Marketing leaders have invested heavily in AI training and testing related use cases, yet they still struggle to demonstrate tangible impact. The productivity gains from AI remain uneven. Marketing leaders don’t have a vision for the future of AI, but they don’t show how to realize it.
Training has been expanded and testing continues, but those efforts alone have not translated into consistent performance improvements. Despite changes in the way work is organized and resourced, the adoption of AI varies across teams and systems.
Automation is where AI delivers ROI
According to Gartner’s 2025 CMO Spend Survey, 36% of marketing budgets are allocated to change and transformation initiatives. Yet less than one-tenth of those funds flow to the area where AI can have its most significant impact: improving organization and operating models.
Additional funds go to other initiatives – such as new or improved products and services, data and insight investments and agency or partner relationships. Spreading resources across multiple bets can make it difficult to get profits from any of them.
Today, the biggest return on AI investment comes from increasing the number of automated workflows. Marketing leaders who report high levels of automation are twice as likely to see a return from their AI investment.
Automation has long been a part of marketing operations, but AI is changing its economics. Martech stacks come with LLM-friendly context: API documentation, integration schemes, code libraries and data flow maps that explain how work is organized across systems.
When paired with a conversational interface, those artifacts become easier to use. Jobs that once required specialized technical skills can now be designed and tested quickly by a wider set of vendors. Developments such as Claude Code and OpenAI Codex offer further opportunities to compress deployment time and increase near-term ROI on AI investments.
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Despite widespread enthusiasm for AI, more than half of marketing organizations have yet to make major changes to the way work is organized or implemented. Although AI training has become more widespread, training alone does not create new ways of working or correlate with increased AI adoption.
Automation forces this to happen. When the workflow is automated, the impact is persistent. It reduces friction whenever the work is done and improves the reliability of all the parties that depend on its output. Instead of helping one person move faster, automation is reshaping the way work flows in an organization.
Despite this, only one-fifth of marketing leaders list integrating AI or automating key tasks as their top action to increase productivity. In fact, the other 10 actions comprise the remaining 81% of the top-ranked actions.
This diverse array of programs can represent a complex transformation of the marketing function. However, it may also reflect a lack of strategic focus or an inability to integrate AI into marketing.
Speed, not ambition, separates leaders from laggards
While marketing leaders show little interest in automation today, they share a desire to double the amount of automated workflows by the end of 2027, from 16% of current workflows to 36%. But this hides the uneven starting line and the difference in the speed of the programmed automatic detection.
Leaders who report the lowest levels of automation achievement aim to increase from 5% of automated workflows to 15%, while those with the highest levels of automation access to increase automation from 31% to 62%. Without a big change in investment, it will be difficult for the laggards to catch the leaders.
What will matter is whether the marketing leader increases his speed compared to others. Today, about 12% of marketing organizations plan to accelerate automation in a way that closes the gap with current leaders – raising their level of automation from 9% of workflows to 40%.
Automation as an operating model advantage
AI ROI in marketing is currently less about successful use cases and more about improving what you’re already doing. With the help of AI, automated workflows become a power multiplier. It shortens development cycles, lowers maintenance costs and enables a unified approach to using tools across the martech stack.
Rule-based automation, augmented by AI, operates under known constraints. It fits existing management models while still delivering efficiency benefits. That makes it one of the few AI applications that can generate near-term ROI without needing to endure organizational disruption that most teams don’t currently have.
Automated design forces decisions about ownership, sequencing and outcomes. Those decisions turn AI capabilities into operational results. Teams that show a desire to increase their speed of automation also show a greater willingness to redefine roles, adjust agency relationships and involve front-line employees in identifying automation opportunities. Together, these skills and behaviors will be increasingly needed as the promise of agent AI begins to materialize.
Learn more about how to drive AI back at Gartner Marketing Symposium/XpoJune 8-10, 2026, Denver, Colorado.
