While every company across every vertical rushes toward AI implementation, the conversation around AI adoption is often secondary, trampled by promises of expediency, transformation, and ROI.  

We recently sat down with four AI leaders who prioritize adoption over acquisition, people over process. Their perspectives and actionable approaches add a much-need layer of practicality to a conversation that often veers into theory, leaving expectations unfulfilled and aspirations unrealized. 

Start with the story, not the technology

One of the most consistent barriers to AI adoption is communicative. Most clients, as Jasmine Presson, Chief Strategy Officer at Mediaplus, pointed out, still don't have mental models for understanding how AI works or what it's doing on their behalf. Flooding them with capability decks and model explanations doesn't help. What does? Traditional storytelling.

Humanizing AI through narrative using metaphor, analogy, and story to explain what's happening and why is vital to make a complex technology accessible. The agencies and vendors that figure this out first will be the ones clients actually trust with their AI spend.

Operationalization is only half the job

For companies sitting in the middle of the media supply chain, AI creates a unique challenge. It's not enough to implement it internally; you must be able to explain it externally. Michael Sadicario, EVP Enterprise Media & Retail Partnerships at Equativ, framed this well: as a trusted infrastructure provider, AI isn't a product feature bolted on top of existing services. It's how you transform and improve what you already deliver, and then articulate that transformation back to the agencies and brands who depend on you.

Precision matters more as the data landscape expands

The promise of LLMs is also one of their biggest risks. As Gregg Galletta, President of Truthset, made clear, the expanding data landscape doesn't make accuracy less important. It makes it more important. When agents are going out and acting on your behalf, the intelligence layer that validates data quality is the foundation everything else is built on.

Applying truth scores to attributes and identity gives AI the added power to trust the data it's working with. In a world where targeting precision directly impacts performance outcomes, that layer of validation is what separates meaningful results from expensive guesswork.

Capability is not justification

Across all three perspectives, one theme surfaced again and again: just because AI can do something doesn't mean it should. Human accountability remains at the center of responsible AI adoption. That means keeping humans not just in the loop, but actively on the loop validating data, questioning outputs, and ensuring AI operates within well-defined boundaries of risk, ethics, and business value.

These are the practical guardrails that make AI adoption sustainable, and the conversations that too many organizations still aren’t prioritizing..

The good news? Leaders like Jasmine, Michael, and Gregg are having them, and the more openly this industry talks about what adoption actually requires, the better positioned everyone will be to get it right.

Ready to take the next step? Discover how leading media agencies are building AI-first cultures in our latest blog: 3 Ways Leading Media Agencies Are Building AI-First Cultures.