Artificial intelligence has firmly established its role within corporate decision-making, especially among those driving revenue growth.
Recent data from a leading revenue intelligence firm reveals that 70% of enterprise revenue leaders now depend on AI to guide their business strategies regularly. This marks a significant evolution from just two years ago, when AI was largely confined to pilot projects and individual productivity enhancements.
Analyzing over 7 million sales opportunities from more than 3,600 companies, alongside a survey of 3,000 revenue executives across the US, UK, Australia, and Germany, the research highlights a rapidly evolving landscape. Companies integrating AI deeply into their go-to-market approaches are 65% more likely to boost their win rates compared to those treating AI as a supplementary tool.
Amit Bendov, CEO and co-founder of Gong, emphasizes that AI is not replacing human judgment but augmenting it. “Decisions remain human-led, but AI provides critical support,” he explains. This partnership between human insight and AI acts as a “second opinion,” offering data-driven validation to traditional sales forecasting and strategic planning.
Addressing Slower Revenue Growth by Maximizing Sales Efficiency
The rise of AI in revenue teams coincides with a challenging economic backdrop. After a rebound in 2024, average annual revenue growth among surveyed firms slowed to 16% in 2025, a 3% drop from the previous year. Concurrently, sales representatives’ quota attainment declined from 52% to 46%.
Interestingly, this slowdown isn’t due to poorer performance on individual deals-win rates and deal cycles remained stable. Instead, sales reps are engaging with fewer opportunities, indicating operational inefficiencies that reduce selling time.
This shift has propelled productivity to the forefront of executive priorities. For the first time, enhancing the efficiency of existing sales teams is the top growth strategy for 2026, climbing from fourth place the year before.
Bendov summarizes the focus succinctly: “It’s about maximizing revenue output per dollar invested.” Supporting this, teams leveraging AI tools consistently generate 77% more revenue per salesperson, translating to a six-figure annual difference per rep.
From Automation to Strategic Intelligence: The Evolution of AI in Sales
AI adoption in sales has matured beyond simple automation tasks like call transcription, email drafting, and CRM updates. While these functions remain widespread, 2025 marked a pivotal shift toward using AI for strategic insights.
The number of US companies employing AI for forecasting and evaluating strategic initiatives surged by 50% year-over-year. Advanced applications-such as predicting deal outcomes, flagging at-risk accounts, and tailoring value propositions to buyer personas-are strongly linked to superior business results.
Firms in the top 5% for AI-driven commercial impact are two to four times more likely to utilize these sophisticated AI capabilities.
Bendov illustrates this with forecasting improvements: “Previously, forecasts relied heavily on human optimism-‘I think this deal will close’-leading to frequent misses. AI now provides an evidence-based second opinion, improving forecast accuracy by 10 to 15%.”
Specialized AI Tools Outperform Generic Platforms in Revenue Impact
The study highlights a critical distinction between general-purpose AI tools and revenue-specific AI solutions designed for sales workflows. Organizations using specialized AI platforms report 13% higher revenue growth and 85% greater commercial impact than those relying on generic tools like ChatGPT.
These domain-specific systems are also twice as likely to be deployed for forecasting and predictive analytics.
While general AI tools are widely used-estimated actual usage at work may approach 90% despite only 59% admitting it-this “shadow AI” usage can introduce security vulnerabilities and fragmented tech ecosystems, limiting enterprise-wide intelligence benefits.
AI’s Role in Transforming Sales Jobs: Enhancement Over Elimination
Contrary to fears of widespread job losses, the research paints a more balanced picture of AI’s impact on sales roles. Forty-three percent of revenue leaders expect AI to reshape jobs without reducing headcount, while 28% foresee some job cuts, and 21% anticipate AI creating new positions. Only 8% predict minimal change.
Bendov points to studies showing that sales reps spend up to 77% of their time on non-customer-facing tasks such as admin, research, and forecasting. “AI can eliminate much of this drudgery, not jobs,” he says. “By making reps fully productive instead of half productive, companies can significantly increase revenue without reducing staff.”
This shift is already leading to role consolidation. Previously, sales teams were fragmented into specialists handling lead qualification, appointment setting, closing, and onboarding-resulting in customers interacting with multiple representatives. AI now enables a single rep to manage many of these functions, improving buyer experience and operational efficiency.
At Gong, for example, AI-driven prospecting allows sellers to generate 80% of their own appointments.
US Leads AI Adoption in Revenue Operations, Outpacing Europe
The research uncovers a notable geographic disparity: 87% of US companies currently use AI in revenue functions, with another 9% planning adoption within a year. In contrast, only 70% of UK firms have adopted AI, with 22% planning near-term implementation-figures comparable to US adoption rates from 2024.
Bendov attributes this lag to a historical pattern of technology adoption delays between the US and Europe. While Europe has led in areas like mobile payments and messaging apps, AI adoption in revenue operations remains faster in the US.
Gong’s Decade-Long AI Expertise Sets It Apart from Tech Giants
As Gong experiences rapid growth in annual recurring revenue, it faces competition from enterprise leaders like Salesforce and Microsoft, who are integrating AI into their platforms.
Bendov highlights Gong’s unique advantage: a decade of AI development built on a three-layer architecture comprising a “revenue graph” aggregating diverse customer data, an intelligence layer combining large language models with proprietary smaller models, and workflow applications on top.
“Building this from scratch isn’t a simple feature-it’s ten years of work,” he notes.
Rather than viewing Salesforce and Microsoft as rivals, Gong sees them as collaborators, citing their participation in Gong’s recent user conference and the rise of multi-cloud platform (MCP) support that allows customers to integrate AI agents from multiple vendors.
Will AI Expand or Contract the Sales Profession?
The implications of AI’s impact on revenue teams extend to broader business functions traditionally reliant on human relationships.
Bendov envisions AI as a growth catalyst rather than a threat. Drawing a parallel to digital photography, he explains that while camera manufacturers struggled, the ease of smartphone photography led to an explosion in photo-taking.
“If AI simplifies selling, the industry could multiply in size, creating diverse opportunities for people with varying skills and locations,” he says.
Reflecting on Gong’s early days in 2015, when AI was a hard sell to business leaders and the technology was nascent, Bendov recalls, “We had to almost hide AI because it was intimidating.”
Today, with 70% of revenue leaders trusting AI to guide their business, the technology has transformed from a hidden asset to an indispensable tool no company can afford to overlook.
