The explosion of Artificial Intelligence is driving billions of investment dollars into the sector and inspiring nearly constant debate about AI’s impact on business and society.
So how is the massive growth in AI technology actually affecting day-to-day corporate reality?
A new ZoomInfo survey of more than 1,000 go-to-market professionals shows frontline workers have eagerly adopted common tools such as ChatGPT, helping users achieve a 47% boost in productivity and saving GTM professionals an average of 12 hours per week.
But compared to frontline professionals, senior leaders remain less satisfied with the impact of mass-market AI tools such as simple chatbots – instead, they’re looking for AI that drives real business value.
That means there’s still a massive opportunity for GTM AI that delivers accurate, reliable, bottom-line impact for revenue leaders – and executives who start leveraging high-quality GTM AI tools now can quickly pull ahead of the competition.
Key Findings: AI Use in Sales & Marketing
- 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 their team wins rates, and increase profits.
- However, many sales & marketing professionals are dissatisfied with the accuracy and reliability of general-purpose AI tools.
Dive Deeper to Uncover:
- A detailed analysis of AI adoption trends, tools, and industry preferences.
- Success stories that show how AI has transformed marketing, sales, and revenue operations.
- Actionable insights to address challenges and maximize the potential of AI in your company.
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 Are Sales & Marketing Pros Using AI?
In our survey, GTM professionals fell into three clear camps when asked about their use of AI:
- Power Users
- Experimenters
- 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.
“It’s clear that mass-market, consumer AI tools are just not suited for business. You can’t just slap some general-purpose AI chatbot on your existing processes and count on transformative results.
The reality is, AI needs to be built directly into specialized applications by people who know what go-to-market teams need to succeed. That’s how we are seeing companies drive real innovation in GTM.”
Which AI Tools Are Used the 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 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.
“The user experience is one of the biggest innovations we’re seeing in the age of generative AI. People are quickly coming to expect a fast, fluid response from AI chat tools as a primary interface.
This is a massive change in user expectations for B2B products. But in the end, this ease of use has to deliver trustworthy, accurate results and recommendations, or it’ll be dismissed as slick but shallow.”
ZoomInfo is among the companies developing innovative AI applications to fill this gap. Our solution, ZoomInfo Copilot, analyzes and synthesizes first-party customer and prospect data, combined with ZoomInfo’s unmatched, proprietary B2B data and market signals, to deliver GTM teams proactive recommendations for outreach, messaging, and account management.
Copilot’s success validates the strong market that awaits AI tools built with GTM intelligence at their core. Since launching in mid-2024, Copilot has helped more than 50,000 users level up their sales motions — they’re booking 60% more demos and meetings, driving email response rate improvements of nearly 90%, and saving more than 10 hours every week by automating administrative work.
Job Seniority and Usage Gaps
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. In its own survey of 400 customers, Google Cloud recently found that 5% of AI use cases qualify as “transformational,” delivering both business growth and internal efficiency benefits simultaneously.
As purpose-built business applications for AI continue to mature — witness the quick rise of AI agents that can perform intricate tasks and deep research — the number of transformational use cases will certainly grow, boosting senior leadership enthusiasm.
“To fully utilize AI, businesses must move beyond experimentation and build a scalable infrastructure that supports a clear vision. Success depends on technology, people, processes, and high-quality data.
This is crucial with Generative AI, which can create content and automate tasks, but also raises ethical concerns. A strong foundation in data governance, skilled workforce, and adaptable processes are essential to integrate Generative AI’s outputs and transform it into a powerful driver of business value.”
AI Adoption Across Generations, 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.
“It’s not that surprising to see younger, frontline professionals a bit more eager to adopt AI tools. But I’d be shocked if this gap persisted five years from now – especially as companies deliver more advanced business applications with AI built into their core.
You can almost think of tools like ChatGPT as a super broad public demo for a new kind of functionality that people will expect to see in critical GTM software across the board.”
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, recently shared some lessons from its own AI agent projects, including the importance of cross-functional data governance and deliberate testing and experimentation in the pilot phases.
“Building effective AI for go-to-market teams is no small undertaking, and high-quality data infrastructure is absolutely critical for success. AI is revolutionizing how we sell at Databricks, freeing our teams from repetitive tasks and unlocking time for what truly matters—building relationships and closing deals.
But the magic only happens with clean data, strong governance, and a mindset of experimentation.”
Conversely, industries like education, non-profits, and government lag behind. These sectors often contend with tight budgets, bureaucratic hurdles, and less access to innovative 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.
AI Impact: Productivity, Profitability, and Relationships
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…
- Shorter deal cycles (78%)
- Larger deal sizes (70%), and
- Improved win rates (76%)
…which clearly highlights AI’s ability to streamline processes and uncover high-value opportunities. In fact, 79% of frequent users said AI helped make their teams more profitable.
And 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.
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.
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.
“Being able to show an increase in efficiency is important, but if you’re selling an AI tool in the go-to-market space, you need to be able to show that you’re driving better business outcomes, not just metrics showing an increase in productivity around emails and calls.
Things like higher average sale price, increase in win rate, and shorter sales cycles — that is what revenue leaders actually care about.”
Broad Challenges: Trust, Data, and Buy-In
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. For instance, 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.
“We’ve been focused on building the best quality, most comprehensive universe of B2B data and buying signals for nearly 20 years, and we’ve seen from the beginning that applying AI tools to incomplete CRM data or poorly sourced signals can lead to poor results.
If you’re going to invest in AI, it’s absolutely critical to have the go-to-market intelligence infrastructure to support it.”
Companies looking to adopt AI at scale also report a shortage of personnel and expertise, which should be expected at the early stages of a major technological change. Lack of skilled personnel (29%), problems with integrating into current systems (28%), general resistance to change (28%), and budget constraints (25%) were the top-cited issues slowing adoption in ZoomInfo’s survey.
Turning Challenges into Opportunities
Non-users said fear of job displacement, a lack of understanding about AI’s benefits, and insufficient training were the most pertinent reasons for their hesitation. However, current users report high confidence in AI’s accuracy when paired with proper implementation and support. This means companies can drive AI implementation by taking a proactive approach.
Here’s what we recommend:
- Invest in Data Quality: Ensure AI tools are powered by accurate, up-to-date information to improve reliability and outcomes. Experts suggest that bad data may cost companies up to 25% of their potential revenue – a figure that could be drastically amplified if AI is given low-quality, inaccurate data inputs.
- Educate and Train Teams: Build internal knowledge through workshops and onboarding programs that help professionals use AI confidently. G2 reports that over 60% of employees take over a month to become proficient with AI tools, so start early.
- Start with Strategic Wins: Focus on areas where AI can drive clear, measurable outcomes, like forecasting or personalization, to build trust among skeptics. For example, when ZoomInfo rolled out Copilot, we focused on gradually adding beta users and features that showed clear ROI — and early users reported strong success that created a flywheel of momentum.
- Gradual Integration: Roll out AI incrementally, starting with accessible tools for individual contributors before expanding adoption to senior roles. Change management takes clear planning, and poorly executed AI initiatives can lead to unexpected costs and security risks.
By addressing these barriers, companies can foster broader adoption and unlock AI’s full potential to drive productivity, profitability, and competitive advantage. There’s plenty of work ahead, and as Snowflake noted in its recent AI + Data Predictions report, the pace of innovation will only increase.
“Everyone I talk to who works in AI says they’re working much harder than they ever have, because there’s so much innovation and change.
Leaders need to focus on goals and ROI, rather than chase either the shiny object or every upgrade.”
About ZoomInfo
By delivering a 360-degree view of buyers through unparalleled B2B data, buying signals, and AI-ready insights, ZoomInfo’s GTM Intelligence platform provides the essential infrastructure for AI that delivers real results. The proof? ZoomInfo Copilot users report these game-changing results:
- 43% increase in Total Addressable Market
- 41% increase in win rates
- 83% increase in average deal size
- 30% faster deal cycles, an average of 45 days per deal
By empowering every seller to be your best seller, ZoomInfo ensures your teams are ready to engage the right prospects with the right message at the right time. The future of AI in sales and marketing is here — and ZoomInfo’s GTM Intelligence Platform is leading the way.
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.
Research and Analysis by Kate Hoffmire
Writing, Editing and Visualizations by Chelsea Verstegen and Curt Woodward
Concept by Michelle Blondin
Partner Marketing by Ben Davies