A Stack That Keeps Growing, Results That Don’t
There is a particular kind of frustration that has become endemic among marketing leaders in 2026. They have more customer data than ever before. They have more tools than ever before. They have more channels, more signals, more segments, and more dashboards than any previous generation of marketers could have imagined. And yet, when a customer moves from a brand’s Instagram ad to its website to its email inbox, the experience often feels as though three different companies are trying to sell them something simultaneously, with no awareness of each other.
This is the paradox at the heart of modern marketing — and it is getting worse, not better. The martech landscape now counts more than 15,000 documented solutions, a figure that has grown a hundredfold since 2011, with over 1,300 net new products added between 2024 and 2025 alone, roughly 77% of them AI-native. Nearly 38% of brands added one to two new applications to their stack in the past year, while 21% added three to five. And yet more than half of organizations still struggle with what industry analysts have taken to calling “Frankenstack” experiences — siloed data architectures where tools don’t talk to each other, customer profiles live in separate systems, and the left hand of marketing genuinely does not know what the right hand is doing.
The investment numbers tell the same paradoxical story. Marketers now allocate roughly 40% of their budgets to personalization — nearly double the 22% they allocated just three years ago. More than half of organizations are actively increasing investment in database strategies. And yet McKinsey research shows that 71% of consumers expect personalized interactions, and 76% report frustration when those interactions don’t materialize. Only 24% of firms are effectively delivering on omnichannel personalization. Most companies are spending more to fail at the same problem.
The Frankenstack Problem
To understand why this is happening, it helps to see how most enterprise marketing stacks were actually built. They were not designed — they accumulated. A CRM here, a marketing automation platform there, a customer data platform bolted on after the fact, an analytics tool integrated through an API that someone on a now-departed team maintained. Each tool was a reasonable decision in isolation, an answer to a specific problem at a specific moment. Collectively, they became an architecture that nobody ever intended, and nobody knows how to fully operate.
The industry term for the result — “Frankenstack” — is grimly accurate. Data collected in one system cannot be activated by another in real time. Customer behavior logged in an analytics platform does not reach the email tool before the message goes out. A customer who just filed a support complaint receives a promotional upsell three minutes later because the two systems do not share signals. Gartner research has found that poorly executed personalized marketing actually generates negative experiences for 53% of customers, making them 3 times more likely to regret a purchase and 44% less likely to buy again. The very investment meant to build loyalty is, in many cases, actively eroding it.
The problem is not a lack of data. Most enterprise brands are drowning in behavioral signals, transactional records, engagement history, and declared preferences. The problem is a structural gap between data collection and data activation — a missing layer of intelligence that can simultaneously ingest all those signals, reason across them in real time, and determine what to actually do next for each individual customer at each moment.
The Middle Layer No One Built
This is where the concept of a marketing “middle layer” becomes both technically precise and strategically urgent. In a well-functioning stack, data flows in from every touchpoint — website behavior, purchase history, email engagement, support interactions, social signals — and is unified into a single, continuously updated customer profile. But having a unified profile is only the starting point. The harder problem, and the one most organizations have chronically underinvested in, is what happens next: the decisioning and orchestration layer that takes that unified profile and determines, in real time, the optimal next action for each customer across every channel simultaneously.
This is what practitioners call “next best action” decisioning — a real-time strategy that uses customer data, business rules, and artificial intelligence to determine the most relevant and valuable action for each individual customer at any given moment. Rather than following predetermined campaign schedules or static audience segments that reflect how a group of customers behaved in the past, a functional decisioning layer evaluates each customer’s live context — their current behavior, their predicted intent, their engagement history, and the business’s goals — and selects the optimal interaction in the moment, whether that is a product recommendation, a content piece, a promotional offer, a service message, or a deliberate choice to say nothing at all.
The phrase “deliberate choice to say nothing” is worth pausing on because it points to a counterintuitive truth about where marketing ROI often hides. AI decisioning systems frequently generate returns not only from better targeting — reaching the right customer with the right message — but from better exclusion: identifying the customers who should not receive a communication because they are already converting, or because outreach at this moment will create friction rather than engagement. Knowing when not to send is as commercially valuable as knowing what to send. Most batch-and-blast campaign architectures, by design, cannot make that judgment.
Why AI Changes the Calculus
For years, the aspiration of real-time, individualized customer decisioning existed in the industry as a stated goal that the underlying technology could not deliver at scale. The data was too fragmented, the models were too slow, and the operational complexity of coordinating decisions across channels simultaneously was too great for most organizations to manage. What has changed rapidly and consequently is that AI has made computation tractable.
AI’s share of marketing activity has risen nearly 85% in the last two years. More than 90% of marketing organizations now use AI agents across their stacks. Gartner projects that more than 80% of enterprises will have tested or deployed generative-AI-enabled applications by 2026. But the most significant shift is not AI in content generation or audience discovery — it is AI embedded in the decisioning layer itself, where it can analyze behavioral signals and optimize customer journeys in real time at a scale and speed that no human team could replicate. Marketing teams using AI-assisted decisioning report measurably faster campaign execution, higher task completion rates, and meaningful improvements in output quality compared to teams relying solely on manual analysis.
The commercial results, where implementations have been done well, are striking. Brands using AI-powered recommendation and decisioning systems have reported conversion rate uplifts of over 11% and ROI multiples that would be difficult to achieve through any other single intervention in the stack. More significantly, these systems improve over time — every customer interaction generates data that sharpens the next decision, creating a compounding loop of relevance that static campaign logic cannot.
The Investment That Actually Matters
The insight that emerges from this landscape is uncomfortable for an industry that has spent a decade adding tools: the answer to the Frankenstack problem is almost certainly not another tool. It is a different kind of thinking about what the stack is for and where its value actually lives. The brands that are winning on personalization and customer experience in 2026 are not the ones with the most sophisticated individual platforms — they are the ones that have built the cleanest, most integrated connection between their data and their activation, with an intelligent orchestration layer in the middle that ensures every customer interaction is informed by everything the company knows.
This requires, above all, a commitment to a unified data infrastructure as a strategic asset rather than an IT maintenance problem. The creative work of marketing — the campaigns, the content, the offers — is the easy part. The data plumbing is where programs succeed or stall, and where the competitive distance between leaders and laggards widens every year. Organizations still managing marketing, sales, and service on separate data systems are not just facing an operational inefficiency; they are giving customers an experience that feels fragmented, because it is — and today’s consumers, who have been conditioned by the most sophisticated personalization engines on the planet to expect relevance as a baseline, will notice.
The broader strategic implication is that the question CMOs should be asking in 2026 is not “what tool should we add?” but “what is happening between the data we collect and the experiences we deliver — and who is accountable for that layer?” For most organizations, the honest answer is that nobody is quite sure, which is itself the most revealing diagnosis of why the gap persists.
The Simplest Way to Say It
The marketing industry has spent billions building the edges of the stack — the data-collection tools that capture customer signals and the activation platforms that push messages into channels. What it has consistently underbuilt is the intelligent middle: the layer that connects those two ends, unifies what is known about each customer in real time, and makes the continuous, context-aware decisions that determine whether any given interaction feels relevant or intrusive, timely or tone-deaf, like a brand that understands you or like one that has never noticed you before.
That middle layer is not glamorous. It does not show up in campaign portfolios or creative awards. It is infrastructure, and infrastructure rarely generates excitement. But it is the difference between a marketing stack that produces data and one that produces results — and in an environment where consumers have more choices, shorter attention spans, and higher expectations than at any point in history, that difference is everything.
The brands that figure it out first will not just have a better quarter; they will have a better year. They will have built something genuinely hard to replicate — and that value grows with every customer interaction they process.
