Marketing’s Silo Problem

8 Lessons from Randy Rothenberg

Vivek and I were back at IAB in Palm Springs this week. This time talking about Kana, super{set}’s latest formation.

As usual, the best conversations weren’t in the keynotes. They were at the quick catch-ups in-between meetings.

I got 40 minutes with Randall Rothenberg. If you know who he is, you know why those 40 minutes mattered. If you don’t, you’re about to find out.

Here’s what stuck.

1. The Digital Audio Workstation Theory

We’re always trying to look at analog examples to help us understand digital+data transformations. I think Rothenberg nailed it with a personal story: as a musician, he was buying some equipment and the sales associate strongly recommended he buy a certain package because it came with a Digital Audio Workstation (DAW). To which Rothenberg responded, “what the hell is a digital audio workstation?”

What he discovered: a global marketplace where any plugin works with any platform, where a $100 digital instrument sounds identical to a $3 million Stradivarius, and where a $2,000 laptop produces audio that’s of higher quality than what used to come out of top-end studios in LA with racks of blinking lights and isolation booths.

The music industry went from its lowest revenues in history (2014) to its highest (2024). Rothenberg wisely observes that this is in no small part because music adopted an open, interoperable standard called MIDI (Musical Instrument Digital Interface). MIDI has provided a universal, enduring standard for the creation and transmission of digital music – a lingua franca that allows the luscious sound of a Steinway piano at Carnegie Hall to live on your laptop or your home studio for the price of a burrito at your favorite joint in the Mission.

In contrast, marketing built walled gardens around a cacophony of incompatible systems, formats, and standards. We’ve spent a hundred years making it harder to align disparate data. It’s time for marketers to tame the complexity and connect data the same way modern musicians make music in a DAW.

2. The Silos Were Technology Artifacts

For the first time, with agentic AI, interoperability for the modern marketer is finally within reach. We CAN bust down the silos and stovepipes and create a seamless backplane of shared, clean, actionable data. But it requires recognizing how the silos and stovepipes came into being in the first place.

Marketing isn’t organized into silos because it’s what’s best. It’s a result of cracks and incompatibilities that system builders confronted at design time decades ago. Let’s be clear-eyed about that, so we can be clear-eyed about how agentic dissolves most of the limitations we’ve been dealing with for no good reason for too long.

Now, to Randy’s point, companies can seamlessly join call center data with advertising creative with data governance.

The constraints are gone. But the org charts remain.

3. Don’t Pave The Cowpath

We’re at the beginning. People are automating simple workflows and calling them “AI workflows.” It’s impressive and fun and novel. But the real use cases—call center data informing creative data integrated with data governance—that’s not happening yet. It will happen, but it’s early.

And therein lies the trap: using extraordinary technology to preserve very ordinary behavior.

4. Nobody’s Asking About Transactions Anymore

At IAB—an advertising conference—not one client asked about advertising transactions. Zero.

Instead: “How do I understand my customers better? How do I connect these threads of data into something that helps me understand the totality of my customer relationships?”

It’s not about trading ad slots efficiently. That’s easy. It’s about understanding human beings well enough to build and sustain meaningful relationships with them.

5. Three Types of Technology Shifts

Type 1: Speed things up without changing industry structure (SWIFT in banking)

Type 2: Transform processes but don’t break boundaries (5G in telecom)

Type 3: Create entirely new things nobody even imagined (HTTP/HTML)

Where does agentic marketing sit? Randy and Vivek agree that it’s somewhere between 2 and 3. It’s possibly a full-on 3. We just don’t know yet.

The further you move from 1 to 3 in agentic marketing, the harder it becomes to weed out the human dimension – and the more uncapped the possibilities.

6. Stop Using the Word “Programmatic”

Randy wants us to stop using the word ‘programmatic,’ and I agree. The word itself is a mental prison. It constrains your mental model to matters of efficiency and buyer-seller relationships (which are inherently adversarial).

Which misses the point. Efficient trading of ad slots is easy. The real question is: how do we understand human beings well enough to catalyze and sustain durable, valuable relationships?

Language shapes thinking. “Programmatic” locks us into yesterday’s framework. It keeps us on the cowpath when we should be building new roads entirely.

7. The Readiness Question Has the Wrong Frame

When we say “marketers,” we picture the CMO at awards dinners still wrestling with how to replace television.

But tens of thousands of marketers are already in the trenches building test-and-learn scenarios with integrated data. Connecting call center data with creative with outcomes. This is happening now. It’s not a pipe dream.

Randy’s prediction: 20-25% of modern marketers will successfully navigate the transition to agentic during the next three years. Which is to say, 75% will be left behind.

The question isn’t “are marketers ready?” It’s “which marketers are you talking about?” A few are already there. Most are still on the cowpath.

8. The Great Reversal: From Big Data to Small Data (Back to Panels)

Vivek and I used to crow on stage about “billions of uniques.” Big Data was everything.

Now we’re celebrating small data—high octane, high-truth data. As Randy observes, panels may actually be better signals for personalization, targeting, measurement, and analytics than cookie pools composed of billions of pseudonymized identifiers of questionable origin.

Facebook proved this after iOS 14 killed its tracking and annihilated approximately 80% of the data they were using to fuel their ad business. More than a few pundits and market watchers predicted the end of Meta.

Meta came roaring back using synthetic data generation, not surveillance.

AI – synthetic data in particular – offers not just a legal but an ethical workaround to the PII problem.

This is what complete transformation looks like: a total reversal of assumptions around which we built the prior generation’s marketing stack.

From today’s vantage point, it’s a Soviet-era contraption crying out for reinvention. Its agentic successor isn’t a state of mind. Ready or not, it’s hurtling towards us at blinding, scary speed.