Martech Stack Benchmarks 2026: Utilization, Integration and AI Overlap

Direct Answer: What the 2026 Martech Stack Evidence Shows

The 2026 martech market is stable only at the surface. Chiefmartec and MartechTribe mapped 15,505 products, just 121 more than the previous edition, while recording 1,488 additions and 1,367 removals underneath that small net change. Their separate survey of 208 tech-forward marketing and marketing-operations leaders reported higher AI use-case adoption in three category comparisons, but the extracted evidence does not disclose the 2024 comparator denominator or composition.

That does not produce one universal martech waste rate. Gartner’s public page reports 49% utilization but does not publish the survey denominator. Flexera’s 20% waste estimate and Zylo’s 36% unused-license figure describe enterprise SaaS populations, not marketing-only stacks. This report keeps those measures separate instead of averaging them into a fictional benchmark.

This page is not a waste calculator, a blended stack-health score, or a revenue-leakage estimate.

This maintained 2026 edition contains 32 source-locked claims: 22 martech landscape or survey claims and 10 explicitly comparison-only enterprise-SaaS claims. Every claim has a stable ID, source section, evidence scope, population, period, methodology, and caveat.

Data years covered: 2024–2026. Last verified: July 14, 2026.

Cite This Report

Canonical URL: https://konabayev.com/blog/martech-stack-benchmarks/

Recommended citation: Tugelbay Konabayev, “Martech Stack Benchmarks 2026: Utilization, Integration and AI Overlap,” Konabayev.com, July 14, 2026, https://konabayev.com/blog/martech-stack-benchmarks/

Machine-readable versions:

• CSV dataset
• JSON dataset
• JSONL claims feed

Use a claim fragment when citing one result, for example https://konabayev.com/blog/martech-stack-benchmarks/#mts-005. The downloadable files preserve evidence scope and limitations so a martech survey percentage cannot silently become an enterprise-wide software benchmark.

Four Most Citable Martech Benchmarks

The strongest headline evidence comes from the disclosed product-landscape method. The separate N=208 State of Martech survey intentionally skews toward tech-forward teams, and its 2024 comparison denominator is not locked in this dataset, so the category changes appear later as supporting evidence.

Claim	Benchmark	Evidence
MTS-005	The 2026 landscape mapped 15,505 martech products.	State of Martech 2026, page 18
MTS-006	Net product growth was only 121 products, or 0.79%.	State of Martech 2026, page 18
MTS-007	The map recorded 1,488 additions and 1,367 removals.	State of Martech 2026, page 18
MTS-008	Content Marketing had 176 removals, 139 additions, and a net decline of 37.	State of Martech 2026, category analysis

Product growth versus product turnover

Additions: 1,488

Removals: 1,367

Net change: +121 products

The two bars share a 1,600-product comparison scale. It is a visual scale, not a target or forecast.

The visual explains why a 0.79% net increase should not be read as a quiet market. A total of 2,855 mapped products changed status. The practical implication is not “buy more tools”; it is that vendor continuity, migration cost, integration ownership, and exit paths belong in stack planning.

Martech Utilization and Budget: Supporting Evidence

Gartner’s public 2025 survey summary is useful but incomplete. It does not disclose the number of respondents, field dates, or geographic weighting. These four results therefore remain supporting evidence and are not used as hero benchmarks.

Claim	Public Gartner summary	Limitation
MTS-001	Gartner reports martech utilization at 49%.	Public N is not disclosed; utilization is not unused-license share or wasted spend.
MTS-002	Gartner says 15% qualified as high performers that met strategic goals and demonstrated positive ROI.	Public N and weighting are not disclosed.
MTS-003	Gartner says martech represented nearly 22% of total marketing spend.	The public summary does not expose the underlying distribution.
MTS-004	Gartner says CMOs oversaw 9 channels on average, while 20% were adopting new channels.	This does not establish that adding channels improves performance.

The useful conclusion is narrower than “half the stack is wasted.” Gartner’s public evidence says utilization is incomplete while martech remains a material budget line. It does not say that every unused capability can be removed, that 51% of spend is waste, or that low utilization directly equals lost revenue.

For the budget layer, keep stack metrics separate from broader B2B marketing budget benchmarks. A budget allocation explains where money goes; utilization explains whether purchased capability is activated. Neither alone proves business impact.

AI Is Expanding and Reshaping the Stack

The evidence supports overlap and stratification more directly than simple consolidation. The State of Martech report argues that the stack is stratifying rather than simply consolidating. Its product map and N=208 category survey support that direction: AI adoption rose in the observed categories while product additions and removals remained high.

The earlier Martech for 2026 report adds detail about deployment and overlap. Its authors explicitly say the participants are more tech-savvy than average, and the accessible report does not disclose N. The following percentages are therefore supporting signals, not market-level estimates.

Claim	Supporting AI signal	Interpretation limit
MTS-009	Advertising and Promotions AI use-case adoption moved from 30% to 50% between the report’s 2024 and 2026 surveys.	2026 N=208; the 2024 comparator denominator and composition are not disclosed in the extracted evidence.
MTS-010	Commerce and Sales AI use-case adoption moved from 28% to 49%.	Same separate-survey limitation; use directionally.
MTS-011	Content and Experience AI use-case adoption moved from 79% to 89%.	Same separate-survey limitation; use directionally.
MTS-012	90.3% used AI agents somewhere in the stack.	Tech-forward sample; N undisclosed.
MTS-013	23.3% reported agents in full production.	Do not force the remaining responses into one mutually exclusive status.
MTS-014	80.6% used agents in assistant-only mode.	Mode and deployment status answer different questions.
MTS-015	85.4% enhanced existing functionality with AI.	Multi-select context; not a share of spend.
MTS-016	42.7% added new functionality and 30.1% replaced existing functionality.	Multi-select; values do not sum to 100%.

The evidence does not support a simple “AI replaces the old stack” story. Enhancement was selected more often than replacement in this tech-forward sample. That makes capability overlap, ownership, and cost observability more important: an AI feature can arrive inside an incumbent platform, through a new AI-native application, or through an internally built workflow.

For marketer-level AI adoption and governance evidence, use the separate AI marketing tool adoption report. It owns marketer usage and ROI intent; this page owns stack architecture, overlap, and integration.

Integration and Data Friction Benchmarks

Integration is not one binary state. A stack may combine vendor connectors, custom APIs, iPaaS workflows, warehouse activation, manual exports, and agent tools. The missing-N Martech for 2026 survey is useful as a map of what tech-forward participants reported, but not as a market census.

Claim	Supporting integration benchmark	Method note
MTS-017	56.3% custom, 47.6% prebuilt, and 40.8% iPaaS integrations.	Multi-select; N undisclosed.
MTS-018	37.9% connected warehouse or lakehouse data directly to AI agents.	Tech-forward sample; connection does not prove data quality.
MTS-019	56.3% selected poor data quality as a challenge.	Multi-select; N undisclosed.
MTS-020	52.4% selected organizational or process readiness.	Multi-select; N undisclosed.
MTS-021	50.5% selected integration friction.	Multi-select; N undisclosed.
MTS-022	26.2% selected cost observability or budget control.	Multi-select; N undisclosed.

These barriers describe different failure modes:

• Data quality: fields are missing, stale, inconsistent, or unusable for activation.
• Process readiness: ownership and approval paths are unclear even when tools work.
• Integration friction: systems cannot reliably exchange the required data or trigger.
• Cost observability: no owner can connect licenses, usage, workflow volume, and outcomes.

Do not compress them into one “stack health” score without first defining units and ownership. For measurement and attribution evidence, use Marketing Analytics Statistics 2026. For workflow adoption and conversion evidence, use Marketing Automation Statistics 2026.

Enterprise SaaS Comparison: Not Martech Data

Enterprise SaaS evidence is a comparison layer, not martech data. This section describes enterprise SaaS and IT asset management because martech tools are often SaaS products. The figures are not martech KPIs and cannot be transferred to a marketing stack without local license, usage, spend, and ownership data.

Flexera: estimated SaaS waste and visibility

Flexera’s 2026 State of ITAM report surveyed 512 vetted professionals worldwide. Its figures are stronger on sample disclosure than the public Gartner and Martech for 2026 summaries, but its population is broader than marketing.

Claim	Enterprise SaaS comparison	Scope
MTS-023	Beginner, intermediate, and advanced ITAM groups each estimated 20% SaaS wasted spend.	Respondent estimate across enterprise SaaS, not measured martech waste.
MTS-024	43% said SaaS waste increased, 42% said it stayed the same, and 15% said it decreased.	Directional survey response, not a spend calculation.
MTS-025	Complete IT visibility declined from 43% to 36%.	Entire IT environment, not marketing only.
MTS-026	64% of surveyed ITAM teams managed SaaS licenses.	ITAM responsibility, not marketer behavior.

Zylo: license telemetry and pricing pressure

Zylo’s evidence comes from 40M+ managed licenses and $75B+ in discovered and categorized SaaS and cloud spend. Cost-shock percentages come from a separate survey of 218 IT leaders. This is observed vendor telemetry, not a probability sample of all companies.

Claim	Enterprise SaaS comparison	Scope
MTS-027	An average 36% of SaaS licenses were unused against Zylo’s recommended utilization levels.	Unused seats are not the same as wasted spend or martech utilization.
MTS-028	Average annual SaaS spend was $55.7M across an average 305 applications.	Zylo portfolio telemetry; not a recommended stack size.
MTS-029	78% of 218 IT leaders reported unexpected AI or consumption-pricing charges.	Separate survey, not telemetry.
MTS-030	61% of 218 IT leaders said unplanned SaaS increases forced project cuts.	Does not identify which projects or prove revenue impact.
MTS-031	Business units controlled 81% of SaaS spend while IT directly managed 15%.	Enterprise SaaS ownership, not marketing budget allocation.
MTS-032	AI-native application spend grew 108% overall and 393% in organizations with 10,001 or more employees.	Spend growth is not ROI or business value.

For the broader buyer-portfolio context, use SaaS Benchmarks 2026. That hub keeps buyer-side software portfolios separate from SaaS-vendor growth, retention, CAC, conversion, and margin metrics.

Why 49%, 36%, and 20% Cannot Be Reconciled

These figures use different units, populations, methods, and denominators. They cannot be presented as peer martech KPIs or blended into a stack-health score.

Figure	What it measures	What it does not prove
49%	Martech utilization in a Gartner marketing survey with public N undisclosed	That 51% of martech spend is wasted
36%	Unused SaaS licenses in Zylo enterprise-SaaS telemetry	That 36% of every martech stack is unused
20%	Estimated wasted SaaS spend in Flexera’s survey of 512 ITAM professionals	That 20% of marketing revenue leaks through tools

The units, populations, and methods differ. Subtracting, averaging, or combining them would create a number no source measured.

Practical Martech Stack Self-Check

Audit capabilities, ownership, data, integrations, outcomes, and cost together. Use one row per paid platform, embedded AI feature, internally built workflow, or material connector. Do not decide “keep or cut” from login frequency alone.

Field	Question
Business capability	Which customer or operating outcome requires this capability?
Owner	Who owns configuration, data quality, adoption, cost, and renewal?
Active users	Which roles used it during the relevant workflow window?
Activated features	Which contracted capabilities are actually configured and used?
Overlap	Which other platform or agent performs the same job?
Data dependency	What data enters, leaves, or remains trapped here?
Integration	Is the connection prebuilt, custom, iPaaS, warehouse-mediated, or manual?
Outcome signal	Which conversion, pipeline, retention, productivity, or risk metric changes?
Total cost	Include license, usage, implementation, data, maintenance, and switching cost.
Renewal or exit	When can the contract change, and how will data and workflows migrate?

This is a diagnostic inventory, not a universal scoring model. A lightly used compliance or seasonal tool may still be essential. A heavily used platform may still duplicate capability or produce unreliable data.

Methodology and Source Notes

This report uses five public evidence surfaces and preserves their incompatible scopes.

1. Gartner 2025 public martech summary: official supporting evidence, but the public page does not disclose N or fieldwork details.
2. State of Martech 2026: product-landscape research plus a February 2026 survey of 208 tech-forward marketing and marketing-operations leaders.
3. Martech for 2026: detailed AI and integration survey results from a tech-forward audience; accessible report does not disclose N.
4. Flexera 2026 State of ITAM: independent-panel survey of 512 professionals worldwide, used only for enterprise-SaaS comparison.
5. Zylo 2026 SaaS Management Index: telemetry covering 40M+ licenses and $75B+ spend; a separate N=218 survey supports two pricing-pressure claims.

Every public record includes source quality, evidence type, population, geography, period, methodology, caveat, and audit date. Multi-select responses remain labeled. Product counts are not presented as adoption counts. Spend growth is not presented as ROI. No source chart or substantial source text is republished.

FAQ

The short answers below preserve the same source and denominator limits as the claim ledger.

What is a martech stack?

A martech stack is the set of technologies used to plan, execute, personalize, measure, and govern marketing. It can include CRM, automation, analytics, advertising, content, customer-data, integration, and AI capabilities. The operating question is not how many tools exist, but whether required capabilities have owners, usable data, reliable integrations, and measurable outcomes.

What is the average martech stack utilization rate in 2026?

Gartner’s public 2025 survey summary reports 49% utilization, but the page does not disclose the sample size or fieldwork details. Treat it as supporting evidence, not a universal target. Do not infer that the remaining 51% equals wasted spend.

How many martech products exist in 2026?

The State of Martech 2026 landscape mapped 15,505 products. The map recorded 1,488 additions and 1,367 removals, so the small 0.79% net increase hides substantial product turnover.

Is AI consolidating the martech stack?

The available evidence is more consistent with overlap and stratification than simple consolidation. In the missing-N Martech for 2026 survey, enhancing existing functionality was selected more often than replacing functionality. That result describes a tech-forward sample and does not prove a universal market transition.

Is unused software the same as wasted spend?

No. An unused license is a seat-level usage observation. Wasted spend requires a cost definition and a judgment that the capability is unnecessary. Martech utilization may refer to activated tools or capabilities. These units should not be substituted for one another.

How often should a martech stack be audited?

Use renewal dates, major integration changes, data-quality incidents, ownership changes, and new AI purchases as audit triggers. A lightweight inventory can run continuously; a deeper decision review should occur before contracts become difficult to change.

Can I download the martech benchmark dataset?

Yes. The same 32 claims are available as CSV, JSON, and JSONL. Each row carries scope and caveat fields to keep martech and enterprise-SaaS evidence separate.

Last verified: July 14, 2026