Top AI Use Cases in Sales: From Pipeline Generation to Forecasting

AI is reshaping how revenue teams operate across every stage of the sales cycle. From automated pipeline generation to AI-driven forecasting, the use cases show how teams are moving from experimentation to measurable results—without adding headcount.

These insights come directly from our expert-led sessions at Unleash 2026, Outreach's annual revenue conference, where sales leaders, RevOps practitioners, and customers shared how AI is being applied across their go-to-market teams today.

The question most revenue leaders are wrestling with is not whether AI has a role in sales. It is figuring out where to start, what actually works, and how to move from pilot to production. Unleash 2026 offered some clear answers. What follows is a breakdown of the most impactful AI use cases discussed, organized by function, grounded in real customer examples, and focused on outcomes.

Why revenue teams are prioritizing AI in sales right now

Revenue leaders are under pressure on two fronts simultaneously: pipeline coverage is tighter, and headcount budgets are constrained. AI directly addresses both.

What is an AI use case in sales? An AI use case in sales refers to a specific workflow, task, or decision where artificial intelligence, including machine learning, natural language processing, or autonomous agents, helps revenue teams work faster, more accurately, or at greater scale.

What separates the teams gaining ground is not whether they are using AI. It is whether they have moved from experimentation to production, deploying AI in actual revenue workflows, not just pilots.

By the numbers

• 45% open rates on AI-automated win-back sequences (SolarWinds) ‍
• 335 meetings covered by AI coach cards in a matter of weeks — 335 hours of manager time saved ‍
• 81% AI forecast accuracy out of the box with three periods of data ‍
• 99.1% forecast accuracy achieved by Outreach's own revenue team by Q3–Q4 2024

AI use cases by function at a glance

AI for pipeline generation: Reaching more accounts without adding headcount

One of the most consistent themes heard across Unleash was the challenge of pipeline coverage. Every team needs more pipeline. Most teams do not have the capacity to go after it manually.

Outreach Chief Product Officer Nithya Lakshmanan put it plainly during the product keynote: "It is getting harder to build pipeline. Buyers are swamped. They are ignoring your so-called personalized outreach. The only way to stand out is with value."

How AI agents identify and engage in-market buyers

AI agents address this by monitoring intent signals across your ideal customer profile, engaging buyers when they are in market, and drafting relevant outreach, whether an email or a call script, on your behalf. That motion can run fully autonomously or with human review, depending on your team's preference.

SolarWinds example: Automating win-back with 45% open rates

SolarWinds shared a concrete example. A year ago, the team was using Outreach primarily as a sales email tool. By Unleash 2026, they had built a fully automated win-back motion for closed-lost opportunities; a segment sellers were not consistently following up with.

Hamish Hill, Head of GTM Technology at SolarWinds, explained: "We have been able to use an agent to identify and do account research, determine which accounts we should go back after, personalize the message, and then automate that message."

The result: open rates around 45%, nearly double the performance of standard outbound sequences.

The broader takeaway: start with the accounts humans are not touching. Underserved accounts, long-tail territories, and closed-lost opportunities are strong first use cases because you can demonstrate value without disrupting existing workflows.

AI for sales coaching: Moving from gut feel to data-driven development

Coaching is widely accepted as the single biggest lever a sales leader has. It is also the first thing that gets cut when calendars fill up.

In the session Uplevel Team Performance with AI Coaching, Duncan Meyers, Lead Professional Consultant at Outreach, led the audience through a useful reframe. Rather than jumping straight to scorecards and call reviews, the more effective approach is to start with conversational data.

Before you decide what to coach, you need to understand what is actually happening across your team's conversations. What do top performers do consistently? Where do deals stall? What objections come up most often?

What are AI coach cards and how do they work?

Once those patterns surface, coach cards can reinforce the behaviors that matter, and AI can score calls automatically so managers are not spending hours in manual review.

One Outreach team covered 335 meetings in a matter of weeks using AI coach cards. That represents 335 hours of sales manager coaching time saved.

How to get started: Three coach card categories

For teams just getting started, three coach card categories are recommended:

• Generic sales skills: rapport building, call structure ‍‍
• ‍Discovery and methodology: ensuring reps are asking the right questions throughout the sales process, not just at one stage ‍
• Specific talk tracks: messaging tied to products or competitors

A Siemens customer attending the session described building a call library for new employee onboarding. The challenge was categorizing which calls were good, which were not, and what made the difference. AI coach cards address exactly that — surfacing the moments that drove a score up or down so the feedback is specific and actionable.

AI for deal execution: Smarter opportunity management at scale

Managing a large book of deals is difficult. Getting accurate, up-to-date context on every opportunity is even harder. AI agents change that by doing the work in the background.

How deal agents automate CRM updates and flag at-risk opportunities

Deal agents listen to conversations and automatically update opportunity fields, methodology, competitive mentions, win reasons, and more, without requiring the rep to manually log the information.

In the session Forecasting Excellence for Predictable Revenue, Logan Rusconi, Solutions Consulting Team Lead at Outreach, talked about how valuable this is. If a rep is not covering the right topics in a late-stage deal, the agent flags it. If a competitor comes up, the agent captures the context and surfaces it in the deal view alongside the source conversation.

"Gone are the days of having a pick list dropdown and trying to figure out how to re-engage closed-lost opportunities," Logan said. AI handles the capture automatically so reps can focus on the conversation, not the admin.

For sales leaders reviewing deals, this translates to richer context without relying on reps to self-report. The executive snapshot in Outreach gives leaders a summary of everything that has happened across an opportunity, grounded in actual conversations rather than rep perception.

What is Outreach Omni and how does it help sales leaders?

Outreach Omni extends this further. Leaders can ask conversational questions across their entire book of business, for example: "Show me the deals forecasted to close this month that are at risk of slipping." What you get is instant, deal-level insight. From there, you can drill into a specific opportunity, review the competitive risk, and ask Omni to draft an email addressing those concerns directly.

AI for sales forecasting: Replacing guesswork with grounded projections

Forecasting is job security for revenue leaders and for the teams that support them. Yet most sales leaders do not have a defined forecasting process.

How AI forecasting accuracy improves over time

AI-driven forecasting in Outreach addresses this by combining historical performance, deal health signals, conversation intelligence, and seasonality into an AI-projected finish. Rather than hiding behind a black box, the platform shows the math: what has already been won, what is weighted in the pipeline, and what is likely to open and close within the period based on velocity and deal momentum.

Out of the box, the AI projection achieves roughly 81% accuracy with three periods of data. Over time, the model learns. Logan shared that Outreach's own internal forecasting reached 99.1% accuracy by Q3 and Q4 of 2024 after several years on the platform.

What is a scenario planner and why does it matter?

For RevOps leaders, the scenario planner adds another layer. You can model "what if" scenarios; an improvement in win rates at the discovery stage, a new marketing campaign bringing in additional pipeline, or a large deal pulling forward from next quarter, and see how those inputs affect your projected range. The platform runs 10,000 simulations and returns a bear, base, and fair case value.

AI for revenue insights: A real-time view across your business

Beyond individual use cases, AI is changing how revenue leaders consume information. Rather than piecing together reports across multiple tools, leaders can get a daily, AI-curated view of what is happening across their pipeline.

Nithya described it as "a newspaper curated by AI for your CRO." The pipeline movement graph shows where revenue has been added, progressed, lost, or pushed. The AI projection updates in real time. Coverage metrics, win-loss trends, and pacing against historical performance are all visible in one place.

Topics Explorer adds a layer that traditional reporting cannot provide. By analyzing trends across thousands of conversations, it surfaces which competitors are coming up more frequently, what objections are emerging, and how those patterns correlate with win rates.

For teams launching new products or responding to competitive pressure, this type of visibility can be the difference between reacting to a problem and getting ahead of it.

Where to start with AI in your revenue organization

Across every Unleash session, the consistent advice was the same: start somewhere, build trust, and expand from there.

The strongest starting points are workflows where humans are not currently active:

• Closed-lost re-engagement: Use Outreach's AI Agents to identify, research, and re-engage closed-lost accounts with personalized outreach, running automatically in the background.
• ‍Underserved accounts and long-tail territories: Reach accounts your team doesn't have the capacity to work manually, without disrupting active sequences.
• ‍Post-call follow-ups: Automate next steps, CRM updates, and summary emails after every conversation so reps stay focused on selling.

From there, teams expand to AI coaching, deal execution, and forecasting; building trust in the platform at each stage before moving to the next.

Frequently Asked Questions About AI Use Cases in Sales

What are the top AI use cases in sales?

The top AI use cases in sales include pipeline generation, sales coaching, deal execution, forecasting, and revenue insights. AI agents can automate prospecting, score calls, update CRM fields, and generate AI-projected forecasts; helping revenue teams improve productivity and pipeline quality without adding headcount.

How does AI improve sales forecasting accuracy?

AI sales forecasting combines historical win rates, deal health signals, conversation intelligence, and seasonality to generate an AI-projected finish. Outreach's forecasting model achieves approximately 81% accuracy out of the box with three periods of data, improving over time as the model learns from your team's patterns. Outreach's own revenue team reached 99.1% accuracy by Q3–Q4 2024.

What is an AI agent in sales?

An AI agent in sales is an autonomous or semi-autonomous system that monitors intent signals, researches accounts, personalizes outreach, updates CRM records, and surfaces deal risks, without requiring manual input from a seller. AI agents run in the background to handle repetitive tasks and flag the right actions at the right time.

What is agentic AI for revenue teams?

Agentic AI for revenue teams refers to AI that doesn't just surface recommendations; it takes action. Rather than waiting for a seller to log a call or update a field, agentic AI completes those tasks automatically, monitors deals for risk, engages buyers when they are in market, and delivers pipeline insights in real time. Outreach is purpose-built as an Agentic AI Platform for Revenue Teams.

Where should revenue teams start with AI in sales?

The strongest starting points are workflows where humans are not currently active: closed-lost re-engagement, underserved accounts, long-tail territories, and post-call follow-ups. These allow teams to demonstrate AI value without disrupting existing workflows.

How long does it take to see results from AI in sales?

SolarWinds built a fully automated win-back motion and saw open rates of approximately 45% — nearly double standard outbound performance. For coaching, one Outreach team used AI coach cards to cover 335 meetings in a matter of weeks, saving hundreds of hours of manager review time. Results vary by use case but teams typically see measurable impact within one to two quarters of deployment.