Everyone nods when you mention storytelling in marketing. It's like saying "content is king" or "customer experience matters"—universal truths that get universal agreement. But here's the uncomfortable reality: maybe 5% of marketers actually do storytelling well.
The rest confuse calls-to-action with storytelling. They mistake positioning statements for narrative. They create campaigns that check the "story" box without ever making anyone feel anything.
Matt Frisbie knows the difference. His journey from Disney artist to CMO of Axomo reveals what real storytelling looks like—and why it's the difference between campaigns that work and campaigns that just exist.
Frisbie's path started with a pencil and a dream of becoming a Disney artist. As a classically trained illustrator, he learned something most marketers never grasp: every element in a frame tells a story by design.
"If you put a figure alone with a lot of negative space, you create the feeling of being alone," Frisbie explains. "If you put the person around a lot of food, that means love. If you have tension, you know, that creates angst."
This isn't accidental. It's intentional storytelling through design—a technical skill that requires understanding how visual elements affect human emotion. As an illustrator creating images from scratch, Frisbie had to master the art of making people feel something specific.
Most marketers never learned this foundation. They inherited templates and best practices without understanding the psychology behind why certain layouts, colors, or compositions work.
In illustration school, Frisbie studied Robert McKee's "Story"—the definitive book on narrative structure. McKee breaks down "Casablanca" not as the best story ever told, but as the best job of telling a story.
"It doesn't mean it's the best story," Frisbie notes. "It does the best job of telling a story of moving a narrative with characters, with supporting characters, shot selection, timing."
The key insight? Supporting characters provide third-party validation instead of self-promotion. Rick doesn't tell you he's honorable—other characters demonstrate his integrity through their dialogue and actions. The audience experiences the story rather than being told about it.
This principle transforms marketing. Instead of declaring your product's benefits, you architect experiences where customers discover those benefits through the narrative journey.
Most people describe Disneyland as a theme park or permanent carnival. Walt Disney had a different vision entirely.
"As a child, Walt would watch animations or films and have the desire to be there," Frisbie explains. "What if I could be inside of a movie, walking around with the characters in the film? That's Disneyland."
Frontierland isn't just Wild West theming—it's walking through a Western with film characters. Tomorrowland is experiencing sci-fi. Adventureland drops you into African jungle adventures.
"You got to be in any movie you want and you just walk to the next movie," Frisbie says. "They want you to be a participant in the story."
This participation principle separates great brands from commodity businesses. When customers participate in your story instead of just consuming your products, you've transcended transactional relationships.
When customers are wrapped up in your story rather than evaluating individual products, something powerful happens: you earn permission to be marketed to.
"As a consumer, I'm wrapped up in your story, not the one or two products that you have to offer," Frisbie explains. "You then have the permission to be marketed to because you've captured me."
This isn't just emotional manipulation—it's sound financial strategy. Story-driven brands can launch new products, enter new categories, and command premium pricing because customers are invested in the narrative, not just individual offerings.
Consider Liquid Death: four-dollar canned water that has people lining up to buy it. On paper, it makes no sense. In practice, it's storytelling genius.
The brand didn't improve the product—water is water. They improved the story around the product, transforming a commodity into a lifestyle statement. Customers aren't buying hydration; they're buying participation in the Liquid Death narrative.
Frisbie identifies a pattern similar to 3D animation's uncanny valley. There's mediocre "food on the table" marketing that optimizes CTAs and gets sites live. Then there are breakthrough storytelling successes like Liquid Death.
The dangerous middle ground is where half-hearted stories fall flat—brands that go halfway on storytelling or tell cute stories that don't actually resonate.
"The only way you hit a home run is if you swing, but when you swing, you have a chance to strike out," Frisbie says about the risk of taking creative swings.
The solution isn't avoiding risk—it's doing homework. "When you make quick decisions, you make the riskiest decisions. When you really do your homework, you practice and you prepare, you're going to be ready to tell that story."
As CMO at Little Giant, Frisbie faced a classic marketing challenge: selling safety products to customers who wanted to be seen as dangerous.
The ladder industry targets professionals in high-risk jobs—electricians, steel workers, construction crews. These buyers work in 11 of the 25 most dangerous jobs in the world. Many receive hazard pay because their work is literally deadly.
Traditional safety messaging missed the mark entirely. "They'll never admit that they like that," Frisbie realized about safety features. "They want to do the dangerous thing and then make sure that they got their back."
The breakthrough moment came at a trade show. Frisbie witnessed a husband and wife arguing in their booth—she was emotional because he'd nearly died in a ladder fall. "You almost didn't come home," she wept.
That conversation revealed the real story: it wasn't just about the buyer. The influencer (spouse) and the end user had different emotional needs, but both centered on respect for the danger these professionals face daily.
Instead of leading with safety, Little Giant pivoted to "respecting danger." Every piece of marketing honored the skill, courage, and risk these professionals take on.
"The whole customer journey was like respecting danger, respecting danger, respecting danger," Frisbie explains. "They felt seen."
The results were immediate. Sales teams said they had "goosebumps" because they finally had weapons to go to war with. Influencers started requesting partnership opportunities. The campaign began taking market share in an established, generational industry where everyone already knew each other.
When COVID hit, Little Giant couldn't do in-person ladder demonstrations. Instead of accepting video conference limitations, they built something unprecedented.
The team constructed a multi-camera studio with nine PTZ cameras and situation rooms. Instead of talking heads, they created immersive experiences where viewers could see ladder demonstrations from every angle—cameras going up ladders, down ladders, all around the room.
"We wanted to be able to blow their minds," Frisbie says. "Most video conferences, most video training is a talking head."
The innovation was so effective that Zoom reached out asking how they were pulling it off. They won awards for retailer training programs. Most importantly, they kept selling when competitors struggled.
The principle behind the success: they had the same number of pixels as competitors but refused to think inside conventional constraints.
Through his experiences, Frisbie discovered a fundamental truth about human psychology: "Feeling understood is the sexiest feeling in the universe. Not being attractive, not being funny, feeling understood."
This insight explains why certain brands create cult-like followings while others struggle for basic recognition. When customers feel truly understood, they don't just buy—they become evangelists.
"That's how affairs happen is at work. People feel understood. That's how you fall in love with someone. That's how you fall in love with a brand," Frisbie explains.
At Axomo, Frisbie applies this principle through what he calls the Queen Vicky method. Instead of building composite personas from data, they identified a real customer who uses their swag management platform exactly as designed.
Queen Vicky isn't a spreadsheet—she's a real person. Frisbie flies out for lunch with her. They sent custom Taylor Swift baby packages when she had a child. The entire company rallies around "All hell to Queen Vicky."
"Everything we build is to get more and more Vickys," Frisbie says. This approach ensures every product decision, marketing campaign, and customer experience improvement serves real human needs rather than theoretical market segments.
Great storytellers practice like elite athletes. Tiger Woods takes a thousand touches with his club before tournaments. Steph Curry shoots hundreds of times before games look effortless.
"In a game, it looks like he's making everything, but you didn't see the thousands of misses before that were very intentional," Frisbie notes.
Marketing storytelling requires the same deliberate practice. Test small campaigns while paying attention to responses. Iterate based on real feedback, not assumptions. Build confidence through repetition before taking big swings.
The best insights often come from unexpected moments—conversations at trade shows, emotional customer reactions, offhand comments during research calls.
But serendipity requires presence. "You can't run across it unless you're out there moving and listening," Frisbie points out.
This drives analytical marketers crazy because it's unknowable. You might talk to 10 people and get the insight, or you might need 50 conversations. The key is accepting that discovery can't be forced—only facilitated through consistent engagement.
Frisbie's final advice is simple but profound: "Take the swing. You're talented. The opportunities have come to you. Take the swing."
Fear of failure keeps most marketers playing it safe. But safe swings rarely create breakthrough results. "When you see yourself do it, you know that you can do it again and again," he explains.
The goal isn't perfection—it's progress through action. Every swing teaches something, whether it connects or misses.
Frisbie poses a provocative question: what's the highest form of art in the world?
His answer: advertising.
"It's creating an asset, an image that gets people to act. Get your wallet out of your back pocket because I made this piece of this image, this video, a sound bite that you're going to act."
When you look at the Mona Lisa, you wonder if she's smiling. When you see great marketing, you reach for your wallet. That's the difference between admiration and action.
Mastering this art requires understanding psychology, human behavior, and the technical craft of narrative construction. It demands the patience to truly listen to customers and the courage to take creative risks.
Most importantly, it requires recognizing that storytelling in marketing isn't about you—it's about helping customers see themselves as the hero of their own journey, with your product or service playing the supporting role that helps them succeed.
The magic happens when customers stop evaluating your features and start participating in your story. That's when marketing transcends transaction and becomes transformation.
“We spent months researching our customers before launching anything. Tiger Woods gets a thousand touches with the club before a tournament—in the game it looks like he's making everything, but you didn't see the thousands of misses that were very intentional. I remember an agency that had 'fail fast' above the door. I agree with that while you're paying attention—take smaller swings, see some responses, then iterate based on what you learn.” - Matt Frisbie
02:35 - From Disney animator to marketing leader
06:51 - Creative skills in the boardroom
13:08 - "Rage clicks" and user frustration signals
23:44 - AI reality check vs. hype
31:47 - Reactive vs. proactive analytics
41:53 - Stay nimble for industry changes
"Story" by Robert McKee - Matt's go-to storytelling reference
"So God Made a Farmer" Super Bowl ad - Perfect example of honoring an underappreciated audience
FREE Content Consolidation Tools: https://97staging.com/articles/podcasts/how-to-consolidate-optimize-and-finally-see-seo-results/
Request a free AI Audit: https://97staging.com/ai-audit/
Connect with Matt on LinkedIn: https://www.linkedin.com/in/matthewfrisbie/
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Matt Frisbee went from Disney performer to marketing executive, and is one of the best storytellers around. In this conversation, Matt breaks down the art of storytelling in marketing, shares how he built breakthrough campaigns at Little Giant Ladder Systems, and explains why "feeling understood is the sexiest feeling in the universe."
This is a masterclass in taking calculated risks, doing the deep research work that most people skip, and why feeling understood by a brand is what drives real customer loyalty.
If you have Googled anything in the last few years, you’ve likely come across an AI overview that summarizes some of the ranking pages to answer your query. Or maybe you wondered about the history of the Ottoman Empire or needed instructions to refill your car’s oil and turned to ChatGPT. AI is taking online search by a storm.
For search engine users, the rise of AI has made getting synthesized summaries of all the top internet easy. For companies and SEO experts, it involves figuring out how to adjust your content strategy to keep your content visible and to reach your customers. That’s why we’ve put together this guide on the future of AI search SEO to help you figure out where and how to tweak your content strategy to be ready for the growth of AI SEO.
Unlike traditional search engines that rely on keyword matching and indexed pages, AI-powered systems use large language models (LLMs) to interpret natural language in an attempt to deliver nuanced, conversational results.
Some of the leading AI search tools you may have used or heard of include:
Search is undergoing a fundamental shift that’s only getting started. In May 2023, Google began rolling out Search Generative Experience (SGE), now rebranded as AI Overviews, which places AI-generated responses above standard results. Microsoft integrated AI mode into Bing in early 2023 using ChatGPT-4, while platforms like You.com and Perplexity launched AI-first search tools that prioritize summarization and citations. Search engines now are prioritizing their own AI summaries at the top of the SERP in what used to be prime real estate for SEOs.
These AI tools are changing the way people interact with SERPs. In fact, a study from the Pew Research Center in May 2025 notes that people are significantly less likely to click on web pages listed in Google search results if there’s an AI summary present. They also only rarely click on the sources listed by the AI summary.
With the shift in how users interact with search engines, SEO is going to shift too.
Traditional SEO is built around optimizing for search engine crawlers and ranking within standard SERPs. This includes tactics you’re likely very familiar with, such as:
GEO—Generative Engine Optimization—targets AI searches. Large Language Models don’t crawl; they try to interpret the context and evaluate:
Because LLMs respond to context and credibility, not just ranking signals, you want to optimize content for semantic relevance, not just visibility. Adding GEO to your content strategy is another way to make your content visible in those AI-generated summaries—though traditional SEO still matters as well.
So if AI is going to change the way people search (and already is starting to do so), you need a content strategy designed to fit in that landscape. These are our four top tips for creating content that your readers will love and that works with AI.
Content these days has to walk a very fine line: being written for humans and for AI accessibility all at the same time. You don’t want to write a brilliant piece of long-form content only for it to be lost in the ether of Google, but you definitely don’t want to end up with AI slop. Some ways you want to cater your content for your readers and for AI include:
The goal here is to write content for your human audience but to make sure it’s fully AI accessible afterward. At the end of the day, good content is still king, so prioritize having well-written content and avoid losing that human touch while optimizing for AI search SEO.
AI works like any other search engine: it will rank your pages higher if they’re correctly set up with appropriate metadata. While AI tools don’t crawl the web like traditional bots, they still rely on structured, well-maintained websites. Technical SEO helps ensure your content is indexed by both search engines and used by AI models that reference top-ranking pages. Prioritize:
Just like with Google Search, AI systems reward content that’s well-structured and technically sound.
Optimizing for search engine results page (SERP) features can improve your visibility in both traditional and AI-generated summaries. Focus on:
Appearing in these SERP features improves your chances of being referenced by AI models—and therefore coming across your readers’ screens.
If you’re looking to build your pages in a way that makes it easy for AI tools to scan your content, focus on these five strategies:
By following these principles, your content becomes easier for AI models to recognize—which then helps you stay visible in the next era of online searches.
Even though it might feel like the search landscape is rapidly evolving, the core principles of technical SEO remain as important as ever. In fact, SEO hasn’t really changed—it’s only expanded to include AI searches. Staying on top of and implementing foundational technical best practices still pays dividends, both in traditional rankings and in AI-generated search results.
Structured data helps both traditional search engines and AI systems better understand the context of your content. Using it can help your content get featured in snippets and AI overview citations. To get the most out of your structured data:
Well-implemented schema makes it easier for AI systems to identify key facts and understand the relationships between ideas—boosting your content’s chances of being referenced in AI search results.
AI-powered search is no longer limited to just text, and your content strategy can capitalize on that. Many search engines and AI assistants now support multimodal inputs and outputs to blend text, images, and video to meet user needs. Make sure your site:
By incorporating diverse formats, you increase your visibility across a variety of SERP features. Your site could end up as the cited image in an AI overview or in an image carousel. That visibility will make your content more accessible and expand your reach.
All the general tips we’ve talked about so far are best practices for any type of AI search tool. While many core principles remain consistent, each model has its unique behaviors and ranking preferences. Some of the most prominent and widely used AI search engines—and the ones offering the most trackable performance insights today—include:
AI systems reward conversational content that mimics how people talk, so here are a few tips to optimize for conversational queries and natural language prompts:
By tailoring your content for the nuances of each platform—and optimizing for how people naturally ask questions—you’ll increase your visibility in both AI-driven and traditional search environments.
Traditional SEO tools may not yet offer complete coverage of AI-driven search experiences—but a new wave of reporting solutions is emerging to bridge the gap.
There are a lot of SEO metrics, but which ones matter for AI search SEO? Key metrics to focus on include:
Since AI search focuses more on credibility and relevance than on traditional rankings, visibility can come in the form of mentions and summaries rather than blue links.
While AI searches are still relatively new, there are tools that are adapting to help you keep track of your most important metrics:
As AI search adoption increases, expect more tracking solutions to emerge. Just like SEO matured with its own analytics stack, AI SEO reporting will become a core part of modern marketing analytics within the very near future. Start experimenting with these tools now to stay ahead of the curve and get a head start above your competitors.
Working with AI to produce AI-optimized content is increasingly essential. Modern AI systems—like ChatGPT, Gemini, and MarketMuse—can help you with identifying content gaps and topic clusters that you can write about, speed up the drafting process, and create content outlines for you.
Don’t think of AI replacing your content creators. Instead, pair AI with human experts to speed up the content creation process without losing what makes human-written content great.
Another way to AI-prep your content strategy is to apply the hub and spoke model. The hub and spoke model is a content architecture that creates a central “hub” page targeting a broad, high-value topic, supported by multiple “spoke” pages that address related subtopics in depth. Each spoke links back to the hub and to one another.
For example, when Maveneer came to 97th Floor in 2023, they wanted content that would rank, so we gave them a hub and spoke strategy with comprehensive overview hubs targeting keywords like “warehouse automation” and “order picking.” After establishing those hubs, we could expand to spokes with drill-down articles like “order picking technology” and “automated sorting systems” linked to and from the hub. This structure improves internal linking, site navigation, and topic authority to search engines and AI systems alike. In fact, for Maveneer, their domain authority R skyrocketed from 3 to 34, and they saw an 886% increase in search impressions YoY.
Search is evolving—and fast. More people are relying on AI to answer their questions and give them potential solutions. With AI browsers popping up, there are only going to be more AI search developments. These platforms don’t just display a list of blue links. Instead, they generate dynamic responses by pulling insights from multiple sources, often without traditional attribution or visible rankings.
For enterprise brands, this shift has major implications.
In this new paradigm, visibility isn’t just about ranking #1—it’s about being referenced, cited, or summarized by AI models at the moment a customer asks a question. Failing to adapt means losing organic visibility at critical touchpoints—especially early in the customer journey when buyers are still gathering information.
Enterprise brands that invest in AI search SEO now can make sure they’re ahead of the curve and stay visible. AI isn’t replacing internet searches—it’s reshaping it. And enterprise brands that evolve their strategies now will be best positioned to lead out in the next era of SEO.
Let us become your strategic AI SEO Partner. If you're ready to win in search, let's talk.
Reddit isn't the niche platform it once was. With roughly 500 million monthly active users, it's evolved into a massive hub where real conversations happen daily. But what makes Reddit special isn't just its size—it's the quality of those conversations.
"Reddit has a tendency to bring the honest opinion out from people and that is where the meat is when it comes to marketing," explains Kiersten Gaffney, a deep tech CMO who's built her content strategy around Reddit listening. Unlike Twitter threads or LinkedIn posts, Reddit's threaded conversation structure creates genuine depth and interactivity that reveals what audiences actually think.
The platform has shattered old stereotypes too. The gender split is now nearly 50/50, contradicting the male-dominated image many marketers still hold. More importantly for B2B brands, there's a massive audience gap that most companies are missing entirely.
Consider these numbers: 68% of Redditors aren't on LinkedIn, 45% aren't on Instagram, and 30% aren't on Facebook. That means while B2B brands flock to LinkedIn, they're ignoring huge chunks of their potential audience who live primarily on Reddit.
This shift represents a fundamental change in how audiences consume and discuss business topics. Reddit has kept its technical roots while expanding to capture professionals, decision-makers, and influencers who value substance over polish. For marketers willing to listen, it's a goldmine of unfiltered audience insights waiting to be discovered.
Effective Reddit listening starts with systematic keyword monitoring, not random browsing. The most successful approach involves using tools like Octalens to cast a wide net across multiple platforms while focusing primarily on Reddit conversations.
The process begins with identifying core keywords related to your industry, product, or audience challenges. Rather than diving straight into specific subreddits, start broad and let the tool surface conversations you might never have found otherwise. This approach reveals unexpected threads and communities where your audience discusses problems in their own language.
Daily monitoring becomes crucial here. Gaffney checks her keyword alerts every single day because conversations move fast and opportunities disappear quickly. "I'll be surprised by new Reddit threads that I wouldn't normally see," she notes. This consistent monitoring uncovers emerging topics before they become mainstream content themes.
The beauty of this system lies in its ability to surface authentic conversations across the entire platform. Instead of limiting yourself to obvious subreddits in your industry, you discover where your audience actually hangs out and what they really talk about when they're not being marketed to.
This discovery process often reveals gaps in your current content strategy. You might find your audience discussing challenges you never considered or using language that's completely different from your marketing materials. These insights become the foundation for content that genuinely resonates because it addresses real problems in familiar terms.
Once you've identified relevant Reddit threads, the next step involves systematic analysis using AI tools. The process is surprisingly straightforward but requires the right approach to extract meaningful insights.
The method involves copying entire Reddit threads and pasting them into AI tools like Claude or ChatGPT. But success depends on asking the right questions. The most effective prompt starts simple: "What is the most important takeaway from this topic?" This creates a foundation for deeper analysis.
Follow-up questions reveal content opportunities: "Are they missing anything? Should I talk about that? Should I write about it next?" These prompts help identify gaps in the conversation that your content can fill.
Tool selection matters for different tasks. Gaffney has found Claude excels at copywriting and any writing-related work, while ChatGPT performs better for research and data analysis. Many successful content creators use both tools for comparison, feeding ChatGPT's research into Claude's writing capabilities.
The key lies in treating AI as a research assistant, not a content creator. The tools help synthesize large amounts of conversation data into actionable insights, but they can't replace human judgment about what matters to your specific audience.
This analysis phase often reveals surprising insights about audience priorities, language preferences, and unmet needs. The goal isn't to find content topics you already knew about—it's to discover the angles, concerns, and perspectives you would never have considered without listening to actual conversations.
The transition from Reddit insights to published content requires a strategic approach focused on creating substantial pillar pieces rather than quick social media posts. This method has proven effective, with Gaffney reporting a 70% success rate when following this systematic process.
The destination for these insights should be comprehensive pillar content—detailed blog posts that thoroughly address the problems and questions discovered in Reddit conversations. This isn't about engaging directly in the Reddit threads themselves, but rather using those conversations as intelligence for creating valuable standalone content.
A smart validation approach involves testing topics first through smaller channels. Having a CEO or founder share the core insight in a LinkedIn post can quickly gauge audience interest before investing in a full pillar piece. If the post resonates, it validates the topic for larger content investments like detailed articles, webinars, or campaign sequences.
This crawl-walk-run approach minimizes wasted effort while maximizing learning. A successful LinkedIn post can expand into a comprehensive blog post, which can then become a webinar, email sequence, or even a full campaign. Each successful piece builds on proven audience interest rather than assumptions.
The content creation process benefits from the authentic language and specific concerns discovered in Reddit conversations. Instead of generic industry content, you're addressing real problems using the exact terminology your audience uses when discussing those problems with peers.
Marketing to technical audiences presents unique challenges that Reddit listening helps solve. Engineers, developers, and other technical professionals have built-in resistance to traditional marketing approaches, making authentic communication essential.
The fundamental principle for technical content is plain language accessibility. "Explain it like you're explaining it to your 10-year-old," Gaffney advises. If a technical audience can't understand your main point within 30 seconds, your content fails regardless of how sophisticated your solution might be.
Technical audiences particularly despise "smarketing"—the combination of sales tactics and marketing gimmicks that feels manipulative. This includes cheesy memes, overly promotional language, and content that prioritizes cleverness over clarity. These approaches backfire spectacularly with audiences who value substance and directness.
Success requires partnership with technical team members who provide expertise while marketers guide communication strategy. Engineers and developers aren't trained writers, but they understand the technical nuances that matter to the audience. Marketers bring the communication skills needed to make complex topics accessible.
The most effective approach focuses on helping technical audiences do their jobs better, faster, and more efficiently. Every piece of content should provide genuine value that improves their daily work experience. If your content doesn't help them solve real problems, they'll ignore it completely.
This audience can detect inauthentic content immediately. They've seen countless vendors trying to trick them into sales conversations through fake educational content. The only way to build trust is through consistently helpful, technically accurate, and genuinely educational materials.
Successful Reddit listening requires realistic expectations about scale and process. The goal isn't to produce hundreds of pieces of content, but rather to create consistently valuable content that genuinely serves your audience.
A sustainable approach involves creating one pillar piece per week that can be adapted into three to five channel-specific pieces. This might include the main blog post, a LinkedIn version, an email newsletter segment, a sales outreach template, and perhaps a social media series. Quality and consistency matter more than volume.
This recommendation particularly applies to founders and early-stage marketing teams without large content operations. As teams grow and add specialized roles like content marketers or developer relations professionals, the scale can increase proportionally. But starting small and building systematically prevents the quality issues that come with overambitious content calendars.
The current landscape includes many companies trying to automate content creation at massive scale, often producing generic content that gets flagged by Google for being AI-generated spam. These approaches hurt everyone by flooding channels with low-quality content that audiences learn to ignore.
Reddit listening prevents this trap by grounding content creation in genuine audience needs and language. When content addresses real problems using authentic terminology discovered through actual conversations, it naturally avoids the generic feel of purely AI-generated material.
The key metric isn't how much content you produce, but how well that content resonates with your intended audience. Better to create one piece per week that generates meaningful engagement than ten pieces that get ignored.
AI tools provide powerful assistance in the Reddit listening process, but they can't replace human expertise and judgment. Understanding this balance is crucial for success with AI-powered content creation.
The general rule suggests AI gets you about 75% of the way to finished content. That remaining 25% requires human expertise in product marketing and copywriting to transform AI output into content that truly serves your audience. This isn't just minor editing—it's substantial refinement that adds personality, brand voice, and strategic focus.
Effective AI use requires skill in both prompt engineering and post-output refinement. Some creators focus heavily on perfecting prompts to get better initial output, while others prefer to work with basic prompts and do more manual refinement afterward. Both approaches work, but success requires expertise in guiding the process.
The relationship between human and AI should be director to directed, not the reverse. As Gaffney discovered when training a founder to use Claude: "He was letting Claude direct him versus him direct Claude." When AI drives the process, results become generic and miss strategic objectives.
Template development helps maintain consistency across content creation efforts. Creating prompt templates for different content types and testing them repeatedly helps identify what works best for your specific needs. However, even with templates, AI outputs can vary significantly, requiring human oversight and adjustment.
The most successful approach treats AI as a research and drafting assistant that helps synthesize large amounts of information and create initial content frameworks. The human expert then shapes that framework into content that serves strategic objectives and connects authentically with the intended audience.
Reddit listening creates a bridge between authentic audience insight and scalable content creation. This approach solves the fundamental tension between understanding what audiences actually want and producing enough content to maintain consistent market presence.
The competitive advantage comes from truly understanding audience language and pain points rather than guessing based on industry assumptions. When content addresses real problems using familiar terminology, it cuts through the noise of generic industry content that dominates most markets.
Long-term success requires focusing on genuine value over clever marketing tactics. Technical audiences, in particular, can immediately detect when content exists primarily to generate leads rather than solve problems. Building trust requires consistent demonstration that your primary goal is helping them succeed in their roles.
This authenticity-first approach naturally leads to better business results because content serves real audience needs. When people find genuine value in your content, they're more likely to remember your brand when they need solutions you provide.
The Reddit listening framework provides a systematic way to maintain this authenticity at scale. Instead of running out of content ideas or falling back on generic industry topics, you have a continuous stream of real audience problems and interests to address.
The key is viewing community listening as an ongoing process rather than a one-time research project. Audiences evolve, new challenges emerge, and language shifts over time. Successful content marketing requires staying connected to these changes through consistent listening and adaptation.
Companies that master this approach create sustainable competitive advantages because they understand their audiences better than competitors who rely on assumptions or outdated research. In a world full of generic content, authentic understanding becomes increasingly valuable.
"Reddit has a tendency to bring the honest opinion out from people and that is where the meat is when it comes to marketing." - Kiersten Gaffney
03:05 - Reddit's honest opinions vs. other platforms
07:23 - Keyword monitoring with Octalens tool
08:31 - AI analysis: Reddit threads to content ideas
20:55 - Director vs. directed: controlling AI tools
25:28 - Marketing to technical audiences without "smarketing"
32:05 - Upcoming Maven course on Reddit mining
Attend a free 60-minute live demo with Kiersten on September 12, 2025 to see her whole in-depth process for transforming tiny insights into incredible content.
Register here: https://maven.com/p/a3bef8/turn-dev-complaints-into-content-gold-with-ai
Request a free AI Audit: https://97staging.com/ai-audit/
Connect with Kiersten on LinkedIn: https://www.linkedin.com/in/kierstengaffney
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Kiersten Gaffney is a CMO advisor helping deep tech software companies build their marketing growth engines. She’s advised hundreds of founders from companies like Airbyte, DragonflyDB, and Codefresh to build systematic, measurable approaches to marketing.
Most marketers think they know their audience because they can rattle off demographics—age, location, job title. But knowing who your customers are isn't the same as understanding what drives them.
Rachel Bascom, Head of Marketing at 97th Floor, puts it simply: "To know your audience intimately... means knowing who they are, but more importantly, what they're doing, where they are, and what they need and what they want."
The problem isn't that companies don't research their audiences. Many invest heavily in market research, creating decks full of insights that end up sitting on hard drives, unused. The real issue is that traditional audience research creates static profiles that never inform actual strategy. Companies know their customer is a "Gen Z influencer" or a "B2B decision maker," but they don't know what really motivates them or how to reach them effectively.
At 97th Floor, we've seen this challenge repeatedly across our client base. Companies arrive with extensive market research but struggle to translate insights into campaigns that convert. That's why we've developed an audience-first approach that flips traditional thinking.
Instead of treating audience research as a one-time project, audience-first marketing becomes an ongoing, cumulative process that builds competitive advantage over time. This isn't about doing more research—it's about using insights differently to drive real business results.
Here's what audience-first marketing is not: It's not just "doing research." It's using insights to inform strategy, messaging, and channel decisions at every level.
Traditional marketing agencies often act as channel managers, following established playbooks. B2B companies get LinkedIn and Google Ads. E-commerce brands get Facebook and Instagram. But at 97th Floor, we build custom channel strategies based on how specific audiences actually behave.
Our methodology has to be flexible, too. Rather than insisting on the same rigid research process for every client, audience-first means taking custom approaches to finding insights. Most clients need results quickly—a lengthy, inflexible audience research process would delay campaigns and hurt outcomes.
This approach challenges conventional wisdom at every turn. Instead of defaulting to "B2B equals LinkedIn plus Google," what if your audience hangs out on Reddit or listens to niche podcasts? What if they aren't even searching for your product yet, but they're discussing related problems in online communities?
As Bascom explains, "We're not channel managers. We build custom channel strategies based on how your audience behaves."
The key is moving beyond surface-level demographics to understand motivations and behaviors that actually drive purchasing decisions.
Today's customers face an unprecedented assault of messaging. AI-powered personalization means they're seeing more targeted ads than ever. Marketing emails flood their inboxes. Social media feeds compete for every spare moment of attention.
When every brand is fighting for the same eyes, understanding exactly who your customers are and what motivates them isn't just nice to have—it's your competitive edge. Generic messaging gets ignored. But when you understand what frustrates your audience in their day-to-day work, what motivates them, and how they actually consume information, you can cut through the noise.
At 97th Floor, we've built our approach around this reality. This isn't marketing fluff or the latest trend. Audience-first marketing delivers measurable results because it's rooted in how real people actually behave, not how we think they should behave. Companies that nail this approach don't just see incremental improvements—they unlock entirely new growth channels and messaging strategies their competitors never considered.
When startups secure funding and invest in marketing for the first time, they often have a product they believe in but limited understanding of who will buy it and why. These companies need everything: strategy, messaging, channel selection, and execution.
For these clients, audience-first marketing means starting with fundamental questions. Why do people actually want this product? What problems does it solve that customers care about most? Where do these people spend time online, and how do they prefer to learn about new solutions?
One startup might discover their audience researches extensively before purchasing and responds well to detailed content and case studies. Another might find their buyers make quick decisions based on peer recommendations in Slack communities or industry forums. The same product category, completely different go-to-market strategies based on audience behavior.
This approach provides clear direction from day one, preventing months of wasted spend on channels and messages that don't resonate.
Larger companies often have marketing teams that know their space well but need fresh perspective on specific initiatives. They might want to expand into new markets, launch a product feature, or improve performance on existing campaigns.
For these clients, audience-first marketing means bringing external insights that sharpen existing strategies. Internal teams have valuable institutional knowledge, but they can also develop blind spots over time.
An enterprise software company might assume their audience only cares about ROI and efficiency metrics. But external research might reveal that individual users are also motivated by how the software makes them look competent to their colleagues, or how it reduces stress in their daily work. These insights don't replace existing knowledge—they add crucial layers that improve messaging and positioning.
Sometimes clients need campaigns launched immediately while building deeper audience understanding over time. This might be a company preparing for a funding round, launching during a crucial sales period, or responding to competitive pressure.
The audience-first approach here means learning while executing. Initial campaigns launch based on existing knowledge and best practices, but every interaction provides data about what resonates with the audience. Click-through rates, engagement patterns, conversion data, and customer feedback all become inputs for the next iteration.
This creates a feedback loop where campaigns improve rapidly over time. Instead of waiting months for perfect audience insights before launching, results start immediately and get better as understanding deepens.
A skincare company approached their marketing with a narrow focus on "Gen Z influencers." That demographic description wasn't telling them much about how to actually reach these customers or what would motivate them to buy.
The company had invested heavily in market research—"decks upon decks of insights"—but couldn't figure out how to leverage it effectively. Sound familiar?
Rather than starting over, the 97th Floor team sorted through existing research and supplemented it strategically. Instead of one broad demographic, they identified five distinct audience segments based on motivations: sustainability-focused buyers, trends-focused customers, product researchers who stick with brands long-term, and others.
One key insight emerged about the trends-focused segment: they were really interested in zodiac signs and astrology content. Most skincare companies talk about ingredient benefits or offer price discounts. But this insight led to a campaign pairing products with zodiac signs.
"It outperformed everything else that we were doing," Bascom recalls. "It was just a small insight that we actioned on... it helped us select the right channel to meet the audience and the exact right message to give the audience."
The campaign succeeded because it reached customers through an interest they cared about—zodiac signs—rather than just talking about skincare benefits. Every other beauty brand was fighting for attention in the same way, but this approach cut through the noise by connecting with broader interests that had nothing to do with skincare.
The real power of audience-first marketing reveals itself over time. Unlike traditional campaigns that treat each initiative as separate, ongoing work with audience insights creates compounding returns that get stronger with every interaction.
Every campaign teaches something new about the audience. A social media test reveals which messaging hooks grab attention. An email sequence shows what content drives clicks. A landing page experiment demonstrates which value propositions convert best. These aren't just performance metrics—they're pieces of a growing puzzle that reveals how your specific customers think and behave.
At 97th Floor, we build a database of what works for each client's specific customers. Over months and years, this becomes an invaluable asset that competitors can't easily replicate. We know which channels perform best for different customer segments, which messaging themes resonate most strongly, and which creative approaches drive the highest engagement.
This isn't a "one-and-done" insight approach. Traditional marketing research creates a snapshot in time, but audience-first marketing creates a living, breathing understanding that evolves with your customers. Markets shift, platforms change, and customer behaviors adapt—but companies with strong audience intelligence can pivot quickly because they understand the underlying motivations that drive their customers.
The key is revisiting your audience regularly to stay ahead of these changes. Major triggers for deeper research include launching new products, entering new markets, or when emerging platforms show potential for your specific audience.
Consider Reddit's transformation in B2B marketing. For years, most business software companies dismissed Reddit as too casual or consumer-focused. But at 97th Floor, we identified early signals that technical professionals were increasingly using relevant subreddits to discuss work challenges and discover solutions. We launched Reddit campaigns for multiple B2B clients and found it became a strong way to connect with technical audiences that traditional B2B channels couldn't reach effectively.
The companies that moved quickly on this insight gained significant advantages—reaching their audience in a native space where competitors weren't present, often at much lower costs than LinkedIn or Google Ads. But this opportunity only became visible through ongoing audience intelligence, not one-time research.
Here's what makes audience-first marketing different from traditional research: it's cumulative, not cyclical.
Most companies treat audience research like buying groceries—they start from scratch every time. But audience-first marketing builds knowledge over time. Every campaign teaches something new about customer motivations, preferences, and behaviors. This creates a database of what works for specific audiences that competitors can't easily replicate.
"It's not a cycle, it's cumulative," Bascom explains. "When you're building up all of these learnings and insights, then the results that you are seeing from them are also going to build."
An initial audience insight might improve campaign performance by 10%. But as understanding deepens over months and years, the batting average for messaging and channel strategy increases dramatically. Teams get better at predicting what will resonate, choosing the right platforms, and crafting messages that cut through noise.
Consider the observability company Chronosphere, a long-term 97th Floor client. Over years of working together, we learned that their highly technical audience was quirky and responded to snarkier messaging than typical B2B approaches. We tested messaging on LinkedIn, tried Reddit campaigns, even created a video game for their audience to play.
This deep audience understanding eventually led to a bold campaign targeting competitor events. Knowing their audience appreciated direct, snarky messaging, we ran "ditch the dog" ads on airport displays, taxi tops, and other out-of-home placements around events hosted by competitor Datadog.
The campaign worked—branded search increased significantly—because it was built on years of accumulated audience insights. A company without that deep understanding never would have attempted such a direct competitive approach.
This demonstrates how long-term client relationships compound results. The initial investment in audience research may yield modest improvements, but continued collaboration creates exponential returns as insights build over time.
Audience-first marketing requires staying proactive, not just reactive to data. Customer behaviors evolve, new platforms emerge, and market conditions change. Companies need triggers for when to invest in deeper audience research.
Major triggers include launching new products, entering new markets, or when emerging platforms show potential. Consider Reddit's evolution in B2B marketing. For years, most business software companies considered Reddit off-limits—too casual, too consumer-focused. But audience behaviors shifted. Technical professionals started discussing work challenges in relevant subreddits, and the platform developed better advertising tools.
At 97th Floor, we identified this trend early and launched Reddit campaigns for multiple B2B clients. Companies that moved quickly gained significant advantages—reaching technical audiences in a native environment where competitors weren't present, often at lower costs than traditional B2B channels.
The key is balancing analysis with exploration. Don't just study existing data—actively seek new insights through tools like SparkToro for audience intelligence, Gummy Search for Reddit research, or simply following rabbit holes in analytics and social platforms.
"It's all about following rabbit holes," Bascom notes. "Where you've seen the biggest insights, look there."
Companies often think they need to start audience research from scratch, but most already have valuable insights scattered across different teams and tools.
"Look at the lay of the land and audit what you already have," Bascom recommends. "See what you know about your audience and how you can action that."
Sales teams know what customers ask about most frequently. Product teams understand why people want specific features. Content teams know which topics drive engagement. SEO data reveals what customers search for. Previous ad campaigns show what messaging resonated.
The key is aggregating these insights and identifying gaps. Maybe sales knows customer pain points but marketing doesn't understand where these customers spend time online. Or content engagement data shows what topics interest people, but there's no insight into their broader motivations and interests.
For storing and building on insights, the approach matters less than consistency. Whether it's spreadsheets, shared documents, or custom AI tools, the important thing is capturing learnings somewhere accessible and revisiting them regularly.
This also can't be one person's job. SEO specialists, advertising managers, content creators, designers—everyone should care about and contribute to audience understanding. The best marketing results happen when entire teams understand who they're trying to reach and why.
Audience-first marketing isn't just better research—it's a fundamentally different approach to growth that builds sustainable competitive advantages.
Most companies will continue treating audience research as a project with a beginning and end. They'll create personas, file them away, and wonder why their marketing feels generic and performs inconsistently.
But companies that embrace audience-first marketing as an ongoing capability will compound their advantages over time. They'll identify new channels before competitors, craft messages that resonate more deeply, and build authentic connections with customers that drive both acquisition and retention.
At 97th Floor, we've seen this transformation repeatedly. Clients who commit to the audience-first approach don't just see better campaign performance—they develop marketing advantages that become harder for competitors to replicate over time.
The choice is clear: stick with static demographics or build dynamic, evolving audience intelligence that gets stronger with every campaign. Companies ready to make this shift should start with an audit of existing knowledge and commit to making audience insights cumulative, not cyclical.
Your customers are complex, evolving people with interests and motivations that extend far beyond your product category. The companies that understand this—and build systems to keep learning—will be the ones that break through the noise and drive sustainable growth.
"To know your audience intimately... means knowing who they are, but more importantly, what they're doing, where they are, and what they need and what they want." - Rachel Bascom
02:27 - What it means to truly know your audience
12:43 - The zodiac campaign breakthrough
18:22 - "It's cumulative, not cyclical"
24:17 - Start by auditing what you already have
27:35 - The difference between great and mediocre marketing
FREE Content Consolidation Tools: https://97staging.com/articles/podcasts/how-to-consolidate-optimize-and-finally-see-seo-results/
Learn about how to create high-quality content that is audience-focused: https://97staging.com/articles/high-quality-content-or-ai-slop/
Connect with Rachel on LinkedIn: https://www.linkedin.com/in/rachelbascom/
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Rachel Bascom is the Head of Content Marketing at 97th Floor, boasting over a decade of expertise in the realm of digital marketing and a fervent dedication to crafting audience-centric content strategies. In her tenure, Rachel has been a trailblazer in the development of the content marketing department, playing an integral role in the transformative journey that positioned 97th Floor as a comprehensive, award-winning, holistic marketing agency.
Most organizations dive into AI with enthusiasm but stumble right out of the gate. They ask the wrong question from day one: "Can AI do this?" It seems logical enough, but this approach puts AI in the driver's seat when humans should be steering.
The better question transforms everything: "What can I do with AI?" This subtle shift changes AI from the protagonist of your story to a powerful tool in your hands. Instead of wondering if AI can write content, generate reports, or analyze data, start asking how AI can help you create better content, deliver more insightful reports, or make smarter decisions.
This mindset difference isn't just semantic—it fundamentally changes how organizations approach AI adoption. When you make AI the hero, you end up chasing shiny tools and impressive demos. When you make your team the hero, you focus on solving real problems and achieving business outcomes.
The path from AI potential to performance requires more than just technology. It demands strategic thinking, cultural change, and a framework that keeps humans at the center. Organizations that get this right don't just implement AI—they transform how work gets done.
The most common conversation AI consultants have with marketing leaders starts with "Can you help me with AI adoption?" When asked what they want to accomplish, there's often an awkward pause. The CEO asked about AI strategy, so they're exploring AI—without knowing what problem they're trying to solve.
This backwards approach leads to scattered efforts and disappointing results. Successful AI adoption starts with establishing a strategic north star built around clear business objectives. These objectives typically fall into two categories: pain points you want to mitigate and opportunities you want to leverage.
Consider a large enterprise marketing organization with 200 global marketers facing two potential AI projects. The first involved using AI to recategorize assets in their decade-old digital asset management system that nobody used effectively. The second focused on AI-powered cold prospecting for a new vertical they were entering.
Both were technically valid AI use cases. But when viewed through the lens of business value, the choice became clear. Developing pipeline for a new vertical directly tied to revenue growth and market expansion. The digital asset management project, while potentially useful, ranked lower on the value chain.
This doesn't mean the asset management project was wrong—just that timing and relative importance matter. Organizations should evaluate AI initiatives against their strategic priorities and plan them in cycles. What bubbles up as critical today might shift in six months based on changing business needs.
The key insight: start with strategy and business outcomes, then find where AI fits. Don't start with AI capabilities and try to force them into your business.
While organizations obsess over AI technology, they consistently overlook the most critical factor: people. Change management represents the single most neglected aspect of AI adoption journeys. Companies focus intensely on what AI can do while ignoring what their people think and feel about it.
The vulnerability manifests differently across organizational levels. Middle managers often feel most threatened, worried they'll be replaced by younger, AI-savvy employees. Senior executives feel lost without a clear vision of what their AI-infused strategy should look like. They're concerned about governance, brand risks, and the fear of AI initiatives backfiring publicly.
Meanwhile, younger employees frequently feel frustrated that their companies aren't investing enough in proper AI training and development. They see the potential but lack the organizational support to pursue it effectively.
A common thread runs through all these groups: confusion about guidelines and governance. Employees want to do the right thing, but they don't know what's okay to use AI for and what isn't. This uncertainty creates paralysis that stifles adoption even when leadership supports AI initiatives.
The training investment gap makes things worse. Survey after survey—from Salesforce, Adobe, and the Marketing Institute—identifies lack of training and knowledge as the primary barrier to AI adoption among marketers. Yet organizations remain surprisingly reluctant to invest in AI education for their teams.
As one executive worried: "If I train my team, now they're AI-savvy marketers and they might leave." The response captures the paradox perfectly: "What if you don't train them and they stay? Now you have a bunch of dinosaurs who don't know AI and struggle to stay relevant."
Creating successful AI adoption requires cultural transformation. Organizations need to build experimentation cultures with risk tolerance for new technologies. They need open dialogue about AI fears and concerns. Most importantly, they need leadership that sets clear vision and expectations while providing time and resources for teams to learn and experiment.
Transforming from AI potential to performance requires fundamental mindset changes. Four key pillars create the foundation for this transformation.
Pillar 1: From "Can AI do this?" to "What can I do with AI?"
This shift makes humans the protagonists of their AI story. Think of it as the human-AI sandwich: humans provide creativity, intuition, and strategic vision at the beginning, AI processes and analyzes in the middle, and humans verify and refine the output at the end.
This approach acknowledges that while AI continues improving rapidly, humans remain essential for directing AI effectively and ensuring quality outcomes. The goal isn't to replace human judgment but to amplify human capabilities.
Pillar 2: From Outputs to Outcomes
Many organizations get excited about AI's ability to increase output volume. They celebrate going from 50 blogs per year to 500 blogs per year without asking whether 500 blogs actually serve their business better than 50 high-quality pieces.
This output mentality misses the point. The real question isn't whether you can produce more content, reports, or analyses—it's whether increased production drives better business outcomes. Focus on the results you want to achieve, then determine if AI helps reach those goals more effectively.
Pillar 3: Reclaiming Time for Human-Centric Work
Nobody works 40-hour weeks anymore. Most professionals put in 60-plus hours, leaving little time for strategic thinking, relationship building, or creative problem-solving. AI offers the opportunity to reclaim time from routine tasks and redirect it toward uniquely human activities.
This means more time to think, imagine, lead, and inspire. It also means better work-life balance and stronger personal relationships. When people understand that AI can free them to do more human things rather than replace human value, they become much more enthusiastic about adoption.
Pillar 4: Embracing the Previously Unimaginable
Consider how Uber combined four existing elements—maps, internet, phones, and apps—to completely revolutionize transportation. The individual pieces existed, but their combination created something nobody had imagined before.
AI presents similar possibilities. The challenge is remaining open to ideas and applications that seem impossible today. This requires willingness to experiment with approaches that don't fit current workflow patterns and comfort with uncertainty about where AI might lead.
Understanding AI's potential requires distinguishing between doing things differently and doing different things altogether. Most organizations start with the first approach—using AI to improve existing processes. But the real transformation comes from reimagining what's possible.
A marketing analytics team at a large B2C organization initially wanted to use AI to accelerate their data pipeline processes. They envisioned taking data through existing systems faster and creating visualizations more efficiently. This represented doing things differently—same process, better execution.
The breakthrough came from challenging that assumption. Instead of faster visualizations, what if there were no visualizations at all? The team developed a conversational interface where marketers could simply "talk to their data." Instead of navigating complex dashboards, they could ask natural language questions like "What was our most successful campaign last quarter?" and receive immediate, contextual insights.
This approach transformed the entire concept of data analysis. The underlying data sources and pipelines remained, but the human interaction became completely different. Marketers no longer needed to interpret charts and graphs—they could have conversations with their data and receive insights served up directly.
This evolution from different to transformational requires patience. Like any major technology adoption, AI follows the familiar bell curve. Early enthusiasts lead the charge while skeptics resist change, and the majority gradually moves from healthy skepticism to cautious optimism. Organizations need patience and consistent leadership commitment to guide this transition successfully.
For individuals feeling motivated but unsure where to start, the key is avoiding overwhelm. The constant AI buzz on LinkedIn and in business publications can create FOMO that leads to paralysis. Instead, take a measured approach.
Start by dipping your toes in rather than diving deep immediately. Plenty of free resources exist, from Coursera's AI courses to industry-specific learning opportunities. If formal courses feel too intensive, follow genuine business leaders in your field who curate and share relevant AI developments. Focus on people who discuss practical applications rather than hype-driven content.
For organizations, the investment strategy requires formalization. Set aside specific budget for AI training and development. Create communities where team members can share experiences and learn from each other. Take a long-term perspective on AI skill development rather than expecting immediate returns.
The goal should be building toward AI self-reliance—the ability to identify opportunities, implement solutions, and adapt to new developments without constant external guidance. This requires both individual skill development and organizational culture change.
Turning AI potential into performance isn't about finding the perfect tool or implementing the most advanced technology. It's about maintaining a human-first, strategy-driven approach that treats AI as an enabler of human potential rather than a replacement for human value.
The organizations that succeed will be those that invest in their people, establish clear strategic priorities, and remain open to transformation beyond their current imagination. They'll ask better questions, focus on meaningful outcomes, and create cultures where AI amplifies human capabilities rather than threatening human relevance.
The future belongs to those who can effectively combine human creativity, judgment, and leadership with AI's processing power and analytical capabilities. That future requires strategic thinking, cultural transformation, and the courage to embrace possibilities that don't exist yet.
The question isn't whether AI will change how work gets done—it's whether your organization will lead that change or get left behind by it.
"The first is to shift from 'can AI do this?' to 'what can I do with AI?' The second is to shift from outputs to outcomes. The third is to shift towards reclaiming time to think, imagine, lead, and inspire. And the fourth is to shift towards embracing ideas that we would have never considered otherwise." - Aby Varma
02:51 - Why "Can AI do this?" is the wrong question
06:22 - Enterprise case study: asset management vs. cold prospecting AI
10:19 - The overlooked change management problem in AI adoption
28:18 - The "human AI sandwich" concept introduced
33:22 - Complete four-pillar mindset framework
38:18 - Practical next steps for getting started
FREE Content Consolidation Tools: https://97staging.com/articles/podcasts/how-to-consolidate-optimize-and-finally-see-seo-results/
Request a free AI Audit: https://97staging.com/ai-audit/
Join Spark Novus's Marketing AI Pulse Community: https://sparknovus.com/marketing-ai-pulse
Connect with Aby on LinkedIn: https://www.linkedin.com/in/abyvarma
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Aby is the founder and principal at Spark Novus, transforming marketing through AI, digital, content, and brand strategies. He aligns marketing, sales, and product teams to drive brand positioning and demand activation. Known for his strategic vision and energy, Aby builds inclusive, high-performing teams.
He advances AI in marketing through the 'Marketing AI SparkCast' podcast and as the founder of the Marketing AI Pulse and Future Crafters communities. Aby is a member of the Forbes Communication Council.
Nick Cawthon got his first job at AltaVista in the late 1990s. When he told his dad how much he was making, his father was shocked. "You're making more than I am now, at the end of my career, in your first job out of college."
The job lasted 18 months. AltaVista became Google. But Cawthon felt something then that he feels again now: change is coming whether we're ready or not.
Today's AI wave feels similar. The question isn't whether AI will reshape work—it's whether leaders can guide their teams through the shift without leaving people behind.
Drive from San Francisco airport into downtown and you'll see billboards everywhere. Cawthon travels this route often and watches the buzzwords change every six months: cloud, Bitcoin, NFTs, edge computing. Companies chase investment dollars with whatever tech term is hot.
"I and others were jaded to what this hype cycle was going to produce," Cawthon says. "Bitcoin hasn't really changed my life very much."
But AI feels different. The potential impact spans different organizations and workflows in ways previous tech waves didn't. We've seen this progression before: businesses moved from pen and paper to spreadsheets, then came mobile-first design around 2010.
Now something more basic is shifting. "What if there is no interface? What if it is a language model instead of a UI?" This isn't just new tools—it's rethinking how work gets done.
Many leaders think AI transformation means starting from scratch. Tear everything down and rebuild with AI-first processes. This creates a false choice between radical change and staying stuck.
The County of San Mateo has a different approach. Despite being home to many AI companies, the county mandates: you cannot eliminate jobs through AI. Organizations must repurpose existing roles. People who used to transcribe town hall meetings now edit transcripts and publish documents online.
"I love that thinking because it acknowledges what we're good at as individuals and teams, then figures out how new tools can amplify that," Cawthon says.
The key word is "augment." Smart leaders ask how to level up existing staff rather than replace them.
For people who work with words, the change is already here. The old way meant spending hours on Google, downloading files, printing them out, highlighting key parts.
Now teams can build their own knowledge bases using RAG (retrieval augmented generation). They create custom language models that can cite sources and maintain transparency about outputs.
This splits into different skill levels. Junior people find sources, tag files, and build references. Senior people handle prompt engineering to get campaign concepts or conduct voice share audits across different brand models.
"If you believe prompting is going to replace searching in the next five to ten years, then figuring out strategy and copy and positioning becomes something to train on," Cawthon says.
Design has changed before. The early 2000s web was wild—everyone had their own visual style with leather textures and unique expressions. It was messy but creative.
Then mobile forced standardization. Google's Project Kennedy said all Google products had to look the same. Microsoft and IBM followed. Now we have standard patterns for tabs, buttons, dropdowns.
AI changes this again. Wireframes that took days or weeks now take minutes or hours. A technologist who knows how to go from concept to production-ready code becomes essential.
Cawthon tells the story of a creative director in his 50s who brought image generation tools like Midjourney into storyboarding for TV campaigns. He learned to keep characters consistent between shots—something that was hard to do even nine months ago.
Jesse James Garrett calls it "AI amnesty"—giving people permission to experiment without feeling like they're cheating. This matters for adoption.
Even when leaders endorse AI use and provide resources, teams still resist. Why?
First, using these tools well is hard. "As anybody who's tried to cheat on a test, it's hard to cheat," Cawthon says. "Using generative tools to make the fidelity of the idea you want to communicate is extremely difficult."
There's a learning curve. People need to stay curious, patient, and confident their process will work. The promise of speed is mostly false right now. It may be true in five years, but not yet.
Cawthon worked with someone who didn't trust the cloud. When SaaS products and cloud platforms emerged, she met them with distrust. Everything had to be email attachments and local files. She needed to see an icon on her desktop to know data was safe.
This limited her tools. She used spreadsheets for time tracking and budgets instead of CRM platforms and management software that could do the job faster.
"I think we're feeling that same thing again today," Cawthon says. Some people need that trust and transparency before they'll suspend disbelief and try new approaches.
When the internet emerged, some people asked how it could help what they were already doing. Seth Godin was working on a catalog—a book index. Larry Page and Sergey Brin saw the internet and said "let's index it" and built Google. Godin saw the internet and said "let me write a book." We know who won.
The lesson: you can't look at new technology through old eyes. Just adding AI to existing processes might not be enough.
Cawthon's young sons started with voice assistants, not point-and-click interfaces. Their first tech interaction is speaking to an algorithm. Within months, their home assistant will have Gemini built in for more complex tasks.
"Are we designing for agents or are we designing for humans anymore?" This generation might reject the awkward pointing, clicking, and typing that defined previous decades.
AI tools create tighter collaboration between departments. Traditional agencies and development shops end up "swimming in the same pool using the same tools."
This means less handoff, more side-by-side work. Instead of separate phases, teams mentor each other: "this is how I see this working" and "this is how it's actually working."
Cawthon found this crucial over the past year—having mentorship around application development and UX that he didn't traditionally worry about.
Cawthon built a survey tool to assess AI adoption maturity after seeing a design team of eight to ten people who might be left behind. He used generative tools to create production-level prototypes, skipping the entire Figma process. But the team was stuck in old workflows of creating interface abstractions to hand off to developers.
The assessment looks at several areas:
Current usage: Are teams using AI for ideation or in actual production workflows?
Process barriers: Is it approval issues, integration problems, or licensing constraints? Some companies only allow Copilot because of their Microsoft relationship, cutting off access to innovative startups.
Culture: Do people feel they have permission to fail, try, and innovate? Are there ethical concerns about AI use?
Data maturity: Can UX design tools integrate with other parts of the agency?
The tool provides scores by section and overall, comparing results to similar industries, organization sizes, and team sizes.
Leaders who want to guide teams through AI transformation should:
Grant amnesty. Make it clear people can experiment without feeling like they're cheating. Be transparent about the learning process.
Focus on skills, not replacement. Ask how to level up existing staff rather than eliminate positions.
Address trust directly. Some people need to see how data stays safe before they'll try new tools.
Create space to fail. The learning curve is real. People need time to get good at this.
Break down silos. When everyone uses similar tools, collaboration gets tighter.
Measure progress. Track where teams stand and what barriers exist.
This transformation is about people, not just technology. Every generation faces the choice between adapting to change or getting left behind.
The leaders who succeed won't be the ones who move fastest or adopt every new tool. They'll be the ones who bring their teams along instead of leaving them behind.
As Cawthon learned from his AltaVista days: change happens whether we're ready or not. The question is whether we help our people get ready for what's coming.
"This notion of amnesty, of AI amnesty in whatever field or process that you're in is to allow it not to feel like you're cheating because you do these things. To be transparent, be a mentor, and be questioning of the process and say, we're trying to figure out this transformation together." - Nick Cawthon
02:38 - Early tech career lessons from AltaVista to Google transition
06:55 - Internet paradigm shifts and building for new vs. old thinking
12:30 - AI workflow changes with prompting replacing search strategies
16:28 - AI adoption barriers and the need for "amnesty" in teams
28:05 - AI readiness assessment for measuring team transformation
Connect with Nick on LinkedIn: https://www.linkedin.com/in/nickcawthon-ux-digital-agency-product-design-leadership/
Fill out Nick’s AI Maturity Assessment to receive a report with a readiness score benchmarking your team against similar organizations: https://retrain.gauge.io/
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Nick helps design teams stay ahead of the curve with their AI transformation. He has been curating self-assessments for UX & Design Teams at retrain.gauge.io, helping analyze industry trends and removing barriers to adoption. Nick founded Gauge in 2001 in the San Francisco Bay Area to help organizations with evidence-based strategy and product decisions. Clients have grown to include Electronic Arts, Genentech, Airbnb, Adobe and many others. Nick is a professor in Data Literacy and Visualization in the Design Strategy MBA program at his alma mater, California College of the Arts.
The difference between SEO at a small business versus a company like Walmart isn't just about scale—it's about treating search engines as customers. While small teams fight for engineering resources and squeeze SEO fixes between other priorities, enterprise SEO teams operate with a completely different playbook.
Patrick Kajirian knows this world well. As Senior Product Manager of SEO at Walmart, he's previously led SEO initiatives at Disney, ESPN, and Realtor.com—sites that serve hundreds of millions of pages to search engines daily. His perspective reveals how enterprise SEO really works and where the industry is heading as AI agents start browsing the web on our behalf.
The lessons from his experience offer insights that extend far beyond large companies, especially as the entire SEO landscape faces another major shift.
Google as a First-Class Citizen
At enterprise scale, SEO stops being a marketing afterthought and becomes a product function. "You treat the search engine as a customer in a way," Kajirian explains. This shift changes everything about how SEO gets executed.
Instead of begging for engineering time or waiting for approval to make technical changes, product-led SEO teams have direct channels to the people building the site. They work alongside dedicated data scientists, brilliant engineers, and experienced designers. The search engine becomes what Kajirian calls "a first-class citizen" in product decisions.
This isn't just about having more resources—it's about building SEO compliance and best practices into the foundation rather than retrofitting them later. When SEO requirements are "woven into the foundation of how you build stuff," they actually get executed correctly.
The practical impact is huge. Product-led SEO teams can build roadmaps, iterate on solutions, and test extensively. They're not stuck with one-shot implementations that have to work perfectly the first time. As Kajirian puts it, they can "release something, look at it long and hard, see if it worked, do something else, do something different, iterate."
Building SEO Into the Foundation
This product-first approach pays dividends when it comes to technical implementation. Features like structured data for rich snippets, proper metadata handling, and mobile optimization get built into content management systems from the start. SEO teams can create templates and workflows that make optimization automatic rather than manual.
The contrast with traditional marketing-led SEO is stark. Instead of fighting for resources and explaining why technical changes matter, product-led teams are part of the conversation from day one. They influence architecture decisions, database design, and user experience flows—all with SEO impact in mind.
Why Indexation is the Real Challenge
Here's something most SEO discussions miss: at enterprise scale, getting pages indexed is often harder than ranking them. Google has become increasingly selective about what makes it into their index, and sites with millions of pages feel this pressure acutely.
"It's pretty clear that in the last decade, Google's actually been putting the brakes on crawling and indexing the entire web," Kajirian notes. "They're being very judicious and very selective about what gets qualified to get represented in the index."
For a site like Walmart with a massive product catalog, or Realtor.com with pages for hundreds of millions of properties, this creates a fundamental challenge. Google will crawl hundreds of millions of pages, but they won't index all of them. The question becomes: how do you signal which pages matter most?
Strategic Link Rotation for Crawl Budget
Kajirian's team developed a sophisticated approach to internal linking that goes far beyond traditional site architecture. They identify pages with the highest crawl activity—typically starting with the homepage—then map out how search engines discover and move through the site.
The strategy involves creating link systems that rotate through target pages, funneling valuable crawl activity to content that needs indexation. "If you understand where the crawler is, where the highest amount of crawl activity is located," Kajirian explains, "you can generate link systems that can rotate through a number of links targeting pages that are highly relevant."
This isn't random internal linking. The team analyzes server logs to understand crawl patterns, identifies authoritative pages that search engines visit frequently, then creates dynamic linking modules that systematically expose important but under-crawled pages to bot traffic.
The results are impressive: "If you do that diligently and consistently, you'll find that those pages get crawled real quick, they get indexed real quick, and they tend to perform better in SERPs."
The Lifecycle Reality
Enterprise sites deal with constant content churn, especially in e-commerce where products come and go. This creates a natural lifecycle for indexed pages. High-quality, relevant pages that serve user intent tend to stay indexed. Pages that decline in quality or relevance eventually drop out.
Kajirian's team uses link rotation as a "churn system" to continuously surface new content for indexation while allowing lower-quality pages to naturally fall out of the index. It's a sophisticated approach to managing what is essentially a limited resource: Google's willingness to index your content.
The key insight is that internal linking isn't just about distributing PageRank—it's about communicating priority to search engines and managing crawl budget strategically.
Disney and ESPN: When Everything Changes
Few people have managed site migrations at the scale Kajirian has. Moving Disney from Flash-based architecture to responsive design while simultaneously changing domains required coordination across hundreds of websites and multiple years.
The Disney project involved creating an entirely new CMS platform called Matterhorn, complete with built-in SEO features for metadata, structured data, and mobile optimization. But the technical complexity was matched by logistical challenges—mapping pages, managing redirects, and coordinating with teams across different properties.
"There were only like three, four SEOs within Disney that were responsible for doing all that," Kajirian recalls. The scale required systematic approaches to problems that smaller sites handle manually.
Battle Scars and Learning Moments
Even with careful planning, migrations at this scale involve risks. Kajirian admits to accidentally taking down Disney Junior during a redirect implementation, affecting "four-year-old girls who wanted to get the Disney Princess coloring books" for over an hour.
These experiences teach important lessons about preparation, testing, and having rollback plans ready. At enterprise scale, small mistakes have large consequences, but they also provide learning opportunities that improve future migrations.
The ESPN migration was particularly challenging because of user reception—the redesign was "very polarizing" with roughly 50% of users either loving or hating the new experience. This highlighted the importance of balancing SEO technical requirements with user experience considerations.
Browser Agents vs. Cloud Agents
The emergence of AI agents that can browse and interact with websites represents another major shift for SEO. But not all agents work the same way, and the differences matter for how businesses should prepare.
Google's Project Mariner and OpenAI's approach involve cloud-based agents that require users to share credentials and authentication details. This creates privacy and security concerns, especially for e-commerce transactions where customer data is involved.
Perplexity's Comet browser takes a different approach—running agents directly in the user's browser where they're already logged in. "It's basically your browser," Kajirian explains. "You're logged in with your credentials, and you're executing tasks as if you were doing it."
This distinction matters because browser-based agents can access authenticated systems without sharing credentials, making them more practical for complex workflows that span multiple tools and platforms.
Real-World Applications for SEO Teams
The potential for automating routine SEO tasks is enormous. Kajirian describes a workflow where an agent could open multiple tabs in Google Search Console, apply filters across different properties, export data to spreadsheets, and perform analysis—all tasks that would normally take an hour but could be completed in minutes.
"What would normally have taken me, if I were to do this on my own, an hour to do, it'll take you two minutes to do it," he notes. This isn't just about speed—it's about being able to perform more comprehensive analysis across larger data sets.
The broader implication is that SEO teams will be able to handle more complex, strategic work as routine data collection and analysis becomes automated.
The Traffic Quality Trade-Off
As AI agents become more sophisticated at answering questions directly, traditional organic traffic patterns are changing. Many sites are seeing fewer clicks from search results, but the traffic they do receive converts at higher rates.
"What we are seeing is the people who do eventually come, they're highly qualified," Kajirian observes. "Even though the traffic's going down, they're more qualified."
This shift requires a fundamental change in how SEO success gets measured. Instead of optimizing purely for traffic volume, teams need to focus on conversion quality and user intent matching. The challenge is ensuring that when AI agents do refer users to websites, those sites provide clear value and competitive advantages.
Why the Wild West Days Are Back
Every major shift in search creates opportunities for those willing to experiment and adapt quickly. The rise of AI agents represents another "Wild West" moment where established practices get disrupted and new winners emerge.
"This is a really great time to be thinking about SEO in general," Kajirian argues. "We're thrown back into the Wild West days where you just had to study and test and experiment and see what works."
The key is balancing experimentation with solid fundamentals. While tactics and tools evolve, the basic principles of how search systems work remain consistent.
Constants in a Changing Landscape
Despite all the changes, SEO fundamentals haven't shifted as much as the noise suggests. Search systems—whether traditional algorithms or AI agents—still need to discover content, understand it, and match it to user intent.
"The fundamentals are still the same," Kajirian emphasizes. "You're still dealing with algorithms or agents or bots still having to discover your content, rank that content and serve it in the context of what the customer wants."
Site performance, content quality, structured data, and technical accessibility remain important. Crawl budget still matters. Pages still need to be indexed to appear in results, even AI-powered ones.
The tools and interfaces will evolve, but the underlying requirements for helping search systems understand and surface content remain constant.
Advice for the Chaos
For teams navigating this transition, Kajirian recommends focusing on fundamentals while staying open to experimentation. The community sharing knowledge through platforms like LinkedIn provides valuable insights about what's working and what isn't.
Most importantly, periods of uncertainty create opportunities for those willing to adapt. As Kajirian's interviewer noted, "new winners are made" during chaotic transitions when established players are slow to change.
The web isn't dying, but it's definitely evolving. Enterprise SEO teams have advantages in navigating these changes—dedicated resources, direct access to engineering teams, and the ability to implement systematic approaches to complex problems.
But the lessons from enterprise SEO apply more broadly. Whether managing a small business website or a massive e-commerce platform, the principles of treating search engines as customers, building optimization into foundations rather than retrofitting it later, and focusing on fundamentals while experimenting with new approaches remain valuable.
The future belongs to those who can master both timeless SEO principles and emerging technologies. In periods of rapid change, that combination of solid fundamentals and adaptive experimentation becomes the key to long-term success.
"This is a really great time to be thinking about SEO in general... We're thrown back into the Wild West days where you just had to study and test and experiment and see what works. The fundamentals are still the same." - Patrick Kajirian
02:26 - Enterprise SEO as a product function vs marketing function
06:24 - Google's indexation challenges and internal linking strategies at scale
16:47 - AI agents and agentic browser automation discussion
30:38 - Disney and ESPN migration war stories
46:51 - Traffic quality vs quantity in the AI era
Request a free AI Audit: https://97staging.com/ai-audit/
Connect with Patrick on LinkedIn: https://www.linkedin.com/in/patrickkajirian/
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Patrick Kajirian honed his career in technology and media in a broad range of roles that involved managing a full-service web design agency, operating world-class e-commerce and media websites, and driving user acquisition initiatives for global brands and fortune 500 companies. Patrick facilitated over a million daily Google searches as a principal product manager for SEO at Realtor.com. He currently works as a senior product manager in SEO at Walmart in the San Francisco Bay area.
Is user confidence in online content at an all-time low? AI-generated content dominates many key topics, and users can easily find themselves frustrated when searching, finding articles they could have generated directly from a chatbot themselves. There is also an increasing volume of content that is becoming commonly known as “AI slop.”
And that’s without getting into the other struggle: LLMs are not only competing for eyeballs on regular search engines, but also stealing traffic directly. As a result, sometimes it can feel like the rest of us are left to fight over scraps.
If the current outrage over AI slop proves anything, however, it’s this: users still want good content. And marketers still want to give it to them. So — with the internet noisier and more crowded than ever — how can we complete the matchmaking experience and find each other?
At 97th Floor, we have cracked the code, and we can prove it.
A brief history lesson
The internet has always been noisy, overcrowded, and full of shoddy content churned out by marketers hoping to maximize their reach. While many of us like to think of marketing as a noble profession (we are helping people solve their problems!), there will always be those who act in bad faith, trying to game the system however they can. It’s the whole reason “black hat” marketing exists.
Luckily, Google is fighting the good fight, and every update they have made over the years is done so in an attempt to improve the experience of the user, and get them closer to the type of content they need. This means that those focusing more on gaming the system and less on quality content are the ones who are typically hit the hardest by algorithm updates.
It’s the reason why, if you have been anywhere around content marketing, SEO, or even digital marketing in general for more than a few years, you will no doubt remember getting asked a question a million times, akin to “how do you balance SEO content and quality content.” Real ones know the truth: the best “SEO content” has always been high-quality content. And that kind of content is what has the power to withstand just about any algorithm update.
If that is not reason enough to focus on high-quality content, then let us also add this: The cost of bad content is steep. Analytics company CreativeX recently recently found that the average Fortune 500 company wastes approximately $25 million annually on content that fails to reach its intended audience or is not fully utilized.
It should come as no surprise, then, that the answer to combatting the current cacophony of AI slop is infuriatingly simple: produce high-quality content.
Ok, But…What Actually Is High-Quality Content?
I know, I know, that’s an incredibly unhelpful piece of advice. Because of course, anyone can claim to produce “quality content” but that means different things to different people. So, what do we mean when we say quality content? 97th Floor has a few principles that we have always lived by when it comes to both content and marketing in general.
1. High quality content is audience-focused
One of the main things that people get wrong about content marketing to this day is the how behind making the content itself audience-focused. As marketers, we can get caught up in how great we believe our solutions to be, that we get evangelical about the value that they bring — resulting in us pushing those solutions on our audience, rather than helping them. Quality content starts from a place of “what does my audience want or need?” rather than “what can we as a brand give our audience?”
| ❌ | ✅ |
2. High quality content is relevant to your brand
Ok, so you have figured out what the audience needs, and you have a ton of great content ideas. The next pitfall that marketers commonly fall into is trying to write everything. To illustrate: Take a quick moment to Google “best” anything and look at all of the sites that wrote about it, despite it being completely unrelated to their brand, product, or mission.
3. You are an expert and/or uniquely qualified to write this content
Authority matters. You might think this is the same as number two, but there’s a slight but significant difference. Something may be relevant to your brand, but you still have to prove yourself uniquely qualified to write it. This might come from expertise, experience, unique insights, or all three. This is also where the human element comes into play — even before AI, but especially now — users want content that they cannot simply generate by asking an LLM themselves. A unique and specialized point of view is more important than ever.
You may have noticed that our three quality content criteria and the use of AI are not mutually exclusive. On the contrary, we are not anti-AI evangelists. In fact, we use AI regularly to aid in efficiency and accuracy in the content creation process. However, it is rare (perhaps impossible) for a piece of content to match all three criteria without first passing by a human expert.
A survey conducted by consulting firm Baringa provides insight into opinions regarding AI-generated content by internet users. A majority of respondents identified at least one reason to value human-generated content above AI-generated content, with 81% citing “authenticity” as the key feature. However, users did not overwhelmingly state that they would avoid AI altogether — especially when it came to the younger demographics.
The fight for quality content is not a fight against AI, rather a delicate dance to make sure that it is used in the most effective way possible.
I Thought You Said You Could Prove It?
Ok, sounds like a nice theory, but does it actually work in practice? And can you prove it? In fact, we can. We have a proven history of this approach to content succeeding time and time again — surviving algorithm updates, changes in user behavior, and more. Here are a few examples.
Blendtec
Earlier, we made the claim that high-quality content will stand the test of time — and withstand algorithm updates. A perfect example of this is an article from way back in 2014 that we produced for Blendtec. A simple listicle of peanut butter smoothies, and accompanying recipes.
It meets our three criteria to a T and was incredibly successful when published. It continued to rank for several important keywords and survive several algorithm updates over the course of the next 10 years, to remain a top-three traffic driver for the site.
Dr Will Cole
Another example can be found in this guide on increasing progesterone levels for Dr Will Cole that we published and optimized in 2022.
This article saw its biggest jump in traffic after an algorithm update in April 2023.
General Kinematics
But what about now? When AI is everywhere and AI Overview is stealing traffic from many pages. Well, we have countless examples of content that has survived the recent AI-pocalypse through following this simple formula for high-quality content. One such example this simple article for General Kinematics about uses for potash.
This content is audience-focused, brand-relevant, and something that General Kinematics — a producer of mining equipment — is uniquely qualified to write about. Published in 2022, it was automatically featured in AI Overview upon rollout of the feature in 2024, and has continued to do so since. What’s more: This page actually saw a 60.4% increase in traffic when you compare pre-AIO rollout to post-AIO rollout.
The bottom line: Google agrees with us. Every major and minor Google update in the past decade and change has been to get the search engine closer to prioritizing one of the three facets of quality content as identified by 97th Floor. For example:
1- Helpful content and other updates intended to prioritize user-first content.
2- Updates around brand authority, including recent updates that are deprioritizing irrelevant content for brands (or worse, brands that have spread themselves too thin and made it difficult for Google to assign authority).
3- This one goes beyond Google. Consider this: In a study of hundreds of thousands of citations, the most cited content type was product pages — by some margin. This means that this facet of quality content matters two-fold: Generic blog content is most likely to be directly replaced by LLMs, and product content — i.e. content that you are most uniquely qualified to write — is most likely to be cited. With optimizing both for and against LLMs becoming an increasing priority, this may be the most significant quality content guidepost of all.
I called out just three examples of this, but there are many more where that came from, and so will that continue.
The pattern across every one of those examples points to the same underlying truth. SEO expert Eli Schwartz makes the case that LLM visibility isn't a data or technical problem — it's a brand problem. This short video captures why the brands that consistently show up in AI-generated answers aren't winning on data. They're winning on authority.
Why It Matters Moving Forward
We talked about the ever-increasing noise of the internet. IBM predicts that AI will only continue to expand over the next decade, influencing more than content creation. High-quality content will continue to perform through both search engines and LLMs. The challenge or “noise” as marketers used to be different, but the solution is the same. If you put your audience first and prioritize quality content, the cream will rise to the top every single time.
Further Reading
Of course, that’s only part of the story. Sometimes you have to give even the cream of the crop the best chance to succeed. Next time, we’ll talk about how to get the most out of your content with an audience-first strategy.
If you're ready to future-proof your content and get in front of your audience—no matter how they search
We’ve all felt it. You pour time into high-quality content, only to see your organic clicks drop—despite impressions climbing. What gives?
Welcome to the era of AI-powered search.
Google’s AI Overviews (AIO) and other generative engines are changing how people discover and engage with content. The game isn’t over—it’s evolving. And if you want to keep winning, it’s time to optimize not just for traditional SEO, but for AI-powered results.
At 97th Floor, we’ve spent the last year testing and refining strategies that help our clients show up and stand out in AI results. This guide breaks down what we’ve learned and how you can use it to grow.
Here’s a fast-track checklist that we stand behind:
We’re seeing a clear trend since the advent of Google’s AI Overviews:
This shift in metrics is significant. Your content is still being seen, but it’s not driving as many clicks.
This is largely due to AI Overviews, which are providing answers directly in search results—without users even having to visit your site.
In fact, research from Ahrefs revealed that AI Overviews reduce clicks by 34.5%. They analyzed 300,000 keywords and found that the presence of an AI Overview in the search results correlated with a 34.5% lower average clickthrough rate (CTR) for the top-ranking page, compared to similar informational keywords without an AI Overview.
This doesn’t mean SEO is dead. It means that SEO needs to evolve.
With this change in how users interact with search results, it’s important to note that KPIs are shifting. While clicks may be down, impressions are up—and brand mentions and search visibility are becoming increasingly valuable metrics. It's no longer just about tracking clicks; it’s about how your brand is being mentioned and perceived in the broader conversation.
At 97th Floor, we’re helping brands adapt to this new search landscape. We’re testing what works—and what doesn’t. In this article, we’ll walk you through how to optimize for AI and stay ahead of the curve.
AEO (Answer Engine Optimization) – Structuring content to appear in AI-generated answers and summaries (like Google's AI Overviews).
GEO (Generative Engine Optimization) – A broader strategy to improve how your content appears in LLM-powered results, including chatbots and voice assistants.
Other helpful terms:
Yes. But traditional SEO on its own won’t cut it.
GEO and AEO prioritize intent, clarity, and usefulness over keyword stuffing or link volume. Search engines (and AI tools) want to deliver satisfying answers, not just keyword matches.
Good keyword research still matters—especially when it covers both primary and secondary search intents.
Unlike traditional SERPs that rank blue links, AI search engines pull and generate answers using two main data sources:
They look for:
Here’s the opportunity: content that works well in LLMs often also ranks well in traditional SERPs. Optimizing for both doesn’t require two strategies—it just requires a smarter one.
AI content optimization is the process of structuring, writing, and formatting your content to be more useful and accessible to AI tools, without losing sight of your human audience.
Let’s be clear: the goal is not to “hack” the algorithm. The goal is to help people. To provide persona-driven content that resonates.
Too often, we see content stuffed with keywords or unrelated FAQs just to rank. That’s not helpful. It’s not what AI wants, and it’s not what readers want either.
Before you go all-in on optimizing for models instead of humans, this quick video breaks down why that approach can actually hurt your content’s real-world performance.
Start with your audience. Understand who they are, what they care about, and how they search.
Consider using audience insights to build Custom GPTs that speak in your brand voice and match your customers' tone. (Here’s a screenshot of what it looks like in ChatGPT to configure a custom GPT.)
We also recommend:
Start with the answer, then explain it.
Example:
Q: How do I optimize for GEO?
A: Focus on clear, structured answers, semantic HTML, and direct responses to user queries.
Then go deeper.
Also:
AI favors content that’s easy to parse. That means:
97th Floor Test Results:
After adding bullet points and clear heading structure to a product page for a 97th Floor client, impressions and AIO rankings for an SEO-optimized article skyrocketed from ranking on the third page of the SERP to the first page (and ranking in Google’s AI Overview) in a short period of time. Here’s the results:
AI wants to serve trustworthy content. Show yours.
Ways to do that:
97th Floor Test Results:
By focusing on tightly-knit topic clusters, we were able to achieve topical authority for Princess Cruises:
AI search engines increasingly value content from recognized, authoritative sources. This makes brand pages, like your About Us or Homepage, vital for building trust with both AI and human users. Additionally, third-party citations, such as mentions from reputable websites or reviews, are becoming more influential in how your content is perceived. Ensuring your brand is recognized across the web not only boosts authority but also increases your visibility in AI-driven search results.
AI is already reshaping how people find information—and how businesses earn attention.
At 97th Floor, we’ve helped our clients weather the shift from traditional SERPs to AI Overviews and GEO. Our strategies have earned AIO features early and consistently. And we’re continuing to test and refine what works as the landscape changes.
If you're ready to future-proof your content and get in front of your audience—no matter how they search
Most marketers think they're data-driven, but they're not actually driving results with data. They collect metrics, build dashboards, and talk about analytics in meetings. But when it comes to making real decisions about where to spend budget or which channels to double down on, they're still flying blind.
The reality is harsh: marketing teams are chronically under-resourced from an analytics perspective. It's common to see one analyst supporting 20 to 50 marketers across an entire organization. That setup might work for other departments, but marketing generates massive amounts of complex data across multiple touchpoints and platforms. One person simply can't handle it all.
The problem gets worse when analytics resources are centralized. These teams usually focus on finance, product, or engineering data first. Marketing becomes a secondary priority, handled by people who don't understand the nuances of attribution, customer journeys, or campaign measurement.
But here's the thing: you don't need a massive data team to become truly data-driven. You just need to be smart about your approach and relentless about making it happen.
Marketing analytics often gets the short end of the stick. While other departments secure dedicated analysts and data engineers, marketing teams make do with borrowed resources and shared dashboards. This isn't just an oversight – it's a fundamental misunderstanding of how complex marketing measurement really is.
Central analytics teams might be brilliant at analyzing product usage or financial metrics, but they struggle with marketing data. They don't understand the difference between first-touch and last-touch attribution. They can't explain why a lead from a webinar should be valued differently than one from a paid search ad. They're not familiar with the quirks of Facebook's API or the limitations of Google Analytics cross-domain tracking.
Meanwhile, marketing teams find themselves in a catch-22. They need better data to prove their value and secure more resources. But they can't get better data without more resources. So they muddle through with incomplete reporting and hope for the best.
Real data-driven marketing means understanding the complete customer journey, not just pieces of it. It's tracking someone from their first website visit through multiple touchpoints – ads, emails, content downloads, webinars – all the way to becoming a paying customer.
This requires connecting data across your entire tech stack. Your front-end analytics platform shows traffic sources. Your marketing automation system tracks email engagement. Your CRM houses lead and opportunity data. Your ad platforms measure clicks and impressions. Most teams look at each of these in isolation, but true insight comes from connecting them together.
The gold standard is having all this data flow into a marketing-specific data warehouse where it can be properly modeled and analyzed. But this requires expensive data engineering resources and dedicated analytics support that most marketing teams simply can't justify to leadership.
Marketing consultant Gallant Chen, who works with companies like DocuSign and Shopify, advocates strongly that "marketers should have their own data warehouse that captures all of the marketing data, puts it into one place where the marketing team can report on that." This setup allows for complete attribution modeling and sophisticated analysis of what's actually driving results.
But Chen is realistic about the challenges. These resources aren't cheap, and marketing teams struggle to justify the investment. Most companies already have a data warehouse – it's just focused on product or finance data, not marketing needs.
The result is that most marketing teams make do with fragmented data across multiple platforms, incomplete attribution, and a lot of guesswork about what's actually working.
The first major benefit of better marketing data is understanding what's actually working versus what just looks like it's working. Most marketing teams spread their budget across multiple channels based on hunches, industry best practices, or last-touch attribution that gives all credit to the final interaction.
Better data reveals the truth. Maybe that expensive display advertising campaign isn't generating any quality leads. Maybe your email nurture sequences are doing more heavy lifting than you realized. Maybe paid search is profitable, but only for certain keyword categories.
As Chen explains: "If you can have all of the data, then it should allow you to understand essentially like where you should focus your marketing resources going forward and better sort of allocate the limited resources that you have from a budgeting perspective."
This isn't just about cutting waste – it's about moving money from low-performing channels to high-performing ones. The same budget can generate significantly more results when it's allocated based on actual performance data rather than assumptions.
The second major benefit is identifying where you have room to grow. Most marketing teams hit a plateau because they don't know which channels have headroom for increased investment. They're afraid to spend more because they can't predict the outcome.
Good data changes this completely. Chen shares a specific example: "If I'm investing in non-brand paid search as a channel and it performs well from a return on ad spend perspective... I can look at impression share, I can look at what my competitors are spending, I can look at how much I've spent in the past and understand that if I invest these incremental dollars that I can expect this incremental return."
This transforms budget conversations with leadership. Instead of asking for more money based on hope, you're presenting a clear business case with expected outcomes. You can say with confidence that an additional $10,000 in paid search will generate X leads, Y opportunities, and Z revenue.
Better data also unlocks the power of AI-driven optimization that ad platforms are heavily investing in. Google and Facebook want you to give them your conversion data and let their algorithms figure out the best audiences, bids, and creative combinations.
But this only works when you give them the right signals. Chen worked with a client who was optimizing Google Ads for leads and getting terrible results. "Google could drive leads at a low cost per lead, but that the lead quality was quite poor. And that's because ultimately, Google is trying to drive as many leads as possible at the lowest cost for you."
When they switched to optimizing for SQL conversions instead of raw leads, Google's algorithms quickly learned which keywords and audiences actually generated qualified prospects. The same budget started producing much better results because the platform had better data to work with.
Most marketing teams can't build the perfect data setup overnight. But they can make meaningful improvements with the resources they have. Here are three practical approaches that don't require massive investment.
If you're a B2B company, you probably already have Salesforce or another robust CRM. Instead of building a separate data warehouse, use your CRM as the central repository for all marketing data.
This means appending attribution data at the lead level – tracking which campaign, channel, or touchpoint generated each lead. Then ensuring that data flows through to opportunities and closed deals. Your CRM becomes your attribution system, and you can build reports that show marketing's impact on revenue.
This approach isn't perfect. CRMs aren't built primarily for marketing analytics, so you'll run into limitations. But it captures most of what you need to understand performance and make better decisions.
For teams with simpler needs or limited technical resources, spreadsheets can be surprisingly powerful. The key is automating data exports from your various platforms into Google Sheets or Excel, then connecting them together.
Pull your Google Ads spending and conversion data into one sheet. Export your CRM lead and opportunity data into another. Use tools like Funnel, Supermetrics, or native integrations to automate these exports. Then pivot the data together to understand which ad campaigns are generating not just leads, but qualified opportunities and revenue.
This approach requires more manual work than a proper data warehouse, but it gives you unified reporting in a familiar format. Most marketers are comfortable working in spreadsheets, and you can build surprisingly sophisticated analysis without any technical skills.
If most of your marketing spend happens in paid channels like Google and Facebook, consider using those platforms as your measurement system. This means sending your downstream conversion data – leads, opportunities, purchases – back to the ad platforms through their APIs.
Once Google knows which clicks generated actual customers (not just leads), its optimization algorithms can focus on finding similar high-value prospects. Facebook's Conversions API can track actions beyond just website visits, giving you a complete picture of campaign performance within the ads manager.
This approach works particularly well for companies with concentrated ad spend. If 80% of your budget goes to Google and Facebook, why not let them be your attribution system? They have sophisticated measurement tools built in – you just need to feed them better data.
Regardless of which approach you choose, focus on future impact when pitching to leadership. Don't emphasize better reporting on past performance – emphasize better decisions about future investments.
Frame your request around growth capability, not just measurement. Explain how better data will help you identify new opportunities, optimize existing spend, and scale what's working. Connect everything back to revenue outcomes that leadership cares about.
Many marketing teams get bogged down trying to build the perfect attribution model. They spend months debating whether to use first-touch, last-touch, or multi-touch attribution. They try to account for every possible interaction and create models that satisfy every stakeholder.
This approach usually fails because it tries to please everyone and ends up pleasing no one. As Chen observes: "You have this sort of like by committee decision-making process that essentially creates, in a lot of cases, an attribution model that is essentially good for nobody, right? Because it's trying to accommodate too many different stakeholders."
Different marketing functions need different measurement approaches. The person running top-of-funnel campaigns to generate new leads needs to understand first-touch attribution. The email marketer nurturing existing prospects cares more about influence on deal progression. The account-based marketing team wants to measure engagement across multiple touchpoints within target accounts.
Instead of building one model that tries to do everything, build multiple models that help different people do their jobs better. Let your demand gen team optimize using first-touch attribution while your sales development team uses multi-touch modeling to understand lead quality.
The key is keeping attribution separate from compensation whenever possible. When people's bonuses depend on attribution models, every conversation becomes political. When attribution is just about optimization and decision-making, you can be more pragmatic about what actually helps.
Keep it simple at first. Pick one primary model that covers 80% of your needs, then add complexity only when it solves specific problems. Perfect attribution is less important than consistent measurement that drives better decisions.
The biggest barrier to better marketing data isn't technical – it's organizational. Too many marketers accept incomplete data as inevitable instead of fighting for what they need to succeed.
Your success as a marketer often depends on having proper measurement in place. Chen shares a blunt perspective: if you can't measure a channel properly, you should question whether you should be spending on it at all.
Take Facebook advertising as an example. Without Conversions API setup, you're probably missing a significant portion of your actual conversions due to iOS tracking limitations. Your campaigns look less effective than they really are, which leads to suboptimal bidding and targeting. In this case, setting up proper tracking is more important than testing new creative or audience segments.
As Chen puts it: "If that channel cannot be successful because you don't have the right reporting, then you need to be spending the time to figure out how to solve for that, as opposed to... change my bids, refresh this creative, do all the things that you're normally going to do because those things are actually more important."
When you hit technical roadblocks, don't just accept them. Research alternative solutions. If your engineering team can't implement Conversions API, look into reverse ETL platforms that can do it for you. If you can't get a dedicated analyst, explore self-service analytics tools.
Build the business case for these solutions and present them to leadership. Show how the investment in better tracking will improve campaign performance and ROI. Be persistent – the first "no" doesn't mean the conversation is over.
Most importantly, don't give up after the first rejection. Chen emphasizes that many marketers try once, get told no, and then never bring it up again. But if your success depends on better data, you need to keep pushing until you find a solution.
You don't need to become a data scientist, but learning the basics will make you much more effective. Understand enough about APIs, data connections, and analytics platforms to have intelligent conversations with technical teams.
Learn basic SQL so you can pull your own data when needed. Understand how tracking pixels work so you can troubleshoot implementation issues. Know the difference between client-side and server-side tracking so you can make informed decisions about data collection.
This knowledge helps you advocate more effectively for solutions and work more productively with technical teams. You'll stop being dependent on others for basic data needs and can focus their expertise on more complex challenges.
The future belongs to marketers who can leverage data effectively, but that doesn't mean you need a massive data team or perfect infrastructure. True data-driven marketing is about consistently using data to make better decisions, optimize spend, and identify growth opportunities.
Start with what's practical for your organization. If you can't build a data warehouse, use your CRM as a system of record. If you can't get dedicated analytics resources, connect your data in spreadsheets. If you can't hire data engineers, use tools that automate the connections for you.
The key is taking action with available resources rather than waiting for perfect conditions. Every improvement in your data capabilities compounds over time. Better tracking leads to better optimization. Better optimization leads to better results. Better results lead to more budget and resources.
Don't let perfect be the enemy of good. The marketing teams that thrive in the coming years won't be the ones with the fanciest data setups – they'll be the ones that relentlessly pursue better measurement and use whatever tools they have to make smarter decisions.
Your competition is probably still making decisions based on hunches and last-touch attribution. While they're waiting for ideal conditions, you can be building competitive advantages through better use of data. The only question is: will you start now, or will you keep waiting for perfect conditions that may never come?
"If you don't have the right data and reporting to be effective in your job, you got to fix that. You have to figure out a way... if your success is tied to some of these analytics or data or reporting issues, to me, those things are far more important." - Gallant Chen
3:17 - Under-resourced data analytics
8:59 - Marketing-specific data warehouses
18:35 - AI/Smart bidding with proper data
27:02 - Practical alternatives
43:22 - Taking ownership of data problems
48:01 - Leveraging platform AI investments
Connect with Gallant on LinkedIn: https://www.linkedin.com/in/gallantc/
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
Check out Gallant’s work: https://gallant.co/
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Gallant is the Founding Partner of Gallant Growth and a seasoned Marketing Executive with a track record of scaling growth for B2B SaaS and marketplace businesses.
He has served as an Advisor and Consultant for companies including Docusign, Shopify, New Relic, Mixpanel, Nutanix, Upwork, and Thumbtack on marketing strategy, customer acquisition, retention, and monetization. He has deep expertise in demand gen and paid acquisition, but also supports areas including the hiring of internal/agency teams, lifecycle and email marketing, CRO, marketing analytics, and marketing operations.
Prior to founding Gallant Growth, he ran Digital Marketing at Zendesk and held marketing roles at SurveyMonkey and Apple. He started his career as a strategy consultant at Bain & Company and holds an MBA from the Kellogg School of Management and a BS from Stanford University.
Most companies are getting messaging wrong before they even write a single word. They jump straight to creating copy without understanding the difference between positioning and messaging, lead with features instead of customer pain points, and treat AI as a magic solution rather than a research amplifier.
The real power of AI in copywriting isn't replacing human insight—it's scaling it. When done right, AI can help companies conduct deeper customer research, create more accurate personas, and test messaging at speeds impossible with traditional methods. But only if they start with the foundation that 70% of effective copywriting is actually research.
Here's a framework for using AI to turn customer insights into copy that actually converts, without losing the human touch that makes messaging resonate.
The first mistake most companies make is confusing positioning with messaging. As conversion copywriter Chris explains, positioning is understanding "what you do, who you do it for, and how you do it better, uniquely, or in a different way." Messaging, on the other hand, is how you communicate all of that across different touchpoints—your pitch, strategic narrative, value propositions, and brand voice.
Most companies skip the positioning work entirely and jump straight to creating creative assets. They treat messaging as the actual deliverables instead of the underlying architecture that should drive every marketing initiative. Without this foundation, companies end up with inconsistent messages that confuse rather than convert.
Even worse, companies that do attempt messaging often lead with features instead of matching customer intent. They land visitors on pages that immediately start talking about the company rather than addressing what brought the visitor there in the first place.
"As soon as someone lands on your page and the first thing they do is start talking about you yourself rather than themselves, they immediately experience some kind of friction," Chris notes. This friction shows up as higher bounce rates, lower engagement, and skeptical prospects who struggle to see themselves in the messaging.
The solution isn't complex: start by matching visitor motivation, intent, and awareness level. When the first thing people read speaks directly to their pain points and desired outcomes, the decision to keep reading becomes automatic.
Another common mistake is creating separate messages for every possible audience. Companies end up with messaging for accountants, HR professionals, mortgage loan officers, and dozens of other segments without any unifying thread.
The problem isn't having multiple audience-specific pages—those are useful for matching search intent. The problem is missing the overarching strategic narrative that ties everything together. This narrative should reflect the unique insight that led to building the product in the first place, the market problem only the company has identified, or the customer mistakes only they've seen.
With this strategic narrative as the foundation, companies can adapt pain points and benefits for specific personas while maintaining a consistent point of view across all touchpoints.
Smart competitive research isn't about copying what others are doing—it's about deconstructing their approach to find gaps and differentiation opportunities. A useful framework divides competitor analysis into four sections: motivation (matching user outcomes and pain points), value (features and benefits), proof (backing up claims), and anxiety (social proof and friction reduction).
This analysis reveals what level of customer awareness competitors are targeting and where opportunities exist. Are they focusing on problem-aware customers while leaving solution-aware prospects underserved? Do their reviews reveal features customers consistently complain about?
The goal is understanding the message landscape prospects encounter when researching solutions, not to copy it but to stand out within it.
Customer interviews remain the gold standard for gathering authentic language, but they're not the only option. Email surveys with open-ended questions often generate surprisingly detailed responses, especially when customers have strong product-market fit. Support chat transcripts, sales call recordings, and review sites like G2 and Capterra provide additional sources of customer language.
The key is building a searchable database of customer language. As Chris explains: "Maybe I'm writing copy and I'm kind of debating, should I use this word or that other word? Then I jump into my voice of customer bank and I literally do a Command+F search and I see what kind of words did they use for this specific use case."
Sometimes entire phrases can be lifted directly from customer feedback. Other times, it's about choosing between two similar words based on which one customers actually use. Either way, the voice-of-customer research provides the raw material for authentic messaging.
One of the most striking examples of voice-of-customer research in action involved a B2B SaaS company selling software to portable toilet and septic container management companies. The challenge was unique: selling software to blue-collar business owners in their 50s and 60s who "hated software" and rarely used anything beyond spreadsheets.
Instead of traditional feature-focused messaging, the company conducted customer interviews and built their "how it works" page like a diary of a typical workday. The copy walked prospects through their daily routine: "When you get to work, this is the first thing that you do with the software. You jump in, you log in your route for the container... And then your driver takes on the software and then this is what happens."
By using the specific language customers used and reflecting their actual workflow, the company increased conversions by 20% across the site. The success came from meeting customers where they were instead of forcing them to learn new terminology or processes.
The PATH framework starts with comprehensive research across three areas. Internal research includes team insights, product knowledge, and support chat transcripts. External research covers current customers, prospects, and importantly, non-buyers who can explain why they chose alternatives. Market research examines competitors and their customer reviews.
This preparation phase creates the foundation for everything that follows. Without solid human research, AI personas become sophisticated guesswork rather than accurate customer representations.
The articulation phase feeds all research into AI personas using platforms with large context windows. The key is creating separate persona chats that "never break character" and include "thoughts" tags that reveal internal motivations before expressing what personas would actually say.
For example, a persona might think: "I'm worried this software will be too complex for my team to learn" before saying: "We need something simple that doesn't require training." These internal thoughts help copywriters understand the emotional drivers behind surface-level objections.
The testing phase runs different messaging scenarios with AI personas, probes for objections, and simulates responses before launching campaigns. As Chris describes it: "It's like having a conversation with any of your customers. You can ask them any questions."
This capability allows teams to test headlines, email sequences, and value propositions at scale. They can explore edge cases, probe for emotional triggers, and identify potential objections before real customers encounter them.
The final phase combines AI findings with real-world testing. If AI personas respond strongly to specific messaging angles, those get tested in sales conversations and marketing campaigns. The results feed back into the system, creating a continuous research flywheel that gets more accurate over time.
This approach turns AI from a one-time tool into an ongoing research system that scales human insight rather than replacing it.
Synthetic research works best for early-stage companies with limited customer access. Even 60-70% accuracy is valuable when the alternative is no customer insights at all. It's also useful for scaling small datasets—turning five customer interviews into 50 detailed personas—or expanding research into questions that weren't asked in original interviews.
The most interesting application is continuous background research. AI personas can monitor market trends, test new messaging angles, and even pre-test ad creative before launch. Platforms like Synthetic Users and Ask Rally are building APIs that could automate these processes entirely.
Synthetic research fails when used as a replacement for customer understanding rather than an amplifier. Companies with extensive customer data and research capabilities likely don't need synthetic personas. More importantly, synthetic research should never be the only research—it works best as a complement to real human insights.
The platforms that do synthetic research well address bias, coordinate persona distribution across roles, and build algorithms that produce realistic rather than overly positive responses.
The copywriters at risk are those who rely on formulas and templates. AI can easily replicate paint-by-numbers approaches to writing. But copywriters who understand that copy comes from research, strategy, and deep customer empathy have a different future ahead.
As Chris puts it: "If you have a strategic vision, so if you know that the copy comes from the research work, then there's the strategy in between, and then you can't really write any word without all of those foundations, then I would say you can still be the effective copywriter using AI."
The role shifts from writer to architect—orchestrating AI systems, knowing when to be the human in the loop, and maintaining the intuition for what resonates with real customers.
Many copywriting critics haven't actually experimented with advanced AI tools beyond free ChatGPT. They're making judgments based on limited experience with basic applications rather than the sophisticated research and persona systems now possible.
The future belongs to copywriters who embrace AI as a research amplifier while maintaining the human skills of empathy, strategic thinking, and taste that determine whether copy actually converts.
AI's real power in copywriting isn't replacing human insight—it's scaling it. The companies that win will be those that use AI to get closer to customers, not further away. They'll conduct more research, create more accurate personas, and test more messaging variations while maintaining the human touch that makes copy resonate.
The framework is straightforward: start with research, not technology. Build comprehensive customer understanding through interviews, surveys, and voice-of-customer analysis. Use AI to scale that understanding into detailed personas and systematic testing. Then maintain the human skills of strategic thinking and emotional intelligence that turn insights into copy that converts.
The future of copywriting isn't human versus AI—it's humans using AI to become better researchers, strategists, and customer advocates.
"If you rely on formulas, templates, when you're writing copy, then probably AI can replace you. But the thing that it can't really replace you now... if you have a strategic vision, so if you know that the copy comes from the research work, then there's the strategy in between." - Chris Silvestri
0:45 - Positioning vs messaging mistakes
4:12 - Avoiding "what we do" friction
14:29 - Research is 70% of the work
26:28 - AI synthetic research tools
33:34 - PATH framework explained
35:52 - AI won't replace strategic copywriters
Get a free scorecard to assess your messaging fit on Chris’ website here: conversionalchemy.net
Connect with Chris on LinkedIn here: https://www.linkedin.com/in/christophersilvestri
Connect with Paxton on LinkedIn: https://www.linkedin.com/in/paxtongray/
AI platforms Chris uses for audience research:
Looking for an agency that'll be worth the investment? 97th Floor creates custom, audience-first campaigns that drive pipeline and conversions. Get started here: https://97staging.com/lets-talk/.
Chris is the Conversion Alchemist. A SaaS message-market fit specialist and conversion copywriter, he worked 10 years as a software engineer in industrial automation. Then, took a sharp turn to enter the digital marketing world as UX lead at the usability testing startup Conversion Crimes (and previously at the conversion design agency Zeda Labs). Chris has been working as a messaging strategist and copywriter for B2B SaaS brands like Moz since 2016.
Marketing has come a long way from the days when creative directors made decisions based purely on gut instinct and artistic vision. Today's marketers operate in a world where every click, scroll, and interaction generates data that can inform strategy. But the pendulum hasn't swung completely to the analytical side—successful modern marketers are those who can bridge the gap between creativity and data science.
The challenge isn't choosing between art and science anymore. It's about blending brand storytelling strategies with behavioral insights and AI capabilities to create marketing that's both emotionally resonant and strategically sound. This new breed of marketer needs to be comfortable presenting to the C-suite while also understanding what a rage click reveals about user frustration.
The winning formula involves three interconnected pillars: authentic brand storytelling that connects with human emotions, deep behavioral data that reveals what users actually do (not what they say they do), and AI that amplifies human intelligence rather than replacing it. When these elements work together, they create marketing strategies that are both scalable and genuinely effective.
One of the biggest myths in marketing is the idea that you're either a "creative person" or a "numbers person." This false dichotomy has held back countless marketers who believe they can't develop skills on the other side of the brain.
Adam Gunn's career journey illustrates how these skills can complement each other. Starting with dreams of working for Disney or Pixar, he moved through graphic design and agency work before being thrust into a marketing leadership role with a multimillion-dollar budget. The transition wasn't easy, but it revealed something important: creative skills translate directly to business success.
"Humans are emotive beings and they respond to emotive narratives," Gunn explains. This truth applies whether you're pitching a brand concept to a client or fighting for budget in a boardroom. The ability to craft compelling stories, use humor strategically, and communicate ideas visually gives creative-minded marketers a significant advantage in business settings.
But there's a crucial caveat—knowing when creative details matter and when they don't. Gunn recalls sitting through a 90-minute meeting about bullet point shapes, with multiple teams debating whether triangles, circles, or squares better represented the brand. His realization: "No one outside of the people in this room care about the shape of bullets."
The key is picking your battles. Brand elements should be beautiful and strategic, but not every design decision deserves a lengthy debate. Creative marketers who learn to focus their energy on elements that actually impact business outcomes earn credibility with their analytical colleagues and leadership teams.
The whiteboard becomes a powerful tool for bridging these worlds. Whether it's a funny doodle that makes a point memorable or a visual way of presenting data, the ability to communicate ideas through both words and images gives marketers a distinct edge in virtual and in-person meetings.
Traditional web analytics tell marketers what happened, but they often miss the why behind user behavior. Page views, bounce rates, and time on site provide a surface-level understanding of user engagement, but they don't reveal the emotional experience of navigating a website.
Behavioral data changes this by capturing sentiment-rich signals that indicate user frustration, confusion, or satisfaction. These signals often predict outcomes better than traditional metrics.
Rage clicks represent one of the strongest behavioral indicators. When a button doesn't work, the natural human response is to click it repeatedly—usually four or more times in quick succession. This simple signal reveals not just that something is broken, but that users are actively frustrated by the experience.
Mouse thrashing provides another window into user sentiment. Erratic cursor movement often indicates that someone is searching for something they can't find or trying to understand a confusing interface. Copy-paste behavior, while seemingly innocent, frequently correlates with user frustration and higher exit rates.
These behavioral signals matter because they reveal the gap between intended user journeys and actual user behavior. Most marketers assume visitors follow a logical path from homepage to product pages to pricing and conversion. The reality is far messier.
The "cow path analogy" illustrates this perfectly. An East Coast college decided to plant grass first and see where students naturally walked before installing sidewalks. The resulting paths were nothing like what architects would have designed, but they reflected how people actually moved through the space.
Website user behavior follows similar patterns. Users might skip carefully crafted platform pages and jump straight from the homepage to pricing. They might enter through blog posts and immediately look for customer testimonials. Understanding these true funnels—not the ones marketers assume exist—provides the foundation for meaningful optimization.
This behavioral data becomes even more valuable when combined with other first-party data sources. Transactional data from CRM systems, email engagement metrics, and customer support interactions can be layered with behavioral signals to create comprehensive user profiles. In a world where third-party cookies are disappearing and privacy regulations are tightening, this first-party behavioral data represents a sustainable competitive advantage.
The future belongs to companies that can warehouse these diverse data streams and use them to personalize experiences, predict churn, and identify opportunities for improvement. The brands that master this integration will have insights their competitors simply can't access.
Despite the hype surrounding AI in marketing, the reality of implementation has been more modest than revolutionary. While executives and boards push for AI initiatives, many marketing teams struggle to achieve the dramatic efficiency gains they've been promised.
The gap between expectation and reality shows up in everyday work. AI-generated strategy briefs often contain comprehensive lists of tactics that are technically correct but lack the nuance of understanding resource constraints, budget limitations, and strategic priorities. The output feels like the work of "a very hard-working intern"—helpful for brainstorming but requiring significant human intervention to become actionable.
Instead of viewing AI as a replacement for human intelligence, successful marketers are learning to use it as a thought partner. This approach recognizes AI's strengths while acknowledging current limitations.
Behavioral analytics platforms are developing AI capabilities along four key pillars. Summation uses AI to create semantic summaries of user session groups, potentially eliminating the need to watch individual session replays. Surfacing opportunities leverages AI to automatically identify conversion problems and optimization possibilities that human analysts might miss. Conversational answers democratize data access by letting non-analysts ask questions in natural language and receive dashboard-style responses. Predicting represents the ultimate goal—AI sophisticated enough to identify problems and opportunities before humans recognize them.
These applications work because they augment human capabilities rather than attempting to replace human judgment. AI excels at processing large volumes of data and identifying patterns, but humans remain essential for strategic context, creative problem-solving, and understanding business nuances.
The key is building the muscle for AI adoption even when current tools provide only modest improvements. The smartphone analogy is instructive—early adopters of mobile apps gained valuable experience that positioned them for success as the technology matured. Banks that initially resisted mobile banking because "no one would ever bank on their phone" found themselves playing catch-up later.
Marketing teams that experiment with AI tools today, even imperfect ones, are developing the workflows and expertise they'll need when more sophisticated solutions emerge. The 4% efficiency gain available now might become a 40% gain in the future, but only for teams that have already integrated AI into their processes.
Traditional analytics setups are fundamentally reactive. Problems are identified after they've already impacted business results, and the process of surfacing insights to decision-makers often involves multiple people and significant time delays.
Consider this scenario: website conversions drop by 20% over a few hours. In most organizations, this insight requires an analyst to notice the change, investigate the cause, prepare a summary, and communicate findings to stakeholders who can take action. Depending on the complexity of the analytics setup and organizational communication, this process might take hours or even days.
The cost of this delay can be substantial. For businesses with high average order values, a 20% conversion drop might represent hundreds of thousands of dollars in lost revenue during the time it takes to identify and address the problem.
Behavioral analytics platforms are designed to flip this model from reactive to proactive. Instead of waiting for humans to discover problems, AI-powered systems monitor behavioral signals in real-time and alert teams to issues as they emerge.
Rage click patterns might spike on a specific page, indicating a technical problem. Mouse thrashing could increase among users from particular traffic sources, suggesting a messaging mismatch. Copy-paste behavior might correlate with form abandonment, pointing to usability issues.
These early warning systems allow marketing and product teams to respond to problems before they significantly impact key metrics. The goal is moving from "what happened last week" to "what's happening right now" and eventually to "what's likely to happen next."
This proactive approach requires rethinking how analytics teams are structured and how data flows through organizations. Instead of periodic reporting cycles, teams need continuous monitoring capabilities. Instead of waiting for monthly business reviews to surface insights, stakeholders need real-time alerts that enable immediate action.
The marketing landscape is entering a period of rapid change that will require significant adaptation from even the most sophisticated teams. Three major shifts are converging to create new challenges and opportunities.
First, the rise of AI agents will fundamentally change how websites and digital experiences are accessed. Instead of humans browsing through carefully designed user journeys, AI agents will increasingly navigate websites on behalf of users, gathering information and making recommendations.
This shift requires marketers to think beyond human-centered design. Experiences that work well for human visitors might be completely ineffective for AI agents, which process information differently and have different expectations for how content should be structured and presented.
Second, the relationship between search engines and websites continues to evolve. ChatGPT and similar tools increasingly provide direct answers to user queries without sending traffic to source websites. This "zero-click" trend means traditional SEO strategies need to account for how AI systems discover, process, and surface content.
Third, the definition of "good" versus "bad" website traffic is becoming more complex. While bot traffic has traditionally been filtered out as irrelevant, the future will require distinguishing between beneficial AI agents and malicious bots. Some automated traffic will represent legitimate business opportunities that deserve optimized experiences.
These changes don't have predetermined solutions, which makes adaptability more important than specific tactical knowledge. Marketing leaders need to develop strong points of view about their strategies while remaining open to new information and different perspectives.
The organizations that will thrive are those that can assess new developments quickly, test hypotheses efficiently, and change course when evidence suggests better approaches. This requires both confidence in core principles and humility about tactical execution.
Building this adaptability muscle starts with current decisions about AI adoption, data infrastructure, and team capabilities. The companies that are experimenting with behavioral data, testing AI tools, and developing cross-functional collaboration skills today will be better positioned for whatever changes emerge next.
The most successful modern marketers won't choose between brand, behavior data, and AI—they'll master the integration of all three. Brand storytelling provides the emotional foundation that connects with human motivations. Behavioral data reveals what users actually do rather than what they claim to do. AI amplifies human capabilities by processing information at scale and identifying patterns that would be impossible to detect manually.
This integration requires marketers who can move fluidly between creative and analytical thinking, who understand both the art of persuasion and the science of optimization. It demands organizations that can combine first-party behavioral signals with traditional business metrics to create comprehensive views of customer experience.
The future belongs to marketing teams that stay grounded in human emotion while leveraging the best available data and technology. They'll use AI as a thought partner rather than a replacement for human judgment. They'll let user behavior guide their optimization efforts rather than assuming they know how customers prefer to navigate digital experiences.
Most importantly, they'll remain adaptable as new technologies and platforms emerge. The specific tools and tactics will continue to evolve, but the fundamental challenge will remain the same: understanding what motivates people and delivering experiences that meet both rational and emotional needs.
The marketers who master this balance—combining brand storytelling, behavioral insights, and AI capabilities—will create sustainable competitive advantages that are difficult for competitors to replicate. They'll build deeper relationships with customers, make more informed strategic decisions, and adapt more quickly to changing market conditions.
The future of marketing isn't about choosing between art and science. It's about blending them skillfully to create experiences that are both emotionally compelling and strategically effective.
"The future is customer-based agents surfing our website. Historically we've built all of our experiences for humans, but agents are often going to be now going out and doing business on our behalf... we'll have to build web experiences that serve the good traffic." - Adam Gunn, VP of Brand at Fullstory
02:35 - From Disney animator to marketing leader
06:51 - Creative skills in the boardroom
13:08 - "Rage clicks" and user frustration signals
23:44 - AI reality check vs. hype
31:47 - Reactive vs. proactive analytics
41:53 - Stay nimble for industry changes
Request a free AI Audit: https://97staging.com/ai-audit/
Connect with Adam on LinkedIn: https://www.linkedin.com/in/adamgunn
Adam Gunn is the VP of Marketing at FullStory, a behavioral analytics platform that’s changing the way teams understand and act on user behavior. He brings a unique perspective around data, storytelling, and how marketing teams can evolve alongside AI.
