AI revenue in action: Real use cases driving results

Co-authored by: Emily Johnson, Senior Product Marketing Manager & Colette Shaw, Director of Customer Experience

It’s no surprise. AI is transforming how sales and RevOps teams work, by shifting from manual, repetitive outreach to automation that is both strategic and scalable. But the change can feel overwhelming without some guidance. That’s why we took the stage at Unleash 2025 to share how Outreach’s AI Revenue Agents are already delivering real results across our go-to-market (GTM) motion.  

Rather than focus on hypotheticals, we walked through real-world use cases we’ve implemented internally with our AI Agents, showing how AI is helping our teams streamline workflows, personalize at scale, and move with greater precision.  

Let's be clear: This isn’t about replacing people with bots—it’s about how automation can free up reps to focus on the work that matters most. Below, we’re breaking down the key insights and strategies we shared in our session, “AI Revenue in Action,” so your team can see what’s working and start putting AI into action today. Let’s get into it.  

A case for real-time AI in sales 

We didn’t want to talk theory. We wanted to show real revenue workflows powered by AI. So we structured our session around how we’re actively deploying Outreach AI Revenue Agents across key parts of the go-to-market motion. 

At a high level, AI Revenue Agents can be used in three overarching ways: 

• Revenue Automation: To surface high-intent accounts and automate next-best actions 

• Data Enrichment: To enhance prospect and account data using first- and third-party insights 

• AI-Personalized Content: To generate relevant messaging tailored to each persona and stage 

But it’s in the real-world execution where these agents really shine. Here’s how we’re putting them to work across five high-impact use cases: 

• Lead Follow-Up: Automatically following up with MQLs using messaging that’s tailored to their persona and lead source. 

• Outbound Prospecting: Sourcing new and existing contacts within our ICP and enrolling them in outbound sequences personalized with enriched data. 

• Retain & Expand: Running targeted plays with current customers based on signals like NPS survey responses. 

• Event Promotion: Driving attendance by enrolling new and existing contacts into sequences that highlight sessions most relevant to their role. 

• Renewal Management: Getting ahead of renewal risks by identifying new above-the-line contacts and proactively reaching out before the renewal period. 

It’s worth nothing that AI-personalized content has been most successful when combined with two key capabilities: data enrichment and intelligent prospecting. On their own, AI-generated messages can sound robotic. But with thoughtful prompt design, tested sequences, and the right data signals, AI can deliver surprisingly strategic and relevant outreach to the right accounts at the right time. 

One early win? When promoting Unleash, we used an AI Agent to tailor email invites by persona and conference track. The result? Registrations doubled the week we launched the campaign. 

For a team laser-focused on customer engagement and GTM optimization, this was our signal. AI works when it’s part of your daily workflow, not some separate initiative. 

3 High-impact AI use cases in sales 

One of the biggest AI use cases in sales is automating an organization’s sales follow-up, which is a task that, for many teams, often slips through the cracks. It’s time-consuming, urgent, and can be stressful. We saw this firsthand: our reps were struggling to keep pace with MQLs, and valuable leads were slipping through the cracks. 

So, we consolidated our scattered snippets and sequences into one automated follow-up flow. Now, leads get contacted faster, and our reps don’t have to lift a finger for the first touch.  

Here are three more ways we’ve used AI to improve sales engagement and RevOps innovation: 

1. Outbound prospecting 

For outbound, we targeted current and new accounts with no open opportunities but signs of competitor usage: using AI Revenue Agents to find the right contacts and initiate outreach. 

AI Revenue Agents source the right contacts and enroll them in a thoughtfully designed sequence, blending 3 automated emails with calls and LinkedIn touches. AI prompts are constructed with clear intent—incorporating persona, product value, and real-time signals—so that every message is grounded in context.  

Each AI-generated message is guided by a four-part prompt structure: 

• Detailed context about why it is reaching out and who it is reaching out to – i.e. the recipient’s role, company, etc.  

• Core GTM messaging (in this case, platform benefits like time savings and improved accuracy) 

• Guidelines with clear do’s and don’ts (such as “use a 7th grade reading level” and “avoid corporate suffixes”) 

• Signals like recent product launches or department size changes 

We started with reps reviewing every output. But after enough feedback and refinement, we switched to full automation because we were confident in the quality. 

2. Retain customers  

Another big win was automating NPS follow-ups. Responding to survey feedback—especially negative—can be tricky and time-consuming. We just uploaded our customers’ comments via CSV to Outreach, and it triggered an email sequence on behalf of our account executives. Everything happened for our reps, all they had to do was follow up with whoever booked a meeting. 

The best part? AI wrote tactful responses that felt natural and nuanced—even for detractors. One response was better than what we could’ve written ourselves. It removed the emotion, while still being empathetic. 

3. Renewal management  

For upsell and renewal outreach, we identified accounts with over 90% seat utilization but no upcoming renewals or expansions in motion. Prime upsell territory. 

Since these are active customers, we usually kick things off with a personal email from a rep, then let the AI follow up with timely, targeted nudges. 

We felt this was most appropriate because we’re talking to current customers. Reps may have gotten off a call with a customer yesterday and we don’t want to send them an automated email if they’ve just spoken to each other. 

Why these use cases matter for GTM teams 

AI is a buzzy topic, but buzz doesn’t help reps hit quota. That’s why we shared use cases: to make AI feel practical, not theoretical. 

When you remove manual effort and eliminate overdue tasks, reps stay focused on what matters: high-intent conversations. It’s not about perfection. It’s about scale and precision. 

AI performance doesn’t improve in a vacuum, it gets better with testing. It’s best to clone successful content to benchmark AI versus human-written sequences, running A/B prompt tests, and even giving the AI some creative leeway.  

We’ve actually found it works better when you’re not saying, do exactly this/say this. The more specific you get, the more it copies your words verbatim. It needs some room to breathe. 

That creative breathing room helps us uncover which prompts and signals generate the best engagement. You might move from A/B testing email copy to A/B testing prompts. Continue to test because then you’ll see what prompt or personalization is landing better. 

KPIs for AI-generated GTM content 

So, what metrics should you measure to understand if your personalized content strategy is working? Each sequence is usually tied to a specific goal, whether that’s event registrations for the Unleash conference or generating replies to outreach—even if it’s negative.  

Even “objection” replies are wins. They move conversations forward and give reps useful data. 

Here are a few key metrics we look at: 

• Overdue tasks  

• Sequence scores  

• Positive sentiment and objection replies  

• Meetings booked  

• Revenue generated  

One sequence had over 2,000 overdue tasks before we automated it. That bottleneck disappeared overnight.  

Scaling revenue with smart automation 

By embedding AI into existing processes, our internal team has eliminated manual friction, improved response times, and enhanced our personalization playbook. No one plans to replace their reps. But you can free up your team to do what they do best, while AI plows through the grunt work in the background. AI is ready to help when you point it in the right direction.