Key findings: AI use in sales and marketing
The state of AI in sales and marketing has shifted decisively. Half of GTM employees surveyed are using AI to support their roles at least once a week. Chatbots such as ChatGPT are the most frequently used AI tools. AI users report that AI increases their productivity by 47%, cutting low-value, manual tasks by an average of 12 hours per week. AI has helped teams shorten deal cycles, increase average deal size, increase win rates, and increase profits. Many sales and marketing professionals remain dissatisfied with the accuracy and reliability of general-purpose AI tools. Senior leaders are less satisfied with general-purpose AI tools and are seeking purpose-built GTM AI that drives measurable business outcomes.
As AI improvements and applications continue to grow, the gap between AI users and late adopters will only widen. The goal of this report is to bridge that gap.
Drawing on insights from sales, marketing, and revenue operations professionals across industries, we'll explore how AI is reshaping workflows, boosting efficiency, and driving tangible results like shorter deal cycles and higher win rates.
We'll also confront some challenges, from data quality to organizational resistance, offering advice to help teams overcome these hurdles.
How sales and marketing pros are using AI in 2025
Drawing on responses from 1,002 GTM professionals across sales, marketing, and revenue operations, we identified three distinct adoption profiles.
In our survey, GTM professionals fell into three clear camps when asked about their use of AI: Power Users, Experimenters, and Skeptics.
Just over 20% of respondents use AI every day in their jobs, and another 29% use it weekly. These users are clearly seeing payoff in AI tools, and have made them a key part of their workday. They likely couldn't do their job as well without AI.
Another 12% use AI on a monthly basis. These are lukewarm users, they use it every other week or every few weeks, a frequency that suggests light, surface-level rewrites or other basic use cases. These users could probably take it or leave it, but might find a use case that works well for them in the future.
About 6% of respondents use AI only a few times a year, with 32% of respondents never using AI or unable to recall how often they used it. This group is skeptical about AI, and sees no compelling reason to make it a core part of their workday. They'll likely need to be shown a different class of AI tools or compelling use cases that impact their business in order to become more enthusiastic.
Which AI tools GTM teams use most
Conversational generative AI apps such as ChatGPT lead the way here. These tools are popular for their user-friendly interfaces and immediate impact, enabling professionals to quickly enhance customer interactions, answer queries, and generate content.
A recent AI adoption report from G2 backs up this finding, reporting that 69% of companies have integrated chatbots and virtual assistants into their tech stack.
More specialized tools like data enrichment platforms or predictive analytics systems are less widely used. On one hand, this is a reflection of the specialized nature of that work, there are simply fewer people doing data enrichment than there are creating content with chatbots.
On the other hand, this curve of AI usage also reflects the fact that the market for sales AI tools purpose-built for revenue-driving use cases is still nascent.
McKinsey came to a similar conclusion in its own survey on AI use: "Most companies are pursuing efficiency gains with gen AI, but leaders believe the real value of the technology will accrue from applications that transform the effectiveness of business functions."
This divide reflects the broader challenge for AI developers: creating tools that are easy and intuitive to use, but also inspire trust through accuracy and seamless integration.
ZoomInfo, an all-in-one AI GTM Platform, is among the companies building purpose-built AI for GTM teams. GTM Workspace uses AI agents to analyze first-party customer and prospect data, combined with ZoomInfo's proprietary B2B data and market signals, to surface proactive recommendations for outreach, messaging, and account management, telling sellers who to contact, when to engage, and what to say.
GTM Workspace's early adoption data reflects the demand for purpose-built GTM AI that has GTM intelligence at its core. Since launching in mid-2024, GTM Workspace has helped more than 50,000 users level up their sales motions. Users are booking significantly more meetings and saving more than 10 hours every week by automating administrative work.
AI adoption by role: sales, marketing, and RevOps
Individual contributors in our survey are the most likely to adopt AI, leveraging it for day-to-day efficiency. In contrast, senior roles, directors, VPs, and especially CEOs, are less likely to use AI tools on a daily basis, likely reflecting the basic content-creation and information retrieval use cases popularized by the first wave of AI chatbots. AI adoption is strongest among senior leaders when it delivers tangible value. Without clear results, senior professionals see less incentive to integrate AI into strategic decision-making.
The high-impact uses of AI sought by senior leaders are starting to emerge. As purpose-built business applications for AI continue to mature, the number of transformational use cases will grow, boosting senior leadership enthusiasm.
AI in sales
About 45% of sales professionals in our survey use AI at least once a week, with AI-powered CRMs mentioned as the most commonly used sales AI tools. For SDRs, AI-powered CRMs reduce the time spent building prospect lists and logging call outcomes; for AEs, AI surfaces account context and buying signals before discovery calls. These role-level efficiency gains aggregate into the business-wide deal outcomes covered in the impact section below.
Sellers who frequently use AI report substantial improvements across all major performance metrics, with shorter deal cycles (81% of respondents), increased deal sizes (73% of respondents), and an 80% increase in win rates.
AI in marketing
Among marketing professionals in our survey, 63% use AI at least once a week. Marketing teams are most likely to use content creation tools.
Marketing AI users overall said they were 44% more productive, saving an average of 11 hours per week. Individual respondents also reported compelling results, including a 30% increase in email open rates and a 40% higher return on ad spend.
AI in RevOps
AI use is prominent among revenue operations teams, with 55% of RevOps respondents to the survey using AI at least once a week.
Data enrichment platforms dominate usage for this group. Top use cases include workflow automation, named as satisfactory by 71% of RevOps users, and sales forecasting, which 69% of RevOps users said was helped by AI. Overall, AI users in RevOps report being 46% more productive.
AI adoption across generations and industries
The range and frequency of AI adoption is shaped by individual and organizational choices and broader demographic and industry trends. These patterns offer critical insights into where AI is thriving and where it faces considerable obstacles.
AI adoption is highest among younger generations: three quarters of GTM professionals under 34 report using AI at least once a month, while less than half of those over 55 use it on a monthly basis. Younger generations often benefit from greater digital fluency, exposure to AI in education, and a workplace culture that embraces technological innovation.
Fast-paced industries like technology, telecommunications, and energy are driving adoption, fueled by their emphasis on efficiency and cutting-edge solutions.
Many companies are also building their own internal AI tools and agents, projects that require expertise across multiple domains. Databricks, for example, has shared lessons from its own AI agent projects, including the importance of cross-functional data governance and deliberate testing and experimentation in the pilot phases.
Conversely, industries like education, non-profits, and government lag behind. These sectors often contend with tight budgets, bureaucratic hurdles, and less access to purpose-built tools, which hinder experimentation and widespread adoption.
This divide highlights the importance of targeted solutions and education tailored to the unique needs of these slower-adopting industries. For GTM teams in fast-moving industries, purpose-built AI tools like GTM Workspace are designed to close this gap, giving sellers AI agents that automatically surface account intelligence and buying signals, eliminating the need to stitch together research across disconnected point solutions.
The business impact of AI: productivity, deal outcomes, and profitability
For frequent AI users, the benefits are clear.
Boosting productivity: AI users report being 47% more productive and saving an average of 12 hours per week by automating repetitive tasks. They also leveraged that extra time to drive greater value for the business, with prospect outreach and client relationship building the top tasks that got more attention.
Driving business outcomes: Teams using AI at least once a week report measurable improvements across the metrics that matter most to revenue leaders:
Outcome | % of frequent AI users reporting improvement |
|---|---|
Shorter deal cycles | 78% |
Larger deal sizes | 70% |
Improved win rates | 76% |
In fact, 79% of frequent users said AI helped make their teams more profitable.
According to G2's Buyer Behavior Report, 83% of companies that purchased an AI solution in the last three months have already seen positive ROI. Additionally, 82% of frequent AI users said they were satisfied with the technology's reliability and accuracy.
Barriers to AI adoption in sales and marketing
Despite its potential, there are still significant challenges with AI adoption in business.
Some 80% of non-users in our survey said they were concerned about accuracy, reflecting a core weakness in the value proposition for many AI tools.
Quality is also a major issue: 42% of survey respondents expressed dissatisfaction with AI tools, pointing to issues such as data quality, security, and generative AI "hallucinations."
Publicly available data shows these concerns aren't merely a case of foot-dragging or general skepticism. A Stanford University study found wide variations in ChatGPT's accuracy between model updates within the same year.
Dirty data is a consistent problem in businesses, costing companies up to 25% of their potential revenue by some estimates. The reliance on accurate data to fuel AI tools highlights the need for robust infrastructure. Without high-quality inputs, even the most advanced tools risk underdelivering.
For quota-carrying sellers, data quality is not an abstract IT concern. Stale phone numbers and bounced emails directly erode outreach capacity and domain reputation, making every sequence less effective before it starts.
Companies looking to adopt AI at scale also report a shortage of personnel and expertise. The top organizational barriers from our survey:
Lack of skilled personnel (29%)
Integration complexity with existing systems (28%)
General resistance to change (28%)
Budget constraints (25%)
As Snowflake noted in its AI + Data Predictions report, the pace of innovation will only increase.
From experimentation to operational: an AI maturity framework for GTM teams
Those barriers are real, but they are not evenly distributed. The state of AI in sales and marketing is not uniform: survey data reveals a wide spectrum of adoption maturity, and where your team sits on that spectrum determines both which barriers you face and how much value you're actually extracting from AI.
Stage 1: Experimental
Teams at this stage use AI as a personal productivity tool. Chatbots handle one-off content generation, ad hoc research, or quick rewrites. Usage is individual and inconsistent, not embedded in team workflows. The 38% of respondents who rarely or never use AI fall here, alongside the 12% of Experimenters who use it monthly but haven't built it into their daily motion.
Stage 2: Operational
AI is embedded in weekly workflows. CRM automation, sequencing tools, and content generation run on a regular cadence. The 29% of respondents who use AI weekly are largely in this stage. The gains are real but still function-specific: marketing teams run AI-assisted campaigns, sales reps use AI-drafted outreach, RevOps teams automate enrichment jobs.
Stage 3: Optimized
This is where the 20%+ of daily AI users operate. AI is no longer a tool layer on top of existing workflows; it's the workflow. Purpose-built GTM AI with verified data, buying signals, and AI agents that synthesize account intelligence replaces the patchwork of manual lookups and disconnected data sources. Sellers know which accounts to prioritize, why, and what to say before they pick up the phone.
Where does your team sit? If AI is still a personal productivity tool rather than a team-wide workflow layer, you are in Stage 1.
The competitive framing matters here. As HubSpot has observed, AI is rapidly becoming the baseline, not a differentiator. In 2026, the competitive gap is no longer who uses AI, it is how well they use it. Teams still in Stage 1 are not just behind on productivity; they are behind on pipeline.
Creatio frames this shift as moving from "AI-versed to AI-first": knowing how to use AI tools is table stakes; building your GTM motion around AI as the operating layer is where the next wave of competitive advantage lives.
How AI is reshaping GTM team structures and roles
AI adoption is not just changing what GTM teams do, it is changing how those teams are structured and who does what.
New roles emerging
The first wave of AI adoption created demand for roles that didn't exist three years ago. AI ops managers are responsible for governing how AI tools are deployed, monitored, and improved across the GTM org. GTM prompt engineers are building and maintaining the prompt libraries and workflow templates that translate AI capability into repeatable sales and marketing motions. AI content strategists are managing the intersection of AI-generated content, brand standards, and performance data.
These roles are not theoretical. Among B2B companies with over $25M in annual revenue, AI adoption in marketing has reached 96%, suggesting the transition is already complete at established mid-market and enterprise organizations. At that level of adoption, governance and workflow design become full-time jobs.
How existing roles are changing
The SDR role is shifting from list-building to signal interpretation. Reps who spent their mornings pulling prospect lists and logging call outcomes are now spending that time evaluating AI-surfaced signals and deciding which accounts to prioritize. The research burden moves to the machine; the judgment call stays with the rep.
AEs are shifting from research to relationship and negotiation. Pre-call prep that used to take 20-30 minutes, pulling org chart data, recent news, tech stack context, is increasingly handled by AI agents that surface account intelligence before the call. AEs who adapt use that time to sharpen their discovery questions and deal strategy.
RevOps is shifting from data cleanup to AI governance. The team that used to spend cycles on manual enrichment runs and deduplication jobs is now responsible for the data quality standards that make AI recommendations reliable. That is a more strategic role, but it requires a different skill set.
Change management: the leadership challenge
The technology is not the hard part. Our survey found that 28% of organizations cite resistance to change as a top barrier to AI adoption. That is a leadership challenge, not a technology problem.
Workload management, not skills gaps or budget, is the single top challenge for B2B marketers today, with 42% citing it as their primary pain point. AI is the primary solution to this specific challenge. But deploying AI successfully requires leaders who can articulate why the change is happening, what it means for each role, and how success will be measured, before asking their teams to change how they work.
A practical roadmap for AI adoption in your GTM motion
Non-users cite fear of job displacement, a lack of understanding about AI's benefits, and insufficient training as the most pertinent reasons for their hesitation. Current users, however, report high confidence in AI's accuracy when paired with proper implementation and support. That gap is closeable. Here is a practical five-step path:
Audit your current AI tool stack and identify workflow gaps. Map which tools your team is actually using, which workflows are still manual, and where the highest-friction handoffs occur. The audit often reveals redundant tools and obvious automation candidates that don't require new investment.
Prioritize the highest-ROI use case for your team. For sellers, that is typically AI-assisted prospecting. For marketers, AI-driven audience segmentation. For RevOps, automated data quality standards and enrichment. Pick one workflow, not five.
Pilot with one workflow before scaling. Run the pilot with a small group, measure against a baseline, and document what worked before expanding. Teams that skip the pilot phase tend to deploy broadly, see inconsistent results, and lose organizational buy-in.
Establish data quality standards, AI is only as good as the data it runs on. Verified contact data, with 200M+ verified business emails and 120M direct-dial phone numbers, is the foundation that makes AI recommendations reliable rather than plausible. If your data layer is degraded, your AI layer will be too.
Measure impact against baseline metrics. Connect rates, email deliverability, deal cycle length, and win rates are the right benchmarks. If you can't show movement on at least two of these within 90 days of deployment, revisit the use case or the data quality foundation.
That data quality step is not incidental. It is the foundation on which every AI recommendation in your stack depends, and it is where ZoomInfo's role in the AI era begins.
About ZoomInfo: built for the AI era of go-to-market
ZoomInfo's all-in-one AI GTM Platform is built on the most comprehensive B2B data foundation in the market: 500M contacts, 100M companies, 135M+ verified phone numbers, and 200M+ verified business emails, verified continuously by 300+ human researchers.
The GTM Context Graph processes 1.5B+ data points daily, fusing that verified data with CRM records, conversation intelligence, and behavioral signals to reveal not just what happened in a deal, but why, giving GTM teams the intelligence layer that general-purpose AI tools cannot replicate.
GTM teams access that intelligence through GTM Workspace for sellers, GTM Studio for marketers and RevOps, or directly via APIs and MCP in any tool or AI agent.
GTM Workspace users report measurable results across the metrics that define GTM performance: a 43% increase in Total Addressable Market, a 41% increase in win rates, an 83% increase in average deal size, and 30% faster deal cycles.
ZoomInfo's all-in-one AI GTM Platform, recognized as a Leader by Gartner and Forrester, is built for the AI era of go-to-market. See what GTM Workspace can do for your team, request a demo.
Survey methodology
Our findings are based on responses from 1,002 sales and marketing professionals in the United States, representing a mix of B2B and B2C, as well as large enterprises and small businesses. The participants ranged from early career individual contributors to seasoned team leaders, executives, and business owners. The respondents represent a mix of ZoomInfo customers and non-customers.
Frequently asked questions about AI in sales and marketing
What is the current state of AI in sales and marketing?
AI has moved from experimental to mainstream in B2B sales and marketing. ZoomInfo's survey of 1,002 GTM professionals found that half use AI at least once a week, with frequent users reporting a 47% productivity increase and 12 hours saved per week. Sales teams using AI weekly report shorter deal cycles (78%), larger deal sizes (70%), and improved win rates (76%). For a deeper look at how teams are putting this to work, see the guide on leveraging AI in sales.
How much time does AI save sales reps per week?
Frequent AI users in ZoomInfo's survey report saving an average of 12 hours per week by automating repetitive tasks. Sales professionals specifically report saving time on prospect research, CRM data entry, and outreach sequencing, time that gets redirected to pipeline-building and client relationship work. For a breakdown of which AI sales tools deliver these gains, see the full tool comparison.
What are the biggest barriers to AI adoption in B2B sales and marketing?
ZoomInfo's survey identifies four top organizational barriers: lack of skilled personnel (29%), integration complexity with existing tech stacks (28%), general resistance to change (28%), and budget constraints (25%). Among non-users, 80% cite concerns about AI accuracy as their primary hesitation. Data quality is the underlying technical barrier, AI tools are only as reliable as the data they run on. See how data quality impact cascades through the GTM stack.
What ROI can businesses expect from AI in sales and marketing?
ROI varies by use case and adoption stage. ZoomInfo's survey found that 79% of frequent AI users said AI made their teams more profitable. Teams using AI at least weekly report 78% shorter deal cycles, 70% larger deal sizes, and 76% improved win rates. According to G2's Buyer Behavior Report, 83% of companies that purchased an AI solution in the last three months have already seen positive ROI.
How are marketing teams restructuring for the AI era?
Marketing teams are creating new AI-specific roles (AI content strategist, AI ops manager), redesigning existing workflows around AI-human collaboration, and building governance frameworks for AI-generated content. ZoomInfo's survey found that 28% of organizations cite resistance to change as a top barrier, making change management as important as the technology itself. Among companies with over $25M in annual revenue, AI adoption in marketing has reached 96%, suggesting the transition is already complete at established mid-market and enterprise organizations. For tactical guidance on how marketers use AI, see the full breakdown.