Marketing leaders have invested heavily in AI training and related use-case exploration, yet still struggle to demonstrate a material impact. Productivity gains from AI remain uneven. Marketing leaders don’t lack vision for an AI future, but they don’t demonstrate a methodical approach to realizing it.
Training has expanded and experimentation continues, but those efforts alone have not translated into consistent performance improvement. Without changes to how work is structured and resourced, AI adoption varies across teams and initiatives.
Automation is where AI delivers ROI
According to Gartner’s 2025 CMO Spend Survey, 36% of the marketing budget is 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.
More 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 many bets may make it difficult to realize gains from any one of them.
Today, the most substantial return on AI investment comes from increasing the number of automated workflows. Marketing leaders who report higher levels of automation are twice as likely to see returns from their AI investments.
Automation has long been part of marketing operations, but AI changes its economics. Martech stacks come with context that’s LLM-ready: API documentation, integration schemas, code libraries and data flow maps that describe how work is orchestrated across systems.
When paired with a conversational interface, those artifacts become easier to leverage. Tasks that once required specialized technical skills can now be designed and tested more quickly by a broader set of marketers. Advances like Claude Code and OpenAI Codex offer further opportunities to compress time-to-deployment and increase near-term ROI on AI investment.
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Misaligned investment
Despite widespread enthusiasm for AI, more than half of marketing organizations haven’t made substantive changes to how work is structured or resourced. While AI training is more widespread, training alone doesn’t create new ways of working or correlate with increased AI adoption.
Automation forces this to happen. Once a workflow is automated, the impact is persistent. It reduces friction whenever the work runs and improves reliability across teams that depend on its output. Instead of helping one person move faster, automation reshapes how work moves through the organization.
Despite this, only one-fifth of marketing leaders rank integrating AI or automating key tasks as their top-ranked action to boost productivity. In fact, 10 other actions comprise the remaining 81% of top-ranked actions.
This diverse range of initiatives may represent the complexity of transforming the marketing function. Still, it may also represent a lack of strategic focus or an inability to integrate AI into marketing.
Pace, not ambition, separates leaders from laggards
While marketing leaders show only partial interest in automation today, they do share the ambition to more than double the number of automated workflows by the end of 2027, from 16% of current workflows to 36%. But this masks an uneven starting line and differences in the pace of planned automation attainment.
Leaders reporting the lowest levels of automation attainment aim to advance from 5% of workflows automated to 15%, while those with high levels of automation attainment plan to increase automation from 31% to 62%. Without a significant shift in investment, it’ll be tough for laggards to catch leaders.
What will matter is whether a marketing leader increases their pace relative to others. Today, only about 12% of marketing organizations plan to accelerate automation in a way that materially closes the gap with current leaders — lifting their automation rate from 9% of workflows to 40%.
Automation as an operating model advantage
AI ROI in marketing is currently less about breakthrough use cases and more about enhancing what you already do. With AI assist, workflow automation becomes an operational force multiplier. It shortens development cycles, lowers maintenance costs and allows for a more composable approach to using tools across the martech stack.
Rule-based automation, augmented by AI, operates within known constraints. It fits existing governance models while still delivering material efficiency gains. That makes it one of the few AI applications capable of producing near-term ROI without demanding a tolerance for organizational disruption that many teams don’t yet have.
Automation design forces decisions about ownership, sequencing and outcomes. Those decisions convert AI capability into operational results. Teams that demonstrate ambition to increase their pace of automation also show greater willingness to redefine roles, adjust agency relationships and involve frontline staff in identifying automation opportunities. Together, these competencies and behaviors will be even more necessary as the promise of agentic AI begins to be realized.
Learn more about how to drive AI returns at the Gartner Marketing Symposium/Xpo, June 8-10, 2026, in Denver, Colorado.
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