Gartner AI Marketing Technology Forecast 2025: Adoption Rates and Spend Benchmarks

Gartner's annual research on marketing technology has become one of the most-cited reference points when marketing leaders need to justify AI investment internally or benchmark their organization against peers. The challenge is that the headline numbers — adoption percentages, spend figures, ROI claims — circulate widely without the context that makes them usable.

This guide breaks down what Gartner's 2025 AI marketing technology data actually covers, where the numbers come from, how they segment by company size and function, and what a practitioner should and shouldn't conclude from them.

What Gartner Actually Measures

Gartner's marketing technology research pulls from several distinct survey instruments, and conflating them produces misleading conclusions. The three most relevant for AI adoption and spend are:

• CMO Spend and Strategy Survey — Annual survey of CMOs across enterprise organizations (typically $500M+ revenue), covering total marketing budget allocation, martech stack spending as a percentage of budget, and AI-specific investment intent. Sample sizes typically range from 300–400 respondents in North America and Europe.
• Marketing Technology Survey — Broader practitioner survey covering martech utilization rates — specifically, what percentage of purchased martech capability is actually being used. AI tools appear here as a subcategory.
• Hype Cycle for Digital Marketing — Analyst-assessed maturity positioning of individual AI marketing technologies (generative AI for content, AI-powered personalization, predictive analytics, etc.) rather than survey-based adoption data.

When a statistic like "X% of CMOs are using AI" appears in a slide deck or vendor pitch, it usually traces back to one of these instruments — but the scope differs significantly. A CMO survey of enterprise organizations does not represent SMBs, agencies, or B2C brands below a certain revenue threshold.

2025 Adoption Rate Benchmarks by Function

Gartner's 2025 data shows AI adoption in marketing is no longer primarily an experimentation story — it has shifted toward selective production use across specific functions. The pattern is uneven: some marketing tasks have reached high AI integration rates, others remain largely manual despite years of vendor hype.

Martech Spend as a Percentage of Marketing Budget

Gartner's CMO Spend Survey has tracked martech as a share of total marketing budget for over a decade. The 2025 data continues a pattern that has surprised analysts: after martech spend peaked around 2020–2022, CMOs have been consolidating stacks rather than expanding them.

The headline figure from Gartner's most recent CMO survey puts martech at approximately 23–25% of total marketing budget across enterprise respondents — down from a peak above 30% in prior years. AI-specific tooling is not broken out as a separate line in most organizations' budgets; it tends to be absorbed into existing platform costs (Google Ads AI features, HubSpot Breeze, Salesforce Einstein) or bundled into content and analytics tool subscriptions.

Where AI Spend Is Actually Going

Gartner's research distinguishes between AI spend embedded in existing platforms versus net-new AI tool purchases. The majority of 2025 AI marketing spend falls into the embedded category — organizations are not primarily buying standalone AI tools; they are using AI features within platforms they already pay for.

• Platform-embedded AI (Google, Meta, HubSpot, Salesforce, Adobe): Estimated 60–70% of AI marketing spend by volume. Costs are absorbed into existing platform fees or incremental add-ons.
• Generative AI writing and content tools (Jasper, Writer, Copy.ai, similar): Growing but still a smaller share. Enterprise contracts typically run $500–$2,000/month for team licenses; SMB plans range from $40–$150/month per seat.
• AI analytics and attribution platforms: Significant enterprise spend, particularly in B2B. Northbeam, Triple Whale, and similar tools represent a meaningful budget line for performance marketing teams.
• Custom AI/ML development and API costs: Primarily enterprise. Organizations building proprietary models or fine-tuned applications on top of OpenAI, Anthropic, or Google APIs are spending on compute and engineering, not just SaaS licenses.

The Utilization Problem

One of Gartner's most consistently cited findings is the gap between martech purchase and actual utilization. Their Marketing Technology Survey has repeatedly found that organizations use only a fraction of the capabilities in their martech stack — the 2023 figure was approximately 33% utilization, and more recent data suggests this has not improved significantly despite increased AI feature availability.

For AI specifically, this means that "adoption" in Gartner's framing often means the tool is deployed and being used by someone on the team — not that it is integrated into core workflows at scale. A content team using an AI writing assistant for 20% of their output counts as an adopter in survey terms. This distinction matters when interpreting adoption rate figures.

Hype Cycle Positioning: Where AI Marketing Technologies Stand

Gartner's Hype Cycle for Digital Marketing provides a different lens than adoption surveys — it assesses maturity and time-to-mainstream-adoption for specific technology categories. The 2024–2025 positioning of key AI marketing technologies tells a useful story about where to invest attention now versus where to wait.

How to Use Gartner Data in Internal Proposals

Gartner benchmarks are most useful when they answer a specific question a stakeholder is already asking — not when they are used to build a general case for AI investment. The figures that tend to land in budget conversations are comparisons: what percentage of peer organizations are doing X, and what are the consequences of not doing it.

What Works in Proposals

1. Cite the specific survey, not just "Gartner." "Gartner's 2024 CMO Spend Survey of 395 enterprise CMOs" is citable. "Gartner says" is not. Finance and procurement reviewers will ask for the source.
2. Match the benchmark scope to your organization. If your company is a $50M B2B SaaS, enterprise CMO data is directional at best. Acknowledge this explicitly rather than letting a skeptical stakeholder raise it.
3. Use adoption rates to establish normalcy, not urgency. "68% of enterprise marketing organizations are deploying AI in content workflows" normalizes the investment. Framing it as competitive pressure often backfires with skeptical CFOs.
4. Pair benchmark data with an internal baseline. External benchmarks land harder when you can show where your organization currently sits relative to them. "We're at roughly 15% AI utilization; the peer median is 40%" is a concrete gap.

What Doesn't Work

• Citing ROI figures without the methodology. Gartner occasionally publishes productivity or efficiency estimates, but these are modeled projections, not empirical results from controlled studies. Presenting them as guaranteed outcomes will damage credibility when results differ.
• Using Hype Cycle positioning to dismiss a technology. Saying "Gartner puts autonomous agents at Peak of Inflated Expectations" is accurate but incomplete — the Hype Cycle doesn't tell you whether a specific tool is worth trialing for a specific use case.
• Stacking multiple Gartner statistics without connecting them. A slide that lists five different Gartner figures from different surveys and years creates more confusion than confidence.

Spend Benchmarks: What Enterprise CMOs Are Actually Allocating

Gartner's CMO Spend Survey data from 2024–2025 shows total marketing budgets recovering from the post-pandemic contraction, but not uniformly. B2B technology companies have seen budget growth; retail and CPG have faced continued pressure. AI-specific line items remain rare — most organizations are funding AI through reallocation rather than net-new budget.

What the 2025 Data Suggests for Practitioners

Reading across Gartner's 2025 research, a few patterns are consistent enough to inform planning decisions — not as predictions, but as calibration points.

The consolidation signal

CMOs are reducing the number of tools in their stack, not adding to it. The average enterprise martech stack peaked at 91 tools in Gartner's 2022 survey. More recent data shows active rationalization. For AI tools, this means the question is increasingly "which AI feature within a platform we already use" rather than "which standalone AI tool should we buy." Vendors who can show native integration with existing stacks are winning deals over best-in-class standalone tools.

The data infrastructure bottleneck

Gartner's research consistently identifies data quality and data infrastructure as the primary constraint on AI marketing adoption — not tool availability or cost. Organizations reporting the highest AI utilization rates are also those that invested in data infrastructure (CDPs, clean CRM data, first-party data programs) 12–24 months earlier. The implication: if your organization's data is fragmented or dirty, buying more AI tools will not move adoption metrics.

The talent and change management gap

When Gartner surveys CMOs on barriers to AI adoption, talent and change management consistently rank above budget and technology access. Marketing teams that lack practitioners who understand how to evaluate, configure, and quality-check AI outputs are underutilizing tools they've already purchased. This is where the utilization gap lives — not in the tools themselves.

Limitations of Using Gartner Data as a Benchmark

Gartner's research is rigorous within its scope, but that scope has real edges. Before citing any figure, check:

• Publication date. AI marketing capabilities are changing faster than annual survey cycles. A Gartner figure from 18 months ago may describe a market that has since shifted materially.
• Survey population. Most Gartner marketing surveys target enterprise organizations. Applying enterprise adoption rates to mid-market or SMB planning is a category error.
• Self-reported vs. observed data. Adoption rates in CMO surveys are self-reported. Respondents may overstate AI usage, particularly in organizations where AI investment is a stated priority.
• Definition of "AI." Gartner's survey instruments define AI broadly, often including rule-based automation and statistical models alongside generative AI. A respondent using basic email send-time optimization may count as an AI adopter alongside one deploying large language models for content generation.
• Paywall access. Full Gartner reports require a subscription. Figures circulating in blog posts, vendor decks, and conference presentations are often paraphrased or selectively extracted. When possible, trace statistics back to the original Gartner publication before citing them.

Complementary Data Sources to Use Alongside Gartner

Gartner is one of several credible sources for AI marketing benchmarks. Using multiple sources — and noting where they converge or diverge — makes a stronger case than relying on a single research organization.

A Note on Forecast Figures

Gartner publishes forward-looking projections alongside survey data — figures like "AI marketing spend will reach $X billion by 2027" or "Y% of enterprise marketing content will be AI-generated by 2026." These are modeled projections based on current trend extrapolation, not measurements.

Gartner's forecasts have historically been directionally useful but imprecise on timing. The generative AI content adoption curve, for example, accelerated faster than most 2022 projections anticipated. Treat forecast figures as scenario planning inputs, not targets. When presenting them internally, label them as projections explicitly — "Gartner projects" rather than "Gartner found."