This is a recap of our recent webinar, How to Use AI to Optimize Revenue Growth in 2026 with leaders from ServiceNow, Sandler, and Acrisure. Watch it on demand here.
Revenue leaders are under increasing pressure to deliver more certainty with fewer resources. AI is often positioned as the solution many organizations should lean on to help close that gap. AI is now appearing in every planning cycle and every board conversation, with the expectation that it will improve how teams plan, forecast, and execute.
But what's becoming clear is that the problem isn’t simply adopting AI. What is? Turning that adoption into performance.
That’s the gap that came through in a recent webinar with leaders from ServiceNow, Sandler, and Acrisure. The conversation centered on how teams can gain actual ROI from AI. How can teams set up AI to deliver not just more activity, but documentable business outcomes? What conditions can leaders set up to make this more likely?
Our new study, Building for Compounding Growth: What 150+ Revenue Leaders Say About AI’s True ROI, points to the same gap. The leaders we surveyed all said AI pays off, but only when it’s applied to the right problems and supported by a system that can carry the impact forward. That's the shift revenue leaders need to prioritize.
On the webinar, our panelists identified four practical ways to turn AI investments into measurable ROI, grounded in both the research and the leaders who are already seeing the impact.
AI Doesn’t Fix Broken Systems. It Exposes Them.
The leaders in our study who reported less AI ROI had one thing in common: fragmented processes and inconsistent data. Many were still relying on disconnected forecasting inputs, territory models pulled from outdated spreadsheets, and handoffs that changed from team to team.
Instead of fixing them, AI made these issues more visible.
Jan Foo, Senior Director of GTM Operations at ServiceNow, said it clearly:
“AI is not a fixer by itself. If your processes or data aren’t clean, AI will make the mess bigger.”
Teams that saw return had a different starting point. They tightened the workflows AI depends on, stabilized forecasting, cleaned data that had quietly decayed, and removed manual steps that created downstream noise.
AI amplifies the system it enters. When the structure is strong, AI accelerates progress. When the structure is weak, it exposes the gaps quickly and visibly. Strengthening the foundation of your system is the first step toward any measurable lift in performance.
Buying More AI Tools Won’t Help. Building a Connected System Will.
In our study, more than 40% of AI spend went toward front-end tools that promised quick wins. Those tools generally created activity, but they rarely moved performance.
Seth Marrs, Chief Strategy Officer at Sandler, explained why:
“Executives have no problem funding AI. The problem is they buy the front end without the infrastructure that makes it work.”
As Seth Marrs noted, leaders will often fund anything with “AI” in the name. The harder work is building the structure behind it. Data needs to be stable, processes need to follow the same logic, and the system needs to support the lift leaders expect. Without that architecture, front-end tools amplify noise rather than improving decisions.
The teams that saw measurable ROI took a very different approach. Instead of buying more tools, they invested in the architecture that allows AI to shape decisions that move revenue, not just automate tasks. They connected planning, forecasting, territories, and incentives, so each part of the model informed the next decision in the chain.
When AI is connected to the operating model, its impact compounds. When it’s tied to isolated tools, it stalls. A system that shares data and logic allows AI to strengthen decision-making across the entire revenue engine, not just within a single task.
AI ROI Lives in the 364 Days After the Deal Closes
Most AI use cases still focus on top-of-funnel productivity: faster research, cleaner notes, better emails. Those gains are helpful, but they rarely change long-term revenue performance.
Our research helped reveal where the real return shows up. Leaders seeing strong ROI were applying AI to the customer lifecycle, especially the period between the initial sale and the renewal. This is the period where indicators of customer health and renewal likelihood often become harder to predict. Onboarding varies from customer to customer, adoption often slows after the first few weeks, and early signs of risk are easy to miss.
Briana Wagner, Director of Sales Operations and Enablement at Acrisure, explained it clearly: “You have to connect back-office intelligence to the customer experience. The 364 days after the deal closes are what matter most.”
That means using AI to surface early signals of customer health, alert reps to risks before they become visible, and strengthen the service experience that drives retention and expansion. These are the moments that determine revenue stability.
You Don’t Need New KPIs. You Need Better Ownership.
Every revenue leader wants to show AI ROI, but many AI business cases default to measuring activity because it’s often the easiest to capture. That includes metrics like prompt volume, minutes saved, or how often a tool is used. Those numbers show AI adoption, but not the impact AI has on the business.
The strongest cases rely on the same measures leaders already trust to evaluate performance, like efficiency gains, effectiveness of selling time, and revenue per rep.
Seth Marrs, Chief Strategy Officer at Sandler described it simply: “Revenue per rep is efficiency plus effectiveness.”
Efficiency is the time AI gives back by eliminating unnecessary work. Effectiveness is what happens when that time is redirected toward higher-value actions.
The leaders in our research who reported the highest ROI quantified both. They measured precisely where AI returned capacity. They assigned that capacity to specific selling activities. And they tracked the revenue impact that followed.
This approach creates accountability and clarity. It also ensures AI investment aligns with how revenue teams already assess performance. The goal isn’t more metrics. It’s more discipline in how existing ones are managed.
The Shift Revenue Leaders Are Making Now
Leaders getting value from AI aren’t adopting more tools. They’re building stronger systems. They’re applying intelligence where revenue becomes predictable, and they’re using the KPIs they already trust to measure what changes.
That’s how AI turns into competitive advantage. Not through volume, but through clarity and discipline.
If you want AI to influence your 2026 plan, that’s where the work starts. Strengthen the structure. Apply AI where performance is truly won or lost. And measure impact through the KPIs your executive team already trusts.
To understand how top teams are doing it, dive into the latest research here: Building for Compounding Growth: What 150+ Revenue Leaders Say About AI’s True ROI.
