Episode 221 | May 11, 2026
From SaaS to retention as a service: how AI is changing customer success
How AI is transforming customer success, retention strategies, and feedback analysis—plus why better conversations drive better outcomes.
From SaaS to retention as a service: how AI is changing customer success
Agentic AI systems are now able to reach out to customers directly—sending emails, making calls, gathering feedback—and in the process, they are capturing a richer, more continuous stream of voice-of-the-customer insight than ever before.
What was once limited to a handful of high-touch accounts can now extend across the entire customer base. The long tail is no longer out of reach—it’s a source of intelligence.
In this Insights Unlocked conversation, Nathan Isaacs talks with Josh Schachter—SVP of strategy at Gainsight and host of the UnChurned podcast—about how AI is transforming customer success—from scaling feedback to redefining retention—and why strong relationships and better customer conversations still matter more than ever.
ON-DEMAND WEBINAR
How to tackle AI adoption to develop a competitive advantage and empower your teams
From SaaS to “retention as a service”
Traditional customer success models segment accounts by value: high-touch, human-led engagement for enterprise customers, low-touch or automated interactions for the long tail, and a hybrid of digital and pooled support for those in the middle.
But that changes with Retention as a Service and the rollout of agentic AI systems, such as Gainsight’s Atlas, that can autonomously send emails and phone calls with your customers.
As Josh points out, teams can now “collect those recordings at scale and synthesize… the trends of the voice of the customer.” It’s not just more data—it’s better visibility into what customers actually need, value, and expect. The companies that lean into this shift won’t just react to churn. They’ll get ahead of it.
Customer success teams, in this model, are no longer reactive stewards of accounts. They are architects of value.
Better data starts with better conversations
It’s tempting to think of customer feedback analysis as a technical challenge. Better tools, cleaner dashboards, more structured data.
But the transcript reveals something more human—and more difficult.
The quality of your data depends on the quality of your conversations.
Josh emphasized that customer success professionals need to evolve beyond note-taking and status updates. Their role is to “get the best stuff out of the conversation,” to uncover the motivations, frustrations, and desired outcomes that actually drive behavior.
This is less about asking more questions and more about asking better ones.
A yes-or-no check-in—“Are you satisfied?”—produces data, but not insight. A deeper conversation—“What would make this indispensable for you?,” and “If you had a magic wand, what would you change in the product?,”—produces something far more valuable: context.
In this sense, customer success begins to resemble journalism or ethnography. It requires curiosity, follow-up, and a willingness to sit with ambiguity. The goal isn’t just to record what customers say, but to understand what they mean.
AI can scale that understanding. But it cannot originate it.
The myth of the perfect workflow
If AI is exposing weak inputs, it’s also revealing another common illusion: that there is a perfect workflow waiting to be discovered.
There isn’t.
Josh spoke candidly about building agentic AI workflows and the gap between expectation and reality. Teams often assume they can design these systems upfront—clean, logical, complete.
But most of the learning happens in the building.
“You’ve got to create a blueprint for the workflow,” he said. “If you don’t have that… you’re not really going to know what you’re trying to improve.”
Even then, the blueprint is only a starting point. Data issues emerge. Edge cases multiply. Assumptions break.
The mistake many teams make is treating AI implementation as a technology project. It’s not. It’s an operational one.
It requires clean data, cross-functional alignment, and—perhaps most importantly—a willingness to iterate in public. To build something imperfect, learn from it, and refine.
In other words, it requires the same mindset that made SaaS successful in the first place.
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The retention problem starts before the sale
One of the more uncomfortable truths in the conversation is also one of the simplest.
“Sell products where the customer is a good fit,” Josh said. “Not oversell.”
It sounds obvious. It rarely is.
In the race to grow, companies often stretch their positioning, promising value that may not materialize. The result is predictable: initial adoption followed by quiet churn.
Customer success inherits this problem, but it cannot solve it alone.
Retention is not a post-sale function. It’s an organizational outcome.
Product teams shape it by building solutions that address real problems. Marketing teams shape it by setting accurate expectations. Sales teams shape it by qualifying customers honestly.
When these functions are misaligned, no amount of customer success strategy can compensate.
But when they are aligned—when the right product meets the right customer with the right expectations—retention becomes less of a battle and more of a byproduct.
Closing the loop (and why most teams don’t)
If there is one missed opportunity that emerges repeatedly in customer success, it is this: closing the feedback loop.
Teams collect feedback. They analyze it. They even act on it.
But they often fail to tell customers what changed.
Josh pointed to this gap as both common and understandable. It’s operationally difficult. It requires coordination between product, design and customer success channels.
But the impact is disproportionate.
When customers see their feedback reflected in the product—when they know they were heard—it reinforces trust. It signals that the relationship is reciprocal.
Without that signal, improvements go unnoticed. Value goes unrecognized.
In a world where switching costs are low, that recognition matters.
The new shape of customer success strategy
What emerges from this conversation is not a set of tactics, but a shift in posture.
Customer success is becoming less about managing and more about designing—designing experiences, conversations, and systems that make value visible and durable.
AI is accelerating this shift, not replacing it.
The teams that succeed will not be the ones with the most advanced tools, but the ones with the clearest thinking. The ones who understand that data is only as good as its source, that workflows are only as strong as their foundations, and that relationships are still the connective tissue of everything.
There is a temptation, in moments of technological change, to look for shortcuts. To believe that a new tool will resolve old problems.
Still, the effectiveness of these systems depends on the quality of the underlying interactions. “The AI can only do the synthesis on the information it has,” Josh said, emphasizing that automation improves access to data, but not necessarily the depth of insight.
Taken together, these shifts point to a changing landscape for customer success. Growth remains important, but retention—measured consistently across cohorts and grounded in real customer value—is becoming a more critical indicator of long-term performance.
Episode links
- Power up human insights with AI. This on-demand webinar explores how AI accelerates customer insight generation, helping teams identify friction points, analyze feedback faster, and improve customer experiences—directly tied to scaling voice-of-the-customer efforts.
- April 2026 product release: scale customer insight faster with AI. A practical look at how AI-powered workflows help teams capture deeper customer feedback, surface meaningful insights, and embed them into decision-making across product, design, and CX teams.
Josh Schachter on Linkedin
Nathan Isaacs on LinkedIn
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