Milena Traikovich is a prominent figure in the marketing technology landscape, renowned for her strategic approach to demand generation and high-quality lead nurturing. With a career built on the pillars of data-driven analytics and performance optimization, she has spent years helping brands bridge the gap between complex technological tools and tangible business outcomes. In an era where corporate investments in artificial intelligence have eclipsed historical infrastructure projects like the interstate highway system, Milena provides the critical perspective needed to move beyond “automation theater.” Her insights are grounded in the latest 2026 industry benchmarks, focusing on how companies can realign their AI initiatives to prioritize customer value over mere internal efficiency.
This conversation delves into the prevailing mismatch between how organizations implement AI and how they measure its success. Milena explores the widening perception gap between C-suite leadership and practitioners, the rise of Generative Engine Optimization as a cornerstone of modern visibility, and the strategic necessity of playing to win rather than simply playing not to lose. Throughout the discussion, she emphasizes that the true power of AI lies not in its ability to perform tasks faster, but in its potential to make a brand more indispensable to its customers.
The data from this year’s research highlights a startling contradiction: while 71% of marketers use AI for internal productivity, nearly half of them are being measured against revenue growth. Why do you believe there is such a significant mismatch between the operational goals of AI and the scorecards used to judge its performance?
This mismatch is one of the most persistent hurdles we see in the industry today, and it often feels like a team is being asked to win a marathon while only being trained to tie their shoes faster. When 71% of professionals focus on efficiency—simply doing their existing jobs at a higher speed—they are looking inward at their own workflows rather than outward at the market. However, because AI is such a high-profile investment, stakeholders naturally want to see a direct impact on the bottom line, which is why 46% of companies use revenue as their primary yardstick. It is essentially the “dishwasher dilemma,” where a business buys a tool for a specific chore but expects it to somehow increase the overall property value of the entire enterprise. To fix this, we have to stop treating AI as a personal assistant for the marketing department and start treating it as a revenue-generating engine that functions by delivering unique value to the customer. Until the use cases match the metrics, we are going to see a lot of frustrated executives wondering why their “faster” teams aren’t bringing in more money.
With traditional search traffic projected to drop by 25% this year as AI-powered answer engines take over, how should companies shift their profitability strategies to avoid becoming invisible to their customers?
The shift in how consumers find information is no longer a looming threat; it is our current reality, and it requires a complete rethink of the search landscape. We have seen AI referral traffic to retail sites grow by a staggering 693% during the most recent holiday season, and the most compelling part is that this traffic converts 31% better than traditional sources. This tells us that when an AI serves as the intermediary, the intent-to-purchase match is far more precise. For a brand to remain profitable, it must earn its way into these AI answers through what we call GEO, or Generative Engine Optimization, which has already seen a 54% adoption rate among forward-thinking marketers. You can no longer just buy a top spot on a results page with a high bid; you have to provide the kind of reputational proof and authoritative content that an AI deems worthy of a citation. If you are not visible within that AI-generated response, your brand effectively ceases to exist for a quarter of your potential audience, making internal productivity gains irrelevant if the “top of the funnel” has moved somewhere you aren’t.
There is a clear divide in perception regarding AI maturity, with 67% of C-level marketers rating their organizations as extremely mature while only 33% of practitioners agree. How can leadership teams bridge this literacy gap to ensure they aren’t just engaging in “automation theater”?
This perception gap is a dangerous blind spot that often results in “top-down” strategies that have no footing in the day-to-day reality of the work. When 73% of leaders see extreme value in their current AI tools but only 25% of the managers doing the work feel the same way, you have a structural failure in communication. To bridge this, leadership must move beyond the conceptual “hype” and actually integrate these tools into their own daily routines to understand the friction points their teams face. We often see firms reporting cost savings of less than 10% from AI, which suggests that the “maturity” the C-suite perceives isn’t actually translating into significant operational breakthroughs. The remedy is to get everyone looking at the same scoreboard—one that emphasizes how proprietary data can be used synergistically with AI rather than just feeding generic prompts into public models. True literacy isn’t about knowing the jargon; it’s about understanding where the technology meets the strategy to create a distinct competitive advantage.
When deciding whether to build, buy, or partner for AI capabilities, 76% of use cases are currently being purchased. In such a homogenized environment, how can a brand maintain its distinct identity and avoid falling into the “Plateau of Indifference”?
The “Plateau of Indifference” is the graveyard of brands that chose efficiency over differentiation, and it is a very crowded place right now. When nearly every company is buying the same off-the-shelf AI tools, “using AI” is no longer a differentiator; it is simply the cost of doing business. If you use these tools to mass-produce marketing content that looks and sounds exactly like your competitor’s output, you have successfully automated your way into being ignored. The key is to buy the common infrastructure for routine tasks but to build or partner deeply where the technology touches your proprietary data or your unique brand voice. Differentiation in 2026 isn’t about the tool itself, but about what the tool is pointed at and the unique insights you feed into it. If your AI-generated experiences don’t make the customer feel more valued or understood than a competitor’s, then you’ve just found a cheaper way to be average.
Most companies are currently focused on measuring AI activity rather than AI impact. What are the specific metrics that marketers should be looking at to determine if their AI investment is actually creating value for the customer?
We need to move away from vanity metrics like “time saved per blog post” and look at the outcomes that actually drive growth, specifically visibility and customer appreciation. While 36% of marketers currently track time savings, this is an internal metric that the customer never sees and frankly doesn’t care about. The true measure of AI impact should be visibility within generative engines and the conversion quality of that traffic, which we’ve seen can be significantly higher than non-AI sources. We should also be looking at the operating margin—much like how Delta aims to lift profitability by 50% through better scheduling and pricing—but only as a reflection of a better customer experience. If your AI isn’t making your service more reliable, your content more helpful, or your pricing more fair, then you aren’t measuring impact; you’re just measuring busywork. Ultimately, the goal is to see if AI is helping you acquire more customers by making them feel like your brand understands them better than anyone else.
What is your forecast for the role of AI in brand strategy over the next two years?
I forecast that the era of “AI as a feature” is ending, and we are moving into an era of “AI as the interface.” Within the next two years, the brands that thrive will be those that have successfully transitioned from trying to attract traffic to trying to attract “citations” from the AI agents that consumers now trust to make decisions for them. We will see a massive shakeout of companies that relied on “automation theater” to boost short-term efficiency, as they will find themselves invisible in an ecosystem where AI filters out generic, low-value content. The successful strategy will be one where AI is treated as an amplifier of a brand’s core human values, making the company more distinct rather than more uniform. Profitability will increasingly depend on a brand’s ability to be the “preferred source” for AI engines, which will require a level of data integrity and brand authority that most firms are only just beginning to take seriously today. Every CEO should be asking not just how AI can make their company more profitable, but how it can make their company more indispensable to the people they serve.
