Trust in the Age of AI: What Brands Can Learn from Mastercard’s Multisensory Approach

At the recent VentureOut Generative AI Summit, I had the privilege of moderating a conversation with Maja Lapcevic , SVP at Mastercard , alongside a group of founders, technologists, and advisors who are actively shaping the next frontier of AI-powered brand experiences. What unfolded was not a debate on AI’s capabilities but a much-needed discussion about trust.

Once Maja introduced herself, it became clear why Mastercard has emerged as a leader in this space. The company has built experiences that go beyond payments, using emotional, multisensory cues to deepen brand trust in both digital and physical environments.

And that trust, as she described it, is not just a brand value. It is the product.

Trust as Infrastructure, Not a Tagline

Maja reminded us that Mastercard’s core business is enabling secure exchanges of value between parties who may never meet. The brand’s promise is embedded in every transaction. As the digital world expands with AI-generated content, synthetic interfaces, and increasingly blurred lines between humans and machines, the traditional cues for trust are no longer enough.

That is why Mastercard has invested in what she called a multisensory trust framework. This includes audio cues, tactile experiences, and visual confirmations that reassure users they are in a legitimate ecosystem. It is not about adding trust later. It is about embedding it from the beginning.

She shared a great example: Mastercard’s sonic branding, a distinctive sound embedded in millions of transactions, instantly signals that a payment has been processed securely. It may seem subtle, but in a world of rising digital impersonation, that sound serves as a meaningful layer of confirmation.

The Paradox of Trust in an AI World

As I mentioned during the conversation, the world is facing a paradox. Cybersecurity spending is projected to hit $212 billion by 2025, yet cybercrime is expected to cost $10.5 trillion, which is 50 times more. At the same time, 70 percent of consumers say they do not trust how companies use AI, according to Deloitte’s Connected Consumer report.

The conversation highlighted a growing disconnect. While companies are investing heavily in backend systems—AI tools, cybersecurity, infrastructure—those efforts often fail to translate into meaningful front-end experiences. Customers don’t feel the investment if the content feels generic, the interfaces are clunky, or trust signals are missing. In many cases, the more companies automate behind the scenes, the less clarity and connection users experience on the surface.

Maja acknowledged this reality. Even at Mastercard, she explained, every partner and acquisition is evaluated rigorously, not just for technical alignment but for trust alignment. “We know that if we break trust, everything else falls apart,” she said.

Building the New Language of Trust

The group then turned its attention to startups and how smaller companies, without Mastercard’s scale or legacy, can build trust from the ground up.

That is where I introduced the AI Trust Stack, an actionable model I’ve been developing that breaks trust into five essential layers. It helps brands move beyond aspirational or theoretical ideas by translating intent into tangible signals that users can feel and machines can verify. The model bridges brand strategy and UX execution, expanding the language of trust to include signals that matter across both human and AI-driven interactions, from clarity of origin to emotional connection to proof of authenticity.

Ironically, building trust in AI-driven environments is pushing marketers back to fundamentals. Clear taxonomy, metadata, and content mapping are once again critical, because AI systems now act as gatekeepers. Trust must be legible not only to humans but also to the machines filtering, presenting, and interpreting digital experiences.

These are not new ideas, but in an AI-driven world, they take on new meaning. Brands need to design these into both the product and the user experience from day one.

The Dual Demand of Trust: Human and Machine

During the session, I raised a point that resonated across the room: trust is no longer just about trusting the human. It is also about trusting the AI system that person is using. That is a fundamental shift. Whether it is a startup sending outreach emails or a global brand deploying AI agents, users are now asking two questions. Do I trust you, and do I trust the technology you rely on?

The group reflected on how even seamless, helpful AI can feel more trustworthy than a human if it gets the job done. That is the new frontier, not human versus machine, but how the two reinforce or erode trust together. 

For Mastercard, where the stakes are high and the user base is global, this means the company must maintain consistency, clarity, and credibility across every signal it sends, whether it comes from a human or a machine.

But even when technology works well, emotional trust is not automatic. As I pointed out during the session, people naturally distrust speed. When things happen too quickly, an instant reply, an auto-generated message — it can trigger skepticism rather than confidence. In an AI-powered world where efficiency is accelerating, building trust requires more than speed. It requires signals of care, consistency, and intention that reassure users something was made for them, not just by a system.

A Call for Deliberate Design

As we wrapped up, Maja offered one key piece of advice for founders: be intentional. Trust is not something you add later. It is something you design for from the beginning. From how you manage data to the signals you use to demonstrate authenticity, every touchpoint matters.

She closed on an optimistic note, encouraging collaboration between startups and large enterprises. When built on a foundation of shared standards and values, those partnerships do more than scale innovation. They scale trust.

If you are a founder, marketer, or builder in this space, the takeaway is clear. Trust is no longer assumed. It must be built by and for both people and machines. Mastercard’s approach shows that doing so is not only possible, but essential to thrive in the AI era.  

This article is based on a live discussion at the Generative AI Leadership Summit, hosted by VentureOut, May 27-30, 2025. It was developed using the session transcript and co-edited by Rori DuBoff to reflect the key insights shared during the conversation.