Expert Mode: Your SEO Playbook is Obsolete. Welcome to the Era of AI Agents
This article was based on the interview with Kimberly Shenk, CEO at Novi by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For the better part of two decades, we’ve been locked in a familiar contest. The battlefield was the search engine results page (SERP), and the weapons of choice were keywords, backlinks, and click-through rates.
As marketing leaders, we built entire teams, strategies, and budgets around mastering the intricate dance of Search Engine Optimization (SEO) and Search Engine Marketing (SEM). We learned to win on the digital shelf by optimizing for rankings, bidding for placement, and ultimately, capturing the click. It was a well-understood game, and many of us became quite good at playing it.
That game is changing, and the new rules are being written not by search engine algorithms, but by artificial intelligence agents. The ground is shifting from a world where consumers browse a list of ten blue links to one where they receive a single, synthesized answer. This transition to what some call “Answer Engine Optimization” (AEO) isn’t just an incremental update; it’s a fundamental rewiring of product discovery. As Kimberly Shenk, a career data scientist and CEO of Novi, points out, this new paradigm demands a complete overhaul of how we think about brand visibility, data integrity, and competitive advantage. The question is no longer simply “How do we rank?” but “How do we become the definitive, trusted answer an AI agent selects on a consumer’s behalf?”
The Great Rewiring: From Ranking to Being Selected
The core psychological and tactical shift for any marketing leader is moving away from a strategy of visibility to a strategy of selection. For years, the goal was to appear high enough on a search results page to earn consideration from a human user. The human did the heavy lifting—opening tabs, comparing reviews, and synthesizing information. Now, an AI agent does that work in a fraction of a second, collapsing what was once a complex, human-driven “messy middle” of the customer journey into a single, automated step.
This means that the signals we’ve traditionally relied on, like ad spend and domain authority, are being re-weighted in favor of something far more foundational: data clarity. The AI isn’t impressed by your marketing budget; it’s impressed by the trustworthiness and structure of your product information. This has the rather interesting effect of leveling the playing field, creating both a significant risk for established incumbents and a massive opportunity for agile challengers.
“Last decade, brands won by ranking. And so that was the SEO and SEM game. And today they’re winning by being selected. And so that’s a totally different game. The consumer is never at this point seeing a search results page anymore. They’re getting a synthesized answer from AI. So if your product data isn’t clear, it’s not consistent, it’s not citable enough for AI to use, you’re just not showing up in that moment.”
This is a stark reality check. All the brand equity and ad spend in the world won’t matter if your product’s data is inconsistent across retailers or lacks the verifiable claims an AI needs to trust it. As Shenk notes, AI rewards brands with “the most trustworthy, structured, verifiable data.” A smaller, AI-native brand with pristine, well-structured product information can now outmaneuver a category leader whose data is a tangled mess across dozens of platforms. The strategic imperative is clear: the focus must shift from optimizing content for human eyeballs and keywords to structuring data for machine comprehension and trust.
Merchandising for Machines: Why Keywords Are a Trap
The muscle memory of many marketing teams will instinctively drive them toward a familiar tactic: keyword optimization. If consumers are asking AI questions, the logic goes, we should optimize for the keywords in those prompts. This is, to put it bluntly, a strategic error. It’s applying the old playbook to a new game with entirely different rules. Chasing prompts is futile because an AI’s response is hyper-personalized based on the user’s entire conversation history and context. Two users can enter the exact same prompt and receive completely different answers.
The goal isn’t to match keywords; it’s to provide the AI with such a deep and clear understanding of your product that it can explain it in its own words, tailored to any user’s context. This requires moving beyond basic product specs and into the realm of what Shenk calls “verified claims”—ingredient disclosures, certifications, safety data, and environmental impact information. This is the rich, structured data that differentiates a product and provides the “trust anchors” an AI relies on.
“SEO is like going and talking to the librarian. You ask, ‘Hey, where can I find a book to learn about communication?’ So that librarian points you to aisle three, shelf seven… But AEO is like actually turning to the research librarian and saying the same question… And they’re gonna say, ‘Charles Duhigg teaches about great communicators, listen first.’ And then this other author talks about empathy and they’re gonna explain the content in their own words, ’cause they deeply understand it. And so that’s the shift.”
This librarian metaphor is powerful because it perfectly illustrates the leap from indexing to understanding. SEO was about making your information findable. AEO is about making your information understandable, interpretable, and trustworthy enough for the AI to become your advocate. This means a product page that says “cruelty-free” at one retailer and “not tested on animals” at another creates a signal of inconsistency, which the AI treats as uncertainty. In a world where an AI is the gatekeeper, uncertainty is the enemy. Your product is no longer just being merchandised to humans; it’s being vetted by a model, and that model demands absolute clarity and consistency.
The New Operating Model: Reallocating Budgets and Blurring Team Lines
Adapting to this new reality is not just a marketing task; it’s an organizational challenge. The data that AI agents need often lives outside the marketing department, in R&D, supply chain, and legal. The product development teams know the precise ingredient breakdowns. The legal and compliance teams validate certifications. The e-commerce teams manage the data feeds and schemas. Winning in the age of AEO requires breaking down these silos.
Shenk observes that the most successful organizations treat their product data not as a technical chore but as a core competitive advantage. This requires new cross-functional workflows where R&D, marketing, and digital teams are in lockstep to ensure that the rich, accurate data about a product makes its way into the market in a clean, structured, and consistent format. This also requires a shift in mindset at the leadership level, particularly around budget allocation. The most agile brands aren’t treating AEO as a new line item to request; they’re reallocating funds from underperforming channels.
“Instead of thinking of AEO as incremental budget, they’re actually reallocating budget. So they’re taking that bottom part of the ad spend and part of their SEO spend and recognizing, ‘we’re not getting as much traffic from SEO anymore. We’re going to reallocate it to AEO.’ And so those are the ones that are winning versus the ones that are thinking of this as incremental… Reallocating budget allows you to do stuff this quarter, not wait for clarity, and then that’s how you show up.”
This is a critical insight for enterprise leaders. Waiting for perfect ROI models and building a business case for an incremental budget is a recipe for falling behind. The brands gaining an early advantage are those who recognize the diminishing returns from traditional channels and have the courage to shift resources toward preparing their data infrastructure for this new era of discovery. They are acting now, understanding that the cost of inaction is invisibility.
Redefining Success: Beyond Clicks to Share of Voice and Attributed Traffic
If we can’t rely on clicks and search rankings, how do we measure success? This is perhaps the most pressing question for marketing leaders who are accountable for proving ROI. The measurement journey, as Shenk outlines, happens in phases. It begins with “readiness”—auditing your data for consistency and structure. Without this foundation, nothing else matters. The next phase is “momentum,” which is measured by a new North Star metric: Share of Voice. This KPI simply tracks how often your brand appears in relevant AI answers.
Once you are consistently appearing in answers, the final challenge is attribution. How do you prove that being mentioned in an AI summary without a direct link is driving business results? The answer lies in tracking shifts in downstream consumer behavior. You can’t measure the click, but you can measure its effect.
“You should see this translate into three types of traffic… First is branded search traffic. And so when an AI engine is repeatedly surfacing your products, consumers will start directly searching for your brand and your products… The second, though, is direct traffic… This is actually going to increase when consumers see your brand and products repeatedly in AI recommendations… But then the third, which is really interesting, is direct referral traffic. And so even though AI is reducing the number of clicks, there’s this moment where they are still going to cite your brand and they’re going to give a link.”
These three metrics—branded search, direct traffic, and direct referral traffic—become the new indicators of ROI. An increase in consumers searching directly for your brand or navigating straight to your site is a powerful signal that AEO efforts are building brand recognition and influencing demand. It’s a shift from measuring the immediate action (the click) to measuring the resulting behavior (the direct search), which, in many ways, is a far stronger indicator of true brand influence.
The rise of agentic AI is not another channel to manage or a new tactic to bolt onto an existing strategy. It represents a fundamental restructuring of the digital shelf and the path to purchase. The skills and playbooks that led to success in the era of search engine rankings are insufficient for the era of AI-driven answers. The new currencies are trust, data clarity, and verifiability. Success is no longer determined by who shouts the loudest with the biggest ad budget, but by who provides the clearest, most consistent, and most reliable information for a machine to consume.
This transition requires more than just new tools; it demands a new organizational mindset. It calls for leaders to break down internal silos, re-evaluate long-held assumptions about budget allocation, and embrace a new set of metrics for success. As Shenk’s insights reveal, the future of commerce won’t be about winning a click on a search page; it will be about earning the right to be the answer. The brands that understand this and begin the hard work of structuring their data for a machine-first world today are the ones that will be selected tomorrow. The question is no longer if your brand will be merchandised by a machine, but how well.
