This article, “SEO Applications for OpenAI’s Deep Research: How to Leverage AI for Smarter Search Strategies,” was originally published in Search Engine Land in March 2025. What you’re reading here is my original, unedited manuscript — the “director’s cut,” if you will. The published version included some thoughtful editorial adjustments (which I don’t begrudge), but this version preserves my original structure, full context, and — perhaps most importantly — my snark.
SEO is evolving faster than a fruit fly population in a genetics lab — constantly adapting, mutating, and surprising even the experts — one day it’s all about long-form content, the next, AI-generated summaries are stealing the spotlight, and staying ahead requires smarter, data-driven insights. AI-powered tools like OpenAI’s Deep Research are changing the way marketers approach content strategy, competitive analysis, and SERP optimization. Unlike traditional AI models that rely on pre-existing training data, Deep Research can pull real-time insights from external sources, making it a powerful asset for SEO professionals.
So how does Deep Research compare to regular ChatGPT, and how can marketers use it to outperform competitors and build better content? Let’s take a look.
Deep Research vs. “Regular” ChatGPT
Until February 2025, Deep Research was only available to OpenAI’s $200/month Pro+ users. Thankfully, regular $20/mo users now have access to this tool that can pull real-time insights from external sources, making it a potential game-changer for research of all kinds. Whether you’re working on SEO strategies, writing academic papers, or conducting competitive analysis, real-time, sourced information with thorough citations is invaluable. Discovering Deep Research is like switching from gloppy rubber cement to spray adhesive for science fair projects — suddenly, everything is faster, cleaner, and prettier — just like how spray adhesive ensures a smooth, uniform finish without bubbles or unevenness. Deep Research not only speeds up the process but also delivers results that are polished and well-organized right out of the box, saving time and effort while improving overall quality.
So before diving into SEO applications, let’s examine how Deep Research differs from traditional ChatGPT responses.
- Regular ChatGPT (GPT-4o, etc.) generates responses based on its internal knowledge and general training data. While it can provide SEO guidance, competitive research, and content ideas, it does not cite external sources in real-time. This means responses are based on historical knowledge rather than up-to-date, sourced insights.
- Deep Research, on the other hand, actively pulls from external sources, synthesizing multiple perspectives and providing links to supporting materials. This makes it far more powerful for research-heavy SEO tasks—like evaluating competitors, validating E-E-A-T signals, and ensuring factual accuracy in content, which, much like a fruit fly experiment, requires constantly monitoring how things evolve in real time.
One of its standout features is the depth and quality of citations in its output. Deep Research doesn’t just provide insights, it backs them up with thorough footnotes and references. Not only does it help substantiate claims with reliable sources, but it also allows SEOs to see exactly where the data or commentary is coming from. Knowing the source — whether it’s a respected publication, a niche forum, or a well-regarded industry expert — helps SEOs evaluate the credibility, relevance, and quality of the information. It can also be a great way to discover new thought leaders or publications worth following.
Example of ChatGPT vs. Deep Research in SEO
Let’s say you want to understand how Google’s latest core update is impacting search rankings.
- ChatGPT prompt:“What are the key ranking changes from Google’s latest core update?”
- ChatGPT will provide insights based on its training data, which may not include the latest updates.
- ChatGPT will provide insights based on its training data, which may not include the latest updates.
- Deep Research prompt:“Summarize the latest analysis of Google’s December 2024 core update from industry experts, including changes in ranking factors and who has been affected.”
- ChatGPT might provide a general summary of past updates but lacks real-time data and direct citations.
- Deep Research, on the other hand, retrieves insights straight from authoritative sources. For example, when testing this prompt, Deep Research returned a 1,068 word analysis (not counting the list of 13 citations with links). Here’s an excerpt:
“Google’s February 2025 update rewards content-rich, trustworthy sites and raises the bar against spam or subpar content[^1]. SEO analysts noted that Google placed even greater emphasis on high-quality, original content demonstrating E-E-A-T[^2]. Sites with thin or duplicate content, especially in YMYL categories, saw declines[^3]. AI-generated content was scrutinized more heavily, with low-quality, auto-generated text being devalued[^4].”
- ChatGPT might provide a general summary of past updates but lacks real-time data and direct citations.
The footnotes and citations in Deep Research’s response allow SEOs to see exactly who said what, in which publication, making it easier to evaluate the credibility of the insights and make informed decisions.
SEO Use Cases for OpenAI’s Deep Research
1. Competitive Analysis & SERP Research
One of the most practical applications of Deep Research in SEO is analyzing competitors and search engine results pages (SERPs) in real time.
Example: Identifying Content Gaps
Imagine you’re optimizing a blog for a keyword like “best AI SEO tools 2025”. Using Deep Research, you can prompt:
Prompt: “Provide a comparison of the top five AI SEO tools as of 2025, summarizing their features, pricing, and pros/cons with links to sources.”
Instead of relying on outdated or generalized knowledge, Deep Research pulls current information from multiple sources, allowing you to craft a content piece that is more comprehensive and up-to-date than competitors.
2. Content Ideation & Topic Research
Creating unique, high-quality content that ranks well requires more than just keyword research. SEOs often need to find trending topics, authoritative sources, and expert insights to craft engaging content.
Example: Finding Trending & Evergreen Topics
Prompt: “What are the emerging trends in AI-powered search optimization in 2025? Provide references to industry reports or expert opinions.”
Deep Research helps ensure your content is timely, relevant, and backed by authoritative sources, improving both E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) and engagement.
3. E-E-A-T & Link Building Research
Google increasingly prioritizes content that demonstrates expertise, authoritativeness, and trustworthiness (E-E-A-T). With Deep Research, SEOs can efficiently:
- Find reputable sources to cite for stronger credibility.
- Discover link-building opportunities by identifying authoritative industry sites that accept guest contributions.
- Locate credible experts whose insights can add weight to an article.
Example: Strengthening Content Credibility
Prompt: “Find peer-reviewed studies or expert analysis on the impact of AI-generated content on SEO rankings.”
(Seriously, try this. The footnotes alone are *chef’s kiss*)
By embedding sourced insights directly into your content, you enhance trust and authority, which can contribute to higher rankings.
4. Automating SEO Research Tasks
SEO professionals spend a significant amount of time manually reviewing sources, extracting insights, and analyzing SERP trends. Deep Research can automate much of this work, freeing up time for strategy and execution.
Example: Generating a Content Brief
Prompt: “Generate a content brief for a 2,000-word article on ‘How AI is Changing SEO in 2025,’ including H2s, key takeaways, and supporting statistics with sources.”
This allows SEO teams to move faster and maintain consistency in content quality and depth.
Reevaluating the Role of Schema in SEO
While structured data has long been considered a key element of technical SEO, recent advancements in AI-driven search have lessened its importance for many types of content. In a December 2024 article for Search Engine Land, I discussed how schema markup is not as critical as it once was, with the exception of certain structured data types, such as product schema.
Deep Research can still assist SEOs by:
- Identifying the most relevant schema types for products, events, or structured content that still benefit from markup.
- Finding industry-specific examples of where structured data is still impactful.
Example: Schema Relevance in AI Search
Prompt: “Analyze the role of schema markup in AI-driven search results and identify which schema types still provide ranking benefits.”
By leveraging Deep Research, SEOs can avoid unnecessary implementation efforts and focus on structured data that truly matters in today’s search landscape.
Why Deep Research Feels Like an SEO Superpower
SEO’s evolution is starting to feel like a sci-fi experiment gone rogue — constantly mutating and throwing unexpected changes our way. OpenAI’s Deep Research helps SEOs track these mutations in real time, ensuring they aren’t optimizing for outdated strategies.
While traditional ChatGPT responses provide helpful general guidance, Deep Research enables SEOs to produce more accurate, authoritative, and competitive content — a necessity in AI-driven search. By integrating Deep Research into their workflows, SEOs can improve competitive analysis, content ideation, E-E-A-T optimization, and automation efforts, ultimately leading to higher rankings and stronger organic performance.
Deep Research shifts the balance of AI-assisted SEO from guesswork to precision. For SEOs who thrive on data-backed decisions, this tool is a game changer.
Postscript
This edition reflects the piece exactly as I wrote it, before the red pen came out. If you’re looking for the unfiltered version, complete with my preferred metaphors, tone, and occasional eye rolls, you’ve found it.
