Gong study: Sales teams using AI generate 77% more revenue per rep
Sales is moving fast toward evidence over opinion. A new Gong study shows seven in ten revenue leaders now trust AI to inform decisions, and teams where reps regularly use AI generate 77% more revenue per seller.
The shift isn't about replacing people. As Gong CEO Amit Bendov puts it: "Humans are making the decision, but they're largely assisted." Think of AI as a second opinion that catches what gut feel misses.
The numbers that matter
- Trust: Nearly 70% of revenue leaders trust AI insights; only 10% are skeptical.
- Performance: AI-embedded orgs are 65% more likely to increase win rates.
- Productivity: Reps using AI generate 77% more revenue per rep-a six-figure gap per seller.
- Market context: Average revenue growth slowed to 16% in 2025; quota attainment fell from 52% to 46%.
Win rates and deal durations held steady. The shortfall? Fewer opportunities per rep. Operational drag is stealing selling time, so productivity has become the top growth strategy for 2026.
From automation to intelligence
Last year, teams leaned on AI for admin work-call notes, CRM updates, email drafts. Useful, but basic. 2025 marked a jump to strategy: forecasting, predicting deal outcomes, spotting at-risk accounts, and understanding which messages resonate with which buyers.
Gong reports U.S. companies using AI for forecasting and strategic measurement jumped 50% year over year. Bendov says evidence-based forecasting alone improves accuracy by 10-15% because it reduces sentiment and overconfidence.
Why specialized revenue AI beats general-purpose tools
Teams using revenue-specific AI outperformed those relying on generic tools with 13% higher revenue growth and 85% greater commercial impact. These teams were also twice as likely to deploy AI for forecasting and predictive modeling.
General-purpose tools are common, but they create blind spots-security risks, siloed workflows, and uneven quality. Specialized revenue AI plugs into your sales motion, not just your inbox.
Jobs aren't disappearing-they're consolidating
Most leaders expect AI to reshape roles, not eliminate them. Only 28% predict job cuts. The most common outlook: transformation without headcount reduction.
Bendov points to a practical win: Forrester data suggests sellers spend 77% of time away from customers. "AI can eliminate, ideally, all 77 percent-all the drudgery work," he said. That unlocks a cleaner buyer experience, too. Instead of five handoffs across the funnel, one rep can now handle most of the motion with AI handling research, prep, and follow-ups.
At Gong, sellers generate 80% of their own appointments because AI does the prospecting legwork.
Adoption gap: U.S. vs. Europe
In the U.S., 87% of companies already use AI in revenue operations, with another 9% planning adoption within a year. The U.K. is 12-18 months behind: 70% use AI today, 22% plan to adopt soon. The lag isn't permanent, but for now, American teams have a compounding advantage.
The stack matters
Gong says its edge comes from a decade of AI work: a revenue graph stitching together CRM, email, calls, video, and web signals; an intelligence layer combining large language models with dozens of smaller specialty models; and workflow apps on top. As Bendov notes, this isn't a feature-it's ten years of infrastructure.
The bigger trend: interoperability. With agent frameworks and consumption-based pricing, companies can mix AI agents from different vendors instead of going all-in on one platform.
What sales leaders should do next
30-day audit
- Map your selling time: quantify admin hours by role; identify the top three time sinks.
- Inventory your AI usage: general-purpose tools vs. revenue-specific tools; document "shadow AI" risks.
- Assess data readiness: call recordings, email threads, meeting transcripts, CRM hygiene, opportunity stages, reason codes.
60-day deployment
- Start with two high-impact use cases:
- Evidence-based forecasting and pipeline health scoring.
- Automated follow-ups and next-step generation from call/email transcripts.
- Pick specialized tools for revenue workflows; avoid patching together generic assistants for core forecasting.
- Create AI operating guardrails: data access, privacy, prompt libraries, approved tools, and review steps.
90-day scale
- Expand to buyer-intent insights: which messages land by segment, risk alerts on stalled deals, churn signals in post-sale calls.
- Consolidate roles where buyer experience improves (fewer handoffs, more context per interaction).
- Build a weekly "AI stand-up": share wins, refine prompts, promote what measurably works.
Metrics that prove it's working
- Revenue per rep (primary)
- Forecast accuracy and bias (over/under)
- Opportunities per rep and selling time reclaimed
- Stage conversion rates and no-decision rate
- Time-to-first-meeting and cycle time by segment
Practical tips for implementation
- Give AI the full picture: connect CRM, calendar, call recordings, and email so recommendations aren't guessing.
- Standardize notes and next steps after every call. Let AI draft; managers review for two weeks, then loosen the reins.
- Score every deal weekly with human + AI confidence. Track gaps and fix the leading indicators, not just the lagging ones.
- Create a "no heroics" rule: if a task is repeatable and low judgment, automate it and move on.
Bottom line
AI is now a sales advantage you can measure. The teams that treat it as a strategic layer-not a keyboard shortcut-are pulling ahead on revenue per rep, forecast accuracy, and customer experience.
Use AI as your second opinion. Keep humans in the loop. And move fast where the data is already clear.
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