Every sales leader is asking the same question right now: "Which AI tools should I actually buy?"
The vendor pitches are relentless. Every solution promises to"revolutionize" your sales process. But behind the marketing noise,which AI use cases actually deliver measurable business value for B2B sales teams?
Gartner® recently cut through the hype by evaluating 19 AI/ML use cases specifically for B2B sales. Their multidimensional analysis evaluated potential business impact and real-world implementation feasibility, giving sales leaders the data-driven roadmap they've been waiting for to de-risk AI investments.
Here's what they found, and what we think it means for your AI strategy.
Too many sales leaders are making AI decisions based on vendor demos and feature lists. Gartner took a different approach, scoring each use case across five critical dimensions.
For business value, they looked at revenue impact, operational efficiency gains,and buyer experience improvements. For implementation reality, they evaluated technical feasibility and internal organizational readiness.
The winning combination? Use cases that deliver medium-to-high value with medium-to-high feasibility. Gartner calls these "Likely Wins,” and they represent your best starting point for AI investments that will scale sales performance.
This framework matters because it forces you to think beyond the shiny object syndrome that's driving most AI purchases today. A use case might sound impressive in a demo, but if your organization lacks the data quality or internal readiness to implement it successfully, you're setting yourself up for failure.
Based on Gartner's analysis, three use cases stand out as clear winners for most B2B sales organizations:
This goes far beyond basic call transcription. Modern conversation intelligence platforms analyze sales interactions to uncover patterns that drive success: which questions convert prospects, which objections signal real buying intent,and which behaviors separate top performers from the rest.
The value is scaling knowledge and coaching. Instead of managers reviewing random call recordings, efficient conversation intelligence surfaces the moments that matter most for rep development and sales productivity. Numerous vendors offer ready-made solutions, but the differentiation lies in how deeply these platforms integrate with your existing sales workflows.
Manual forecasting is broken. Reps sandbag, managers guess, and executives make decisions based on incomplete information. Machine learning changes this by analyzing historical patterns, deal progression data, and external signals to predict outcomes more accurately than human intuition alone. Success depends heavily on data quality, specifically consistent pipeline updates and structured deal information. But when implemented correctly, AI-powered forecasting reduces bias and gives leadership the reliable information needed to shift limited sales resources to accelerate high-quality deals or resuscitate stalling ones.
Every sales leader has experienced the painful surprise of deals falling apart at the last minute. Deal risk analysis prevents this by identifying warning signals early: declining engagement, missing stakeholders, or stalled progression that indicates trouble ahead. The real value isn't just in predicting which deals might be at risk, but in giving reps specific actions they can take to address those risks before it's too late.
Here's where we think many AI evaluations and implementations go wrong: sales leaders treat each use case as a separate purchasing decision. They buy conversation intelligence from one vendor, forecasting from another, and deal risk analysis from a third.
This creates vendor sprawl, data silos, and user fatigue, in addition to exacerbating sales process bottlenecks. Your reps end up juggling multiple “AI agents” and platforms, your data gets fragmented across systems, and your total cost of ownership skyrockets.
We believe the smarter approach is thinking holistically. These three use cases all draw from similar data sources: your CRM, communication platforms, and interaction history.
Rather than deploying separate point solutions, select AI Agents that can address multiple use cases within a unified interface. An AI teammate doesn't just complete isolated tasks; it collaborates across the entire sales process,connecting insights from conversations to deal risk to forecasting accuracy.This integrated approach delivers better results while reducing complexity for your team. In short, look for AI teammates vs. one-off agents.
Based on the Gartner research, here's how we think sales leaders should approach AI investments:
Rather than deploying separate tools for each use case, find platforms that address multiple "Likely Wins" simultaneously. Look for AI teammates that can handle conversation intelligence, deal risk analysis, forecasting, and deal intelligence within a unified workflow.
This approach avoids vendor sprawl while ensuring your AI investment creates compound value across multiple use cases rather than isolated improvements. Wethink it also improves the likelihood of successful adoption, since end users have a lower activation cost using one new tool vs. multiple.
Focus your initial deployment on the use cases Gartner identifies as having the best combination of value and feasibility. Here’s what we recommend as key for each use case:
The key is ensuring your chosen AI sales tool can address multiple use cases while understanding how to sell, not just how to analyze data.
Gartner emphasizes the criticality of internal readiness, your team's ability and openness to incorporate AI insights into decision making. This is where many AI implementations fail.
Sales professionals are already overwhelmed with tools and processes. An AI teammate that integrates deeply into existing workflows and thinks with sellers (not just for them) creates a collaborative dynamic that drives AI adoption rather than resistance.
Avoid tool sprawl by resisting the temptation to deploy separate point solutions foreach use case. Also avoid jumping to "Calculated Risks" use cases like churn prediction or negotiation optimization until you've proven ROI with core selling activities.
Not every AI use case deserves your investment, but those outlined above represent likely opportunities to drive measurable business impact.
The key is approaching AI strategically rather than tactically. Think collaborator and teammate, not tool. Think unified interface, not army of point solutions. Think value delivery, not feature lists.
Your competition is making AI investments right now. The question isn't whether you should invest in AI for sales; it's whether you'll invest wisely.
Ready to dive deeper? Download the full Gartner® report "19 Artificial Intelligence Use Cases for B2B Sales" to access the complete scoring framework and detailed analysis of all use cases.
This research provides the strategic foundation you need to become an AI-first sales organization.
According to Gartner®, the best AI use cases for B2B sales include conversation intelligence, sales forecasting, deal risk analysis, price optimization, andsales activity intelligence. These are considered “Likely Wins” because they combine high business value with high feasibility for implementation.
Gartner®scored each of the 19 use cases based on two main criteria:
This scoring framework helps sales leaders prioritize the most practical and impactful AI investments.
Not always. Gartner® states: “Generative AI is not a perfect solution; it is often not the right fit for most AI use cases.” Many of the highest-value sales applications use traditional AI/ML models for pattern recognition, forecasting, and decision support to inform agenerative output.
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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