I’m thrilled to sit down with Milena Traikovich, a seasoned Demand Gen expert who has dedicated her career to helping businesses craft powerful campaigns for nurturing high-quality leads. With her deep expertise in analytics, performance optimization, and lead generation, Milena offers invaluable insights into how AI can transform marketing strategies—when implemented with trust and transparency at the forefront. In this conversation, we dive into the critical role of trust in AI adoption, the importance of making AI systems understandable and observable, and the business advantages of transparent technology. We also explore practical ways to build confidence in AI tools among marketing teams.
Can you share why trust is such a cornerstone in marketing, particularly when integrating AI tools into strategies?
Trust is the foundation of everything in marketing—whether it’s trust in a brand, data, or the tools we use. When it comes to AI, trust is even more critical because these systems often operate in ways that aren’t immediately clear to everyone on the team. If marketers don’t trust the AI, they won’t use it, no matter how powerful it is. This hesitation can slow down campaigns, stifle innovation, and ultimately hurt results. Trust issues often stem from not understanding how AI makes decisions, which can make teams feel like they’re flying blind. Building that confidence through transparency is key to getting everyone on board.
How does a lack of trust in AI specifically impact a marketing team’s willingness to embrace it?
When trust is absent, you see resistance at every level. Marketers might second-guess AI recommendations, stick to old manual processes, or even create workarounds that defeat the purpose of the tool. This not only wastes time and resources but also means missing out on the efficiency and precision AI can offer. I’ve seen teams become skeptical after a single bad experience with AI—like a poorly targeted campaign—and that skepticism can linger, making adoption an uphill battle. Without trust, the technology becomes a liability rather than an asset.
Could you walk us through what happened with Zillow’s AI-driven home-buying program in 2021 and why it serves as a cautionary tale?
Zillow’s iBuying program is a stark example of what can go wrong when AI lacks transparency. In 2021, they relied on an algorithm to buy homes for resale, but the model overestimated property values in a volatile market. Because the system wasn’t clear or well-monitored, the company overpaid for thousands of homes, leading to massive financial losses—hundreds of millions of dollars—and ultimately, they had to shut down the program and lay off a significant portion of their staff. The core issue was that there weren’t enough checks or visibility into the AI’s decision-making, so errors snowballed before anyone could intervene. It’s a reminder that AI without oversight can be disastrous.
What does it mean to you when we say the most successful marketing organizations focus on AI systems their teams can understand?
To me, it means prioritizing tools that aren’t just powerful but also accessible in terms of comprehension. Successful organizations invest in AI that their marketers—many of whom aren’t tech experts—can grasp at a basic level. This understanding fosters confidence to act on AI insights, whether it’s targeting a new audience or optimizing a campaign. When teams get how the AI works, they’re more likely to collaborate with it, test new ideas, and integrate it into their daily workflows. It’s about making the tech a partner, not a mystery.
Can you explain the concept of ‘observability’ in AI for marketing and why it’s so valuable?
Observability in AI refers to having real-time visibility into how the system operates—from the data it uses to the decisions it makes. For marketers, this means being able to see what’s happening under the hood, like why a certain ad placement was chosen or how a segment was prioritized. This visibility is invaluable because it lets teams spot errors or biases early, before they turn into costly mistakes. It also builds trust by showing that nothing is hidden. When you can monitor AI in action, you’re empowered to step in with human judgment when needed, which is crucial for maintaining control.
How would you define ‘explainability’ in AI, and why does it matter so much to marketing teams?
Explainability is about making AI’s recommendations clear in a way that makes sense to non-technical folks. Instead of spitting out complex stats or jargon, an explainable AI might say, ‘We’re targeting this group because they’re 40% more likely to engage with your content based on past behavior.’ For marketing teams, this matters because it bridges the gap between tech and strategy. When you understand the ‘why’ behind AI suggestions, you’re more likely to trust and act on them. Without it, teams can feel alienated, and the tool becomes a black box they’re reluctant to rely on.
What are some of the key business benefits of having transparent AI systems in marketing?
Transparent AI offers a huge edge in several ways. First, it reduces risk by making compliance with regulations much easier—you can show exactly how decisions were made if questioned. It also protects brand reputation by preventing misguided campaigns that could offend or alienate customers. Beyond risk management, transparency drives innovation; when marketers trust the system, they’re more willing to experiment with bold personalization or creative strategies. Finally, as privacy laws get stricter, companies with transparent AI will adapt faster than those stuck with opaque models, giving them a competitive advantage.
What practical steps can companies take to ensure their AI tools are transparent and trustworthy for their marketing teams?
Start by choosing platforms designed with transparency in mind—look for ones with user-friendly dashboards, detailed logging, and metrics that non-tech users can understand. Next, focus on making outputs interpretable; use methods to break down AI decisions into plain language. Tailor those explanations to different roles—executives might need high-level summaries, while analysts want specifics. Also, build in human feedback loops so marketers see their input refining the AI over time. Lastly, consider independent audits to validate the system; third-party reviews can go a long way in building stakeholder confidence.
Looking ahead, what is your forecast for the role of trust and transparency in AI for marketing over the next few years?
I believe trust and transparency will become non-negotiable in AI for marketing as we move forward. With increasing scrutiny from regulators and consumers on data use and privacy, companies will have to prioritize observable and explainable systems to stay compliant and maintain public confidence. I also see transparency becoming a differentiator—brands that can show they use AI responsibly will build stronger loyalty. On the tech side, I expect more tools to emerge with built-in features for visibility and user-friendly explanations, making trust a standard rather than an exception. It’s an exciting time, but only for those who adapt early.
