Scott Brinker’s Martech for 2026 report offers a lucid map of the terrain GTM teams must now navigate: a marketplace no longer defined by sequential buyer journeys, increasingly shaped by agentic AI, destabilized by volatility and governed by nonlinear patterns of evaluation and decision-making.
Yet during the same period in which martech blossomed into a sophisticated, multi-layered discipline, GTM effectiveness collapsed.
The GTM-martech paradox
The data is unmistakable. Across datasets representing 478 B2B companies, GTM effectiveness has fallen from 78% in 2018 to just 47% in 2025. The decline is not cyclical. It is structural. And it has accelerated sharply over the past three years.
Let’s repeat that: less than $0.50 of each B2B GTM dollar is effective. More than 50% is wasted spend.
Meanwhile, martech investment has grown. Sales teams have become more specialized. Marketing organizations are more data-driven. AI has flooded the revenue engine with tools capable of performing work once reserved for experts.
Yet GTM performance moved in the opposite direction. How did GTM become more sophisticated while its underlying value creation engine deteriorated across industries, company sizes and maturity levels?
The answer begins with an uncomfortable but clarifying truth: martech scaled the wrong worldview. It industrialized the deterministic logic that once governed GTM, and it did so at the very moment the marketplace was abandoning determinism wholesale.
The environment shifted from stable to volatile, from linear to nonlinear, from internally influenced to externally dominated. Buyer decisioning collapsed. Lag dynamics expanded. Volatility became structural. The deterministic metaphors that underpinned 20 years of GTM thinking — funnels, stages, journeys and attribution paths — could no longer make contact with reality.
Martech did not cause the collapse. But by faithfully encoding the old logic, it accelerated the divergence between GTM’s internal understanding and the external world it sought to influence.
Dig deeper: B2B firms suffer from poor GTM understanding
Marketplace realities finally blew up deterministic machine GTM
The structural decline in GTM effectiveness coincides almost perfectly with the onset of deep marketplace volatility.
Around 2018, intensifying through the pandemic era and its aftermath, buyer behavior became unpredictable. Internal corporate risk tolerance fell. Procurement gained authority. Committees expanded. Budget cycles elongated. Decision rights fractured. Most critically, buyers increasingly defaulted to inaction. In our dataset, 83%-84% of opportunities now end in “no decision,” a figure so extreme it forces a re-evaluation of GTM’s operating assumptions.
This behavioral reversal cannot be fixed by optimizing internal motions. It reflects a causal shift in the environment. Nothing in the traditional GTM playbook anticipates a world in which the buyer’s most likely action is to do nothing at all.
Traditional martech systems, built to guide buyers through linear journeys, were never designed to interpret this level of entropy. They read movement where none exists, infer influence where none is present and generate predictions anchored to patterns that no longer reflect the system’s underlying physics. Internal dashboards remain orderly, but the reality beneath them has dissolved into uncertainty.
Sales effectiveness collapsed first —and hardest
Marketing effectiveness has declined gradually, but sales effectiveness has collapsed catastrophically. Three forces define this collapse. Sales cycles have doubled, resulting in a significant decline in throughput, even for top-tier performers. Year 1 deal sizes have fallen by more than 60%, undermining the economics of customer acquisition. And the “no decision” phenomenon now erases the economic value of four out of five opportunities.
This is not a performance failure. It is a physics failure. When the environment hinders decision-making, sales cannot achieve outcomes at historical rates, regardless of the team’s skill or the process’s optimization. The deterministic assumption that a well-executed process inevitably yields a decision no longer holds.
Marketing, which sits upstream from economic commitment, can remain functional under these conditions. Sales, which sits squarely in the decisioning layer, cannot. Because sales effectiveness multiplies marketing effectiveness, the decline in sales becomes the dominant driver of GTM collapse.
Dig deeper: Designing the GTM model for marketing’s revenue era
CAC didn’t rise because of spend. It rose because causality broke.
The surge in customer acquisition cost over the past three years is often framed as a budgeting problem, a market-saturation phenomenon or a sign of deteriorating efficiency. But CAC is not a marketing metric. CAC is a system metric — a reflection of the entire revenue engine’s causal integrity.
When sales cycles double, CAC rises because capital is tied up longer. When deal sizes shrink, CAC payback extends because the system produces less revenue per unit of acquisition effort. When 84% of opportunities end without a decision, CAC becomes nearly unmanageable because most of the system’s labor never converts into value.
The CAC loan — the assumption that acquisition cost can be repaid within a predictable window — collapses when the causal structure of the revenue engine dissolves. It is not that GTM is spending too much. It is that the marketplace no longer converts GTM actions into revenue at the expected rate.
No amount of process refinement or pipeline hygiene can solve this. Only a return to causal understanding can.
Martech became the amplifier of misalignment
None of this means martech is defective. Quite the opposite. Martech performed exactly as designed. It automated workflows, refined processes, increased visibility, orchestrated cross-channel execution and delivered unparalleled reach. It did everything the logic model asked it to do. The problem is that the logic model was wrong.
The tools encoded a world of sequential steps, stable patterns, attributable influence and linear persuasion. They kept that world alive long after the market had abandoned it. As a result, martech became the enterprise’s distortion layer. It preserved the illusion of order while the underlying system devolved into volatility and decision paralysis. It allowed GTM leaders to believe their motions remained effective, even as the causal connection between those motions and economic outcomes deteriorated.
Because the dashboards looked sophisticated and the models looked mathematically rigorous, those illusions became harder to question. Martech gave GTM precision, but not truth.
GTM’s decline is now a governance issue
This divergence between internal representation and external reality has elevated the GTM crisis from a commercial problem to a governance problem. Boards and CFOs are increasingly relying on systems that cannot accurately describe the real world. Under Delaware’s 2023 duty-of-oversight standards, that reliance is no longer tenable.
Officers are now responsible for ensuring that critical areas of the business — including revenue generation — are supported by reliable, causally accurate information systems. GTM’s deterministic dashboards and correlation-based attribution models no longer qualify.
At the same time, the SEC’s emerging AI governance agenda demands explainability, model transparency and defensible logic in any market-facing claims influenced by automated systems.
Forecasts, marketing claims and revenue projections derived from pattern-based models will face heightened scrutiny. The enterprise cannot continue to speak in deterministic forecasts when the underlying system is probabilistic.
GTM has therefore become the enterprise’s largest blind spot. Blind spots are inherently fiduciary.
Dig deeper: AI is transforming GTM teams into fiduciary powerhouses
The path forward cannot be achieved through better playbooks, cleaner funnels, improved attribution or more refined orchestration. These are optimizations of a worldview that no longer matches the environment. The enterprise does not need more martech. It needs a new mental model — one that reflects the causal mechanics of a volatile market.
A causal GTM operating system replaces deterministic premises with a worldview capable of representing the marketplace as it is — nonlinear, externally influenced, dynamic and probabilistic. It begins not with processes but with mechanisms — the causal relationships that drive outcomes. It explicitly models the role of external forces, quantifies the impact of volatility, captures the effect of lag and distinguishes signal from noise.
In a causal system, GTM activity is not assumed to create value. It is tested against reality. Sales performance is not evaluated by quota attainment but by its causal influence on outcomes relative to environmental forces. Marketing investment is not justified by engagement metrics or attribution reports but by its measurable contribution to the system’s causal architecture. Forecasts do not reflect pattern extrapolation but mechanism-based projections.
Most importantly, a causal operating system gives the enterprise something it has long lacked: a shared language across GTM, finance, the CEO and the board.
Finance finally understands GTM — and GTM finally becomes governable
For the first time, GTM becomes legible to finance. CFOs can see:
- Which investments produce measurable causal impact.
- Which levers reduce CAC payback.
- Which actions suppress or enhance deal velocity.
- How external forces shape performance.
- Where marginal returns truly lie.
This resolves the mistrust cycle between GTM and finance. Budget discussions shift from persuasion to capital allocation because both functions now operate inside the same logic model. CAC becomes interpretable again. Forecasts become credible again. Sales projections regain relevance because they are grounded in mechanism rather than optimism.
Boards also receive what they need: causal explainability, transparency and compliance-ready visibility into GTM performance. They can distinguish between functional failure and environmental suppression, as well as strategic error and probabilistic outcomes.
Dig deeper: The hard truth about what AI will do to GTM
Causal AI is the bridge between GTM and business impact
The deeper value of a causal operating system is that it bridges the enterprise. It translates strategic intent into operational mechanism. It gives the CEO a coherent view of how the business behaves in volatile conditions. It aligns product, finance, marketing, sales and customer success around a single model of value creation. It restores the connection between activity and impact, between investment and return, between decision and outcome.
It also satisfies growing regulatory and fiduciary expectations surrounding AI. Once GTM becomes causally grounded, AI becomes auditable. Models become explainable. Forecasts become defensible. Time lag is revealed. Leadership can distinguish genuine insight from algorithmic correlation.
GTM effectiveness did not fall because GTM teams failed. It fell because their maps no longer matched the territory. Martech did not underperform. It overperformed in the service of a worldview that had already expired. Sales did not collapse because it lost discipline. It collapsed because the environment erased the decision-making pathways that once supported it.
The path out of this decline is not found in more technology, more process or more scale. It is found in a causal operating system that restores GTM’s connection to reality, bridges internal divisions and gives boards and CFOs the model of truth they need to govern responsibly.
The next era of GTM will not be defined by automation or orchestration but by understanding.
Not by more data, but by correct data.
Not by motion, but by mechanism.
Not by funnels, but by causal maps.
Not by control, but by clarity.
The causal era has begun. And the enterprises that embrace it will be the ones capable of navigating the volatility ahead — not by guessing, but by knowing when to change course and speed.
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