The promise of AI in B2B sales is arguably oversaturating the market, from predictive analytics to intelligent automation. For sales leaders navigating an increasingly crowded landscape of AI solutions, the real challenge isn't whether to adopt AI. It's knowing which AI use cases will actually move the needle for your organization, without creating tool sprawl.
Recent research from Gartner® provides crucial clarity on this question. Their report "19 Artificial Intelligence Use Cases for B2B Sales" evaluates AI applications across two critical dimensions: business value and implementation feasibility. The analysis reveals three use cases that stand out as clear priorities for sales leaders looking to maximize their AI investment.
These aren't experimental or speculative technologies. They're proven, practical applications that deliver measurable impact with manageable implementation risk.
Gartner's evaluation framework assesses AI use cases across five key dimensions, ultimately categorizing them into three groups: Likely Wins, Calculated Risks, and Marginal Gains.
The researchers recommend "use cases combining medium-to-high feasibility with medium-to-high value, making them wins in most circumstances."
The framework evaluates each use case on:
The three use cases highlighted here all earned Gartner's "Likely Wins"designation, meaning they offer the optimal combination of high business impact and implementation feasibility.
Gartner defines conversation intelligence as a solution that "analyzes calls,emails or chat transcripts to uncover best practices, improve sales performance and coaching, and reduce manual note-taking."
We believe the real value of conversation intelligence extends beyond simply recording what was said. Advanced implementations can analyze conversation patterns to recommend specific coaching actions for individual reps, identify which messaging resonates with different buyer personas, and surface deal risks before they become problems. This creates a systematic approach to scaling enablement—transforming successful rep behaviors into repeatable guidance that can accelerate new hire ramp time and elevate underperforming team members.
The strategic advantage becomes clear when conversation intelligence acts as a deal strategy partner. By analyzing stakeholder engagement patterns, competitive mentions, and buyer concerns across similar deals, it can recommend next-bestactions, suggest optimal follow-up timing, and help reps navigate complexbuying committees with greater precision. This transforms reactive coaching into proactive deal guidance.
The feasibility factor is equally compelling. Conversation intelligence solutions are widely available from established vendors and often integrate seamlessly with existing sales technology stacks. Many organizations already have the foundational infrastructure in place, making adoption relatively straightforward.
According to Gartner's research, "Sales forecasting uses machine learning to predict future sales based on historical data, both structured and unstructured."
Thebusiness case for AI-powered forecasting extends far beyond number-crunching.Tools with advanced forecasting capabilities can act as strategic partners,surfacing deal-level insights that inform immediate sales actions. Rather than just predicting outcomes, intelligent forecasting can identify which deals need intervention, recommend specific actions or resources to improve close probability, and flag pipeline risks before they impact quarterly results.
Thistransforms forecasting from a backward-looking reporting exercise into forward-looking deal strategy. Imagine AI that not only predicts a deal will slip but explains why, analyzing stakeholder engagement patterns, competitive dynamics, and buying signals to recommend precise next steps. Then imagine this AI teammate can proactively alert managers when deals require coaching intervention, suggest optimal timing for executive engagement, and identify which opportunities deserve additional resources.
The feasibility advantages are significant. Forecasting capabilities are often pre-integrated with existing CRM and Sales Force Automation (SFA) platforms.The underlying data requirements, deal stages, historical close rates, pipelinevelocity, are typically already captured in most sales organizations' existingsystems.
However,the real competitive advantage emerges when forecasting AI operates continuously and autonomously across your entire pipeline, learning from every interaction and deal outcome to improve its recommendations. This creates a self-improving system that gets smarter with each quarter, transforming sales forecasting from administrative overhead into strategic advantage.
Gartner describes deal risk analysis as a system that "identifies potential deal obstacles or red flags so teams can address issues proactively."
This use case addresses one of sales leaders' most persistent challenges: late-stage deal surprises. But advanced deal risk systems go beyond simple red flag alerts. They can automatically generate comprehensive deal reviews that synthesize stakeholder sentiment, competitive positioning, and progression patterns to provide actionable intelligence. Instead of waiting for weekly pipeline calls, AI can continuously monitor deal health and proactively surface specific recommendations, like when to engage executives, which stakeholders need attention, or what competitive threats require immediate response.
The transformative value lies in systematic deal coaching at scale. These systems can analyze successful deal patterns to recommend proven strategies for similar situations, identify when deals are deviating from winning playbooks, and suggest course corrections before problems compound. This creates a continuous feedback loop where every deal outcome improves the system's ability to guide future opportunities.
From a feasibility standpoint, deal risk analysis works well with the structured data most organizations already capture in their CRM, deal stage progression,engagement metrics, stakeholder mapping.
However, the most sophisticated implementations integrate conversation analysis, email sentiment tracking, and competitive intelligence in a single AI interface to provide comprehensive deal intelligence that transforms reactive management into proactive deal orchestration.
These three applications earned Gartner's "Likely Wins" designation because they deliver strong business impact with manageable implementation risk. They don't require revolutionary changes to existing processes or massive technology investments. Instead, they enhance and optimize the work sales teams already do.
For sales leaders evaluating investments in AI for sales, these use cases representthe sweet spot: proven value, available technology, and realisticimplementation requirements. They're ideal starting points for organizationsbuilding their AI capabilities, providing early wins that can build momentum for more sophisticated applications down the road.
The AI transformation of B2B sales is no longer a question of "if" but "how" and "when." The organizations that succeed will be those that make strategic, evidence-based decisions about where to focus their AI investments.
These three use cases (conversation intelligence, sales forecasting, and deal risk analysis) aren't speculative or experimental. They're proven, practical applications that can deliver measurable results for sales organizations ready to embrace AI-powered selling.
Thequestion isn't whether AI will reshape your sales process. The question is whether you'll be proactive in guiding that transformation or reactive to competitive pressure.
Ready to dive deeper? Download the complimentary Gartner® research "19 Artificial Intelligence Use Cases for B2B Sales" to explore all 19 use cases and see how they compare across value and feasibility dimensions. We believe this comprehensive analysis will help you build a strategic roadmap for AI adoption that aligns with your sales organization's specific challenges and objectives.
Gartner, 19 Artificial Intelligence Use Cases for B2B Sales, Adnan Zijadic, Melissa Hilbert, Guy Wood, Ilona Hansen, Sandhya Mahadevan, Mark Lewis, 28 March 2025.
GARTNERis a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission.All rights reserved.
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