The 7 MarTech Findings That Define 2026
The seven defining findings: (1) average stack is 121 tools, up from 91 in 2022; (2) only 34.1% of stack tools are actively used in any given month; (3) MarTech spend has dropped to 22.7% of marketing budget (down from 29.5%); (4) AI tool adoption hit 84.1% (up from 22.4% in 2023); (5) CDP adoption doubled to 47.2%; (6) best-of-breed preference dropped to 54.2% from 67%; (7) average tool tenure compressed to 18.4 months from 27.1.
In 2026, the MarTech story is no longer just "more, more, more". Tool count is still climbing - but driven entirely by AI-native tools displacing manual processes, not by additional legacy tools. Spend share has dropped. Utilisation has dropped. CMOs have started consolidating. The stack is being quietly rationalised even as headline tool counts continue to rise.
The 121-Tool Average - And Why It Won't Drop
The average marketing team runs 121 tools in 2026 - up from 91 in 2022 and 24 in 2014. But the growth is entirely concentrated in AI-native tools (now 14% of stack); non-AI tool count is essentially flat versus 2022. Mid-market teams have seen the steepest growth (+64%).
| Company size | Avg tools | AI-native share | Growth vs 2022 |
|---|---|---|---|
| Enterprise (5,000+) | 287 | 11.4% | +22% |
| Mid-market (250-5,000) | 142 | 14.7% | +64% |
| SMB (under 250) | 47 | 21.4% | +31% |
| Cross-segment avg | 121 | 14.0% | +33% |
Source: Visionary 240-Client MarTech Audit + Mass Marketer Survey 2026.
Stack size by company segment, split AI vs non-AI. Source: Visionary 2026.
The Dormant Tool Audit: (1) export tool list from finance/procurement; (2) cross-reference with login activity from each tool; (3) flag any tool with <2 unique logins in last 30 days; (4) review for cancellation or consolidation. Time: ~2 weeks for a 121-tool stack.
The 34% Utilisation Problem
Only 34.1% of marketing stack tools are actively used in any given month - down from 42% in 2022. The remaining 65.9% are dormant licences, redundant tools, stalled pilots or tools tied to departed staff. The average team is paying for 79.9 tools per month they do not use.
| Dormant category | Share of total stack | What it represents |
|---|---|---|
| Dormant licences | 32.4% | Tools paid for, never logged into in 30+ days |
| Redundant tools | 18.7% | Duplicate another tool's primary function |
| Stalled pilots | 8.4% | Trialled, never graduated to production |
| Tools tied to departed staff | 6.4% | Owner left; tool stayed in stack |
| Active (used monthly) | 34.1% | Logged into by ≥1 user in last 30 days |
Source: Visionary 240-Client MarTech Audit 2026.
Composition of the average 121-tool stack. Source: Visionary 2026.
MarTech Spend Has Dropped to 22.7% of Budget
| Year | MarTech % of marketing budget | Median spend (USD) | Median spend (GBP) |
|---|---|---|---|
| 2022 | 29.5% | $1.24M | £976K |
| 2023 | 27.4% | $1.18M | £929K |
| 2024 | 24.8% | $1.04M | £819K |
| 2025 | 23.1% | $984K | £775K |
| 2026 | 22.7% | $1.02M | £803K |
Source: Visionary Mass Marketer Survey 2026.
CDP Adoption Has Doubled Since 2023
| Year | CDP adoption | Active utilisation | Dominant use case |
|---|---|---|---|
| 2022 | 21.4% | 47% | Email segmentation |
| 2023 | 27.8% | 52% | Audience suppression |
| 2024 | 34.6% | 58% | Server-side tracking |
| 2025 | 41.4% | 61% | Server-side + AI features |
| 2026 | 47.2% | 64% | AI personalisation + identity resolution |
Source: Visionary 2026.
AI Tool Adoption: 84% and Climbing
The fastest category adoption curve we have ever measured in MarTech. 84% of teams use ChatGPT, 47% Claude, 38% Gemini, 32% Perplexity. Native AI features inside existing MarTech tools have hit 78% adoption.
| AI tool | Adoption 2026 | Adoption 2023 | Dominant use case |
|---|---|---|---|
| ChatGPT (any tier) | 84.1% | 22.4% | Content drafting + ideation |
| Claude | 47.2% | 8.4% | Long-form editing + analysis |
| Gemini | 38.4% | - | Workspace integration |
| Perplexity | 31.7% | - | Research + competitor mapping |
| Midjourney / image gen | 28.6% | 11.4% | Creative production |
| Native AI in MarTech tools | 78.4% | 32.4% | In-app summarisation, suggestions |
Source: Visionary Mass Marketer Survey 2026, n=2,400.
Best-of-Breed vs Suite: The Reversal
| Preference | 2022 share | 2026 share | Change |
|---|---|---|---|
| Best-of-breed | 67.0% | 54.2% | -12.8pp |
| Suite (Hubspot, Adobe, Salesforce MC) | 24.4% | 38.4% | +14.0pp |
| Hybrid (suite core + best-of-breed edges) | 8.6% | 7.4% | -1.2pp |
Source: Visionary 2026.
Tool Churn and Tenure Trends
| Metric | 2022 | 2026 | Change |
|---|---|---|---|
| Avg tool tenure | 27.1 months | 18.4 months | -32% |
| % replaced in last 12 months | 21.4% | 34.6% | +13.2pp |
| Annual MarTech churn rate | 18.4% | 27.8% | +9.4pp |
| Active consolidation programmes | 31.4% | 67.4% | +36pp |
Source: Visionary 2026.
Integration: Still the #1 Pain Point
| Pain point | % citing as top-3 frustration |
|---|---|
| Integration / data unification | 61.4% |
| Tool sprawl / redundancy | 54.2% |
| Cost / pricing creep | 47.8% |
| Onboarding / adoption | 38.4% |
| Vendor lock-in | 31.7% |
| Security / data residency | 28.4% |
Source: Visionary 2026.
Sector-Specific Stack Profiles
| Sector | Avg stack size | AI-native share | Top spend category |
|---|---|---|---|
| B2B SaaS | 184 | 17.4% | ABM + intent data |
| eCommerce | 142 | 14.8% | Personalisation + reviews |
| B2B services | 121 | 12.4% | CRM + automation |
| FMCG | 94 | 13.8% | CDP + analytics |
| Financial services | 84 | 10.4% | Compliance + CDP |
| Healthcare | 71 | 9.4% | Compliance + CRM |
| Local services | 47 | 16.4% | Lead-gen + reviews |
| Charity | 38 | 11.4% | Email + donor CRM |
Source: Visionary 2026.
The Marketing Engineer Headcount Boom
| Company size | Marketing engineers (avg) | Marketing ops (avg) | Ratio of dev:marketer |
|---|---|---|---|
| Enterprise (5,000+) | 4.2 | 8.4 | 1:14 |
| Mid-market | 1.4 | 2.8 | 1:24 |
| SMB | 0.3 | 0.7 | 1:48 |
Source: Visionary 2026.
How MarTech Stacks Actually Got to 121 Tools
The average marketing organisation did not decide to buy 121 tools. It bought 8, then 14, then 27, then 60, and then stopped counting. What our 240-client audit shows is that stack size is almost never the outcome of a strategy - it is the outcome of a decade of point-solution decisions made by different people, in different budget cycles, solving different problems, and then never revisited. The tools accumulate the way silt does: quietly, in layers, until someone tries to run analytics across them and discovers there is no shared identity spine, no shared taxonomy, and no shared owner.
Three structural forces drive the accumulation. First, the fragmentation of the vendor market: every marketing sub-discipline now has 40-200 credible SaaS vendors, and each one is engineered to be trivially easy to buy on a card, hard to fully deprecate, and priced to grow inside the account. Second, the shift of budget authority away from a single CMO decision-maker toward function-level owners (SEO, paid, lifecycle, RevOps, product marketing), each of whom now controls their own procurement micro-budget. Third - and this is the one CFOs underestimate - AI-native tooling has expanded the total surface area of "MarTech" itself. Categories that didn't exist in 2022 (AI copilots, GEO/AEO monitoring, synthetic research, LLM observability) now account for 14% of the average stack.
The consequence is that "rationalising the stack" is not a procurement exercise. It is an operating-model exercise. You cannot cut tools if you do not first assign owners and consolidate the workflows those tools sit inside. Every stack rationalisation programme we've run that started with "let's list the tools" stalled. The ones that worked started with "let's list the workflows" and then asked what tools each workflow actually needed.
The 34% Utilisation Number - And Why It Understates the Problem
Utilisation is the most quoted and least understood metric in MarTech. When we say 34% of tools are actively used, we mean at least one login by at least one user in a 30-day window. That is a generous definition. Tighten it to "the tool influenced a customer-facing decision this month" and the number falls to somewhere between 18% and 24%. The rest is either passive data collection nobody actions, dashboards nobody reads, or workflow steps that were automated once and never audited since.
The financial exposure is larger than most CFOs realise, because the median unused seat sits inside an annual contract that auto-renews. The 66% of tools with no active use in a given month represent roughly £180-£420 per employee per year in enterprise stacks - and because these are annual contracts, the real cost to remove them is not the licence, it is the internal effort to prove they can be safely killed. That is why so few get killed. It is easier to keep paying than to defend the decision to cut.
The workaround the best-run teams use is an annual "kill list" cycle: every tool in the stack must be re-justified in writing by an accountable owner every 12 months, with the null default being termination at renewal. Once the burden of proof shifts from "prove we should cut it" to "prove we should keep it," 20-40% of the stack tends to fall away in the first cycle without operational damage. In our audit cohort, teams that ran this cycle recovered £2.40-£4.80 in annual budget per £1 of internal effort spent - one of the best-ROI operational moves marketing leaders can make in a flat-budget year.
Best-of-Breed vs Suite: The Debate That Won't End
The best-of-breed vs suite argument re-emerges every three years like clockwork, always with the same shape. Best-of-breed advocates point to functional depth; suite advocates point to integration cost and TCO. The 2026 version has one new variable: AI native suites (Adobe GenStudio, HubSpot Breeze, Salesforce Agentforce, Klaviyo AI) are making the suite side of the argument stronger than at any point since 2018, because they collapse categories that used to require dedicated vendors - content generation, audience prediction, subject-line optimisation - into the suite subscription you already pay for.
Our audit data suggests the honest answer is not "pick a side" but "match architecture to team maturity." Teams with fewer than 15 marketers, no dedicated marketing engineer, and no in-house data function almost always get more value from a consolidated suite, because their bottleneck is integration effort not feature depth. Teams with 40+ marketers, an in-house RevOps or MarOps function, and a data engineer available at least part-time can extract more value from a best-of-breed stack, because they have the internal capacity to actually integrate it. Teams in the middle - 15-40 marketers, patchy internal engineering - get the worst of both worlds and are the most over-tooled cohort in our dataset.
Where AI Is Actually Landing in the Stack
AI tooling in 2026 is not evenly distributed across the stack. It is concentrated in four zones. The first is content production - brief, outline, draft, edit, publish - where AI has genuinely collapsed cost per unit output by 40-70%. The second is analytics and reporting, where LLM-driven natural-language interfaces have made previously-locked-away data accessible to marketers without SQL. The third is campaign creative for paid media, particularly asset generation for Performance Max and Meta Advantage+. The fourth is customer service and lifecycle automation, where AI agents now handle 30-60% of tier-1 volume in the accounts we audit.
The zones where AI has landed least well: attribution (where the underlying data problem is not solvable by better tooling), audience insight for genuinely new segments (where the LLM has no training data), and anything requiring first-party research design. These are the zones where the "AI tool" is really an AI-flavoured wrapper on an unchanged underlying capability. Buyers who confuse the two end up with 30 AI subscriptions and no additional output.
Integration Is Still the Bottleneck (And Always Will Be)
Every large-scale MarTech report of the last decade has named integration as the top pain point, and every year the industry acts surprised. The reason it persists is structural: vendors optimise for standalone value (that is what wins the buying decision), not for integration cost (which the buyer discovers 90 days later). Even where APIs exist, they rarely share identity models - the "customer" in your CDP is not the same object as the "contact" in your CRM, which is not the same as the "user" in your product analytics tool, which is not the same as the "lead" in your MAP.
Two design decisions predict integration pain more than any other. First: do you have a single system of record for the customer object, and does every other tool consume that record via ID rather than via matching keys (email, hashed email, cookie ID)? Second: do you have an event-model schema that every tool emits into, or does every tool ship events in its own format? Teams that answer yes to both spend 60-70% less on integration than teams that answer no. Teams that answer no to both are the ones with the "121 tools and none of them talk" complaint.
The Marketing Engineer Is Now the Highest-Leverage Hire
The single strongest correlate of stack health in our 240-client dataset is not the number of tools, the total spend, or the vendor mix. It is the presence of at least one dedicated marketing engineer - someone whose job is the plumbing between tools, the data model, the workflow automation, and the audit of what is actually running. Teams with a marketing engineer had 41% lower stack cost per marketer, 68% higher active utilisation, and 3.2x faster time-to-launch on new campaigns.
The role is under-hired because it doesn't fit the traditional marketing org chart. It sits somewhere between RevOps, MarOps, data engineering, and product management. In many companies, when we recommend the hire, the response is "we'll get IT to help." IT cannot help - IT owns systems, not marketing workflows, and the two require different priorities. The teams that hire this role early recover the salary many times over within 12 months through stack rationalisation alone. The teams that don't hire it end up paying an agency to do a stack audit every two years and never fixing the underlying operating model.
What "A Healthy Stack" Actually Looks Like in 2026
A healthy 2026 MarTech stack is not necessarily small. Some enterprise stacks legitimately need 250+ tools because their scope spans multiple brands, geographies, and channels. A healthy stack is one where every tool has a named owner, a documented workflow it serves, a defined success metric, and a scheduled renewal review. It is one where the identity spine is coherent, the event schema is shared, and the data flows are documented. It is one where AI-native tooling has been intentionally selected against category maps rather than accumulated from vendor demos.
The measurable signals of a healthy stack are: active utilisation above 55%, tool-per-marketer ratio under the sector median, integration incident count trending down year-over-year, and - the honest one - the ability of a new joiner to explain the stack architecture within their first month. That last signal is the one most often missing. If a stack cannot be explained, it cannot be governed; and if it cannot be governed, the accumulation continues by default.
Stack Health Calculator
Score your own stack. Enter total tool count, active utilisation, annual spend, and operational signals to benchmark against 2026.
Stack Health Index
43/100
Utilisation 68 · Ops 17
Estimated dormant-tool spend
£417K
Annual spend on tools likely unused.
vs 2026 benchmark
Below
2026 industry median Stack Health = 62/100.
Indicative model. Pair with a finance/SSO audit for actual figures.
Methodology
Source 1: Visionary Mass Marketer Survey 2026 (n=2,400) via Pollfish nationally representative panel. Fielded 1-28 February 2026. Margin of error ±2.0% at 95% confidence.
Source 2: Visionary 240-Client MarTech Audit Q1 2026. Full stack inventory across 240 managed accounts: tool count, ACV, login activity, vendor renewal status, integration depth.
Limitations: self-reported stack data shows ±8% variance vs procurement records; mid-market over-represented vs enterprise; sector mix tilted toward UK and Western Europe. For media enquiries: press@visionary-marketing.co.uk.
