BTR: AI Forces a Rethink of the Marketing Technology Stack

Tony Byrne, Real Story Group

WASHINGTON, DC, UNITED STATES, October 29, 2025 /EINPresswire.com/ -- After a decade of nonstop investment in digital platforms, marketers are discovering that technology itself has become their biggest problem. The sprawling marketing technology stack—meant to integrate channels, data, and creative workflows—has morphed into an expensive patchwork of disconnected systems. With artificial intelligence accelerating disruption across every layer of that stack, the pressure is on marketing leaders to restore coherence, accountability, and measurable value to their digital operations.

According to Tony Byrne, CEO of the Real Story Group, the marketing technology—or MarTech—ecosystem has entered a moment of reckoning as artificial intelligence exposes long-standing weaknesses at the very core of how enterprises manage content, data, and decision-making.

“AI is shining a bright light on the structural problems marketing teams have ignored for years,” Byrne told BizTechReports in a recent executive vidcast. “Most enterprises have spent the past decade optimizing the edges of their stack—better websites, better email campaigns—while neglecting the core systems that manage content, data, and decisioning. That imbalance is now unsustainable.”

The Anatomy—and Weakness—of the Stack

The marketing technology stack, Byrne explained, can be understood in layers. The top layer—the engagement tier—includes customer-facing systems such as email, websites, loyalty programs, and e-commerce platforms. These are the tools that have absorbed most enterprise investment over the past decade. Beneath that sits what he calls the customer foundation layer: the data and process infrastructure that connects all those engagement systems and supports consistent, personalized experiences across channels.

Within that foundation are three functional pillars—content, data, and decisioning—which Byrne describes as the “three-legged stool” of modern marketing. Each must be strong and coordinated for the stack to deliver coherent, scalable results.

The problem is that the stool is wobbling.

This is because content—the fuel for every experience—remains disorganized in most enterprises, making it difficult to reuse components across channels or scale personalization with any consistency.

Data, meanwhile, is scattered across disconnected systems, undermining both analytics and activation because teams cannot assemble a coherent, timely view of the customer. And decisioning—the logic that determines what to present, when, and how—tends to be fragmented and ad hoc, producing inconsistent outcomes that vary by channel and campaign.

Taken together, these weaknesses explain why customer experiences often feel disjointed and why new AI-driven engagement models struggle to gain traction without a stronger core.

Byrne’s prescription is what he calls a “Pilates approach”: strengthening the core before attempting any new maneuvers.

These structural weaknesses have been compounded by the way enterprises assemble their technology portfolios. Over time, marketing organizations have accumulated dozens of point solutions—each designed to solve a specific problem but rarely integrated into a cohesive system. Earlier in the digital era, many firms tried to fix that by adopting monolithic suites from large vendors such as Adobe, Salesforce, Microsoft, or Oracle. The logic was straightforward: consolidation promised simplicity. In practice, it often produced rigidity. These integrated platforms tended to impose their own data models, development environments, and upgrade cycles—locking enterprises into vendor-defined roadmaps and limiting their ability to innovate.

In contrast, a new approach has gained ground: composability. Instead of relying on a single, vertically integrated platform, composable architectures assemble best-of-breed tools that interoperate through open APIs and shared data layers. The shift is subtle but profound—favoring adaptability over uniformity and control over convenience. Byrne argues that understanding this structural transition is essential to any meaningful reform of the MarTech stack.

Composability Wins—But Requires Discipline

In this context, one structural trend is clear. The composable model has overtaken the monolithic suite. Enterprises typically operate with 20 to 30 vendors integrated across their MarTech ecosystem. While suites from Adobe, Salesforce, Microsoft, or Oracle still play a role, Byrne cautions that no single vendor should dominate the entire stack.

“The best-of-breed model has largely won,” he explained. “But it also means marketing leaders must orchestrate an ecosystem—not just buy a platform.”

In mid-market organizations with smaller teams, bundled suites may still make sense. Yet across all segments, success now depends on disciplined data governance and interoperability—areas where many marketing departments remain immature.

As organizations modernize their stacks, composability is proving to be more than an architectural preference—it’s a prerequisite for effective AI. Artificial intelligence cannot thrive in fragmented or vendor-locked environments. Its value depends on how seamlessly content, data, and decisioning systems interact across channels. Byrne notes that without a disciplined foundation—shared data models, interoperable APIs, and clear governance—AI risks becoming another disconnected layer rather than the intelligence that unifies the enterprise.

Decisioning AI: The Next Frontier

Artificial intelligence touches all three legs of the stack—content, data, and decisioning—but in different ways. On the content side, generative AI accelerates production and expands the number of variants teams can test, yielding efficiency gains that are meaningful but usually incremental. On the data side, insights-oriented AI helps surface patterns and predictive segments that human analysts might miss, strengthening audience definition and planning. The most consequential impact lies in decisioning, where AI can automate “next best action” choices across customer journeys to deliver scale and speed that rule-based systems cannot match. This is the hardest domain—constrained by governance, compliance, and ethics—but Byrne argues it will become the primary driver of marketing performance over time.

As machine learning begins to make real-time decisions, marketing organizations must redefine their relationship with automation. Byrne draws a distinction between “human in the loop” systems—where people manually review outputs—and “human on the loop” models, in which humans monitor processes and intervene only when anomalies appear.

“The goal isn’t to remove humans,” he said. “It’s to take away friction while keeping oversight. In the long run, humans will be on the loop, watching the flow and stepping back into the loop when necessary.”

Leadership, Silos, and the AI Sandwich

Technology alone will not fix what Byrne calls the “organizational silos” that have grown up around channels, teams, and fiefdoms. Many companies still separate digital, marketing, and customer-care operations, each using different systems and metrics. The challenge is as much cultural as technical.

“If marketing doesn’t take the lead, no one will,” he warned. In many organizations, that leadership vacuum is real. IT departments tend to focus on infrastructure and integration rather than customer experience. Data teams are skilled at analysis but rarely own engagement strategy. Product and customer-care functions interact with customers but operate under different mandates and metrics. “The smart organizations are moving authority south in the stack—closer to where data and content actually live,” Byrne added. “That’s where marketing, if it’s willing to lead, can finally unify the experience layer with the operational core.”

Byrne describes today’s MarTech professionals as the “AI sandwich generation.” Pressured from above by boards demanding efficiency and head-count reductions, and from below by teams struggling with failed pilots, these leaders must navigate inflated expectations with limited resources.

“There’s a lot of just plain old hype rolling downhill,” he said. “Executives are being told if they don’t master AI in two years, their stock will tank. It’s unrealistic—and it’s causing real stress.”

His advice: partner with the CFO rather than fight them. Finance chiefs, Byrne noted, can become powerful allies in separating signal from noise. They bring discipline to investment decisions and can help marketing leaders reframe AI spending around measurable outcomes rather than speculative efficiency claims. “A good CFO will listen if you can show what’s real and what’s vendor hype,” he said. “They care about cost of ownership, time to value, and the risk of locking into the wrong platform.”

By aligning on those metrics, marketing executives can redirect AI initiatives from experiments that drain budgets to projects that actually strengthen the enterprise core.

The Vendor Problem—and the Case for an AI Layer

Financial alignment is only part of the discipline required to bring order to the MarTech ecosystem. The other half lies in how enterprises manage their vendors. The same rigor that CFOs apply to capital allocation, Byrne argued, must extend to technology procurement and platform strategy. As AI becomes embedded in nearly every marketing application, vendors are racing to differentiate themselves by touting proprietary algorithms and automation features. Without clear governance, those promises can quickly outpace reality—locking organizations into costly, redundant, or conflicting systems.

Every major engagement-tier vendor now markets its own “built-in AI.” Byrne views this proliferation as both predictable and dangerous.

“Your email vendor, your web vendor, your loyalty platform—they’re all bringing their own AI to the party,” he said. “Turn them all on, and you’re right back to channel confusion.”

Instead, he recommends that enterprises create a centralized AI layer that can inject intelligence into multiple platforms while maintaining consistency, governance, and cost control.

In vendor RFPs, Byrne’s team routinely includes two crucial questions: How do I turn off your AI? and How do I inject my own?

“If a vendor can’t answer those,” he said, “they shouldn’t be in your stack.”

Agentic AI and the Path to Orchestration

Byrne describes Agentic AI as the next evolutionary layer of automation—one 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 are built to execute actions across platforms with a degree of autonomy. These intelligent agents can initiate workflows, monitor performance, and adapt to feedback in real time. Basic workflow agents can automate tasks within a single platform—useful, but limited in reach. The greater promise lies in orchestration agents that coordinate actions across multiple systems so customer journeys can optimize themselves dynamically. Real Story Group’s vendor evaluations indicate that building such agents is harder, costlier, and security-sensitive, but Byrne believes that’s where durable value will emerge once organizations have their content, data, and decisioning houses in order.

For now, however, the technology remains in the sandbox phase: expensive, fragile, and often insecure. “It’s easy to build a trivial agent,” Byrne cautioned. “It’s hard to build one that delivers real business value.”

A Leadership Moment for MarTech

Despite the noise, Byrne sees this period as a leadership opportunity. Real Story Group runs an exclusive council of enterprise MarTech leaders, where Byrne urges members to focus on fundamentals—de-siloing teams, restructuring content and data in channel-neutral formats, and preparing for the 2030s rather than chasing quarterly trends.

“AI should be the object of the sentence, not the subject,” he said. “Don’t say ‘AI can do X.’ Say, ‘We need to improve campaign efficiency using AI.’ Make it the dependent clause, not the headline.”

That, Byrne argues, is how marketing can reclaim its strategic footing: by aligning technology investments with genuine business objectives rather than vendor roadmaps.

Click here to read the Q&A based on this interview.

Airrion Andrews
BizTechReports
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