1. Search is undergoing one of its biggest transformations with the rise of AI-powered answer engines and generative search experiences. How do you see the relationship between traditional SEO and AI-driven discovery evolving over the next few years?
The fundamentals haven’t changed as much as people think, but the end result has. The things that made a page rank well a decade ago, like real expertise, clean structure, and a direct answer to a real question, are still the foundation a large language model rewards. What’s different is everything built on top of that foundation. Citation in an AI answer depends on signals SEO never had to care about, things like how clearly an entity is defined, how structured the content is for extraction, and how consistently a brand’s authority shows up across the web. The base layer carries over. The work on top of it doesn’t.
What has changed is where the race ends. Ranking position used to be the finish line. Today, that finish line is being pulled directly into the AI answer itself, with your brand name attached as a citation if you’ve earned it.
Data shows how quickly this is shifting. Industry studies reveal that the moment an AI Overview appears for a keyword, the top-ranking page can lose over half of its traditional click-through volume. I see this on client dashboards constantly. Rankings remain untouched, yet traffic quietly softens because the AI answer already gave the user what they needed before they had a reason to click through to the page. Over the next few years, I don’t think SEO disappears. It becomes the foundation, and earning that AI citation becomes its own specific, increasingly important layer on top of it.
2. Marketers today have access to more data than ever before, yet many still struggle to translate insights into meaningful business outcomes. What separates organizations that successfully leverage data from those that merely collect it?
Having access to data is easy. Holding someone accountable for acting on it is the hard part. Major industry surveys show that while roughly 70% of CMOs believe becoming an AI and data leader is critical, only about 30% feel their organizations are actually prepared to execute at scale. That mismatch is the gap we are fighting.
The companies pulling ahead actually have fewer dashboards, not more. They just ensure that someone owns the decision attached to the data. At AdLift, when our Tesseract platform shows a client’s citation share dropping, that metric doesn’t sit passively on a slide. It comes with an owner’s name attached and a specific action item for that week. I’ve sat through too many forty-slide decks where I ask what changes on Monday morning, and nobody has an answer. A metric no one acts on is basically just a number on a screen.
3. Having worked across global technology companies and built a successful digital marketing business, what has been the biggest lesson you’ve learned about adapting to disruption and staying ahead of industry shifts?
The biggest lesson I’ve learned across global tech and agency environments is that disruption doesn’t care how good your existing playbook is. Real disruption usually sneaks up on you while you are busy optimizing a system that is quietly becoming obsolete. I saw this during the major shifts like Panda, Penguin, and mobile-first, where companies failed simply because they kept trying to apply old rules to a completely new game.
When AI answers started transforming search results, the immediate instinct across the industry was to defend traditional web positions. We decided to look at the problem differently by asking what visibility even means when search engines prioritize immediate answers over traditional links.
That shift in perspective is what drove the development of Tesseract. Traditional analytics tools lump all search performance into a single metric, which hides how differently users are actually behaving. With our new Search Console AI integration, we wanted marketers to see what’s really happening underneath that number. The dashboard breaks performance apart by AI surface, so you can see exactly how a brand shows up inside AI Overviews, Google’s AI Mode, or the Discover feed.
4. As AI increasingly influences content creation and customer journeys, how can brands maintain authenticity and build genuine connections with audiences while embracing automation at scale?
Automation is incredible at scaling output, but one thing it cannot scale is trust. I can spot content that was entirely AI-written and untouched by human hands from a mile away, and so can your audience, even if they can’t quite articulate why.
At AdLift, we let AI do the heavy lifting with the initial research, drafting, and reshaping a core piece of thought leadership for five different platforms. What AI never gets to do is decide our point of view. If a piece of content doesn’t have a specific, sharp claim that an actual professional stands behind, we don’t publish it.
The brands that are getting it right are using it to free up time for the creative work that cannot be automated, such as primary research, strong opinions, and genuine customer stories. The ones falling behind are mistaking speed for connection. And readers can tell. They’ll stick with a brand that posts less but actually has something meaningful to say.
5. Looking ahead, what changes do you believe marketers and business leaders need to make today to remain visible, relevant, and competitive in an increasingly AI-first digital ecosystem?
If you are still measuring marketing success solely by traditional rankings and click-through rates, you are optimizing for a surface that is shrinking. Users are staying on the search results page because the answers are comprehensive.
Leaders who want to stay relevant need to adapt to two realities right now. First, you have to track what AI platforms are saying about you. We just integrated Anthropic’s Claude AI into Tesseract’s intelligence layer specifically to solve this. It analyzes the context, sentiment, and intent behind AI-generated brand mentions so teams can see exactly how their brand is being positioned inside conversational answers across ChatGPT, Perplexity, and Google.
Second, you must measure where your actual AI referral traffic is coming from. Traditional analytics weren’t built for this, which is why we launched AI Traffic Analytics across all Tesseract plans to give brands clear visibility into which AI assistants are driving real discovery and traffic to their sites over time.
The time to build category authority and measure it across these new AI surfaces is right now, while most competitors are still pouring effort into a search experience that’s already changing underneath them.
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jeevika@thefoundermedia.in
