Rebuilding the MarTech Core: How AI Is Forcing Marketing Leaders to Rethink the Stack — Real Story Group - October 29, 2025

By Staff Reports - October 29th, 2025

Marketing leaders are entering an era where ambition and architecture are colliding. Artificial intelligence is forcing a new level of transparency inside the marketing technology stack — revealing how years of rapid digital investment have left many organizations with systems that are powerful in isolation but fragile in combination. For Tony Byrne, founder and CEO of The Real Story Group, this moment represents a turning point: AI isn’t simply adding capability to marketing, it’s demanding coherence.

In this conversation with BizTechReports, Byrne explains why the path to effective AI runs straight through better content, cleaner data, and disciplined decision-making — the core elements that define the modern MarTech ecosystem. He also explores how composability, governance, and cross-functional leadership are becoming the real differentiators for marketing organizations determined to compete in an AI-driven future.

NOTE: This interview has been edited for clarity and length. The Q&A has been organized into four sections — Strategic Assessments, Operational Imperatives, Financial Implications, and Technology Development — to highlight the most pressing issues for healthcare executives.

STRATEGIC ASSESSMENTS

Full Interview w/ Tony Byrne, CEO, Real Story Group

BTR: Tony, before we get into AI, let’s define the terrain. What exactly is the MarTech stack, and why is it such a challenge for organizations to manage?

Byrne: The MarTech stack is basically the digital nervous system of modern marketing — a layered ecosystem of technologies that connect brands to customers. At the top is what we call the engagement tier: customer-facing tools like email platforms, websites, loyalty programs, and e-commerce systems. Those are the touchpoints that have received the most investment over the past 15 years.

Beneath that sits the customer foundation layer, which should act as the connective tissue linking those experiences together. Within that foundation are three functional pillars — content, data, and decisioning — what I call the “three-legged stool” of modern marketing. Content fuels the experiences; data defines who the customer is; decisioning determines what you show, when, and how. Each has to be strong and synchronized.

The problem is that most enterprises have built outward rather than downward. They’ve invested heavily at the edge — better websites, better campaigns — while neglecting the core systems that manage content, data, and decision logic. AI is now shining a light on that imbalance, exposing just how brittle many of these stacks have become.

BTR: You’ve described marketing teams as aspirational — sometimes even overly optimistic — when it comes to technology. Why has that mindset created so much risk?

 Byrne: Marketers are visionary by nature, and that’s great. But that optimism also makes them vulnerable to hype. There’s enormous disappointment across the industry with MarTech investments that promised transformation but delivered complexity. Many teams believed integrated suites would give them an end-to-end solution; instead, they found themselves managing overlapping tools that don’t talk to each other. The strategic takeaway is that ambition has to be balanced with architecture. You can’t buy your way into coherence — you have to design for it. 

BTR: The term composability has become popular in enterprise IT. What does it mean in marketing?

Byrne: Earlier in the digital era, enterprises leaned toward monolithic suites from vendors like Adobe, Salesforce, Microsoft, or Oracle. The idea was to simplify by consolidating everything under one roof. In practice, those suites often imposed rigidity — their own data models, upgrade cycles, and pricing schemes.

Composability flips that model. It’s about assembling best-of-breed tools that interoperate through open APIs and shared data layers. Large enterprises may run 20 to 30 vendors in a single stack. That sounds messy, but when governed properly it’s actually more adaptable. You can replace weak components without disrupting everything else. For mid-market firms, bundled suites can still make sense, but across the board success now depends on disciplined data management and interoperability.

The message for strategists is that composability isn’t an end state — it’s a philosophy. You win by designing systems flexible enough to evolve with the market.

OPERATIONAL IMPERATIVES

BTR: Let’s talk about operations. What does it actually take to bring coherence to these fragmented environments?

Byrne: It starts with leadership. Too often, no one truly owns the customer experience end-to-end. Marketing controls messaging, IT manages infrastructure, and data teams live in their own analytics silos. If marketing doesn’t take the lead, no one will.

IT departments are great at reliability and integration but aren’t built to drive engagement strategy. Data teams can surface insights but don’t run campaigns. Product and customer-care groups interact with customers every day but under completely different KPIs. The smartest organizations are moving authority south in the stack — closer to where data and content actually live — so marketing can coordinate across those functions. When leadership happens at that level, AI and automation become enablers rather than distractions.

 BTR: Where does artificial intelligence fit operationally?

Byrne: AI interacts with every layer of the stack — especially the three foundational legs of content, data, and decisioning. Generative AI helps create and personalize content more efficiently. Insights-oriented AI analyzes data to uncover segments or trends that humans might miss. And decisioning AI is where the real power lies: automating “next-best-action” choices across customer journeys so experiences adapt in real time.

Decisioning AI is difficult because it touches governance, compliance, and ethics, but it’s also where scalability will come from. In marketing, rules and workflows don’t scale well. Algorithms can, provided you train and monitor them properly.

Operationally, organizations must also evolve how humans interact with machines. We talk about moving from humans in the loop — checking every output — to humans on the loop, monitoring automated processes and intervening only when something looks off. That’s how you combine efficiency with oversight.

 BTR: You’ve said marketing leaders are caught in an “AI sandwich.” What do you mean?

 Byrne: At Real Story Group, we run a private council of enterprise MarTech stack leaders, and they tell us privately they’re getting squeezed from both sides. Boards and C-suites are demanding instant efficiencies, often based on hype from consultants or futurists promising massive head-count reductions. Meanwhile, teams on the ground are running pilots that rarely produce measurable ROI. MarTech leaders are stuck in the middle, trying to manage expectations upward and reality downward.

That’s why a partnership with the CFO is crucial. Finance chiefs can be allies in separating signal from noise. They bring rigor to investment decisions and can help marketers reframe AI spending around time-to-value and total cost of ownership instead of vague notions of transformation. If you can show a CFO what’s real and what’s vendor hype, you’ll find support for projects that strengthen the core instead of draining budgets.

 BTR: And beyond finance, where else should marketing build alliances?

 Byrne: With IT and data leadership. The same financial discipline you apply to budgeting must also apply to platform governance. As AI features proliferate, every vendor is claiming differentiation through proprietary algorithms. Without joint oversight, those add-ons can create redundant costs, compliance risks, and inconsistent experiences. Marketing leaders need shared governance with IT to evaluate where AI actually adds value — and where it’s just marketing wear.

FINANCIAL IMPLICATIONS

BTR: You’ve talked about CFOs as partners in accountability. What’s the bigger financial picture here?

 Byrne: The financial story of MarTech is one of hidden duplication. Organizations often buy multiple tools that do the same thing in slightly different contexts — one for loyalty, one for web, one for social — and then spend even more integrating them. AI has the potential to reduce those inefficiencies, but only if it’s built on a stable foundation. 

This is where composability meets finance. When your architecture is modular, you can swap parts as costs or performance change. That flexibility protects budgets. Monolithic suites, by contrast, lock you into long-term licenses and high switching costs.

CFOs understand this inherently. What they need from marketing is transparency — clear documentation of the stack, cost attribution, and measurable outcomes. If you can quantify the ROI of cleaner data or faster campaign cycles, you’ll turn AI from a speculative investment into an operational efficiency play.

BTR: Many organizations are chasing AI to gain short-term efficiency. What’s the risk in that?

Byrne: The danger is mistaking acceleration for transformation. AI is an accelerant; done right, it should remove friction and speed up what you already do well. But if your underlying processes are broken, you’re just automating dysfunction. That’s why the strategic work of de-siloing content, data, and decision-making remains critical. AI amplifies both strengths and weaknesses — it doesn’t magically fix them.

Financially, the winners will be those who use AI to tighten operations: reducing cycle times, improving data quality, and increasing reuse of assets. Those are real gains. The fantasy is that AI alone will eliminate labor costs. Marketing is still a human discipline; it just needs better tools and smarter allocation of effort.

TECHNOLOGY DEVELOPMENT

 BTR: Let’s turn to the technology itself. Vendors are all claiming to be “AI-powered.” How should enterprises navigate that?

 Byrne: Carefully. Real Story Group’s vendor tracking indicates that every engagement-tier vendor — your email platform, web CMS, loyalty system — now markets its own built-in AI. It’s predictable, but it’s also dangerous. If you light them all up, you’ll recreate the same channel confusion we suffered a decade ago, except now it’s algorithmic. 

Our advice is to create an enterprise AI layer — a centralized framework for intelligence that plugs into various systems through APIs. That doesn’t mean building AI from scratch; it means choosing partners for specific capabilities like generative content, decisioning, or analytics, and ensuring they can integrate cleanly. In the RFPs we develop for enterprise clients, we always ask vendors two questions: How do we turn off your AI? and How can we inject our own? If they can’t answer those, they don’t belong in your stack.

BTR: How do you see Agentic AI fitting into that framework?

Byrne: Agentic AI is the next evolutionary layer of automation — software designed to act on behalf of the enterprise, not just within it. Unlike generative AI, which creates content, or decisioning AI, which optimizes choices, agentic systems can execute actions autonomously across platforms. They initiate workflows, monitor performance, and adapt based on results.

The simplest form is a workflow agent that automates repetitive tasks within a single platform. Useful, but limited. The greater promise lies in orchestration agents that connect multiple systems so customer journeys can self-optimize in real time. Those are harder to build — they raise issues of security, cost, and governance — but they represent the future.

We’re still in the sandbox stage: it’s easy to build a trivial agent, much harder to build one that’s secure, auditable, and delivers measurable value. But once enterprises strengthen their core — content, data, and decisioning — they’ll be ready for agents that truly orchestrate experiences rather than just automate tasks.

BTR: That brings us back to humans. How do marketers maintain control as systems become more autonomous?

Byrne: Through human-on-the-loop oversight. Think of it like a control room: you’re watching the flow, monitoring exceptions, and stepping in when needed. Early on, there will still be humans in the loop, approving and editing. Over time, the goal is to elevate humans above the process so they focus on governance, creativity, and ethics. You still need people; you just need them doing higher-order work.

Conclusion

As the conversation with Byrne makes clear, the next era of marketing technology is less about invention than re-invention. Artificial intelligence isn’t rewriting the laws of marketing — it’s exposing the weaknesses already there. Enterprises that treat AI as an accelerant and integrator, rather than a magic bullet, will find themselves better prepared for the 2030s. 

For marketing leaders, that means re-centering on fundamentals: strong content operations, unified data, coherent decisioning, and cross-functional leadership. It means partnering with finance and IT to bring the same discipline to technology governance that they bring to budgets and infrastructure. And it means resisting the temptation to outsource intelligence to vendors when it belongs at the heart of the enterprise.