Many B2B MarTech and sales tech vendors built their data capabilities for one type of buyer: the fast-moving, technically confident team that can find, assemble, and act on data without much help. A growth hacker who pulls his own lists. A RevOps analyst who stitches together enrichment sources in a spreadsheet. A startup or scaleup where one person controls the full data pipeline because one person controls everything.
The data gets found. The data gets used. And it works — because the person who needs it and the person who manages it are the same person.
That's not how established enterprises works. And that gap is where the GTM DATA problem lives.
For example: enterprises have legal asking where the data came from and whether you have the right to use it. It has IT with a backlog that doesn't include your project. It has marketing ops, RevOps, demand gen, field marketing, and sales each owning a different slice of the picture — and nobody owning the whole thing. And it has a marketing automation contract that doesn't expire for another 18 months, so wholesale platform changes aren't on the table. I can go on but you get the gist.
In most cases, someone in the business raised the need. A head of marketing, a CMO, a commercial leader who could see the data problem clearly. And then the natural next step happened: they asked IT for help. That's where things went sideways.
IT engaged. Vendors got involved. Relationships that were formed between IT teams and platform providers in the past took over and the business had little visibility into what happened next. The conversation shifted from "what does marketing need to reach the right accounts" to "what architecture can we build to centralise all customer data." Data lakes were proposed. CDPs were procured. Infrastructure projects were scoped that would take 18 months before the first use case went live.
The result, more often than not, was disappointing — and the industry data backs this up. According to Gartner, CDPs are now in the Trough of Disillusionment phase of the Hype Cycle, as implementations have repeatedly failed to deliver on early promises.
One survey found that 87% of marketers still describe data as their company's most underutilised asset — despite years of investment and growing budgets. Almost 75% of marketers say technology has made personalisation harder, not easier.
The reason isn't hard to find. CDPs were designed to solve an IT data problem, not a marketing campaign problem — and as the market evolved, the line between who the technology was supposed to serve became increasingly blurred. It aimed to centralize existing first party data but neglected the need to augment it sensibly with a 3rd party influx of information and data.
The architecture was also built by people too far from the field. The field — demand gen, campaign teams, the people who actually needed to reach accounts in motion — kept throttling along without ever really getting what they asked for in the first place.
Stuck. And the reasons are stacking up.
Big infrastructure investments have stalled. Nobody wants to sign off on a multi-year platform project in an environment shaped by budget pressure, geopolitical uncertainty, and investors who've been handed AI as a justification to cut headcount in their portfolio companies. The self fulfilling and partly false SaaS apocalypse prophecy — the wave of rationalizations, consolidations, and quiet sunsets that's been reshaping the vendor landscape — has made procurement committees even more risk-averse than they already were.
Then there's AI itself, which has created its own particular paralysis. In theory, agentic AI and LLM-powered automation will solve much if not everything. Yeah...
In practice, enterprise legal and compliance teams are now grappling with a tokenonomy (token-economy) that turned out to have concentration risks nobody fully priced in — models retracted overnight (saw Fable5?), pricing changed unilaterally, dependencies on a handful of players who are making increasingly aggressive moves.
Prudence, which was already a feature of enterprise decision-making, is now being applied to AI infrastructure too.
Meanwhile, the existing vendors are doing what vendors do when their core product is running out of road: borrowing concepts (headless CMS that makes sense is being applied to headless CRM that makes less sense as long as the data(model) is flawed), rebranding roadmaps, and adding the word "next" and "AI (MCP)" to product names built on decade-old code.
But the technology problem runs deeper than vendor roadmaps.
In the middle of all this noise, a body of evidence has been quietly accumulating that much of the GTM theory these platforms were built on was wrong to begin with. The work we have done together with researchers like Kerry Cunningham at 6sense has made it increasingly hard to ignore: the MQL-driven model didn't just underperform — it built an industrial complex around a metric that was never a reliable proxy for buying intent. Chasing the hand-raiser, the form-fill, the "ready to buy" signal created entire demand generation programmes optimised for the wrong moment. And the consequences are now visible. If your budget and strategy concentrate exclusively on buyers already in-market ready to buy, you will find — too late — that those buyers were never going to buy from you. Because you weren't there when the committee was forming. Because you weren't in the consideration set before the shortlist was built. Because 70% of the decision was already made.
This has given branding a renewed urgency in B2B that would have seemed odd five years ago. Not brand for its own sake — but the transition from brand to demand, the careful work of activating a buying committee at a moment when only one or two members have moved into an in-market state while the majority are still out of market. Orchestrating that — across multiple stakeholders, parallel journeys, different phases, different levels of awareness — is genuinely hard.
It requires a technology infrastructure whose data model acknowledges that complexity from the ground up. But it also requires the data! Not "an account plus one contact". Not a lead score but data on the buying group, in motion, with every member tracked and every engagement mapped. And it requires infrastructure that can actually populate that model — not as a one-time exercise, but continuously, as committees shift and signals change.
Here's a reality. For most of the existing marketing technology the underlying data model hasn't changed. And that is the elephant in the room. Lead-centric architectures dressed up in new language are still lead-centric. They cannot accommodate what buying group engagement actually requires — multiple stakeholders, parallel journeys, committee-level signals — because they were never designed to.
Through all of this, the marketer in the middle is being asked to do more, faster, with fewer colleagues, and with a stack that was never quite right and is now visibly aging.
So if you're in the process of buying your "next" marketing cloud, wait! (or at least talk to us)
The ability to build a live, continuous, connected GTM data capability — one that keeps ICP definitions current, surfaces buying committee members at accounts in motion, monitors competitive and macro signals at scale, and feeds all of that into the workflows that actually run the GTM motion — no longer (just) requires (only) a heavy infrastructure project, an IT dependency, or a seven-figure data contract.
First off, in the short run, it does not necessarily longer requires waiting for the right platform cycle or the right budget year. Parts of it can be built now, around what already exists. Your existing tech stack complimented with some new "threads" can be woven into a much more agile GTM enabler.
But then sometime later, it will require the right scalable architecture with the right foundation both in terms of business as well as data model.
You'll probably also need a partner who knows how to build it inside the complexity of a real enterprise environment — across the legal, IT, ops, and GTM functions that all have a stake in how data moves.
We call all of this AI GTM Fabric. Built by people who start with what the field actually needs — not what the infrastructure can theoretically support, not what a system integrator is pushing because it suits their IT vendor/partner margins. The right technical depth on legacy stacks. But always marketing team-first.
We've been working on this for a while. We're not ready to share all the details yet. We will unveil that GTM enabler I referred to on June 22nd.
If you're a marketing or commercial leader at a mid-to-large B2B organisation in Europe, and you've been wondering whether there's a smarter way to feed your GTM motion — without waiting for the platform renewal, without a major IT project, and without starting from scratch — it's worth a conversation.
We've spent the last year building toward a capability we haven't been able to offer before — not just better data and data provisioning, but also the infrastructure that connects it to how buying groups actually form and move. In other words both the data threads and the machine that weaves it together.
If that's you: reach out. Happy to talk through what we're seeing and whether it's relevant to your situation. Stay tuned for more announcements....yet, why wait? 🙂
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LeadFabric is a B2B GTM agency specialising in marketing automation, ABM, and buying group-led GTM strategy. We've been building GTM infrastructure for enterprise B2B organisations in Europe since 2008.