Over the last year, I’ve had the same conversation with a lot of support leaders.
They’ve deployed AI and are seeing initial efficiency gains, but want to push beyond these early results and achieve meaningful transformation.
When AI is first introduced, the gains show up quickly. Teams resolve higher volumes of queries, free up capacity, and deliver faster responses. But the real opportunity for impact extends well beyond those initial wins. As AI becomes more deeply integrated into support operations, taking on harder, more complex work, those results compound, new ways to create and measure value open up, and the economics of support change entirely.
This is part two of our five-part deep dive into our new research: “The 2026 Customer Service Transformation Report.” We’ll be sharing all five editions on our blog and on LinkedIn.
If you’d like to get straight to the report, download it here.
This sits at the heart of our 2026 Customer Service Transformation Report. This week, we explore how deeper integration compounds impact, and why that makes business value easier to articulate across the organization.
The teams going deeper are seeing higher returns
Our research shows that 62% of support teams have seen their customer service metrics improve since implementing AI, with early wins showing up most clearly in speed and efficiency. But for teams that have reached mature deployment (where AI is fully integrated into operations) that number jumps to 87%.
The same pattern holds for the ability to measure ROI. Among teams in early exploration, just 35% say they can measure their return on AI investment, but for teams at the mature deployment stage, that rises to 70%.
How measurement evolves as deployment deepens
As AI becomes more embedded in support workflows, what teams choose to measure starts to change.
In the early stages of deployment, ROI is typically understood through improved customer response times, lower cost to serve, and freeing up capacity. Teams focus on how much time AI creates and whether it’s relieving pressure on the support organization. These signals help validate that the system is working, but they say little about how that capacity is ultimately used.
As deployments mature, measurement starts to reflect a different intent. Instead of stopping at time saved, teams look at where that capacity is reinvested – into higher value customer work, and revenue-generating activities. ROI becomes less about relief and more about leverage.
The report data shows this clearly. Across all maturity stages, the most commonly cited measure of ROI is “time freed up that the support team can use to focus on value-adding activities for customers.” But at mature deployment, that signal intensifies, with 73% of teams citing it, compared to 56% at early exploration.
What’s also interesting is that 56% of mature teams say freed capacity is being directed toward revenue-generating activities, up from 34% at initial deployment.
The result is a shift in economic intent: from measuring what AI saves to demonstrating how the capacity it creates is reinvested to drive growth.
The limits of legacy economics
As AI takes on more work, the question moves from “does it save money?” to “how does it change the economics of support?”
That’s where many teams get stuck. Legacy support economics were built for linear growth: more customer tickets meant more headcount, more outsourcing, and more software costs. Success was measured through containment – the number of queries that didn’t reach human agents. These models worked when volume and effort were tightly linked.
But AI doesn’t scale linearly, and it needs to be evaluated differently.
The new economics of customer service
To sustain AI investment and expand its impact, teams need to move beyond cost-cutting narratives and build a clearer case for business value. That requires a different economic model, one that redefines success, links performance to outcomes, and reflects the way AI actually creates value at scale.
When done right, AI goes far beyond improving support efficiency. It rewires the financial model, breaking the link between support costs and revenue growth, and turning support into a contributor to customer activation, retention, and lifetime value. This means treating your AI Agent as a new workforce capability that changes how your support function creates and captures value. Here’s what value looks like in an AI-first model:
- Human productivity: Your team focuses on more strategic areas, not the queue.
- System improvement: Every resolved query makes the system smarter.
- Revenue influence: Support becomes a lever for activation, retention, and growth.
- Organizational agility: You scale service without scaling headcount.
And here’s how that transformation looks visually:
How this looks in practice
We’ve seen this shift clearly in how we’ve deployed Fin at Intercom. What started as a focused effort to improve our customer support experience has become one of the clearest examples of what happens when AI is fully embraced across an organization.
Since 2022, Fin has helped us absorb more than a 300% increase in customer demand while improving the consistency of our delivery – including supporting new routes into support for trial customers and website visitors. Today, Fin is involved in 97% of our customers’ conversations. Of those, it resolves 83.5% end-to-end, putting our overall automation rate at 81%.
That depth of deployment has allowed us to scale service without scaling headcount. Without Fin, we would have needed at least 100 additional support teammates to meet rising demand and service standards.
As Fin has taken on the majority of day-to-day volume, our support team has also been able to focus more on consultative work, where they help customers adopt Fin more deeply, succeed faster, and unlock more value from the platform. We now track metrics like “direct revenue generated” and “expansion revenue influenced” to understand the impact of these consultative support activities.
That change has turned support from a cost center into an active contributor to long-term growth.
The deployment gap is getting wider
The throughline from The 2026 Customer Service Transformation Report is that deployment depth makes a significant difference.
Teams that are investing in deeply integrating AI are reshaping how support scales and contributes to growth. Value becomes clearer as AI takes on more work, and support leaders can articulate that value to the rest of the business.
The gap between these teams and those still in the early stages is widening. A select group of pioneers are setting a new bar for what AI-powered customer service can deliver, and understanding what they’re doing differently is the first step toward closing that gap.
Next week, we’ll explore what happens as AI becomes foundational to the customer experience. As it takes on more responsibility, expectations shift from proving it works to ensuring it delivers consistently excellent experiences.
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