How Zeta Cracked The AI code for the next gen of martech

AI in Martech: How Zeta Built a Data Empire

In the episode titled Data Dominion: How Zeta cracked the AI code for the next gen of martech, Matt Britton sits down with David A. Steinberg, Co-Founder, Chairman and CEO of Zeta Global, to unpack what it really takes to win with AI in martech.

The central tension is clear. As artificial intelligence reshapes marketing technology, will enterprise platforms be disrupted by large language models, or will they become the disruptors? For CMOs and CEOs, the stakes are measurable. Billions in media spend, customer acquisition costs, and enterprise data strategies hang in the balance. Ignore the AI shift and risk margin compression. Embrace it without a strategy and risk handing proprietary data to someone else’s model.

Matt Britton, AI futurist and author of Generation AI, frames the conversation through a builder’s lens. He has long argued that AI will not just optimize workflows but redefine how companies compete. As CEO of Suzy, an AI-powered consumer intelligence platform, Britton has seen firsthand how enterprise buyers are reevaluating their tech stacks.

Steinberg brings rare credibility to the table. He has built and scaled multiple companies across telecom, ecommerce, and digital marketing. Today, Zeta Global serves 51 percent of the Fortune 100 and has delivered four consecutive years of more than 30 percent growth. His perspective is operational, financial, and grounded in execution.

What follows is a blueprint for how to build AI in martech from the inside out, before the market forces you to.

How Zeta Re-Architected Its Martech Platform Around AI

The AI in martech conversation often starts with tools. Steinberg’s story starts with architecture.

In 2017, Zeta was operating eight disparate platforms assembled through acquisition. The company had significant data assets, but no efficient way to process them in real time. Steinberg recognized a fundamental constraint. Decisions that should have been made in milliseconds were taking hours.

Rather than layering AI on top, Zeta rebuilt its foundation. In 2021, it launched the Zeta Marketing Platform with AI and data native to the application layer. That architectural choice reduced latency and increased return on ad spend. According to a Forrester study cited in the episode, Zeta returns between six and seven dollars for every dollar spent through its platform.

The business results followed:
• Four consecutive years of 30 percent or greater revenue growth
• 50 percent-plus compounded EBITDA growth
• 75 percent-plus free cash flow growth

While headlines warned of a “SaaS apocalypse,” Zeta positioned itself as the disruptor, not the disrupted. In a marketing ecosystem growing at roughly 10 percent, Zeta grew three times faster, taking share.

Companies that treat AI as a feature will lose to those that treat it as infrastructure. The winners build proprietary data assets, integrate AI at the core, and create measurable ROI.

For enterprise leaders, the application is direct. If AI is not embedded into your architecture, it is a temporary layer.

Why First-Party Data and Enterprise AI Strategy Matter More Than Ever

One of the most pressing questions in AI in martech is whether large language models will disintermediate enterprise platforms. Steinberg’s answer is pragmatic.

Zeta built a consumer data platform for each client, ingesting first-party data and merging it with a proprietary data cloud of 552 million opted-in individuals. Each profile contains thousands of data elements. Critically, that data is not fed into public large language models.

The implication is clear. Fortune 500 companies are unlikely to hand over proprietary data to external models at scale due to privacy, compliance, and control concerns.

Zeta’s edge rests on three pillars:
• Proprietary first-party and opt-in data
• AI embedded natively within the platform
• Demonstrated revenue return rather than cost center positioning

If a platform consistently returns six to seven dollars for every dollar spent, the conversation shifts. Marketing becomes an investment line, not an expense line.

Enterprise adoption accelerates when AI is tied to measurable outcomes. Insight without action is noise. AI in martech must connect data ingestion to acquisition, retention, and monetization in a closed loop.

Owning first-party data is no longer just a privacy play. It is the raw material for AI differentiation.

What Is Athena and How Does Voice AI Change Martech?

Zeta’s launch of Athena signals the next phase of AI in martech. Steinberg describes Athena as a voice-enabled, fully conversational copilot.

Most enterprise software is underutilized. Companies build highly complex systems, but teams only use a fraction of their capabilities.

Athena removes the learning curve. Instead of navigating dashboards, users speak. A marketer can define a goal and receive recommendations on audience segments, media allocation, and expected performance based on real data.

The business impact is direct:
• Increased return on ad spend
• Reduced workflow management overhead

Steinberg’s target is a 1000 percent return on investment through Athena-enabled campaigns. Early beta users are already exceeding prior benchmarks.

Voice interfaces collapse the distance between intent and execution. When software becomes conversational, access expands and adoption increases.

AI in martech is moving from dashboards to dialogue.

How Generative Search Is Reshaping Digital Marketing Strategy

Consumer behavior is shifting. Generative interfaces are beginning to replace traditional search patterns.

A year ago, 97 percent of Google searches resulted in a click off-platform. Today, roughly 60 percent of answers are delivered directly within the interface.

Fewer outbound clicks increase cost per click and reduce efficiency in traditional paid search. Brands are beginning to see diminishing returns.

Zeta’s response includes:
• Building a generative engine optimization strategy
• Partnering with emerging AI platforms as advertising evolves
• Ensuring brands are included in AI-generated answers

This shift requires a new approach. Brands must optimize for inclusion in AI outputs, not just rankings.

Distribution is fragmenting. The advantage goes to those who identify where attention is moving early.

How to Lead Organizational Change in the Age of AI

Technology alone does not drive transformation. Execution is driven by culture.

Zeta moved early during COVID, enabling remote work and restructuring operations ahead of competitors. The same mindset applied to AI adoption.

The company embedded AI across teams, provided tools to all employees, and tracked usage. High performers were highlighted and used to drive adoption internally.

The result:
• Engineering productivity reached 125 percent of prior output on a net basis
• Gross productivity gains approached 150 percent

The company also made difficult decisions. Employees who resisted AI adoption were replaced with talent fluent in these tools.

For emerging professionals, the takeaway is direct. Build hands-on experience early. Develop creativity and problem-solving skills alongside technical fluency.

AI reallocates opportunity. It does not eliminate it.

Key Takeaways for Business Leaders

• Embed AI into architecture, not as a surface feature
• Protect and operationalize first-party data
• Use conversational interfaces to drive adoption and ROI
• Diversify media strategy beyond traditional search
• Use disruption as a catalyst for acceleration

Frequently Asked Questions

What is AI in martech and why does it matter now?
AI in martech refers to embedding artificial intelligence into marketing technology platforms to automate decisions, optimize campaigns, and personalize customer experiences. It matters now because generative AI is reshaping acquisition and media strategy.

How is generative search impacting marketing budgets?
Generative search reduces outbound clicks, increases cost per click, and forces brands to diversify spend toward new channels and optimization strategies.

Why is first-party data critical?
First-party data enables differentiation, protects privacy, and allows companies to build proprietary intelligence systems without relying on external models.

Will AI eliminate entry-level marketing jobs?
AI will automate repetitive work but increase demand for creativity, strategy, and technical fluency. Roles will evolve rather than disappear.

AI in martech is now a structural shift. Companies that rebuild around intelligence, control their data, and move decisively will take share.

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