In the rapidly evolving digital landscape, where artificial intelligence shapes how brands are discovered and perceived, understanding the intersection of AI and branding is more critical than ever. Today, I’m thrilled to sit down with Milena Traikovich, a seasoned Demand Gen expert who has dedicated her career to helping businesses craft impactful campaigns and nurture high-quality leads. With her deep expertise in analytics, performance optimization, and lead generation, Milena offers unique insights into how brands can maintain visibility and meaning in the age of AI. In our conversation, we explore the essence of a meaningful brand, the role of AI in interpreting brand value, the shift from traditional search to AI-driven discovery, and the strategies brands must adopt to stay relevant in this transformative era.
How would you define a ‘meaningful brand’ in today’s digital world, and why does it carry so much weight now?
A meaningful brand is one that resonates deeply with its audience on both a rational and emotional level. It’s not just about a logo or a tagline; it’s about the promises you keep, the problems you solve, and the values you embody in every interaction. Today, with AI playing a huge role in how people discover products and services, meaning matters more than ever because AI doesn’t just look at surface-level stuff like visuals—it digs into the associations, stories, and trust signals tied to your brand. If those elements aren’t clear or compelling, you risk being overlooked in favor of brands that have a stronger, more defined presence in the minds of consumers and machines alike.
Can you unpack the concept of ‘earned meaning’ and why you believe it’s more powerful than simply buying attention through ads?
Earned meaning comes from building genuine connections with your audience over time—through consistent actions, authentic storytelling, and delivering on your promises. It’s the kind of impact you can’t buy with a flashy ad campaign because it’s rooted in real experiences and trust. Unlike paid attention, which can be fleeting, earned meaning sticks because it’s based on what people truly feel and say about your brand. In the AI era, this is even more powerful because AI systems pull from user-generated content, reviews, and social proof to gauge a brand’s relevance. So, a brand with deep, earned meaning will naturally surface in recommendations, while one relying solely on bought attention might struggle to stand out.
How does AI go about interpreting a brand’s meaning when someone asks for a recommendation or searches for a solution?
AI interprets a brand’s meaning by analyzing a massive web of data points in real time. It looks at things like the content on your website, social media posts, customer reviews, and even the tone and consistency of your messaging. It’s not just about what your product does—AI is also picking up on emotional cues, like how people feel about your brand based on their shared experiences. For instance, if users consistently associate your brand with trust or innovation in their feedback, AI will weigh that heavily in its recommendations. It’s almost like AI is curating a personalized story about your brand for each user, based on the signals it can read.
What happens to a brand’s visibility if its meaning isn’t clear or consistent across platforms, especially from an AI perspective?
If a brand’s meaning isn’t clear or consistent, AI struggles to piece together a coherent picture of what that brand stands for. Imagine a brand that says one thing on social media but something completely different on its website—AI might not know how to categorize or prioritize it, which often leads to invisibility in search results or recommendations. It’s like being a ghost in the system; if the signals are fuzzy or contradictory, AI just moves on to brands with sharper, more unified messaging. This can be a real problem for businesses that haven’t aligned their narrative across touchpoints, leaving them out of the conversation when potential customers are looking for solutions.
With AI chatbots and virtual agents replacing traditional search methods, how does this change the way brands need to approach visibility?
This shift is a game-changer because it moves discovery from a broad, keyword-driven process to a highly personalized, conversational one. Brands can no longer rely solely on SEO tricks or traditional branding elements like logos to get noticed. The biggest challenge is ensuring that AI understands and accurately represents your brand’s value when responding to user queries. This means focusing on content that reflects real human experiences and values, rather than just optimizing for algorithms. While visual identity still plays a role, it’s secondary to the deeper associations and trust signals AI picks up from user interactions and content consistency.
What specific types of content should brands prioritize to ensure AI interprets their meaning correctly?
Brands need to focus on content that’s clear, authentic, and reflective of their core values. This includes detailed ‘About Us’ pages that tell your story, customer testimonials that highlight real experiences, and social media posts that consistently reinforce your mission. User-generated content is also gold—reviews, social mentions, and community discussions give AI a rich pool of human sentiment to draw from. The key is to avoid vague or generic messaging and instead create content that paints a vivid, cohesive picture of who you are and what you stand for, making it easy for AI to connect the dots.
How can brands avoid sending mixed messages across different platforms and ensure a unified presence that AI can recognize?
Avoiding mixed messages starts with a strong internal alignment. Every team—marketing, customer service, product—needs to be on the same page about the brand’s core values and tone. Create a style guide that dictates how you communicate everywhere, from your website to social media, and stick to it. Regularly audit your content to spot inconsistencies, like a playful vibe on one platform clashing with a serious tone on another. Also, make sure your messaging reflects the same promises and values across channels. When AI sees that kind of uniformity, it’s much more likely to interpret your brand accurately and boost your visibility.
What’s your forecast for how AI will continue to shape brand discovery and meaning in the coming years?
I believe AI will only deepen its role as the gatekeeper of brand discovery, making it even more crucial for brands to prioritize meaning over mere visibility tactics. As AI systems get smarter, they’ll become even better at understanding nuanced human emotions and values, pulling from an ever-wider range of data sources like voice interactions or even wearable tech feedback. Brands that fail to build authentic, consistent meaning will struggle to keep up, while those that invest in genuine connections and transparent practices will thrive. It’s not just about being seen—it’s about being understood, and I think the gap between meaningful brands and the rest will widen dramatically in the next few years.
