This article was based on the interview with Moburst CEO Gilad Bechar on winning in the age of AI-driven discovery by Greg Kihlström, Agentic Commerce keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For the better part of two decades, the game for marketers was clear, if not always simple: appease the Google algorithm. We built teams, budgets, and entire strategies around the central question of ranking. Our organizational charts reflected this reality, with neat little boxes for SEO, Paid Media, Social, and Content, each with its own set of KPIs and, too often, its own version of the truth. This functional, siloed approach served its purpose in an era of channel optimization. But that era is decisively over. The rise of generative AI hasn’t just given us a new set of tools; it has fundamentally altered the landscape of discovery.
The new central question is no longer “How do we rank?” but “How do we become the definitive answer?” This isn’t a semantic shift—it represents the emergence of a new operating system for marketing, where Generative Engine Optimization (GEO) plays a key role. AI-powered answer engines like ChatGPT, Gemini, and Perplexity don’t just crawl a website; they synthesize information from a vast ecosystem of sources to determine trust and authority. In this new reality, a siloed marketing structure isn’t just inefficient; it’s a liability. Your social media engagement, third-party reviews, and video content are no longer separate concerns—they are all crucial inputs into a single, interconnected system that determines your brand’s visibility and credibility. As leaders, our challenge is to move beyond optimizing channels and begin engineering a truly connected growth engine for the entire business.
1. The Collapse of the Marketing Silo
The most immediate casualty of the AI era is the traditional, channel-specific marketing department. In the past, the SEO team could focus on technical optimizations and on-page content, largely ignoring what the social team was doing, whose primary concern was engagement metrics. This separation of duties is no longer tenable. AI engines don’t care about your org chart; they care about “trust signals,” and they look for them everywhere.
Gilad Bechar explains how this holistic evaluation renders silos obsolete. It’s not just about your website’s domain authority anymore. It’s about the total sum of your brand’s digital presence, from high-level thought leadership to the sentiment in user-generated content.
“AI doesn’t just care about your Google ranking for a specific query. It cares about the trust signals. And when it carries the trust signals, it basically looks about what happens on every single one of those venues. So it will read your reviews on third party sites that are actually ranking you compared to your competitors. It will look on what happens on Reddit… You can see questions on Quora that are impacting it and how you’re looking on Wikipedia is making an impact of it. And if you have YouTube videos, how much views they got and how they engagement there was… it’s not just an SEO problem, it’s the brand problem.”
For marketing leaders, the implication is clear: you are now managing a single, interconnected brand ecosystem. The work of your PR team to secure a mention on a reputable site, the diligence of your community managers on Reddit, and the clarity of your product descriptions on review sites all contribute directly to whether an AI will recommend your solution. This requires a fundamental shift in collaboration. KPIs must be shared, strategies must be integrated, and teams must understand that their individual efforts are part of a larger, collective push to build brand authority in the eyes of the machines that are increasingly shaping customer decisions.
2. The Tactical Shift from SEO to AEO
As the strategic foundation shifts from silos to systems, the tactical execution must evolve as well. The practice of Search Engine Optimization (SEO) is giving way to what is now being called Answer Engine Optimization (AEO). While they share a common lineage, their approaches differ significantly. SEO was often a game of volume and keywords—longer articles, more backlinks, and meticulous keyword density. AEO, however, is a game of precision and structure.
The goal is no longer to create a 3,500-word article to outrank a competitor’s 3,000-word piece. Instead, it’s about providing clear, structured, and easily digestible answers to the specific questions your ideal customers are asking. AI engines are designed to find the best fit, not the longest page. This requires a deep understanding of your niche and the ability to articulate your value proposition with surgical clarity.
“The more that you have the structured content on your website to help dictate, ‘I’m the best in this niche. We are the this product of ours is the ideal solution for that audience.’… It can’t be that you are the best at everything for everyone. Like usually it’s not the case… it tries to match that with what it knows about the user who is searching for it… The more information that you have within the site that kind of gets the AI to understand they are ideal for these cases… it helps them to dictate the relevant score that you are getting compared to the others.”
This is a powerful directive for content and product marketing teams. The low-hanging fruit, as Bechar notes, often lies dormant in your own organization. Your sales team’s handbooks, your customer service FAQs, and your internal product documentation are treasure troves of structured, question-and-answer content. By publishing this information in an accessible format on your resource centers and blogs, you are directly feeding AI engines the exact material they need to understand your specific strengths and recommend you to the right audience. It’s a shift from broadcasting to educating, from keyword stuffing to providing genuine utility.
3. Balancing Automation with the Human-in-the-Loop
As we integrate AI more deeply into our operations, particularly in areas like media buying, the temptation is to “set it and forget it.” AI’s ability to analyze thousands of data points and identify patterns far exceeds human capability. It can tell you to increase bids on a Saturday evening and decrease them at 3:00 AM with a level of precision that would take a team of analysts weeks to uncover. However, ceding control entirely is a mistake. AI is brilliant at optimization but lacks a key ingredient: strategic context.
A successful AI-powered marketing operation isn’t one where humans are obsolete, but one where they are elevated. The human-in-the-loop becomes the strategic arbiter, the one who judges the AI’s recommendations against the broader business goals that the machine can’t possibly comprehend.
“It will tell you, ‘Okay, you’re running the same campaign in five languages. Kill the Spanish speaker type of campaign because it performs lower than the English speaker one.’ But if your goal is to expand from the US to Spanish markets, it doesn’t make sense to hear that recommendation. It doesn’t have the context of understanding that we’re trying now to increase the market share in here or on there… I wouldn’t let it do everything it wants by itself because it’s not entirely there just yet.”
This principle holds true across the marketing function. An AI might recommend content topics based on search volume, but it doesn’t know you’re about to launch a new product line that requires foundational educational content. It might flag a customer segment as low-value based on initial purchase data, but it doesn’t have visibility into their lifetime value. The role of the marketing leader and their team is to provide this essential context—to use AI as a powerful co-pilot that can process the data, while the human sets the destination.
4. Cultivating the AI-Native Mindset
Ultimately, navigating this new landscape requires more than just new technologies and processes; it requires a new type of marketer. The most critical skill for the future is not expertise in a specific tool, which will inevitably be replaced, but rather the ability to continuously learn and challenge the status quo. The marketer of the future is AI-native, not in the sense of being a coder, but in their instinct to look at a problem—especially a repetitive, low-value one—and ask, “How can I build a system to solve this?”
This mindset shift must be cultivated at all levels of the organization. Innovation no longer comes exclusively from the top down or from dedicated R&D teams. As tools become more accessible, anyone in the organization can become an agent of change.
“One of my team members from the HR division just created us an HR bot. And she’s not an AI expert. She just said, hey, you know what, if we can actually create this type of a thing that let’s train it with the handbook that we are giving for each and every employee… I feel like if an HR person can actually create something like that, there is no reason that every single team member on every single department will challenge the status quo.”
This is perhaps the most profound opportunity for us as leaders. Our job is not just to implement AI, but to foster a culture of curiosity and empowerment. We must encourage our teams to experiment, to automate the mundane parts of their jobs, and to reinvest that reclaimed time into higher-level strategic thinking. The employee who saves five hours a week by automating a report is not just more efficient; they now have five more hours to think about customer strategy, creative concepts, or new market opportunities. Building this capacity for distributed innovation is what will separate the agile brands from the ones left behind.
The transition we are experiencing is not just another cycle of technological disruption. It is a fundamental rewiring of the connection between brands and customers. The old operating model, built on the logic of discrete channels and siloed expertise, is breaking under the weight of this new, integrated reality. To thrive, we must dismantle the old machinery and architect a new kind of growth engine—one that is holistic, data-driven, and relentlessly focused on providing definitive, trustworthy answers.
This requires us to think less like channel managers and more like systems engineers. It demands that we foster a culture of radical collaboration and continuous learning, where human strategy guides machine execution. The pace of innovation is, to put it mildly, intense. But for leaders who embrace this complexity and empower their teams to become co-creators in this new world, the opportunity to build more meaningful connections with customers has never been greater. The question is no longer if we need to adapt, but how quickly we can build the marketing organization of the future.
