We’re joined today by Anastasia Braitsik, a global leader in SEO, content marketing, and data analytics. In a world where marketing budgets are shrinking and the promise of AI seems to offer a tantalizingly cheap alternative to human creativity, Anastasia brings a much-needed dose of strategic reality to the conversation.
This interview explores the dangerous parallels between today’s cost-cutting AI strategies and historical infrastructure blunders, delving into why treating content as a cost center is a critical mistake. We’ll discuss how to move beyond the generic “minimum benchmark” of AI-generated articles, transform AI into a “force multiplier” for your creative team, and produce uniquely human content that stands out in an increasingly automated landscape. Anastasia will break down how to build a content infrastructure that delivers long-term value, ensuring your brand remains visible, credible, and trusted.
The article notes that average marketing budgets have dropped to 7.7% of company revenue. How have you seen this financial pressure lead companies to misinterpret AI’s role, and what are the first measurable, negative impacts they experience when they swap skilled writers for prompts?
It’s a completely understandable reaction, born from immense pressure. When you see your budget shrink from 11% to 7.7% in just a few years, the idea of a machine that can churn out articles for pennies on the dollar feels like a lifeline. The misinterpretation is seeing AI as a like-for-like replacement for a writer. They think they’re swapping one “content producer” for another, cheaper one. What they’re actually doing is swapping a strategic thinker, a brand steward, and an audience expert for a very sophisticated text synthesizer. The first negative impacts are almost immediate, even if they aren’t always tracked correctly. You’ll see engagement metrics like time on page and comment rates plummet because the content is generic and lacks a compelling human perspective. Then, search rankings begin to stagnate or drop because the articles are just rehashes of existing online information, failing to demonstrate the expertise and trustworthiness that algorithms are getting better at identifying. The content ecosystem becomes a ghost town of recycled ideas, and the brand’s unique voice simply vanishes.
You draw a powerful parallel between current AI strategies and the “Beeching Axe” in the 1960s. Can you share a modern business anecdote where a purely “data-led” decision to cut content backfired, creating unforeseen costs or undermining the broader brand infrastructure in the long run?
The Beeching Axe is such a perfect, heartbreaking analogy for this mindset. The data showed those 5,000 miles of track were “unprofitable” in isolation, but it completely missed their value to the entire network and the communities they served. I saw a tech company make a very similar mistake. They looked at the data and saw that their in-depth, expert-led webinars and whitepapers were expensive to produce and had a smaller audience than their snappy blog posts. So, following a purely data-led approach, they cut the “unprofitable” high-effort content to focus entirely on volume. What they didn’t measure was that those deep-dive assets were the bedrock of their brand’s authority. They were what sales teams used to close major deals, what industry experts cited, and what built immense trust with senior decision-makers. A year later, their sales cycle had lengthened, their reputation as a thought leader was diminished, and they were suddenly competing on price because they had dismantled the very content infrastructure that proved their premium value. Rebuilding that trust will cost them far more than they saved.
The text argues that AI-generated content is merely the new “minimum benchmark.” If everyone is using the same tools, can you provide a step-by-step process for how a content team can add their “secret sauce” and create work that consistently stands out from the noise?
Absolutely. Thinking of AI as the benchmark is the right starting point. To add the “secret sauce,” you need a process that systematically layers human value on top of that automated foundation. First, use AI for the grunt work: generate a rough outline, transcribe an expert interview, or summarize existing research. This is your base layer. Second, and this is crucial, you must generate new knowledge. This means interviewing your own subject matter experts—the engineers, the data scientists, the sales leaders who have insights that don’t exist anywhere else online. You’re unearthing proprietary wisdom. Third, you add the human experience. This is where a skilled writer comes in, weaving in compelling anecdotes, a unique perspective, or even a personal story that creates an emotional connection. It’s the difference between stating a fact and telling a story like the one about my great-grandmother surviving on one almond a day—a detail you’ll never find in a history database. Finally, you align it all with strategy, ensuring the piece not only informs but also builds authority, aids brand recall, and guides the reader, all while being structured perfectly for both search engines and human attention spans. That multi-layered process is the secret sauce.
You mention that law firms hired more lawyers to vet AI output. What does this “force multiplier” model look like for a marketing team? Please describe a specific workflow where AI handles the drudgery, freeing up creators to produce higher-value, strategic work they couldn’t before.
The legal industry example is fantastic because it shows how a smart industry adopts a new tool. They didn’t fire lawyers; they supercharged them. For a marketing team, the workflow looks like this: Imagine you need to create a pillar page on a complex technical topic. Before, a writer might spend a week just researching, structuring, and writing a first draft. In the “force multiplier” model, the AI handles the initial drudgery. It can transcribe a 90-minute interview with your lead engineer, pull out the key themes, and generate a structured outline in under an hour. It can even draft some basic definitional sections. This frees the human writer from perhaps 10-15 hours of foundational work. Now, they can invest that time in what really matters: conducting two more interviews to add unique perspectives, crafting a compelling narrative arc, developing custom graphics with a designer to explain complex points, and ensuring every sentence is infused with the brand’s voice and authority. The AI handles the “what,” freeing the human creator to focus entirely on the “so what” and “why it matters,” which is where true value is created.
You highlight the irony that to be cited by AI search, content must be uniquely human. What types of information—such as expert interviews, proprietary data, or unique personal experiences—have you seen perform best, and how can brands systematically unearth and scale this kind of knowledge?
It’s the ultimate paradox, isn’t it? To impress the machine, you have to be more human. The content that performs best, and that I see large language models citing, is invariably built on information that AI can’t find elsewhere. This includes original research, like a brand conducting its own industry survey and publishing the exclusive data. It includes deep, insightful interviews with named experts whose credibility lends the content immense authority. And it absolutely includes unique case studies and personal experiences that provide authentic, grounded proof of a concept. Think of the story about the comic book collector; AI can give you market times, but it can’t tell you which dealer has the specific comic you need. To scale this, brands need to treat knowledge extraction as a core business process. This means creating an internal content pipeline. Schedule recurring monthly interviews with your heads of product, sales, and customer success. Create simple templates for employees to submit interesting customer stories or personal industry insights. Systematize the process of turning raw, internal knowledge—the conversations happening in meetings and Slack channels every day—into polished, authoritative public content. It’s about building an infrastructure to mine the most valuable resource you have: the collective brainpower of your people.
Do you have any advice for our readers?
My advice is to resist the temptation of the “data-led” trap. Before you wield the axe on your creative team, take a hard look at your metrics and ask yourself what they aren’t capturing. Your spreadsheet might show you the cost of a writer, but it won’t show you the cost of lost authority, eroded trust, or a diminished brand. Don’t view your content team as a cost center; view them as the architects of your brand’s infrastructure—the very system that makes you discoverable and credible in an increasingly noisy world. The most intelligent AI strategy isn’t about replacing skilled people. It’s about equipping them with powerful tools so they can do what humans do best: create new knowledge, share unique stories, and build genuine connections. Focus on that, and you’ll build something far more valuable and enduring than a library of cheap, machine-generated articles.
