Generative AI adoption in marketing is accelerating
Generative AI adoption in marketing is accelerating faster than any other go-to-market function, according to a generative AI marketing adoption survey of more than 1,000 GTM professionals. Marketers are the most enthusiastic adopters of AI tools across go-to-market, with significantly higher rates of weekly and daily AI use compared to sales and operations peers.
Those marketing power users are using AI to get work done faster, launch higher-performing campaigns, and drive deeper audience engagement.
But the data also shows that many marketers still face key roadblocks to using AI effectively, including reliable data quality and the push-pull between AI efficiency and human connection. Digging deeper into the survey's insights highlights how marketers are using AI today, where it's working best, and the challenges that could limit further adoption.
How marketers are adopting AI in 2025
A near-supermajority of marketers are adopting AI regularly, with 63% of survey respondents reporting they use AI at least once a week, according to the State of AI 2025 report. Another 15% of marketers are "experimenters," using AI monthly. Only 22% of marketers in our survey qualified as "skeptics," using AI only a few times per year at most.
That makes marketers the most enthusiastic AI adopters in GTM, with "power user" rates 37% higher than AI users in sales and 15% higher than RevOps survey respondents.
To put that 63% weekly use figure in context: the St. Louis Fed's Real-Time Population Survey found overall generative AI adoption among U.S. workers reached 54.6% by August 2025. Marketers are outpacing the general workforce by a meaningful margin, suggesting that AI use cases in content generation and campaign optimization have gained traction faster in marketing than in most other professional functions.
The gap reflects how quickly specialized sales and RevOps use cases are still catching up to the mass-market AI tooling that marketers adopted early, and signals where the next wave of GTM AI investment is likely to land.
However, the survey also reveals a gap between basic and advanced AI adoption. While content creation tools dominate, more powerful solutions such as predictive analytics or data enrichment platforms remain underused. These capabilities could dramatically enhance segmentation, campaign targeting, and ROI if better integrated into marketing strategies.
Where marketing teams are putting AI to work
No surprise here: marketers' most commonly used AI tools are focused on content creation. From generating copy to producing design assets, AI content tools streamline creative workflows and allow teams to scale output much more quickly than before.
But content creation is just the start. Emerging marketing AI use cases, such as natural language audience building and signal-triggered play activation in GTM Studio, still have untapped potential.
Marketing Function | Primary AI Application | ZoomInfo Capability |
|---|---|---|
Content Creation | AI copy and asset generation | GTM Studio content workflows |
Audience Targeting | Natural language segment building | GTM Studio |
Campaign Optimization | Signal-triggered play activation | GTM Studio |
Personalization | Behavioral signal analysis | GTM Context Graph |
Analytics | AI-powered funnel dashboards | GTM Studio |
Teams that invest in more sophisticated capabilities are seeing clearer, more strategic value from AI. Smartsheet, for example, saw 84% more MQLs and a 40%+ increase in form fills after implementing AI-driven audience targeting and form optimization through GTM Studio.
Measurable impact: what the data shows
Survey respondents report strong gains from adopting AI in marketing, especially in areas that combine automation with customer data analysis:
Productivity: Marketers are 44% more productive thanks to AI, saving an average of 11 hours per week, time that can be reinvested into more strategic work.
Personalization: AI analysis and message creation is driving better audience engagement. One survey respondent said AI personalization increased open rates by 30% and conversions by 20%.
Named customers report similar gains. Smartsheet saw a 26% increase in opportunity rates after implementing AI-driven audience and form optimization, downstream pipeline impact that followed the top-of-funnel MQL lift.
These results suggest that marketing teams are using AI not just to work faster, but also to work smarter, with data-backed insights that power more effective campaigns.
Top barriers holding marketing teams back from AI at scale
Despite these benefits, marketing teams still face unique challenges when it comes to scaling AI. The barriers below surface consistently across the ZoomInfo State of AI 2025 report and the PwC 2025 Responsible AI survey:
Barrier | What it means for your team |
|---|---|
Data quality gaps | Stale audience lists and poor match rates undermine campaign targeting before a campaign launches |
Operationalization difficulty | Half of organizations cite turning AI principles into repeatable processes as their top hurdle (PwC 2025 Responsible AI survey) |
Governance and brand safety | Legal and compliance teams block scaling when AI content policies are undefined |
Skill gaps and tool fragmentation | Marketing teams run campaigns across disconnected tools with different audience definitions, making AI outputs inconsistent |
These barriers are interconnected. Multi-channel campaigns that run off different audience definitions produce inconsistent AI outputs, and stale data compounds the problem by feeding AI recommendations that reflect last quarter's reality rather than current buying behavior. Teams that address data quality first report the fastest path from experimentation to scaling. An intelligence layer that processes 1.5B+ data points daily and connects CRM records, behavioral signals, and intent data directly addresses the data quality and operationalization barriers at their root. For a deeper look at how GTM Intelligence for marketing addresses these gaps, the linked resource covers the mechanics in detail.
AI success stories from marketing teams
Teams that have embraced AI are already seeing outsized returns:
Simmers Crane Design & Services used AI-driven analytics to automate marketing reports, saving valuable time and reducing manual work. "AI-driven analytics automated marketing reports, eliminating hours of manual work for us," the company's chief commercial officer said.
MajorKey, a security technology company, saw a 40% increase in return on ad spend and cut marketing costs by 15% by implementing predictive analytics.
Embroker, a business insurance company, used AI to set up engagement-based workflows, leading to better deal closure rates. "Using AI to set up workflows based on customer engagement to our marketing emails helped us close more deals," one survey respondent said.
The examples above illustrate what's possible when AI is applied with clear intent. The maturity model below maps those outcomes to where teams typically sit on the adoption curve, so you can identify which stage describes your team and what the next step looks like.
What separates AI leaders from AI experimenters in marketing
Not all marketing teams are getting the same return from AI. Where a team sits on the adoption curve largely determines what they're able to do with it. Note that the stage percentages below reflect a framework overlay on the survey data: the 22% skeptics from the survey map roughly to Stage 1, the 63% weekly users span Stages 2 and 3, and the 15% monthly experimenters represent teams transitioning between stages.
Stage 1: Experimenter
Experimenters represent roughly 22% of marketers in the ZoomInfo survey. These teams are using AI primarily for content generation on an ad hoc basis: drafting copy, resizing assets, summarizing research. The tools are accessible and the wins are real, but the work is disconnected from the broader campaign architecture.
What's missing at this stage is a consistent data foundation. Without verified, real-time audience data, AI recommendations are grounded in stale inputs. The next step for experimenters is establishing a single source of truth for audience data before layering more AI capability on top.
Stage 2: Adopter
Adopters represent roughly 41% of marketers. These teams use AI regularly for campaign optimization and audience segmentation. They've moved past ad hoc content generation and are running AI-assisted workflows across multiple channels.
What's missing is closed-loop attribution. Adopters can measure engagement and MQL volume, but they can't consistently connect campaign activity to closed revenue. The next step is integrating intent signals with CRM data so that campaign performance can be traced to pipeline outcomes, not just top-of-funnel metrics.
Stage 3: Leader
Leaders represent roughly 22% of marketers. These are the power users the ZoomInfo survey identified: teams running predictive analytics, engagement-based automation, and closed-loop measurement. They report the strongest productivity and pipeline gains, and they're the ones most likely to absorb the next wave of AI capability as it arrives.
The gap between leaders and experimenters is widening. By 2026, agentic AI will enter campaign orchestration workflows in a meaningful way, according to the PwC 2025 Responsible AI survey's predictions on agentic AI governance. The Digital Marketing Institute (DMI 2025) projects that 75% of companies using AI for marketing expect to shift resources toward more strategic activities as automation handles execution. Marketing teams that have already operationalized data quality and signal-based automation will be positioned to absorb these capabilities. Those still running on disconnected tools with inconsistent audience definitions will fall further behind.
How ZoomInfo helps marketing teams move from data to action
What separates leaders from experimenters comes down to the same factors the maturity model surfaces: verified data, connected intelligence, and execution tooling that works from a single audience definition. ZoomInfo is built around exactly that combination.
ZoomInfo is an all-in-one AI GTM Platform built on three interconnected capabilities.
The foundation is data: 500M contacts, 100M companies, and 1.5B+ data points processed daily. That scale matters because AI recommendations are only as reliable as the data they're built on. When audience lists go stale before campaigns launch, or when match rates fail because contact data was built from email domain parsing rather than verified business identifiers, AI amplifies the problem rather than solving it. ZoomInfo's data foundation is continuously verified, which means AI recommendations reflect current buying behavior rather than last quarter's snapshot.
The intelligence layer is the GTM Context Graph, ZoomInfo's reasoning engine that fuses B2B data with CRM records, behavioral signals, and conversation intelligence. It captures not just what accounts are doing, but why, so marketing teams can build audiences that reflect real buying behavior rather than static demographic segments. Most intent data surfaces a list of companies visiting topic pages, the GTM Context Graph tells you which accounts in your ICP have the right buying committee composition, the right engagement pattern, and the right timing to be worth a campaign dollar today.
For marketing teams, that intelligence flows directly into GTM Studio, ZoomInfo's marketer-facing workspace for natural language audience building, signal-triggered play activation, and closed-loop campaign measurement. Teams that want to pipe the same verified intelligence into their own AI tools and agents can do so through ZoomInfo MCP or one API.
Ready to see how ZoomInfo's AI GTM Platform accelerates your marketing programs? Request a demo.
Frequently asked questions about AI adoption in marketing
What percentage of marketers are using generative AI in 2025?
According to ZoomInfo's State of AI 2025 survey of 1,000+ GTM professionals, 63% of marketers use AI at least once a week, making them the most frequent AI adopters across go-to-market functions. Only 22% qualify as skeptics using AI a few times per year at most. For broader context, the St. Louis Fed's Real-Time Population Survey found overall generative AI adoption among U.S. workers reached 54.6% by August 2025, meaning marketers are outpacing the general workforce.
What are the biggest barriers to generative AI adoption in marketing?
The top barriers marketing teams face are data quality gaps that cause audience lists to go stale before campaigns launch, operationalization difficulty (half of organizations cite turning AI principles into repeatable processes as their top hurdle, per the PwC 2025 Responsible AI survey), governance and brand safety concerns that stall scaling in regulated industries, and tool fragmentation where campaigns run across disconnected platforms with inconsistent audience definitions. Teams that address data quality first report the fastest path from experimentation to scaling. See how GTM Intelligence for marketing addresses these structural barriers.
How is generative AI changing marketing jobs and roles in 2025?
Generative AI is shifting marketing roles from execution to strategy rather than eliminating them. ZoomInfo's survey found marketers are 44% more productive and save an average of 11 hours per week, time reinvested into higher-value strategic work. The Digital Marketing Institute reports 69% of marketing professionals feel hopeful about AI's impact on their roles, and 75% of companies using AI for marketing expect to shift to more strategic activities. The pattern is consistent: AI handles repetitive content generation and data processing while marketers focus on campaign architecture, audience strategy, and revenue attribution. For a closer look at specific applications, see how marketers use generative AI.
What is the ROI of AI for marketing teams?
Marketing teams report strong ROI from AI adoption across productivity and campaign performance. ZoomInfo's State of AI 2025 survey found marketers are 44% more productive and save 11 hours per week on average. Named customer examples show even larger gains: Smartsheet saw an 84% increase in MQLs and a 40%+ increase in form fills after implementing AI-driven audience targeting and form optimization. MajorKey, a security technology company, reported a 40% increase in return on ad spend and a 15% reduction in marketing costs through predictive analytics. PwC's 2025 Responsible AI survey found nearly 60% of executives report that responsible AI adoption boosts ROI and efficiency.
How does AI help marketing teams improve personalization?
AI improves marketing personalization by analyzing behavioral signals, purchase intent, and engagement patterns to tailor messaging to individual accounts and contacts in real time. ZoomInfo's survey found one respondent reported AI personalization increased open rates by 30% and conversions by 20%. GTM Studio brings those capabilities together in a marketer-facing workspace, connecting verified buying signals to campaign execution so personalization reflects current account behavior rather than static demographic segments. The result is campaigns that reach the right accounts at the right moment in their buying journey.