Absolutely. We ran a content audit using an AI tool that flagged a weird blind spot. We had plenty of content around high-level SEO topics and some solid how-to guides, but nothing targeting beginners who were just learning what SEO even was. It wasn't something we noticed because we were too close to the material. The AI grouped queries by intent and showed us we were missing a big chunk of low-competition, high-traffic searches like "what is title tag" or "how do search engines work." The lesson was simple but important. You need a full content ladder. Not just the expert stuff at the top but the intro-level rungs that help people climb up to it. AI didn't magically create the strategy for us, but it gave us a new lens to see where people were falling through the cracks. Sometimes you need an outside view, even if it's coming from a bot.
Yes - one of the most useful moments came when we used AI to analyse our existing Google Reviews to better understand recurring themes, language patterns and emotional drivers across our reviews and compare them to our marketing i.e. website content, blogs and social messaging. What became obvious very quickly was a gap between why clients actually choose us and what we were leading with in our content. Our reviews consistently highlighted things like clarity, calm guidance, translation of complex ideas, trust, and feeling supported - especially during periods of uncertainty or change. Yet our content was far more focused on services, deliverables and technical capability. AI helped us see that we were underselling the decision-making support and reassurance clients value most. We were explaining what we do, but not clearly enough how it feels to work with us or why that matters. The lesson was powerful: AI is incredibly effective at surfacing insight from qualitative data you already own. It doesn't replace strategy or judgment, but it helps remove bias and familiarity, showing you what's resonating emotionally - not just functionally. Since then, we regularly use AI to analyse reviews, feedback and client language to shape messaging, content themes and proof points. The result isn't more content - it's more relevant content that reflects what clients genuinely care about and trust.
We manually conducted a series of individual customer interviews ahead of our marketing conference and then used AI to take notes, summarize, and cluster the transcripts. Doing it ourselves not only would have taken a while, but might have been more difficult to keep personal biases out of the recommendations. Note-taking ourselves would probably also have missed some things and not allow the interviewer to focus on the body language, this allowed us to have the human interviewer observing for these things and updating the AI's notes afterwards with these additional details. We noticed than in previous years, the manual notes often focused on the specific skills people were interested in, probably because those were the most practical takeaways, but when we took all the notes together and had AI cluster it, it noticed patterns that we didn't ourselves. For example, the clustering from these interviews showed us that the biggest challenges for marketers were organizational politics and confidence. That was a pretty big gap in our content strategy, because we were teaching people how to do the work, and trying to have skill-related content on marketing fundamentals to attract them to our site, when more general leadership topics would be more interesting to them both in our content marketing and in our programming on the day of the event. From this experience I learned that using AI to help fact-check your data can be a helpful gut-check and also help bring together insights from various contributors (in our case interviewers) in the most data-centric way.
We started using AI to do a competitive content gap analysis at scale. We used AI tools to crawl our sitemap alongside the sitemaps of our top five competitors in injection molding and thermoforming. It analyzed hundreds of URLs, grouped content into topic clusters, and surfaced patterns showing what competitors were publishing that we were not. The gaps were pretty obvious once we saw the data. We had solid content around our services and capabilities, but we were missing educational content focused on real technical problems customers deal with every day. Meanwhile, competitors were ranking for very specific troubleshooting searches like warping in injection molding, short shot causes, and splay defects. We adjusted our marketing plan to include more of this educational content and since then, many of the blogs have become top performing blog posts and brought in qualified traffic. The biggest takeaway for us was that AI does not replace strategy, instead it just speeds up pattern recognition in a way humans just cannot do efficiently. We could have reviewed competitor sites manually, but it would have taken weeks and we still might have missed important details. AI helped us see both the big picture and the specifics. Not just that we needed more content, but exactly what kind of content we were missing.
Hi! I have more than 10k followers on LinkedIn and my posts didn't get views. AI literally restored my reach. I had been posting consistently for years and had a lot of projects going on. I was getting tagged in podcast features, receiving reviews on my documentaries... But my DMs were dead. Anyone who has spendt some time on LinkedIn knows this: DMs are the most important metric. I was worried. So I fed my last 20 LinkedIn posts into Claude and asked it to analyze them. I wanted to know why they weren't working. In hindsight, I should've seen the pattern. All of my posts were very Peter-centric. Me, me, me. "I just finished a documentary in Ohio...". The hook was always about what I did, not what the reader was dealing with. I hopped on Perplexity (my favorite research AI tool) and asked it to find 10 LinkedIn creators in my space (fractional CMOs, people working in "boring" industries, documentary filmmakers) whose posts were going viral. I copied their posts into Claude, and said: "Compare these to mine. What are they doing that I'm not?" Their hooks focused on broad industry pain points. "Nursing homes are losing staff faster than they can hire them" or "Every healthcare association thinks they need a rebrand. They don't." Then they'd offer their personal story or solution. It clicked: even if there was value in my post, it was hidden below my 'look at me' type of intro. The next problem was voice consistency. I've got a team of 15 people spread across the world, if I just said "copy this structure" we'd all sound like the same generic consultant. We built a Claude Project. Put everything in there: transcripts from our nursing home documentary, People Worth Caring About, podcast, keynote, drafts of my upcoming book Interns to A-Players. Our interns wrote an SOP with my social media manager: here's how Peter talks, what he cares about, the 95/5 rule (AI generates to 95%, humans add 5% authenticity). Now when we create content, AI knows my voice, my stories, my methodology. But we never publish what AI spits out. It's always a draft. I have a writer who turns it into something I would actually write. The result was a 195.5% increase in LinkedIn impressions in the first two weeks of January, typically my worst time of year. And I'm getting inquiries again. AI works great to identify what's performing, it can see patterns that I have become blind to due to exposure. Happy to walk you through our process, or how this looks like in my remote team!
AI helped me identify a gap in my content strategy by showing that my "good" content wasn't actually doing its most important job. Engagement seemed fine at first, but when I used AI to examine posts based on reader intent instead of topic, a pattern appeared. My safest content was underperforming. Most of what I published focused on best practices and ideal frameworks. This content was useful, but also comfortable. AI revealed that posts about failures, rather than successes, consistently led to deeper engagement, especially from senior leaders. Stories about edge cases, trade-offs, and consequences outperformed polished advice. The lesson was uncomfortable but useful: educational content rarely drives decisions. Leaders respond to risk, not theory. AI didn't generate better ideas; it revealed where I was avoiding difficult topics. Now I use AI for pressure testing, not for generating content. If a piece doesn't highlight a real failure or force a decision, it probably won't matter. AI helped me shift from optimizing for engagement to focusing on relevance.
At EMILY, we use AI tools like SERanking, Google Search Console, and ChatGPT in combination with our Gaps and Pillar Strategy to continually refine and improve content strategies for clients. One example involved a national training brand whose site had solid blog content but still struggled to rank for core service keywords. Using AI-assisted analysis, we mapped their content against a pillar cluster model, where one primary "pillar" page supports and links to a set of more focused "cluster" pages. AI helped us identify that while their site covered related topics in the fitness industry, it lacked a comprehensive, authoritative pillar page for the keyword "elite sports training." This was a critical gap; they had plenty of supporting content but no strong centerpiece. We created a new optimized pillar page and linked existing cluster content back to it. We also used ChatGPT to assist with internal linking suggestions and topic modeling to enhance the content's depth. The lesson? Even with high-quality content, SEO performance can stall if there's no clear content hierarchy. AI helped us visualize the structure and identify missing strategic pieces, proving that content quality must be matched with intentional content architecture. Thanks to this process, the client's visibility for competitive keywords increased, bounce rates dropped, and their site started showing up more frequently in AI-powered search features like featured snippets and Google Discover.
At Thundr Digital, we recently used AI to audit a comprehensive content cluster for a client in the competitive Edinburgh property sector. While our manual keyword research suggested we had covered all the "high-intent" bases—such as mortgage advice and neighborhood guides—we fed our existing sitemap and top-performing competitor URLs into an AI analysis tool to check for thematic gaps. The AI identified a significant "intent gap" that we had overlooked: the psychological transition of first-time buyers moving from the city centre to the suburbs. It highlighted that while we had technical data on house prices, we lacked "lifestyle integration" content. This insight allowed us to pivot our strategy, creating a series of briefs focused on the community culture and commuting realities of specific postcodes. The primary lesson we learned is that AI is most effective when used as a "logic checker" rather than just a generator. It excels at spotting patterns and omissions that the human eye might miss due to cognitive bias or over-familiarity with a project. We now view AI as an essential partner in our strategic process; it doesn't replace the local expertise of our Edinburgh-based team, but it ensures our creative intuition is backed by an exhaustive analysis of the wider information landscape.
The Situation: While running an AI intent-mapping audit on Enstacked's organic traffic, I uncovered a funnel stage mismatch that was silently killing conversions. The tool analyzed ranking pages against actual user search intent and revealed 68% of bottom-funnel traffic was landing on informational blog content instead of decision-stage pages. The AI pinpointed that a comprehensive blog was ranking position 4 for a high-commercial-intent phrase, pulling in 800+ monthly visits from ready-to-buy searchers. But the page had zero conversion architecture: no portfolio callouts, no service CTAs, and a 91% bounce rate. Meanwhile, the actual service page sat on page 7, optimized for brand terms instead of buyer queries. We were essentially intercepting purchase-ready traffic with educational content, then watching them leave to find our competitor. The Lesson: You can't optimize your way out of an intent mismatch. To fix it, I transformed the content architecture entirely. High-commercial keywords get decision-stage landing pages with conversion paths, not guides. Educational content targets purely informational queries, with strategic internal linking to push readers toward service pages when they signal buying intent.
AI Helped Us Find the Missing Link Between Content and Conversion We once used AI to analyze our blog and landing pages alongside conversion data, and it quickly showed a gap we hadn't noticed. We had lots of great content about tracking, attribution, and ad performance, but the AI pointed out that we were missing clear "next-step" content for people who were already convinced and ready to buy. In other words, our content was great at education, but weak at helping users move from learning to taking action. The lesson was that content strategy isn't just about attracting traffic, it's also about guiding people through the buying journey. After that, we created more comparison guides, case studies, and clear "how to choose the right plan" pages. It helped reduce friction and improved our sign-up rate. The biggest takeaway was that AI doesn't just help you create content, it helps you see where your funnel is leaking.
In robotics and logistics, our biggest challenge was striking a balance between technical authority and usability. To compete with established companies working with household brands in our sector, our content had to be highly detailed, including specifications, workflows, and engineering nuances, to be taken seriously in search engines. But that level of detail was overwhelming for less technical decision-makers, who are often crucial in the buying process. AI helped us identify this gap and suggested a more modular approach. Now, instead of one long wall of text, we break pages into sections with accordions, anchor links, videos, and visual explainers. Engineers can dive into the technical details, while other stakeholders can quickly get the high-level overview. We also became more intentional with SEO, ensuring that the depth of each page matches what the user is actually looking for. The number one takeaway for us is that authority alone isn't enough in a complex field; content needs to be accessible to users in every stage of the sales funnel, and AI helped us both spot the problem and shape the solution.
This is recent and I have to say it's the best gap filler I have found and I'm rolling it out to my customers. I don't want this to be an advert for obvious reasons, so I won't mention the AI product by name, but I will describe it and you can search out the product that's right for you, there are a few brands doing roughly the same job. So, what are the gaps? First, good quality detailed article content that targets medium search volume and low SEO scores. And second, a way of gaining good quality backlinks without paying disproportionate money or spending loads of time registering on directories. This software allows me to enter my URL, it identifies your target market and your keywords (you can manually add more), and then it creates a 30-day content calendar that, to be honest, I'm shocked at. It then produces high-quality articles on the subject matter, not just 1,000 words long, but up to 5,000 words and very good quality. You write a command like any AI, for example, include internal links and video and tables and infographics, highlight headlines in your brand colours, to name just a few of the customisable areas. However, what I've been experiencing over the past few weeks is that you can refine these prompts to give you very specific results. For example, it's always a grind to get your local SEO going and internal linking to place names and local pages does take a back seat. With this, you can ask it to link to specific local pages internally. The second gap it's been filling is backlinks. In just over 6 weeks, I've got over 50 good quality backlinks, all within my monthly payment. So, what's the catch, I hear you yell? Well, for now, I do have to monitor the content that's created daily. I have to make sure that the AI gremlins don't pop up, as in just over 40 posts, I've found the odd error with feature image creation and I've even had a Rick Astley video inserted into a post where there was no reference for it! ... hahaha, love Rick. So, what did I learn? AI technology is making it easier for us that are resource and time poor to create such powerful content and boost our SEO. My site has doubled its Ahrefs authority ranking from 15 to 31 in just 6 weeks, and I've just onboarded a customer with a zero rating and it's at 1.4 within a week. So the moral is: utilise AI, but you need to keep tweaking it and managing it, so we're not redundant... yet!
By implementing AI into the formation of our content strategy, we've found that it's not just about tracking what's working, but about understanding why something might not be resonating with our audience. We learnt that while design inspiration is a pillar of our content, we needed to show some more love to the first-time renovators audience. With this data, we shifted our approach, adding more educational content and guides that empowered our customers to feel confident in their renovation decisions from the get-go - we called it our Reno-Readiness month (last October). With our sister brand, TileCloud, we uploaded a series of digestible videos to our socials that helped us connect with our audience and reflect who we are as a brand.
Yes, AI helped us identify a clear gap in our content strategy when we analyzed performance data across multiple channels and noticed a consistent drop-off between high engagement and actual lead intent. While our top-of-funnel content was performing well in terms of views and clicks, AI-driven content analysis revealed we were underserving mid-funnel audiences looking for practical use cases, implementation insights, and decision-stage information. The key lesson was that engagement doesn't equal effectiveness. AI didn't just surface what content was popular; it highlighted what was missing. By using AI to map content topics against user intent and journey stages, we rebalanced our strategy toward more problem-solving, use-case-driven content. The result was better-qualified traffic and stronger alignment between content performance and business outcomes.
At HeyOz, AI helped us identify a blind spot in our content strategy that we likely would have missed on our own. We were producing a steady stream of short-form content across various platforms, and our engagement looked healthy at first glance. However, when we used AI to analyze performance patterns across different formats, hooks, and audience segments, a clear gap emerged. The data showed that while our educational content resonated with existing users, it consistently fell short with new audiences. AI pointed out that our top-of-funnel content assumed too much prior knowledge. We were speaking like insiders instead of explaining problems from the ground up. This insight did not come from a single metric; it came from analyzing patterns in watch time, drop-offs, saves, and comments. We adjusted by creating a new layer of content focused on framing problems rather than highlighting product features. Within six weeks, first-time profile visits increased by about 30 percent, and conversions from content to product pages improved noticeably. The lesson was that volume and consistency do not equal coverage. AI is most effective when it helps you see what you are missing, not just how well your current content is performing. It pushed us to design content for different stages of awareness instead of assuming one message works for everyone.
We're currently updating content for 2026, and I've been using ChatGPT as a sounding board to help me analyze and assess the top ranking content and how it compares to ours. I still give it all the important information such as difference in page rank and links etc, but it helps analyze what the top content includes or misses, or how its structured. Sometimes its as simple as small things missing or mentioning resources that we have elsewhere. So far, some posts are seeing a 900% increase in traffic. It's not perfect, but it is helpful to have a 2nd set of 'eyes' and then look at it objectively with my own years of experience. Daniel
We thought we had our content strategy pretty dialed in—steady publishing schedule, solid traffic—but things had flatlined. So we started using a few API-based tools that pull in real-time keyword and competitor data to help us spot gaps. That's when it became clear: we were completely missing out on the kind of content people search for when they're actually making decisions, not just learning. The AI helped surface those overlooked opportunities way faster than we could have done manually. We filled the gaps with targeted pieces, and within a couple of months, we saw a real lift in both traffic and leads. What I learned is that AI can handle the heavy lifting—scanning, comparing, surfacing insights—but you still need someone with context to make the right calls. It saved us hours, but we still had to guide the ship.
I first saw the gap when I asked ChatGPT to draft thought leadership posts. The drafts were clean but did not sound like me, and the ideas felt generic. That told me our process was missing a way to capture my verbal identity before any writing began. I shifted to short interviews to capture my voice, then used AI to turn those recordings into posts. The difference was clear, and writing no longer felt like starting from a blank page. The lesson was to use AI to sharpen and structure ideas that are already yours, not to create them from scratch. Since then, our strategy begins with defining key messaging through voice interviews, and we use AI to ship a week's worth of authentic content from a 10-minute session.
Last year, we were overtaken by a competitor in organic traffic for certain key keywords. Rather than being reactive, we turned to an AI tool to interpret how LLMs were summarizing content in our sector. We found that our content was great for humans but NOT good enough for AI search and LLMs, as competitors explained use cases and definitions in simple language. The bottom line is that your content must cater to both humans and AI, as a lack of understanding by AI means loss of visibility. We audited a core page and found that it wasn't very clear about "who this is for" right away. We used AI to test a few summaries and updated the page to include transparent context and examples. Within weeks, our brand was popping up more often in AI-generated responses during sales calls. The lesson here is that content should be tested from an AI point of view, to make sure it's clear and structured - because unclear value won't get AI attention.
One gap we didn't realize we had was that our content jumped straight into services without answering the basic questions people had first. Visitors wanted to know what happens before a project starts, not just what we deliver at the end. We used AI to review search queries and on-site behavior, and it showed us that people were looking for simple things like timelines, preparation steps, and what they needed before reaching out. For example, many users visited our pricing page and left because they didn't understand what information they needed to get started. We fixed this by creating short, easy-to-read pages that explained the process in plain language before any commitment. After that, people stayed on the site longer and asked fewer repetitive questions. The lesson we learned is that AI works best as a mirror. It doesn't replace strategy, but it helps you see what your audience is confused about so you can fill the gaps.