How Sales Teams are Using AI in 2025: Survey & Statistics

AI adoption rates among sales teams in 2025

Sales teams are splitting into two camps. Per ZoomInfo's State of AI in Sales and Marketing survey of more than 1,000 GTM professionals, 45% of sellers use AI at least once a week. At the same time, 42% use it only a few times a year or not at all. That gap between power users and skeptics is the central story in AI adoption right now, and the distance between those two groups is widening fast.

LinkedIn's 2025 State of Sales report puts daily AI usage among sales professionals at 56%, and 87% of companies identify AI as a top business priority for 2025. The momentum is real. But the survey data makes clear that mass-market AI tools like basic chatbots and generic CRM assistants are not the reason power users are pulling ahead. The tools that drive bottom-line results are purpose-built for go-to-market teams.

As ZoomInfo Chief Revenue Officer James Roth notes: "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."

How sales teams are using AI: a use-case breakdown

The most commonly used AI tools in sales are AI-powered CRMs, which embed capabilities like lead prioritization, forecasting, and automated follow-ups directly into existing workflows. But the use cases extend well beyond CRM automation. Here is a taxonomy of where AI is creating real leverage across the sales motion:

AI-powered CRMs are the most common entry point because they meet reps where they already work. They reduce the friction of data entry and surface basic recommendations without requiring a new platform. For many teams, this is where AI adoption starts.

For teams that want bottom-line impact beyond workflow convenience, GTM Workspace is the next-level tool. ZoomInfo's GTM Workspace analyzes CRM activity, market signals, and B2B contact data through the GTM Context Graph to recommend next steps in outreach, messaging, and pipeline management. The GTM Context Graph processes 1.5B+ data points daily, fusing ZoomInfo's verified B2B data with customer CRM data, conversation intelligence, and behavioral signals into a unified reasoning layer that tells sellers not just what happened but why. Per ZoomInfo GTM Workspace launch data, GTM Workspace users have booked 60% more meetings, improved email response rates by nearly 90%, and recovered 10+ hours a week through workflow automation.

ZoomInfo is an all-in-one AI GTM Platform built on three capabilities that work together: the most comprehensive B2B data platform (500M contacts, 100M companies, 200M+ verified business emails), the GTM Context Graph reasoning layer that turns raw signals into actionable intelligence, and universal access that puts that intelligence wherever sellers work. Teams that prefer to wire verified B2B intelligence into their own tools and agents can do so through ZoomInfo's MCP integration, which connects firmographic, technographic, and signal data to any AI agent without requiring a new interface.

What the data shows: AI impact on sales performance

The performance gap between teams using AI frequently and those that don't is no longer marginal. Per the ZoomInfo survey, among sellers who use AI at least once a week:

• 81% said their deal cycles got shorter

• 73% reported increases in average deal size

• 80% saw higher win rates

Bain and Company (2025) found that early AI deployments have boosted win rates by more than 30% in organizations that have moved past pilot stage. Sopro research shows that 86% of sales teams see positive ROI within the first year of AI adoption. And 38% of sellers using AI for research save more than 1.5 hours per week on prospecting tasks alone (LinkedIn, 2025). For a 10-rep team, that is 780 hours of additional selling time per year.

GTM Workspace's ability to surface in-market accounts via the GTM Context Graph, prioritize leads based on buying signals, and automate routine tasks empowers sales teams to deliver results with greater speed and precision.

Named customer outcomes confirm the pattern. Seismic saved 11.5 hours weekly per rep and achieved a 54% productivity gain using GTM Workspace. And the Thomson Reuters closed-won lift tells the quota attainment story: a 40% increase in closed-won deals and 115% average monthly quota attainment.

These are not efficiency metrics. They are revenue outcomes, and they show what becomes possible when AI is integrated at the platform level rather than bolted on as a point solution.

Where AI delivers the fastest ROI for sales teams

Sellers spend only about 25% of their working hours on direct selling activity, with the rest consumed by administrative tasks (Bain, 2025). That is the core business case for AI in sales: the problem is not that reps are bad at selling, it is that they spend most of their day not selling. AI is the structural fix.

Prospecting and pipeline building

38% of sellers using AI for research save more than 1.5 hours per week on prospecting tasks alone (LinkedIn, 2025). For a 10-rep team, that translates to 780 hours of recovered selling time per year. AI automates the research loop: pulling firmographic context, identifying decision-makers, and surfacing accounts showing buying signals before a rep ever picks up the phone.

Outreach and engagement

AI-driven campaigns launch 75% faster and generate 47% better click-through rates compared to manually built sequences (Sopro research). The mechanism is personalization at scale: AI drafts outreach based on account context, adjusts messaging by buying stage, and recommends send timing based on engagement patterns. Reps spend their time reviewing and sending, not building from scratch.

Forecasting and deal health

Predictive AI surfaces deal risk signals before they become lost deals. Rather than waiting for a deal to go quiet, AI flags changes in engagement patterns, buying committee activity, and competitive signals early enough for reps to intervene. This shifts forecasting from a backward-looking reporting exercise to a forward-looking action trigger.

CRM and data quality

Stale contact data is the top barrier to AI ROI. When the underlying contact records are wrong, every AI recommendation built on top of them is wrong too. Email bounces erode sender domain reputation. Bad phone numbers waste call blocks. Contacts who changed jobs create a hidden outreach failure rate that only surfaces when sequences start collapsing. Continuous enrichment is the fix: keeping contact records current so that AI recommendations are grounded in accurate data.

Teams that deploy AI across all four functions see compounding returns. The gains in prospecting feed better pipeline quality, which improves forecasting accuracy, which reduces wasted effort on deals that were never going to close.

Barriers to AI adoption that sales teams still face

Despite the performance data, most sales teams are still working through structural obstacles. The barriers are real, and understanding them is the first step to getting past them.

• Data quality: Sales teams depend on accurate, up-to-date data to prioritize leads and forecast effectively. Any discrepancies in data erode trust in AI recommendations. The data quality impact is compounding: stale contact records produce email bounces, bounces damage sender domain reputation, and degraded deliverability shrinks outreach capacity. 28% of survey respondents cited integration difficulties as a top barrier, but for outbound-heavy teams, data quality is the root cause underneath the integration problem.

• Training gaps: 29% of organizations cite lack of skilled personnel as a top AI adoption barrier. The fix is not a company-wide AI training program on day one. Phased rollouts that start with a single high-ROI use case, such as lead scoring or prospecting research, reduce the training burden to something manageable. Reps learn one workflow change, see results, and extend from there.

• Integration issues: 28% cite resistance to change as a barrier. Tools that require reps to log into a separate platform face the highest adoption friction. AI embedded directly in existing CRM workflows, the way GTM Workspace integrates with Salesforce and other major CRMs, reduces that friction because the rep never has to leave the tool they already use.

• Intent signal overload: Too many unprioritized signals overwhelm reps rather than guiding them. When 25+ intent signals are active simultaneously with no grouping or prioritization logic, reps face analysis paralysis instead of clear next actions. The solution is signal hierarchy: fewer, higher-quality signals mapped to specific messaging playbooks so reps know exactly how to respond.

45% of sales teams have already adopted a hybrid AI+human SDR model for prospecting (per industry research), signaling that the replacement narrative is giving way to an augmentation reality. AI handles the tasks that erode selling time. Human reps handle the conversations that close deals.

Real results: how sales teams are winning with AI today

Survey respondents reported results that cut across company size and industry. Sellers at MajorKey Technologies, an enterprise identity security provider, credited AI use with increasing their team's revenue by 16% in the past year. One business development manager reported doubling their sales since starting to use AI with their existing CRM system. Executives at Simmers Crane Design and Services saw a 20% jump in sales in just one month after implementing AI tools into their sales process. Reps at Embroker, an innovative business insurance provider, have doubled productivity and sales since starting to implement AI.

These are survey respondents reporting their own outcomes, not controlled studies. But the consistency of the pattern across different companies and use cases points to something real.

Per ZoomInfo GTM Workspace user data, GTM Workspace users reported a 43% increase in Total Addressable Market, a 41% jump in win rates, and a 30% reduction in deal cycle length. These are competitive advantages that deliver serious ROI.

GTM Workspace customer outcomes

Named customer results confirm what the aggregate data shows:

• Snowflake (data scoring + pipeline): Snowflake doubled conversion rates on ZoomInfo-scored accounts, with 90% higher opportunity open rates and 2x customer conversion compared to unscored accounts.

• Spekit (pipeline qualification): Spekit qualified pipeline faster using GTM Workspace, with accounts 43% more likely to turn into qualified pipeline and 58% faster qualification overall.

How to implement AI in your sales team: a phased approach

Most AI adoption failures trace back to the same mistake: trying to deploy everything at once. A phased approach reduces risk, builds rep confidence, and creates the measurement baseline that justifies expanding to the next function.

Phase 1: audit and prioritize

Start by assessing your current tech stack and identifying the single sales function with the highest administrative burden. For most teams, that is prospecting research or CRM data entry. Pick one AI use case to pilot. The goal at this stage is not transformation; it is a controlled test with a clear success metric. If you cannot define what "working" looks like before you start, you will not be able to prove it afterward.

Phase 2: pilot and measure

Deploy AI in the chosen function and set a 30-day baseline metric before you expand. Useful baseline metrics include hours saved per rep per week, email bounce rate, connect rate on outbound calls, or meetings booked per rep. Track against the baseline. If the metric moves in the right direction, you have the proof point you need to fund the next phase. If it does not, you have learned something specific rather than concluding that AI does not work.

Phase 3: scale and integrate

Once the pilot function shows results, expand to adjacent functions. The sequencing matters: prospecting improvements feed better pipeline quality, which makes forecasting more accurate, which reduces wasted effort on low-probability deals. Ensure AI tools are embedded in existing CRM workflows rather than requiring reps to log into a separate platform. Establish a recurring training cadence so new reps onboard into the AI-enabled workflow from day one rather than learning it as an add-on.

The broader shift underway is from AI-versed to AI-first. Organizations that are AI-versed have added AI tools to their stack. Organizations that are AI-first have rebuilt their seller workflows around AI, so that every rep starts their day with AI-generated account briefs, AI-prioritized call lists, and AI-drafted outreach ready to review. The difference is not the tool; it is the workflow design.

GTM Workspace is built for the AI-first model. It brings AI agents, verified B2B data, and the GTM Context Graph into the seller's existing workflow, so reps get the intelligence they need without switching platforms or learning a new interface.

See how GTM Workspace helps your team reclaim selling time and close more deals.

What's next: the future of AI in sales

The trajectory is clear. Gartner projects that by 2027, 95% of sellers' research workflows will begin with AI. By 2028, 60% of B2B sales tasks will be executed through AI-powered conversational interfaces. These are planning inputs, not hype. The teams building AI-first workflows now are accumulating a structural advantage that compounds over time.

ZoomInfo's own survey data shows the gap is already opening: power users report 80%+ win rate improvements and 73% larger deal sizes compared to sellers who use AI only occasionally. The performance delta between frequent AI users and skeptics is not a rounding error; it is the difference between hitting quota and missing it.

Teams that combine verified B2B data with the GTM Context Graph reasoning layer are building a structural advantage in pipeline generation and deal execution that compounds over time. The question is no longer whether AI belongs in the sales motion. It is whether your team is building the workflows to capture the advantage before your competitors do.

Frequently asked questions

What percentage of sales teams are using AI in 2025?

Per ZoomInfo's State of AI survey, 45% of sales professionals use AI at least once a week. LinkedIn's 2025 research puts daily AI usage among sales professionals at 56%. Despite this momentum, 42% of sellers in ZoomInfo's survey still use AI only a few times a year or not at all, highlighting a significant adoption gap between power users and skeptics.

What are the biggest benefits of AI for sales teams?

Sales teams using AI at least weekly report 81% shorter deal cycles, 73% larger average deal sizes, and 80% higher win rates (ZoomInfo survey, 2025). Bain and Company research shows early AI deployments have boosted win rates by more than 30%. The most immediate productivity gain: 38% of sellers using AI for research save more than 1.5 hours per week on prospecting tasks alone (LinkedIn, 2025). Named customer outcomes bear this out: Seismic's 54% productivity gain and 11.5 hours per week saved per rep is one of the clearest proof points available.

How does AI improve sales prospecting?

AI improves prospecting by automating lead research, scoring accounts by conversion probability, and surfacing in-market accounts based on buying signals. Sellers using AI for research save more than 1.5 hours per week on prospecting tasks (LinkedIn, 2025). Platforms like GTM Workspace combine verified B2B contact data with the GTM Context Graph to identify which accounts are actively researching solutions and recommend the next best action for each.

What are the biggest challenges sales teams face when adopting AI?

The top barriers are data quality barriers (teams depend on accurate contact data for AI recommendations to be trustworthy), training gaps (29% of organizations cite lack of skilled personnel), and integration complexity (28% cite difficulties embedding AI into existing CRM workflows). A fourth barrier: too many unprioritized intent signals overwhelm reps rather than guiding them. Phased rollouts starting with one high-ROI use case, such as lead scoring or prospecting research, reduce both the training burden and adoption friction.

Will AI replace sales reps?

No. The evidence points to augmentation, not replacement. 45% of sales teams have already adopted a hybrid AI+human SDR model where AI handles initial research and outreach while human reps focus on relationship-building and complex negotiations. AI handles the tasks that erode selling time so reps can spend more time on the activities that actually close deals. Gartner predicts that by 2028, 60% of B2B sales tasks will involve AI-powered conversational interfaces, but human judgment remains essential for strategic account management. Thomson Reuters quota attainment at 115% average monthly quota is a strong example of augmentation in practice: AI handling research and prioritization while reps focus on closing.

How can AI tools integrate with existing CRM and sales workflows?

The most effective AI tools embed directly into existing CRM workflows rather than requiring reps to log into a separate platform. GTM Workspace integrates with Salesforce and other major CRMs, surfacing AI-generated account briefs and next-best-action recommendations inside the tools reps already use. For teams that want to wire B2B intelligence into custom AI agents or existing tools, ZoomInfo's MCP integration connects verified contact, firmographic, and intent data to any AI agent without requiring a new interface.