveiled in 2025, Deep Research represents a leap beyond the familiar chatbots. It works more like an autonomous research analyst: you give it a complex question and it independently hunts down answers across the internet, evaluates them, and returns with a well-supported report. Think of it as an AI version of a caffeinated, overeager graduate student, meticulously sleuthing through information and piecing together the full story, citation by citation, so you can validate and trust the results.

This introduction of agentic AI (AI “agents” that can act with minimal supervision) comes at a critical time: Business leaders are under pressure to move faster and smarter. In fact, industry experts note that tools like Deep Research are reshaping how companies approach business intelligence and strategy. Early adopters in various sectors are already seeing productivity boosts and cost savings by offloading tedious research tasks to AI.

For SMBs with limited manpower and PE firms racing to identify the next acquisition, the ability to instantly tap an AI-driven research pipeline can be revolutionary.

In the next sections, we’ll break down the release of Deep Research, how it functions, and – most importantly – how you can harness it for immediate impact on your business.

Release of OpenAI Deep Research

On February 2, 2025, OpenAI officially launched “Deep Research,” a new feature within ChatGPT designed to supercharge online research tasks. Announced during an online event (OpenAI launches new AI tool to facilitate research tasks -February 02, 2025 at 07:19 pm EST | MarketScreener), the release came as competition in the AI field intensifies (notably, China’s latest chatbot DeepSeek was making headlines as a rival). OpenAI’s CEO Sam Altman introduced Deep Research as part of a vision to offer advanced AI services to businesses globally, underscoring the high stakes and high hopes riding on this technology.

So, what exactly is Deep Research? In OpenAI’s own words, it’s an AI-driven research assistant that scours the web for in-depth information on a given topic and then produces a detailed, analyst-level report. The tool was publicly demonstrated on its launch day, impressing observers with its ability to handle complex, multi-layered queries.

Crucially, Deep Research doesn’t just spit out a single answer; it provides a well-structured report complete with sources for each claim, much like a human researcher would—with footnotes and references included. OpenAI touts that it is especially good at finding “niche, non-intuitive information” that might be buried in obscure corners of the internet. In other words, it can surface hidden gems of insight that you might miss in a cursory Google search, the kind of stuff that would require hours of digging by a real person.

The value proposition is clear: speed and depth. According to OpenAI, Deep Research can accomplish in tens of minutes what would take a human many hours or even days. During the launch, OpenAI showcased examples like a complex market analysis that the AI completed in around 20 minutes – a task that normally might keep a team of analysts busy for a week.

Each output comes with full citations and even a summary of the AI’s reasoning process, ensuring transparency and trust in the results. As OpenAI’s Chief Research Officer Mark Chen explained, this tool is a significant step toward the company’s broader vision of AI that approaches human-level intelligence in diverse tasks. In fact, Chen described Deep Research as one step closer to AGI (artificial general intelligence) – a system that can not only use knowledge but also generate new insights on its own.

Upon release, Deep Research was made available to ChatGPT Pro subscribers (a premium tier of OpenAI’s service that costs $200 per month) with an initial cap of 100 queries per month. OpenAI plans to roll it out to other user tiers (Plus, Team, Enterprise) over time as they optimize the tool’s running costs.

The gradual rollout hints at the computing intensity under the hood – thorough research doesn’t come cheap or easy, even for AI. Nonetheless, the early access users, many of whom are business professionals, began experimenting with Deep Research from day one. Their verdict so far: it has the potential to transform how businesses gather intelligence and shape their corporate strategy, albeit with some caveats.

In summary, the release of OpenAI Deep Research marks a milestone in practical AI applications for business. By introducing an autonomous research agent capable of delivering comprehensive, cited reports in a fraction of the usual time, OpenAI is equipping decision-makers with a powerful new tool.

Next, let’s demystify how Deep Research actually works, and how it manages to produce such in-depth results so quickly.

How Deep Research Works

At its core, Deep Research works like a highly skilled (and very tireless) research analyst that you can summon on demand. The magic happens behind the scenes in a few key steps – and we’ll explain them here.

1. Intelligent Web Crawling: When you pose a question or task to Deep Research, it doesn’t just recall information from memory. Instead, it goes out to the internet much like an investigator heading to the library archives. The AI breaks down your query into smaller sub-tasks and systematically searches for answers, browsing websites, databases, and public data sources. You can even give it additional context, like attaching a PDF report or a spreadsheet, and it will incorporate those into its hunt. Think of it as sending an army of diligent interns to scour every corner of the web – except these interns work at light speed and never take breaks.

2. Multi-Step Reasoning: As information comes in, Deep Research doesn’t just dump it on your desk. It uses an advanced reasoning engine called o3 (an evolution of the GPT models) to connect the dots. This means if one article gives a statistic and another provides a definition and a third offers an expert quote, Deep Research will piece together these bits to form a coherent narrative or answer. It’s a bit like assembling a jigsaw puzzle: the AI places each piece of information where it belongs, building up a complete picture of the topic. OpenAI developed this capability by training the AI on complex real-world tasks requiring reasoning and tool use, so it can “synthesize large amounts of online information” and navigate knowledge gaps during its research. In practical terms, while a normal search engine might give you 10 separate links to click, Deep Research reads links for you and summarizes selectively to determine what the articles mean in context for your query.

3. Cited Report Generation: Perhaps the most impressive part is the output. After taking 5 to 30 minutes (depending on the complexity of the task) to gather and process information, Deep Research delivers a detailed report right in the chat interface. This report isn’t a loose collection of facts; it’s written in a narrative form, often with an introduction, breakdown of findings, and even a conclusion or recommendation. This enables the end user to verify every claim or data point that comes with a citation – formatted in a way not unlike an academic paper or a professional analyst’s report. You’ll see references (with links) to the exact sources the AI used, making it easy to validate the information. OpenAI highlights that this level of documentation is what differentiates a quick AI answer from a well-substantiated answer you can use as a work product.

In effect, Deep Research provides the evidence for every statement it makes, giving you confidence in the result and saving you the step of double-checking sources. It’s as if you asked a PhD level expert to perform research on a topic and they returned with both a full report and a stack of annotated articles used to build that report.

4. User-in-the-Loop (Minimal Effort Required): Using Deep Research is straightforward. From within ChatGPT, you select the ‘deep research’ mode, type in your question or research prompt (for example, “Analyze the competitive landscape of electric scooter startups in Europe and identify market gaps we could exploit”), and hit enter. At that point, after the agent asks you some astute clarifying questions, you can literally step away and do other work (or grab dinner, or sleep) while the AI does its thing.

A sidebar will show you a live summary of the steps being taken and sources being consulted, almost like watching a detective’s case board being filled in. You’ll be notified when the research is complete. The process is hands-off; you don’t need to babysit it or provide additional prompts to refine the query after seeing initial results. This is what OpenAI refers to as an “agentic workflow,” meaning the AI agent can carry the task through multiple steps autonomously. The result: you ask once, and you get a polished result, versus the back-and-forth of traditional search or even standard AI Q&A sessions.

To put it metaphorically, using Deep Research is like deploying a skilled spy into the information wilderness: it maps the terrain, photographs points of interest (collects data), and returns with a comprehensive reconnaissance report – all while you focus on higher-level strategy. Under the hood, cutting-edge AI models and reinforcement learning techniques make this possible, but as a user you don’t see that complexity. You simply experience the outcome: a reliable, in-depth answer to a complex question, delivered faster than any human team could achieve.

How to Best Use Deep Research to Win at Work

Knowing how a tool works is one thing – knowing how to apply it effectively in your work is another. Deep Research may be powerful, but its value truly comes to life when integrated into your daily business decision-making. Here we blend thought leadership with practical tips, spotlighting how SMB owners and PE professionals in rollup strategies can use Deep Research to gain a winning edge at work.

First and foremost, think of Deep Research as your on-demand strategy consultant and research team. Whenever you face a question that makes you think, “I need to look into that” – consider handing it off to Deep Research. Here are some concrete ways to leverage this tool for maximum business impact:

  • Market Research in Minutes: Whether you’re exploring a new market to enter or sizing up demand for a product, Deep Research can compile market data, consumer trends, and niche insights remarkably fast. Instead of spending days pulling reports and statistics, ask the AI something like, “What are the latest trends and growth figures in the organic pet food industry?” and get a synthesized answer with references. It will scan the internet, collect data, and generate thorough reports in a matter of minutes – tasks that would take human researchers hours to complete. The speed here means faster go-to-market decisions and less guesswork.
  • Competitive Analysis and “Voice of the Customer”: SMB owners often need to keep tabs on competitors or understand customer sentiment, but hiring dedicated market analysts using multiple expensive tools (SEO analyzers, social listening platforms, etc.) might be out of reach. Deep Research can be your all-in-one competitive intelligence unit. It can review competitors’ websites, marketing materials, and even scrape platforms like Reddit or review sites to see what customers are saying. One marketing professional noted that normally this kind of competitor content gathering and sentiment analysis is labor-intensive, entailing manual review of competitor sites and scouring forums. Now, Deep Research could eliminate the need for many expensive social listening tools by providing an out-of-the-box AI solution. You can quickly get a report on how your product or service stacks up, what customers love or hate about alternatives, and where there’s an emotional gap you can exploit in your branding.
  • Due Diligence and Partner Vetting: For PE professionals engaged in rollups, as well as SMBs considering partnerships or acquisitions, due diligence is paramount. Typically, you’d dig through financial filings, news archives, industry databases, and maybe pay third-party research firms – a process that can take weeks. With Deep Research, you can expedite initial due diligence by tasking the AI to evaluate a potential business partner or acquisition target. For instance, you might prompt: “Assess Company X’s online reputation, key financial metrics available publicly, any regulatory or legal issues, and overall market position.” In tens of minutes, you’ll have a dossier that at least gives you a solid starting point, highlighting any red flags or unique strengths. Business developers report using it for exactly this purpose: Businesses can use it for market research, evaluating potential business partners, or keeping up with new technology and trends with the primary advantage being speed and cost savings. By lowering the manual research required, you reduce consultant fees and internal labor hours – directly improving ROI on deal sourcing.
  • Investment Thesis & Rollup Strategy Formation: If you’re a PE professional crafting a rollup thesis (say, consolidating local HVAC companies or dental clinics), you need to identify industry fragmentation, margin disparities, and growth opportunities. Deep Research can gather fragmented data – like how many players are in a region, average EBITDA multiples in recent sales, customer reviews of the smaller competitors – and present an informed view of the industry landscape. It’s like having a junior analyst tirelessly gather all available intel for you overnight. Armed with that, you can quickly validate whether your rollup idea has merit or adjust your strategy. The ability to iterate on strategy faster is invaluable in competitive investment environments.
  • Technical Research and IP Checking: Many SMBs don’t have in-house research departments for technical questions, and PE firms might not employ full-time scientists or lawyers for each deal. Suppose you need to research a new technology’s potential or check if a product concept might infringe on patents. Deep Research shines here as well. It was designed for intensive knowledge work in fields like finance, science, and engineering where thoroughness is key. For example, in pharmaceuticals, a human analyst might spend days researching drug interactions and usage data across various sources, whereas this takes mere minutes with Deep Research.
  • Continuous Learning and Trend Tracking: In fast-moving industries, staying updated is a job in itself. You can task Deep Research with periodic check-ins on emerging trends. For instance, a savvy use case is setting a monthly query like, “What are the newest developments in fintech regulations for online lenders?” The AI will generate a fresh report each time, citing the latest articles, statistics, and expert opinions. It’s like subscribing to a custom research brief tailored to your needs. This helps SMB owners who wear multiple hats – you can’t be reading every journal or tech blog, but your AI agent can, and it will hand you the distilled knowledge.

To truly win at work with Deep Research, a few best practices are worth noting. Treat the AI’s output as a starting point for action: it gives you the insight, but human judgment is needed to apply it. Always glance over the cited sources, especially for mission-critical decisions; Deep Research does a lot of quality filtering but it’s always good to verify anything that seems surprising (more on its limitations in a moment).

Importantly, integrate it into your workflow. Make it a habit: before a big meeting or when brainstorming a new strategy, ask yourself “Did I Deep Research this?” – meaning, did you let your AI assistant gather all available intelligence? Many early users report that this habit has led to better-prepared pitches, quicker strategy pivots, and a noticeable competitive edge. When a rival is still waiting on a week-long research report from an analyst, you could already have your answers and be moving forward.

The ROI case here is straightforward: by collapsing research timelines from weeks to hours, you reduce labor costs and seize opportunities faster. Time truly is money. And Deep Research is all about saving time without sacrificing depth or quality of information. For SMBs fighting to compete with larger firms, and PE investors hunting for the next big consolidation play, that combination of speed and insight can directly translate to dollars – and success – gained.

Deep Research While You Sleep: Autonomous AI Agents for Effortless Scale

One of the most captivating promises of tools like Deep Research is the idea of “work while you sleep.” Imagine delegating a crucial research task to an AI agent at the end of your workday. You go home, get a good night’s sleep, and by the next morning, a detailed report is waiting in your inbox, replete with insights and opportunities you hadn’t even considered. This isn’t just wishful thinking – it’s quickly becoming reality. Deep Research is a prime example of an autonomous AI agent that can carry out complex, multi-step objectives with minimal human intervention. In practical terms, it means you can scale your output (do more research, analyze more ideas) without scaling your headcount. It’s about working smarter, not harder, by letting the machine intelligence handle the heavy lifting in the background.

The concept of autonomous AI agents or “agentic AI” is a major trend in 2025. Businesses large and small are experimenting with AI systems that have a degree of agency – the ability to take independent actions to achieve a goal. Deep Research operates in this agentic fashion: once you give it a goal (your query), it decides how to break that down, where to find information, and how to compile the answer, all without needing hand-holding. Industry predictions show that this trend is only growing. According to Deloitte’s 2025 analysis, roughly 25% of companies using generative AI will pilot some form of agentic AI (autonomous AI workflows or agents) in 2025, and that figure could double by 2027. In other words, one in four companies may soon have an AI working on tasks semi-independently – and those that do will likely pull ahead in efficiency.

Why is “AI while you sleep” so powerful? Consider the traditional limitations of business: a human team has a fixed number of hours in a day. If you needed round-the-clock progress on a project, you’d have to hire a night shift or outsource to teams in different time zones.

Now, even a small business can achieve a 24/7 workflow by deploying AI agents that don’t clock out. Deep Research can be running analyses at midnight, so that by the time you sip your morning coffee, you have fresh intelligence to act on. This kind of leverage was previously only available to big corporations with big budgets. Today, an SMB owner with an AI assistant can out-hustle a larger competitor simply by sheer agility – decisions get made faster because the research got done faster.

Another angle is effortless scale. PE professionals juggling multiple acquisition targets or SMB owners handling various projects can clone themselves, in a sense, by running multiple Deep Research queries in parallel. While you focus on one high-level task, your AI agents could concurrently be exploring, say, three different market expansion ideas, five potential suppliers, and a full SWOT analysis of your top competitor. This parallel processing means you can cover vastly more ground in the same 24 hours. One founder described the workflow as having an extra pair of cofounder quality hands on deck that doesn’t add to payroll. It’s not just about working faster; it’s about scaling out the number of initiatives you can investigate without diluting your team’s focus.

It’s worth noting that autonomous doesn’t mean uncontrolled. You set the objectives for Deep Research, and you review the outputs – think of it like managing a team of extremely efficient interns. There is still an oversight role for you as the business leader, ensuring that the work the AI did aligns with your needs and that any critical decisions are vetted. This is where effortless doesn’t imply careless. The best outcomes arise when you pair the AI’s relentless efficiency with your human judgment. For instance, if Deep Research surfaces a surprising market trend, you might want to sanity-check it or discuss it with your human team to decide strategy. In essence, you become a coach or director, orchestrating both human and AI contributors toward your business goals.

The concept of Deep Research running while you sleep also speaks to quality of life and sustainable productivity. As an SMB owner or PE deal-maker, burnout from all-nighters and constant information overload is a real risk. If an AI agent can take away some of that burden, you not only get more done but can also reclaim your nights and weekends. It’s like having a highly competent night shift that you can trust to handle things until you’re back online.

Finally, consider the competitive landscape: if you aren’t using autonomous agents and your competition is, you might be at a disadvantage. Early adopting companies are already pouring investment into these technologies, with over $2 billion invested in agentic AI startups in the last two years targeting enterprise applications. The tools will keep improving, becoming faster and more capable. We’re already seeing hints of what’s next – OpenAI has mentioned plans to integrate data visualizations and even the ability for Deep Research to generate charts/graphs in its reports in the near future (imagine waking up not just to a written report, but to a fully illustrated pitch deck for your 9am meeting!

The bottom line: leveraging AI agents like Deep Research can lead to effortless scale – you dramatically increase your capacity to research, analyze, and strategize without proportionally increasing effort or cost.

In summary, Deep Research encapsulates the newfound freedom and firepower that autonomous AI agents grant businesses. By delegating the grunt work to machines that never tire, you free yourself and your team to focus on creativity, strategy, and execution – the things humans do best. It’s a recipe for scaling up success in a sustainable way.

Conclusion

OpenAI’s Deep Research heralds a new chapter in the AI revolution for business – one where intelligent agents handle the heavy lifting of research, allowing leaders to make faster, data-driven decisions. Throughout this article, we’ve explored how Deep Research was born and how it functions as a tireless research analyst, scouring the web and delivering gold-standard reports in a fraction of the time a human would take. We used metaphors to demystify its complex workings, but the upshot is simple: this tool can turn hours of work into minutes of results, and that has very real implications for ROI and competitive advantage.

For SMB owners and PE professionals engaged in rollups, Deep Research offers an immediate edge. It levels the playing field by giving small teams capabilities that were once reserved for firms with big research budgets. Need to understand a market’s dynamics? Deep Research can brief you by Monday. Vetting a list of acquisition targets? Deep Research can highlight the gems and red flags before your competitors have even finished their first calls. The practical applications – from market analysis, competitor intelligence, due diligence, to trend spotting – translate directly into faster project cycles, lower research costs, and smarter strategic moves. In a business landscape where agility is often the difference between capturing an opportunity or missing it, those benefits are transformative.

We should, of course, acknowledge the caveats (every rose has its thorns, and every AI its quirks). Deep Research, while more reliable than its predecessors, can occasionally wander off-track or hallucinate – making up an inference that isn’t backed by facts. OpenAI notes that it sometimes struggles to tell authoritative information apart from mere internet rumors and might not always convey uncertainty as well as a human expert would. In our own usage, the motto “trust, but verify” applies.

The good news is the tool’s design makes verification easy: because sources are cited, you can quickly check if that statistic on page 5 came from an industry journal or a random blog. A quick sanity check or a review by a team member is usually enough to catch any oddities. The output is typically only as good as the sources it gets information from, which means discerning users will still want to ensure the AI isn’t leaning on dubious sources (“garbage in, garbage out”).

These are not deal-breakers but reminders that AI agents work best in partnership with human oversight. When used wisely, the minor drawbacks are easily managed, and the productivity gains far outweigh the occasional hiccup.

In conclusion, Deep Research exemplifies the authoritative yet accessible power of modern AI. It brings authoritative research capabilities to your fingertips in an accessible, user-friendly way. For those focused on rapid ROI and business value, it’s a no-brainer to explore.

If you can accomplish in one afternoon what used to take two weeks of research, that acceleration in itself is ROI – time saved is money saved (or earned, if it lets you act faster on a lucrative opportunity). Moreover, by freeing your brain from drowning in information gathering, you can reallocate your energy to higher-level strategic thinking and creative problem-solving. It’s as if you hired a brilliant analyst who works 24/7 for the cost of a software subscription – and that analyst never gets tired or distracted.

Businesses that embrace tools like Deep Research and the broader wave of autonomous AI agents stand to benefit from compounding competitive advantages: more knowledge, sooner, at lower cost, leading to better decisions. Those that hesitate may find themselves outpaced by more nimble rivals who have AI working around the clock for them. The message is clear: the AI-driven future of work is here, and it’s time to take advantage of it.

Call to Action

Ready to harness the power of OpenAI’s Deep Research and autonomous AI agents for your own business? It’s time to turn insight into action. Book a meeting with Michael Weinberger for a free Deep Research and Agentic AI audit. In this complimentary session, we will assess your organization’s current research and decision-making workflows and identify how tools like Deep Research (and other agentic AI solutions) can be integrated to deliver rapid ROI. Whether you’re an SMB owner looking to outsmart bigger competitors, or a PE professional aiming to turbocharge your rollup strategy, this audit will uncover immediate opportunities to work faster and smarter with AI.

Don’t miss the chance to stay ahead of the curve – and the competition. Schedule your free audit with Michael Weinberger today and discover how you can put OpenAI’s Deep Research to work for you. Embrace the future of effortless, AI-powered scale and transform the way you win at work, starting now.