Making AI deep research work for strategic marketing tasks

Recently, I put some of the newer, more powerful deep research tools through their paces to see if AI could handle the kind of deep, strategic research marketers have to do but rarely have time for. What I found surprised me.

It wasn’t about automating work. It was about surfacing research, case studies and trends past the first page of search results. Here’s what I discovered when I handed AI the keys to some of my most time-consuming research tasks.

When the first page isn’t enough

If you’re like me, you’re drowning in information but starved for synthesis. Dozens of articles, reports, posts and analyst insights arrive daily — what’s missing is the time to actually read and make sense of them. That’s where my exploration of deep research started. Could it not only surface interesting new information, but also help me make sense of topics and trends? 

Unlike search, which delivers results optimized for algorithms, deep research tools look at the totality of the results at once. They can scan multiple sources, follow links, extract key data and actually reason across them. If search is your librarian, pointing you to resources, deep research is your research assistant, coming back with the sources alongside a full report.

I wanted to test this in a real-world setting, so I started with a common marketing task: conducting a competitive landscape analysis for a new product. Usually, this means combing through websites, analyst commentary, product pages, content libraries and even Reddit threads. It’s messy, manual and takes hours, leaving me bleary-eyed.

To conduct deep research, I gave the AI a clear goal. It needed to:

• Map the top five players in the project management software space.
• Analyze their messaging.
• Identify content gaps.
• Surface emerging trends across digital touchpoints.

I asked it to focus on the past 12 months, organize the messaging into a comparison table and highlight any key stats. I also had it search for analyst reports — like those from Gartner and McKinsey — for added insight. The result was a surprisingly well-structured, 80-percent-there strategic brief, delivered in under an hour.

Dig deeper: How AI makes marketing data more accessible and actionable

What deep AI can (and can’t) do 

Like most AI, deep research tools aren’t 100% accurate. They can hallucinate with the best of them. If you make business decisions based on the data, double-check the source and context. They can, however, serve as fast, scalable analysts that don’t get tired or distracted. Here’s what worked well.

AI identified top market players and pulled in data from various sources. It synthesized messaging themes across competitor websites and highlighted patterns I might have missed. It suggested holes in the competition’s positioning that we could fill. It gave me a sense of how the competition’s messaging had changed over time. It even found a few verbatim quotes from analyst reports that bolstered my positioning.

That kind of synthesis would have taken me two full days. I got it in less than 90 minutes, including citations and source links.

The painful part was vetting the sources, which still took several hours. I had to spot the occasional hallucination and decide what mattered to the strategy. Even with that, I had a finished report in under a day.

Research that’s actually useful

One of the most significant advantages 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 complete research. I could also ask it to search public forums like Reddit to spot trends.

When I asked the AI to help me understand customer sentiment, it pulled insights from social media, forums and review sites. Then it grouped the feedback into common frustrations, feature requests and competitor comparisons. I felt confident that we had our finger on the pulse of the customer. And I did it without a full-on research sprint, without hiring a freelancer and without cutting deep into capacity.

Deep research helped me get to the good stuff faster, skipping the grunt work and allowing me to focus on the nuance.

Dig deeper: Meet my research team: Gemini, ChatGPT and Perplexity

Building better prompts and smarter workflows

If you use AI for deep research, I’d recommend knowing how to prompt. A question like “What’s the market size for B2B influencer tech?” might get you a decent summary. But “Analyze recent industry reports, news articles and financial commentary to summarize the current market size, projected growth and top five players, with source citations” will get you something strategic.

The best prompts are structured, specific, and goal-oriented. You’re not just asking for facts — you’re assigning a task. AI itself generated some of the strongest prompts I used. Here’s the one I gave it to start:

“Create me a deep research prompt to analyze the project management software space. I am doing competitive analysis of the top five players and want to analyze their messaging, identify content gaps and surface emerging trends across digital touchpoints. focus on the past 12 months, put the messaging into a comparison table and highlight any key stats. I also asked it to search for analyst reports like those from Gartner and McKinsey for insights.”

Here’s the deep research prompt it generated using ChatGPT:

“I want you to conduct a comprehensive, competitive research analysis of the top five project management software providers over the past 12 months. Focus on the following dimensions:

1. Company Selection Criteria

Identify the top five project management software players based on a combination of:

• Market share
• Brand visibility
• Analyst rankings (e.g., Gartner Magic Quadrant)
• Popularity among enterprise and mid-market customers

Examples likely include: Asana, Monday.com, ClickUp, Wrike, Smartsheet or others you determine through current rankings.

2. Messaging Analysis (Comparison Table Format)

Compare each company’s core messaging and positioning across channels:

• Website homepage + product pages
• Feature pages (e.g., integrations, AI, automation, templates)
• Blog posts
• Video content (YouTube, webinars)
• Ad copy (LinkedIn, Google Display, etc.)
• LinkedIn company page (post content + about section)

Create a messaging table with these headers:

Provider / Tagline / Headline / Primary Messaging / Themes / Differentiators / Target Audience / Signals / AI Messaging / Presence / Tone of Voice

3. Content Gap Analysis

• Identify recurring themes or formats used by the top five.
• Highlight what content types or topics are missing or underserved across the ecosystem (e.g., not enough use case breakdowns, lack of vertical-specific messaging, weak thought leadership).
• Highlight gaps your brand (or a new entrant) could credibly fill.

4. Emerging Trends (Across Digital Touchpoints)

Surface trends based on:

• Shifts in positioning (e.g., toward AI, productivity, collaboration, workflow automation)
• Emerging feature categories (e.g., AI copilot, workflow automation, predictive insights)
• Evolving customer personas (e.g., shift from PMs to RevOps, marketing ops, IT leads)
• Style or format innovations (e.g., use of TikTok-style video, gamified onboarding)
• UX/feature messaging shifts (e.g., emphasis on integrations, scalability, security)

5. Key Stats to Highlight

Extract or cite relevant:

• Market share stats
• Website traffic or engagement stats (if available)
• Customer growth or funding data
• Product usage data (from sources like G2, Capterra or BuiltWith)
• Analyst evaluations or Magic Quadrant / Wave positioning summaries

Create a summary table of stats and performance indicators.

6. Analyst Reports

Search for and summarize recent (past 12–18 months) analyst reports or whitepapers from sources like:

• Gartner
• Forrester
• McKinsey
• IDC
• CB Insights
• G2 Grid Reports
• PitchBook (for startup activity, funding trends)

Highlight any key findings or language around:

• Buyer priorities
• Pain points or opportunity areas
• Predicted category shifts (e.g., PM evolving into Work OS or hybrid ops platforms)
• Strategic moves or acquisitions shaping the landscape

Output Format

Please organize findings into a structured Google Doc or Markdown-friendly format, with:

1. Executive Summary
2. Messaging Comparison Table
3. Content Gap Insights
4. Emerging Trends Report
5. Key Stats Table
6. Analyst Insights Summary

Cite all sources directly or via footnotes.”

Once I started thinking this way, things started to shift. I began to see AI as a junior strategist who could think super fast.

Dig deeper: A co-pilot approach to genAI (with prompt examples)

The risks are real, but manageable

I won’t sugarcoat it. AI gets things wrong. It can cite outdated or irrelevant sources. It can miss nuance. That’s why I took hours to verify key points. However, the benefits of getting answers to all of the above were amazing and much more thorough than I would have done given time constraints. And I had tons more context than usual.

How to start putting deep research AI to work

If you’re ready to experiment, pick a high-effort research task that eats up more time than it should or where you are forced to cut corners. Maybe it’s mapping competitor messaging. Maybe it’s identifying content gaps. Maybe it’s analyzing consumer sentiment in an emerging category.

Then build a thoughtful prompt (or have AI create a prompt for you). 

• Be specific. 
• Define the scope. 
• Ask for structure. 
• Expect to iterate.

Use the output as a draft and keep track of what works and what doesn’t. Like anything, it gets better with practice.

Final thoughts

Working with AI deep research helped me reclaim hours of time, produce better research and explore ideas I might not have reached on my own. It felt like a shift in what great marketing work could look like — less about opinions, more about deep, actionable insight at your fingertips.

Ask AI a smarter question. Give it a complex challenge. Let it do the heavy lifting. Then step in, verify the output and refine it in the way only you can.

Dig deeper: HubSpot announces deep research connector to ChatGPT