Recently I put some of the newer, more powerful deep research tools to the test to see if AI can deal with the kind of deep, strategic research marketers, but rarely have time. What I found surprised me.
It was not about automating work. It was about popping up research, case studies and trends beyond the first page of search results. This is what I discovered when I handed AI the keys to some of my most time -consuming research tasks.
If the first page is not enough
If you are like me, you drown in information but starved for synthesis. Dozens of articles, reports, messages and analysts arrive daily – what is missing is it time to actually read and understand them. That is where my exploration of deep research began. Can it not only come up with interesting new information, but also help me to understand topics and trends?
Unlike search, which provides results that are optimized for algorithms, deep research tools look at the totality of the results at the same time. They can scan multiple sources, follow links, extract important data and actually come over it. If a search is your librarian that you point to sources, deep research is your research assistant and comes back with the sources next to a full report.
I wanted to test this in a Real-World setting, so I started with a common marketing task: performing a competitive landscape analysis for a new product. Usually this means combing websites, analyst commentary, product pages, content libraries and even Reddit -Threads. It is messy, manual and takes hours, so I leave a so -called eyes.
To conduct deep research, I gave the AI a clear goal. It had to be:
- Map the top five players in the software space for project management software.
- Analyze their messages.
- Identify Content Hiates.
- Emerging trends on surface over digital contact points.
I asked myself to concentrate on the past 12 months, to organize the messages in a comparison table and to mark any important statistics. I also had it searching for analyst reports – such as those of Gartner and McKinsey – for extra insight. The result was a surprisingly well-structured, 80 percent strategic assignment, delivered in less than an hour.
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What deep ai can (and not) do
Like most AI, deep research tools are not 100% accurate. They can hallucinate with the best of them. If you make business decisions based on the data, check the source and context double. However, they can serve as fast, scalable analysts who are not tired or distracted. This is what worked well.
AI identified top market players and withdrew data from different sources. The synthesized messaging themes on competitive websites and emphasized patterns that I may have missed. It suggested gaps in the positioning of the competition that we could fill. It gave me an idea how the messages from the competition had changed over time. It even found a few fonts from analyst reports that strengthened my positioning.
That kind of synthesis would have cost me two full days. I have it in less than 90 minutes, including quotes and source tires.
The painful part was the screening of the sources, which lasted a few hours. I occasionally had to recognize hallucination and decide what the strategy was important. Even with that I had a completed report under a day.
Research that is actually useful
One of the most important benefits was finding sources I would have missed. Because it goes deep, it found sources that didn’t even show up in the top 100 search results. That helped me to do more full research. I could also ask to look for public forums such as Reddit to spot trends.
When I asked the AI to help me understand the customer sentiment, the insights of social media, forums and assessment sites attracted. Subsequently, it grouped the feedback in common frustrations, job requests and competition comparisons. I had convinced that we had the finger on the wrist of the customer. And I did it without a full research print, without hiring a freelancer and without cutting deep into capacity.
Deep research helped me faster about the good things, skipped the grunt work and allowed me to concentrate on the nuance.
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Build better prompts and smarter workflows
If you use AI for deep research, I would recommend knowing how to be asked. A question such as “What is the market size for B2B Influencer Tech?” Maybe you get a considerable summary. But “analyzes recent industrial reports, news articles and financial comments to summarize the current market size, projected growth and top five players, with source citations,” will make you something strategic.
The best instructions are structured, specific and targeted. You don’t just ask for facts – you allow a task. AI itself generated some of the strongest instructions I used. Here is the one I gave to start with:
“Create a in -depth research prompt to analyze the space for project management software. I do a competition analysis of the top five players and I want to analyze their messages, identify content and surface -up trends in digital contact points. Focus on the past 12 months. Put the messages in a comparison table.
Here is the deep research prompt it generated with chatgpt:
“I want you to have an extensive, competitive research analysis of the Top five project management software providers over the past 12 months. Focus on the following dimensions:
1. Criteria for business selection
Identify the Top Five Project Management Software -Players Based on a combination of:
- Market share
- Visibility of the brand
- Analyst rankings (eg Gartner Magic Quadrant)
- Popularity at customers of companies and medium -sized market
Examples are likely: Asana, Monday.com, ClickUp, Girl, Smartsheet Or others that you determine through the current ranking.
2. Messaging analysis (comparison table division)
Compare the core of every company Messages and Positioning About channels:
- Website Homepage + Product pages
- Function pages (eg integrations, AI, automation, templates)
- Blog messages
- Video -content (YouTube, Webinars)
- Advertising copy (LinkedIn, Google Display, etc.)
- LinkedIn Company -page (Posting content + about section)
Create a messaging table With these headers:
Provider / Tagline / Headline / Primary Messaging / Themes / differentiators / target group / Signals / AI messages / Presence / tone of voice
3. Analysis of content gap
- Identify recurring themes or formats used by the top five.
- Modest What content types or topics Missing or one after the other in the ecosystem (for example, not enough use outputs, lack of vertical-specific messages, weak thinking).
- Mark Hiaten Your brand (or a newcomer) could fill credibly.
4. Emerging trends (about digital contact points)
Surface trends based on:
- Shifts in positioning (eg to AI, productivity, cooperation, workflow automation)
- Emerging function categories (eg AI Copilot, workflow automation, predictive insights)
- Evolving customer personas (eg shift from PMS to Revops, Marketing OPS, it leads)
- Style or format innovations (for example the use of video in TIKTOK style, Gamified onboarding)
- UX/Functional shifts (eg the emphasis on integrations, scalability, security)
5. Important statistics for marking
Extraheer or quote relevant:
- Market share statistics
- Website traffic or engagement statistics (if available)
- Data from customers or financing data
- Product use data (from sources such as G2, Capterra or Builtwith)
- Analyst -Evaluations or Magical Quadrant / Golf positioning Allowances
Make one Summary table with statistics and performance -indicators.
6. Analyst reports
Search for and recently summarized (last 12-18 months) Analy failure or white papers From sources such as:
- Gartner
- Forrester
- McKinsey
- IDC
- CB insights
- G2 Grid Reports
- Pitchbook (for Startup activity, financing trends)
Mark any important findings or language around:
- Buyer Priorities
- Pain points or opportunities areas
- Predicted category shifts (e.g. evolve to work OS or Hybrid OPS platforms)
- Strategic movements or acquisitions that form the landscape
Output size
Organize findings in a structured Google Doc or Markdown-friendly format, with:
- Executive summary
- Message comparison table
- CLASSIFICATIONS VIEWS
- Emerging trend report
- Main statistable
- Summary of the analysts
Quote all sources directly or via footnotes. “
When I started to think this way, things started to shift. I started to see AI as a junior strategist who could think super fast.
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The risks are real, but manageable
I won’t be a sugar jacket. Ai is wrong. It can call outdated or irrelevant sources. It can miss nuance. That is why I have had hours to verify the most important points. However, the benefits of getting answers to all the above were great and much more thorough than I would have done, given time restrictions. And I had much more context than usual.
How to do deep research to work
If you are ready to experiment, choose a research task with a high mind that eats more time than needs or where you are forced to cut corners. Perhaps it has been mapped out the messages from competitors. Perhaps the content of content identifies. Perhaps consumer sentiment analyzes in an emerging category.
Then build a well -considered prompt (or have AI made a prompt for you).
- Be specific.
- Define the scope.
- Ask for structure.
- Expect to repeat.
Use the output as a concept and keep track of what works and what doesn’t. Like everything, it gets better with practice.
Last thoughts
Working with AI Deep Research helped me to reclaim for hours, produce better research and explore ideas that I may not have reached alone. It felt like a shift in what great marketing work could look like – less about opinions, more about deep, usable insight within reach.
Ask AI a smarter question. Give it a complex challenge. Let it do the heavy work. Then get in, verify the output and refine it in the way you can only.
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