Let me introduce Milena Traikovich, a seasoned Demand Gen expert who has dedicated her career to helping businesses craft impactful campaigns that attract and nurture high-quality leads. With her deep expertise in analytics, performance optimization, and lead generation, Milena is uniquely positioned to guide us through the evolving landscape of AI-driven brand discovery. In this conversation, we dive into how AI is transforming the way customers find brands, the risks of being invisible to AI tools, and the critical shifts marketing teams must make to stay relevant in this new era.
How is AI reshaping the way customers discover brands online compared to traditional methods?
AI is fundamentally changing the game by shifting discovery from user-driven search to machine-driven recommendations. Unlike traditional search engines where users actively look for information, AI tools like chatbots and virtual assistants are now proactively suggesting brands based on parsed data and user queries. This means customers often make decisions without ever visiting a website or seeing a social media post. It’s a more predictive, personalized process, and brands that aren’t optimized for these tools risk being completely overlooked.
What makes AI-driven discovery so critical for brands today, even more so than website traffic or social media metrics?
The importance of AI-driven discovery lies in its scale and speed. Billions of prompts are processed daily by AI platforms, and these tools are becoming the first point of contact for many consumers. If your brand isn’t visible to AI, you’re missing out on a massive audience that’s making decisions in real-time without ever engaging with traditional channels. Website traffic and social media still matter, but they’re secondary if AI can’t surface your brand in the first place.
Can you break down the concept of ‘AI invisibility’ and why it’s such a pressing concern for companies?
AI invisibility means that AI tools can’t read, understand, or recommend your brand because your digital presence isn’t structured for machine parsing. It’s a huge concern because if AI can’t find you, your potential customers won’t either. With more people relying on AI for recommendations, being invisible essentially erases you from the consideration set, no matter how great your product or marketing is.
How can a business recognize if it’s falling into the trap of AI invisibility?
One clear sign is if your brand isn’t showing up in AI-generated responses on platforms like chatbots or AI search tools, even for relevant queries. You can test this by running queries related to your industry or products and seeing if your content is mentioned. Another red flag is a lack of structured data on your website—things like poor metadata or messy HTML can make it hard for AI to extract meaningful information about your brand.
What are some practical steps marketers can take to ensure AI tools can understand and recommend their brand?
Marketers need to focus on making their digital assets machine-readable. That starts with clean website structure—think clear HTML, proper sitemaps, and strong metadata. Implementing schema markup is also key because it helps AI categorize and contextualize your content. Beyond that, content should be formatted for natural language processing, meaning concise, factual information that answers user questions directly. It’s less about flashy design and more about clarity for machines.
Why should marketing leaders, rather than tech teams, take ownership of AI visibility strategies?
Marketing leaders are the voice of the brand and understand how to communicate value to audiences, whether those audiences are human or machine. While tech teams can handle implementation, marketers are best positioned to align AI optimization with brand messaging and customer needs. They’re the ones who can bridge the gap between technical requirements and storytelling, ensuring the brand’s essence isn’t lost in the process of becoming AI-friendly.
What skills or knowledge do you believe marketing teams need to develop to thrive in this AI-dominated landscape?
Marketers don’t need to become coders, but they do need a working understanding of how AI processes information. Familiarity with concepts like structured data, metadata, and natural language processing is crucial. They should also learn to analyze AI visibility through testing and diagnostics. It’s about adopting a hybrid mindset—blending traditional marketing creativity with a data-driven approach to ensure content resonates with both machines and people.
How should companies rethink their marketing budgets to prioritize AI optimization without neglecting other essentials?
Budgets should start with the foundation of AI visibility—allocating funds for technical audits, schema implementation, and content formatting that AI can parse. These aren’t glamorous spends, but they’re non-negotiable. From there, balance can be struck by integrating AI-friendly practices into existing efforts, like ensuring ad content or videos are also optimized for machine readability. It’s about building a solid base before spending on flashier campaigns.
What role does earned media play in boosting a brand’s visibility in the AI era?
Earned media is incredibly powerful right now because AI tools often prioritize trusted, third-party sources when making recommendations. Press coverage, interviews, and awards act as credibility signals that AI can easily index and surface. For marketers, this means treating earned media not just as a reputation boost but as a core part of their visibility strategy. Every mention becomes a digital asset that can amplify your brand’s reach through AI channels.
What is your forecast for the future of AI in brand discovery over the next few years?
I believe AI will become even more dominant as the primary gatekeeper for brand discovery. We’re likely to see an increase in autonomous AI agents that don’t just suggest but also act—making purchases or bookings on behalf of users. This will make AI visibility not just a marketing priority but a business survival factor. Brands that adapt early by structuring their digital presence for machines will have a significant edge, while those who lag behind may struggle to regain relevance in an increasingly machine-driven world.
