Introduction
In the past few years, artificial intelligence has moved from the realm of speculative research to a practical, revenue‑driving tool for sales organizations worldwide. A recent Gong study, which analyzed 7.1 million sales opportunities across more than 3,600 companies and surveyed over 3,000 revenue leaders, confirms that AI is no longer a niche experiment but a core component of modern go‑to‑market strategies. The findings reveal a dramatic shift: seven out of ten enterprise revenue leaders now rely on AI to inform their decisions, and teams that embed AI into their processes generate 77 % more revenue per representative than those that do not. This blog post explores the implications of those numbers, the evolving nature of AI adoption in sales, and what it means for the future of the profession.
The study’s context is critical. While the global economy rebounded in 2024, the average annual revenue growth of surveyed companies slowed to 16 % in 2025, a three‑percentage‑point decline from the previous year. Sales quota attainment fell from 52 % to 46 %, not because individual deals were harder to close, but because representatives were working fewer opportunities. In a world where operational inefficiencies are eating into selling time, AI has emerged as a powerful productivity lever.
The narrative that AI will replace human judgment is misleading. According to Gong’s co‑founder Amit Bendov, AI functions as a “second opinion” – a data‑driven check on the intuition that has traditionally governed sales forecasting and strategy. This distinction is crucial because it frames AI as an enabler rather than a replacement, a perspective that resonates with the majority of sales leaders who expect AI to transform jobs without reducing headcount.
Main Content
AI as a Second Opinion
The Gong study highlights that AI is not about delegating decisions to machines; it is about augmenting human decision‑making. Bendov emphasizes that “humans are making the decision, but they’re largely assisted.” In practice, this means that sales managers and reps use AI to validate their gut feelings, to quantify the probability of closing a deal, and to identify which accounts are most likely to convert. By turning subjective sentiment into objective evidence, AI improves forecasting accuracy by 10–15 %, a margin that can translate into millions of dollars in incremental revenue.
This approach also mitigates the risk of overconfidence that often plagues sales forecasting. When teams rely solely on human judgment, they may over‑estimate the likelihood of a deal based on a single positive interaction or a vague sense of buyer enthusiasm. AI, by contrast, aggregates signals from calls, emails, CRM updates, and even web activity to produce a probability score that is grounded in data. The result is a more disciplined, data‑driven culture that reduces the variance between expected and actual revenue.
Productivity Pressure in a Slowing Market
The slowdown in revenue growth and quota attainment underscores a broader industry challenge: how to extract more value from existing sales talent. The Gong study shows that teams using AI generate 77 % more revenue per representative, a gap that represents a six‑figure difference per salesperson annually. This figure is not a theoretical benefit; it reflects real, measurable gains that have already been realized by companies that have embraced AI.
The underlying driver is the elimination of “drudgery” – the 77 % of a sales rep’s time that is spent on non‑customer‑facing tasks such as administrative work, research, and forecast updates. Forrester research cited by Bendov indicates that AI can automate these tasks, freeing reps to focus on high‑value activities. When a rep spends less time on paperwork and more time engaging with prospects, the probability of closing deals rises, and the overall productivity of the team improves.
From Automation to Intelligence
In 2024, most revenue teams used AI for basic automation: transcribing calls, drafting emails, and updating CRM records. While these use cases remain valuable, the 2025 data shows a clear shift toward “intelligence” – applications that use AI to predict outcomes, identify at‑risk accounts, and measure the resonance of value propositions with different buyer personas.
The study reports a 50 % year‑over‑year increase in U.S. companies using AI for forecasting and measuring strategic initiatives. These advanced applications correlate with dramatically better results. Companies in the 95th percentile of commercial impact from AI were two to four times more likely to deploy these strategic use cases. In other words, the most successful organizations are not just using AI; they are integrating it into the core of their decision‑making processes.
Revenue‑Specific AI vs General‑Purpose Tools
One of the study’s most provocative findings concerns the type of AI that delivers results. Teams that use revenue‑specific AI solutions – tools built explicitly for sales workflows – reported 13 % higher revenue growth and 85 % greater commercial impact than those relying on generic platforms like ChatGPT. These specialized systems were also twice as likely to be deployed for forecasting and predictive modeling.
The distinction is significant because general‑purpose AI, while more prevalent, often creates a “blind spot” for organizations. When employees adopt consumer AI tools without company oversight, security risks arise and fragmented technology stacks undermine the potential for organization‑wide intelligence. In contrast, revenue‑specific AI platforms can be tightly integrated with a company’s CRM, email, and call systems, ensuring that data flows seamlessly and that insights are actionable.
Impact on Jobs and Roles
The question of whether AI will eliminate jobs is a perennial concern. The Gong study offers a nuanced view: 43 % of respondents expect AI to transform jobs without reducing headcount, 28 % anticipate job eliminations, and 21 % foresee AI creating new roles. Only 8 % predict minimal impact.
Bendov frames the opportunity in terms of reclaiming lost time. He cites Forrester research indicating that 77 % of a sales representative’s time is spent on non‑customer tasks. By automating these tasks, AI can eliminate the drudgery that currently limits productivity. Rather than reducing headcount, AI can enable a leaner, more efficient organization where each rep can focus on high‑impact activities.
The study also highlights a trend toward role consolidation. Over the past decade, sales organizations have splintered into hyper‑specialized functions – lead qualification, appointment setting, closing, onboarding. This fragmentation can frustrate customers, who may interact with five or six different people during the buying journey. AI can enable a single rep to handle multiple stages of the process, improving the buyer experience and reducing operational friction.
Geographic Adoption Gap
The study reveals a notable divide in AI adoption between the United States and Europe. While 87 % of U.S. companies use AI in revenue operations, only 70 % of UK companies do so, with a 12‑18‑month lag. This pattern reflects a broader historical tendency for enterprise technology trends to cross the Atlantic with a delay. However, the gap is not permanent; Europe sometimes leads on specific technologies, such as mobile payments and messaging apps.
Gong’s Competitive Edge
Gong’s decade of AI development gives it a substantial advantage over larger competitors like Salesforce and Microsoft. The company’s architecture comprises a revenue graph that aggregates customer data from multiple sources, an intelligence layer that combines large language models with proprietary small language models, and workflow applications built on top. Bendov argues that building such a system would require at least ten years of development, creating a significant barrier to entry.
Rather than viewing Salesforce and Microsoft as threats, Bendov sees them as partners. The rise of Model Context Protocol (MCP) support and consumption‑based pricing models means customers can mix AI agents from multiple vendors, allowing for greater flexibility and innovation.
Future of Sales Professions
The implications of AI extend beyond sales departments. If AI can transform revenue operations – a traditionally relationship‑driven, human‑centric function – it raises questions about which other business processes might be next. Bendov suggests that AI could expand the sales profession rather than hollow it out. He draws an analogy to digital photography: while camera manufacturers suffered, the total number of photos taken exploded once smartphones made photography effortless. Similarly, AI could make selling as simple as taking a photo, potentially creating ten times more jobs than we have now.
Conclusion
The Gong study paints a compelling picture of an industry in transition. AI is no longer a novelty; it is a strategic imperative that can unlock significant revenue growth, improve forecasting accuracy, and free sales talent to focus on high‑value activities. The data shows that companies that embed AI into their core go‑to‑market strategies are 65 % more likely to increase win rates and generate 77 % more revenue per rep. Moreover, the most successful organizations are those that move beyond basic automation to deploy AI for strategic decision‑making and use revenue‑specific tools that integrate seamlessly with their existing technology stack.
The human element remains central. AI serves as a second opinion, augmenting intuition with data‑driven insights. It is reshaping roles, consolidating responsibilities, and potentially expanding the sales profession rather than shrinking it. As AI adoption accelerates, especially in the United States, organizations that invest in specialized, integrated AI solutions will be best positioned to thrive in a market where productivity is the new growth engine.
Call to Action
If you’re a revenue leader looking to stay ahead of the curve, it’s time to evaluate how AI is currently embedded in your sales processes. Start by identifying the most time‑consuming tasks that could be automated, and assess whether your organization is using revenue‑specific AI tools that can deliver the same level of insight as generic platforms. Engage with vendors that offer a full stack – from data aggregation to predictive modeling – and consider how AI can serve as a second opinion in your decision‑making. By embracing AI strategically, you can unlock higher revenue per rep, improve forecast accuracy, and create a more efficient, customer‑centric sales organization. Reach out today to explore how AI can transform your revenue operations and position your team for sustained growth.