AI budgets are growing faster than evidence of returns
Two-thirds of companies have dedicated AI budgets, but only 10.8% of agent projects are scaled and 40.2% cannot prove financial returns, according to the Enterprise Martech Outlook 2026 report.
Two-thirds of enterprise organizations have already allocated dedicated budgets for artificial intelligence, yet 40.2% of martech leaders still cannot demonstrate a measurable financial contribution from their technology investments. These findings come from The Martech Weekly's "Enterprise Martech Outlook 2026" report.
The gap between ambition and results looks substantial. 95.3% of companies said AI agents are present in their roadmaps. At the same time, only 10.8% of agent-based projects are fully scaled in production. Nearly 90% of organizations are at the planning, proof-of-concept, or limited production stages.
Companies are spending money and experimenting, but mostly without success. Most AI agent initiatives have not reached full scale, and marketing technology teams cannot confirm the ROI of new tools.
Where does AI help more: marketers or customers?
Today, artificial intelligence is more effective at transforming marketing's internal operations than the customer experience. 69.3% of respondents note a noticeable, clear, or significant impact of AI on the martech stack. Only 59.9% say the same about customer experience.
This gap is explained by where AI works best right now:
- writing and editing texts;
- data analysis and reporting;
- creating creative variations;
- copilots and video generation.
Internal tasks are easier to test, control, and embed into familiar processes. Agents for journey optimization, decisioning, audience selection, campaign creation, attribution, loyalty programs, and offers have advanced less and show weaker returns.
Why does the agent roadmap outpace production?
The problem is not getting an agent to produce an answer. The problem is verifying that the answer is correct. Journey optimization, decisioning, attribution, and campaign creation inherit complex and unstable data flows. If a workflow is shaky, an agent merely accelerates its collapse. Fully autonomous marketing remains a distant prospect for now.
Are humans still in the loop?
Enterprise brands are clearly aware of AI's limitations and risks. They are ready to apply the technology broadly, but most prefer to keep a human between the model and the customer.
The breakdown looks like this:
- only 1.6% allow fully automated customer-facing AI-generated content;
- 43.3% permit external use of generated content only after review, editing, and verification;
- another 24.4% restrict generative AI to internal use.
Herein lies a paradox. AI's high cost was supposed to be justified by increased employee productivity. But there is no gain if the time saved on content production is spent checking the result.
What will the budget committee change?
A separate line item for AI spending hasn't solved martech's overall measurement problem. 40.2% of leaders cannot show a clear contribution from technology to financial goals or recognized proxy metrics.
Evidence directly influences money. Teams that managed to confirm value were significantly more likely to get budget increases: 56% versus 37.5% for organizations relying on what the report calls "belief instead of demonstration" of value.
At the investment stage, AI may enjoy special treatment. But in the end, it enters the same budget discussion as every other line item. Marketers have to distinguish between AI that improves work and AI that merely increases its volume. The former is easier to defend — the results are visible. The latter dissolves into vague talk about productivity, transformation, and future potential.
Dedicated AI funding is now abundant. What's scarcer is the other currency: evidence.
The full report is available for download on The Martech Weekly website (registration required).
Original: semrush.com
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