Simon Farthing, VP of Customer Success and Client Services at Bloomreach, joined host Josh Schachter for the [Un]Churned episode “204. He Cut Exec Prep From 4 Hours to 10 Minutes.” They cover what 60 tool evaluations across 18 months actually taught his team, how he built a personal mission control that turned four hours of exec prep into ten minutes, and what institutional AI leadership looks like inside a 100-person post-sales org.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: Who Owns the Outcome?

The AI outputs filling our workflows right now are good enough to be dangerous. So good, you might find yourself skipping that second check or easing up on your LLM interrogations. In most cases, good enough gets the job done. Until it doesn’t.

The question of accountability isn’t new, but AI makes it harder to answer. When a Customer Success Manager (CSM) prepares a QBR by hand, it’s clear who is responsible. But if an AI tool pulls data from three sources, another model formats it, and someone reviews it for ten minutes before a customer call, who is accountable? Is it the CSM who reviewed it, the tool that made it, or the team that picked the tool?

Most of us are figuring out as we go, but if you never tackle the question directly, you risk never addressing it at all.

At the Individual Level, You’re the Stopgap

On this week’s episode of the [Un]Churned podcast, Simon Farthing, VP of Customer Success at Bloomreach, gives us a good place to start. A self-labeled “failed computer science graduate,” he had the basic knowledge to build software, but the power of vibe coding gave him the tools he needed to bring his ideas to life. He created a personal mission control in Claude that brings together customer health data, one-on-one tracking, and executive prep into one place. What used to take three or four hours now takes him just ten minutes.

“I now get that in 10 minutes, and then I can spend the rest of the time really interrogating the insight, really building the talk track, and hopefully it’s making me better at my job and more useful to the customers who I speak to because I know more about their businesses than I would have done in the past.”

The mission control works because Simon’s the one responsible. He’s the only user and checks every output before it’s used. If something’s wrong, he finds it because he handles both creating and reviewing the work. His standard for his broader team reflects the same logic:

“The sentiment we have in my team is anything that you produce, ‘AI did it’ is not an excuse.”

On an individual level, responsibilities are cut and dry. If you build it and review it, you own it. When one person is involved, accountability is easy to map back to.

When the Workflow Gets More Complex, So Does the Answer

As AI continues to rapidly accelerate, it’s taking on more complex workflows. For example, the renewal process. Renewals aren’t handled by one person. They involve CSMs, account managers, finance, legal, and executive sponsors. Data comes from a variety of systems, and the process needs consistent results. With retention more important than ever, AI now plays a role at every stage, whether that’s gathering health scores, flagging risks, or generating renewal decks.

When something goes wrong in a renewal—like a wrong number, a missed risk, or a customer leaving despite a good health score—it’s hard to know who’s responsible. Was it the data, the model, the CSM who didn’t look closer, or the leader who set up the process?

This is the layer where the industry is starting to ask a different kind of question. In a recent article in the Silicon Valley Business Journal, Gainsight CEO Chuck Ganapathi described how software companies are rethinking what they actually sell as AI changes the equation. Rather than simply providing tools for customers to operate, some vendors are moving toward owning the work itself. As Chuck put it, “The vendor should own the outcome.“

In other words, the role of accountability is moving. If a vendor manages renewals with AI and human oversight, they’re not just giving customers a tool; they’re responsible for the outcome. Now, accountability shifts from the customer’s team to the vendor.

The Playbook’s Still Being Written

As Chuck mentions in the SVBJ article,

“The playbook hasn’t been written.”

This rings true for all aspects of AI. Simon’s still improving his mission control. Customer Success (CS) teams are still learning what AI governance really means in practice. Vendors offering AI Native Services (AINS) are still figuring out what it means to take responsibility for results at scale.

What’s clear is that we’re moving toward more AI in more workflows, creating more polished results, but making accountability harder to find. The solution—whether it’s a personal mission control, a team standard, or a vendor who owns the outcome—requires a human who has thought carefully about their role.

As the playbook continues to evolve, real success still hinges on having someone willing to own the outcome.

🎧 Listen for These Moments

For any leader trying to figure out where personal experimentation ends and institutional AI begins, this one is worth the full listen. Here are three moments to listen for in this week’s episode:

• Strategic AI vs. factual AI: There’s a hard line between using AI for open-ended planning and using it for customer-facing numbers. Simon shares why knowing the difference matters more than most CS leaders realize.

• The comp restructure: Simon shifted his team from 100% GRR-focused comp to a 50/50 GRR/NRR split. What drove that decision, and what it signals about where CS ownership is heading.

• The FDE role and what it signals: Learn where the Field Deployed Engineer sits in Simon’s customer interaction stack, and why he’d always fill that role from within.

🔎 Where to Find the Speakers

• Simon Farthing — LinkedIn: https://www.linkedin.com/in/simon-farthing/

• Josh Schachter — LinkedIn: https://www.linkedin.com/in/jschachter/

📎 Referenced in This Episode

• Von — go-to-market intelligence platform

• LoomiAI — Bloomreach’s AI product suite

• Claude — AI assistant and development platform

• Glean — enterprise knowledge management and search

• Gong — revenue intelligence platform

• Granola — AI meeting notes

• “Software companies face existential question as AI changes what customers buy” — Silicon Valley Business Journal, July 2026

Wrapping Up

We may be figuring it out as we go, but that doesn’t mean we can’t stop to answer the important questions.

See you next week 🧠

He talks about how to manage AI adoption in a 2,000-person go-to-market team, why vibe coding often causes more coordination issues than it fixes, and shares a simple rule for making AI decisions for Customer Success (CS) and beyond.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: The Answer to AI Chaos Is Hiding in Plain Sight

Ever since AI tools entered the chat, we’ve seen a reinvigoration of debates around build vs. buy, native intelligence vs. Model Context Protocol (MCP), and point solutions vs. platform consolidation. In this week’s UnChurned episode, Saumyo Mukherjee, VP of Business Systems at Braze, shared his thoughts on balancing AI innovation with your existing tech stack.

Saumyo’s team manages the entire application stack for all external-facing teams: marketing, sales, BDRs, partnerships, support, CS, and services. He refers to his 24-person team as “the point of convergence.” Every tool decision, overlap, and gap between what a team needs and what the stack offers starts and ends with Business Systems. This bird’s-eye view into the entire GTM tech stack and how everything works together offers a unique perspective that a single team usually doesn’t get.

“I very carefully call it [Gainsight] a system of interaction, not a system of record. Because I want the user group, which is CSM in this example, to basically spend their day within that platform.”

A system of record just stores data. A system of interaction is where people actually do their daily work. For a Customer Success manager (CSM), Saumyo believes these should be in the same place.

What Context-Switching Actually Costs

When it comes to Customer Success, the debate over AI tools is often framed as a capability question. Does this tool surface the right signals? Does it write a better QBR prep? Can it predict churn more accurately than the one we have?

What tends to get overlooked is the fatigue that comes with context switching. When your CSM gets an alert, opens an account, prepares for a call, and logs notes, how many apps do they use? If it’s more than one or two, you have a context-switching problem that AI alone can’t solve.

“If I’m a CSM, I would rather like to wake up in the morning and log into a platform, let’s say Gainsight. And then, I should be able to log off at 5 p.m. from Gainsight without having to ‘swivel chair’ anywhere else. That is my ideal state of affairs for an end user like a CSM.”

Outside tools are useful, but every time someone switches between tools, it adds a small burden that slowly adds up. By the end of the day, a CSM who spends forty minutes switching between four tools limits the amount of impactful work they could’ve done in a single, complete environment.

Real progress comes from building within, not chasing every shiny new tool. The real challenge is harnessing this power without drowning in complexity, and that’s where smart governance comes in.

You Wouldn’t Put a Band-Aid Over a Band-Aid

When Braze rolled out access to Claude and Gemini, Saumyo’s team found themselves faced with what he referred to as “good chaos.” Teammates were inspired to be creative and explore the capabilities of their new AI tools, but without governance, nobody knew what was being built, where, and by whom. Without clear direction, CSMs can be the first to feel this chaos.

Let’s say your CS, RevOps, and product teams are all creating their own AI workflows across the customer journey. While each team may be solving their own part, without management for the gaps between workflows or handoffs, it can fall on CSMs to fill them.

“Oftentimes [vibe-coded, point solutions] become a band-aid over another band-aid... get rid of all that, build from scratch, a fresh solution that doesn’t require any other band-aids.”

Saumyo’s solution was to hold a weekly AI Idea Box meeting every Thursday. During these sessions, a core team reviews ideas based on business impact and effort before they spread. It’s not flashy, but it turns scattershot innovation into holistic additions to teams’ day-to-days.

Good governance doesn’t restrict innovation. It creates the guardrails and clarity that let teams use AI and existing features to their fullest potential, without overwhelming themselves in the process. Sometimes, the smartest move isn’t adding more, but making the most of what’s already there.

Finding Focus Within the Chaos

New AI tools are constantly launching, and this means the internal workflows being built with them will only compound. What separates the “chaotic” teams from the impactful ones is governance that filters out what actually improves workflows, rather than adding technical debt.

What updates have your existing platforms launched that can be utilized the same way as that shiny new AI platform? Are you really making building QBR decks and meeting preparation easier, or does it feel like you need to add a new monitor for every new program you have to use? Nobody wants to feel like the hacker in an action movie juggling a hundred screens, typing into a dozen systems at once, scrambling to keep up. When AI tools force CSMs to jump between platforms and piece together information, we’re not empowering them—we’re overwhelming them.

Real impact happens when everything a CSM needs is right in front of them in a single environment, so they can focus on customers.

🎧 Listen for These Moments

Saumyo and Josh talk about how to manage AI adoption in a complex GTM stack, and what CS leaders can learn by looking at how operations teams handle these challenges. Listen for these moments:

• Token budgets by department — Saumyo shares a simple way to show CFOs that AI investment is working. It includes giving every team an AI spending limit and adjusting from there.

• The quarterly hackathon — Braze runs a two-day event where anyone can pitch and build an AI idea. Saumyo explains why the person with the best idea usually isn’t who you’d expect.

• The handoff problem — Saumyo shares where Braze discovered customer relationships were quietly breaking down and what they did to fix it.

🔎 Where to Find the Speakers

• Saumyo Mukherjee — LinkedIn: https://www.linkedin.com/in/saumyo/

• Josh Schachter — LinkedIn: https://www.linkedin.com/in/jschachter/

📎 Referenced in This Episode

• Braze — the customer engagement platform where Saumyo leads Business Systems across all GTM-facing applications

• Gainsight MCP — referenced by Josh as part of Gainsight’s current AI architecture, connecting Gainsight, Staircase AI, and Skilljar data into your LLM of choice.

Wrapping Up

If your CSM had to describe their experience with AI in one word, would it be the same word as yours?

See you next week 🧠

Matthew Kropp, Managing Director and Senior Partner at BCG and Chief AI Officer at BCGx, joined host Josh Schachter for the [Un]Churned episode, "201. Why BCGx’s Chief AI Officer Thinks Writing Code By Hand Is a Waste of Time."

They cover a lot of AI bases, including why most companies are still early in their AI transformation even when adoption looks healthy, what token-based competition means for every function, and the four psychological barriers keeping smart teams from changing how they work.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: The Identity Crisis Impacting Your AI Adoption

Your team’s AI adoption numbers might not tell the whole story. It’s not that anyone is hiding something, but there’s a big difference between logging in and actually using AI to change how you work. Matthew Kropp, Managing Director and Senior Partner at BCG and Chief AI Officer at BCGx, has spent three years working with hundreds of companies on AI strategy. He keeps seeing the same pattern: adoption looks high on paper, but real change is rare.

He had an “aha moment” during a BCG partner meeting. A practice lead spent a month building a client strategy and presented it, but then the CEO used ChatGPT and got the same answer in 30 seconds. As he puts it in this week’s [Un]Churned podcast episode:

“What ChatGPT produces, that is the baseline. That is the starting point.”

If AI can do something, it’s no longer your differentiator. The weight of this realization is what a lot of Customer Success (CS) professionals are feeling right now, and without a clear direction, this hesitancy will impact AI adoption. What’s important now is to shift the mindset from, ‘Am I safe?’ to ‘What is it that only I can do?’

What’s Actually Blocking Your Team

When CS teams push back against AI, it’s easy to blame time, skepticism, or change fatigue. Those reasons are real, but Matthew’s research found that the deeper fear was the threat to identity.

“It’s not about ‘I’m worried about losing my job,’ it’s ‘I’m worried about losing my identity.’ And that’s a very scary thing for people.”

For CS teams, identity often comes from being the expert customers rely on. A Customer Success Manager (CSM) is the person who knows the account, manages complexity, and builds relationships. When AI starts taking over parts of that, like drafting strategies, spotting risks, or flagging renewals, it doesn’t just change the job description. It challenges the story people tell themselves about why they’re good at their work.

That’s why telling people “AI won’t replace you” doesn’t work. You can’t talk someone out of feeling their identity is threatened. The only way past it is through experience. Matthew found that everyone who gets through this process says the same thing:

“What happens every time as you get people through this process, they come to the realization that this tool is not taking something away from them, but it’s actually making them more successful and more powerful…every time they will say, I’m never going back. I love working this way. Not only can I get more done, but I’m more creative. It’s more fun. I’m not doing the toil part of my job.”

Reassurance alone doesn’t make the fear go away. It fades when people actually use the tool and see that it gives them more space to use their strengths.

Where the Job Rewrites Itself

Once the fear is gone, something interesting happens. The shift that felt threatening starts doing the work for you. When AI handles the account summary, the CSM has time for the conversation the health score missed. When it preps the QBR deck, there’s suddenly space to read on the room and drive strategic initiatives. The job redefines itself, because the toil that used to fill the day is gone.

That’s the moment the adoption metric stops mattering. Logging in was never the point. What changes is what people do with the time AI gives back, and whether they’ve gotten far enough past the identity threat to actually take it.

The teams that get there don’t look like teams that adopted AI. They look like teams doing a fundamentally different job.

🎧 Listen for These Moments

Matt and Josh get into what’s actually holding AI adoption back and where leaders should focus instead. Listen for these moments in the full episode:

• Why AI adoption is really stalling — Matt breaks down the four biggest barriers to AI adoption: lack of time, skepticism, habit change, and the deeper identity threat underneath it.

• The “brain fry” from heavy AI use — Matt shares what happens when people work across multiple AI tools and agents at once, and why leaders need to think more carefully about overload, recovery time, and focus.

• How middle management is under pressure in AI-first orgs — a sharp discussion on how AI-native companies are rethinking coordination, spans of control, and what managers are actually expected to do.

🔎 Where to Find the Speakers

• Matthew Kropp — LinkedIn: https://www.linkedin.com/in/matt-kropp/

• Josh Schachter — LinkedIn: https://www.linkedin.com/in/jschachter/

📎 Referenced in This Episode

• BCGx — BCG's engineering and AI build arm, responsible for designing and building technology solutions alongside BCG's strategy work.

Wrapping Up

Getting through the fear is hard. What's on the other side of it is worth it.

See you next week 🧠

Margo Martin, Chief Customer Officer at Deltek, and Jason Goldsmith, VP of Customer Strategy and Services, joined host Josh Schachter for the [Un]Churned episode, “199. Claude Code Cut Dev Cycles 60% Then Forced an Org Redesign.” They discussed what it takes to rebuild the customer journey, make build-vs-buy decisions, and what the duplication across three siloed orgs revealed about their structure.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: AI Isn’t Creating Chaos. Your Org Structure Is.

When Margo Martin, Chief Customer Officer at Deltek, reviewed what her three post-sales teams had built with AI this year, she noticed directors were making tools that others had already created. Managers were starting similar projects at the same time, and there was no shared list of these efforts across implementation, Customer Success (CS), and support.

This repeated work was actually a good sign. Teams were using AI and turning their ideas into real workflows. What they lacked was a way to connect with each other, encourage collaboration, and build on each other’s progress.

Duplication as a Signal, Not a Flaw

It’s easy to view this kind of overlap as a management problem that can be solved by adding more coordination, such as steering committees, more meetings, or working groups. But when teams independently come up with the same AI projects, it points to a deeper issue. The structure itself is causing problems.

What Margo discovers is a valuable lesson. There were clear improvements being made from her team’s AI innovation. Claude Code reduced custom integration times by 50 to 60 percent. Customer documentation was completed 80 percent faster. Post-sales directors were told to focus on AI and did exactly that, but they didn’t know what the others were working on. Instead of waiting for new instructions, they moved forward even without a shared plan.

The real question for your organization isn’t why teams aren’t working together better. It’s whether your structure gives them a reason to work together at all. When new tools make one team much more efficient, the reason for keeping teams separate becomes less clear. The people doing the work often notice this before leadership does.

Seeing Opportunity in Team Initiative

Margo created a new executive role, filled by Jason Goldsmith, VP of Customer Strategy and Services at Deltek, to focus on bringing teams together without giving any one team full control. She expected some resistance. Instead, people at different levels volunteered before anyone even asked. They had already seen the benefits, and openly admitting that teams were coming together made them feel free to stop working around the old system.

“We got incredible feedback... a lot of people raised their hands and said, I’d be interested in maybe being on that team at some point. I thought people would be like, ‘stay out of my org.’”
— Margo Martin

If you can’t create a new executive role, the same lesson still applies. Are your people already finding workarounds to do their jobs well? If so, your current structure probably isn’t meeting their needs.

Don’t Trade Connection for Speed

Even as AI increases efficiency, customer retention still needs to be the north star. Jason’s main goal in his new role isn’t just speed; it’s making sure customers renew. That might sound simple, but automation can remove important parts of the relationship. The steps that AI speeds up, like weeks of close contact during integration or the support history that helped a rep really get to know a customer, weren’t just inefficient. Some of those steps helped build real connections.

“We could develop something that is a touchless implementation that doesn’t actually give the customer what they want or provide them a great experience, and then they’re immediately a churn risk at their next renewal.”
— Jason Goldsmith

You can use the same logic here as you would with org structure. If your CSMs are finding reasons to stay involved longer than the process requires, or your customers are going quiet after a faster-than-ever onboarding, those aren’t edge cases to manage. They’re the structure telling you something it doesn’t have language for yet.

The Big Picture

Smart teams will always find ways to adapt when the structure no longer fits. Instead of treating overlap or unusual team behavior as problems to fix, pay attention to what they reveal about how your organization really works. Use these signals as an opportunity to rethink your structure, so you can unlock even more value for your teams and your customers.

🎧 Listen for These Moments

Margo and Jason are two weeks into a structural bet that most CS orgs are still debating whether to make. Listen for these moments in the full episode:

• The initiative inventory Jason built in his first weeks on the job: 65 to 100 active AI initiatives spread across three orgs, none of them visible to each other, and what surfaced when he finally put them all in one place.

• The build-vs-buy moment where Deltek built something solid and bought it anyway, and the real reason the math worked out that way.

• How both Margo and Jason have built personal AI chiefs of staff. What Margo’s caught her forgetting, and why she says it’s less about summarizing meetings and more about surviving the tyranny of the day.

🔎 Where to Find the Speakers

• Margo Martin — LinkedIn: https://www.linkedin.com/in/margomartin-/

• Jason Goldsmith — LinkedIn: https://www.linkedin.com/in/jwgoldsmith/

• Josh Schachter — LinkedIn: https://www.linkedin.com/in/jschachter/

📎 Referenced in This Episode

• Deltek — the ERP software provider for government contractors and professional services firms where Margo and Jason lead post-sale

• Claude Code — the agentic coding tool Deltek’s services team used to compress custom integration timelines

Wrapping Up

Trusting your team’s instincts can turn duplication into a powerful force for improvement.

See you next week 🧠

Yash Tekriwal, who leads the Go-To-Market Engineering Ecosystem at Clay, joined hosts Josh Schachter and Samantha Murray for the [Un]Churned episode, “He Created a Job Title 1,000+ People Now Have.” He talked about the origins of the GTM engineer role, why Clay renamed its education team, and what it looks like to build a learning ecosystem around community and live experience rather than content libraries and completion rates.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

Subscribe now and get the [Un]Churned take straight to your inbox.

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🎯 The [Un]Churned Take: The Job Already Changed. Has Your Title Kept Up?

In August 2023, Varun Anand, Clay’s co-founder and Head of Operations, noticed Yash Tekriwal’s work and pointed out that he wasn’t really doing a traditional salesperson’s job or an operations person’s job. It needed a term all its own.

Clay, a go-to-market (GTM) software platform and data operations canvas used by sales and marketing teams, didn’t create the GTM engineer title just to stand out. The label fit work that was already happening. Yash, now Head of Go-To-Market Engineering (GTME) Ecosystem, was running 30+ sales calls a week, building automated scoring systems, cleaning data, and designing the go-to-market infrastructure. No existing title described all of that. The role came first out of necessity, and the name came later.

Now, GTM engineers have grown in popularity, appearing on job boards and LinkedIn profiles. The entire industry needed the role; they just didn’t know what to call it yet.

The Same Inflection Is Happening in Customer Success

Post-sales and Customer Success (CS) roles have changed more in the past three years than in the previous decade. Many organizations have seen their books of business double almost overnight. Customers are pushing back on QBRs and want more outcome-focused conversations. More and more companies are vibe-coding on top of existing solutions, and need their Customer Success Manager (CSM) to help, not hinder.

Bain’s 2024 CS Practitioner Survey shows that customers view technical assistance as their top priority from CS partners, but most CS teams rank it last. This gap has been growing for years, even though the official CSM job description hasn’t changed much.

Most CS orgs are still structured, measured, and resourced around a version of the job that no longer reflects where the real leverage lives. Now CSMs are expected to read signals before a customer feels pain, exhibit technical fluency, and connect your product directly to a customer’s desired outcomes.

What Yash’s Ecosystem Decision Reveals

When Clay changed its education team’s name to the “Go-To-Market Engineering Ecosystem,” Yash was making a point about structure. If the name doesn’t match what the team actually does, you end up hiring and measuring for the wrong things.

The old name suggested content production, video libraries, and completion rates, which shaped everything from hiring to success metrics. The new name allowed the team to run in-person workshops for prospects, share deal intelligence with sales, manage certification programs that test real skills, and build a talent placement process for certified people. This is a growth function built from what used to be called education, and the change happened because someone let the name reflect the real work.

There’s a clear and uncomfortable parallel for CS. If the CSM role is still based on 2019 assumptions, then commercial skills, data literacy, and technical know-how will seem like extra tasks instead of the main focus of the job.

The Leaders Who Move First

Yash didn’t plan this from the start. Clay didn’t set out to create a new category or structure. Instead, they kept solving real problems, and the solution took shape over time.

“Don’t go out and try to do something abstract and then reverse-engineer your way into it because you don’t know what doing the actual work and solving the real problems in front of you will yield.”

Most changes in CS organizations will happen the same way. Not through a big reorganization, but through leaders who notice the gap between what their CSMs actually do and what their job descriptions say.

🎧 Listen for These Moments

Yash gets refreshingly candid about how Clay stumbled into one of the most talked-about roles in GTM, and what he's still figuring out. Listen for these moments in the full episode:

• Why Clay didn’t let students touch the product for the first week of a technical bootcamp — and why it ended up being the most valuable part of the program.

• The case against multiple choice certification — how Yash is using AI role-plays to assess whether someone can actually do the job.

• Why one customer logged 16 touchpoints with Clay before converting — and what that means for how you think about community as a growth channel.

🔎 Where to Find the Speakers

• Yash Tekriwal — LinkedIn: https://www.linkedin.com/in/yashtekriwal/

• Samantha Murray — LinkedIn: www.linkedin.com/in/samantha-murray613

• Josh Schachter — LinkedIn: https://www.linkedin.com/in/jschachter/

📎 Referenced in This Episode

• Alt MBA — the Seth Godin-founded program that Yash modeled Clay’s Alpha Forge bootcamp after

• Clay — the go-to-market data platform where Yash leads the GTM Engineering Ecosystem

Wrapping Up

If you had to describe what your CSMs actually do today, would the job description match?

See you next week 🧠

On this week’s episode of [Un]Churned, “How to Sell Community to a CEO Who Doesn’t Care About Community,” Chris Catania, Head of Community at Esri and author of The Community First Advantage, joins host Josh Schachter and co-host Erica Kuhl, EVP and General Manager of Gainsight’s Digital Customer Hub.

Together, they discuss how to make a strong internal case for community, use it as a cross-functional voice-of-customer tool, and build a program that earns executive trust over time.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

Subscribe now and get the [Un]Churned take straight to your inbox.

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🎯 The [Un]Churned Take: Community Is the Answer to AI’s Trust Problem. Now Sell It Like One.

As AI becomes part of every customer interaction, people are asking a question that no language model can answer for them: can I trust this? The peer-to-peer connection that community creates, with real customers helping each other, working with vendors, and advocating on their own, is one of the clearest answers. This is something AI can’t create or imitate.

That’s why now is the worst time to lose the budget battle for your community program.

The Pitch Problem Predates AI. The Stakes Don’t.

Chris Catania, Head of Community at Esri and author of The Community First Advantage, interviewed over 100 executives for his book and found that most executives already have their own idea of what ‘community’ means, like Facebook groups, Reddit, or even a city footprint. But those aren’t the definitions you’re pitching. But this isn’t new. Erica Kuhl, who built Salesforce’s Trailblazer community into a leading example of community-led growth in B2B SaaS, says she’s avoided using the word “community” in executive conversations for years.

What’s different now is what happens if you get it right. If community is the trust layer that AI can’t copy, then organizations that fail to make a strong internal case for it are giving their competitors an advantage that grows over time and can’t be regained quickly.

Chris’s solution is to stop using the word altogether and instead speak in terms each executive understands.

• For a CMO, it’s about brand advocacy, with customers sharing on their own channels without being asked.

• For a product leader, it’s about co-creation and getting better feedback.

• For a CFO, it’s about improving retention and reducing support costs.

It’s the same program, just described differently each time. Erica does this too: she calls it a digital customer hub, since everyone knows what a hub is. The name focuses on what the company receives, not what the team creates.

The best example of this approach is Erica’s story about how she earned Marc Benioff’s ongoing support for Trailblazers. She didn’t pitch a community strategy. Instead, she gave him honest, direct feedback from real Salesforce customers about his keynote presentations, coming from people with no reason to hold back. Benioff wanted more. She wasn’t explaining what a community program was; she showed him something he couldn’t get any other way.

The Trust Layer AI Can’t Build for You

The moat around your community pitch or existing program just got stronger, now that AI is degrading every other signal executives rely on.

Vendor content is now generated, and outreach is automated. Community is the only input that remains truly human. Real customers help each other and share honest feedback without any reason to hold back. Co-creation reflects what users actually need, not just what they say in surveys. As Chris says, “How do I balance authenticity with efficiency of AI? Customers are really looking for that.” Organizations that have built real community trust can answer that question. Those that haven’t will see the gap grow.

To get there, community leaders need to make a clear internal case that can last through the budget cycle and grow over time. The pitch still needs to be strong, but now there’s an even better reason to make it.

🎧 Listen for These Moments

Chris and Erica get into the mechanics of how community programs actually earn executive support. Listen for these moments in the full episode:

• Why community can't be siloed — the argument that community only works as a cross-functional service, not owned by any one department.

• The three E’s — Chris’s operating system for community: enablement, experience, and evaluation.

• North Star + small wins — how Erica thinks about keeping executive patience through a long-term community build by delivering staged ROI along the way.

🔎 Where to Find the Speakers

• Chris Catania — LinkedIn: https://www.linkedin.com/in/christophercatania/

• Erica Kuhl — LinkedIn: https://www.linkedin.com/in/ericakuhl/

• Josh Schachter — LinkedIn: https://www.linkedin.com/in/jschachter/

📎 Referenced in This Episode

• The Community First Advantage — Chris Catania’s book on using community as a competitive advantage across the organization

• Salesforce Trailblazers — Salesforce’s customer community program, built by Erica Kuhl

• Gainsight Digital Customer Hub — Gainsight’s platform for centralizing customer engagement across community, learning, and support

Wrapping Up

The case for community has never been stronger and for the first time, the market conditions are making that argument for you.

See you next week 🧠

The conversation covers a lot of ground: cutting mid-touch entirely, packaging every post-sales activity into a 318-item self-serve catalog, and the 45% consumption gap that Jean calls ERP’s “dirty secret.”

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: Keeping the Human Advantage as AI Advances

Jean de Villiers and his team at Unit4 are in the middle of an AI transformation. Right now, you could say the same about almost any CCO. As LLMs and agentic AI take on more work, post-sales teams are adapting and finding ways to use these tools to their advantage.

But AI can be a double-edged sword. As much as it helps the humans using it, what happens when AI steps confidently into those roles that once were uniquely human?

The Role AI Just Took From Your Team

AI has already absorbed the part of the job that “could have been an email.” Now, Customer Success Managers (CSMs) are expected to spend time on the harder, relationship-driving aspects, such as judgment calls and difficult conversations. However, as AI continues to advance, that capability gap is already starting to close.

At Unit4, Jean’s team is using an agentic CSM designed to read product telemetry, benchmark a customer’s usage against their vertical peers, identify a specific gap, and surface a success plan that challenges the customer. An example output can sound something like, “You’re not using this module, here’s what companies like yours are doing with it, here’s what it’s costing you not to.” While the agent’s job isn’t to maintain the relationship, it’s entering the realm of provoking action.

“The term ‘trusted advisor’ is kind of overused and redundant in the modern world. Claude and co-pilots are the trusted advisors these days.”

If technology can push customers to act and even inspire new thinking, the bar for what makes a human contribution truly valuable is suddenly higher.

What “High-Touch” Actually Means Now

When the agentic layer is designed to be challenging and specific, then a human CSM who shows up to a QBR with a relationship and a slide deck summarizing product updates isn’t offering anything unique. The agent already sent the personalized, telemetry-driven, peer-benchmarked recommendation, so by the time the CSM enters the meeting, they’re just bringing noise.

As AI catches up to the human skillset, CS leaders need to ask themselves, “What does your human layer actually do to move a customer forward?”

The answer lies in defining explicitly what high-touch means for your customers.

Jean’s strategy includes rolling out Challenger Sale training to his whole post-sales team—not just CSMs, but also architects, project managers, senior consultants, and the teams running cloud migrations and managed services. He isn’t focused on making them sell, but on helping them guide customers to new insights and directions they wouldn’t reach on their own. By teaching his team the Challenger Sale and changing how they engage with customers and deliver value, he’s equipping them to work better together alongside their agentic solutions.

Most CS orgs haven’t had to define their high-touch strategy to this level because CSMs have carried the load. The more impactful your agentic layer becomes, the more you’ll need to define how the human layer can sustain that impact.

Raising the Bar for Human Impact

If your agentic layer is doing its job well, and your human layer looks redundant by comparison, that’s not an AI problem. In order to get ahead, you need to be honest about what value your CSMs provide and work towards building out those strengths.

🎧 Listen for These Moments

Jean gets deep into the specifics about his decisions behind Unit4’s post-sales rebuild. Listen out for these moments in the full episode:

• The 50-point NPS gap between high-touch and self-serve customers — Jean breaks down exactly why it exists and why it’s the clearest internal argument for investing in the human layer.

• Running post-sales as a profit center under PE ownership — how the 30% EBITDA contribution margin target shaped every structural decision Jean made, including cutting mid-touch entirely.

• Challenger Sale training for the whole post-sales org — why Jean is teaching architects, PMs, and consultants to pitch ideas confidently, and why he thinks most CSMs have never had to develop that skill.

🔎 Where to Find Jean

• LinkedIn: https://www.linkedin.com/in/jeandevilliers/

📎 Referenced in This Episode

• The Challenger Sale — sales methodology by Matthew Dixon and Brent Adamson, built around teaching and reframing rather than relationship-building

• Gainsight — platform Unit4 uses to deliver digital success plans

Wrapping Up

Most CS orgs will adopt agentic tools and call it a transformation. The ones that actually transform will use it as a reason to get honest about what their humans are doing and make them better at it.

See you next week 🧠

Kellie Snyder, Chief Customer Officer at LinkSquares, sat down with Josh Schachter on this week’s episode of [Un]Churned, “191. The Retention Killer Hiding in Your Product Demos,” to share what it really takes to rebuild a Customer Success (CS) approach with AI, how a platform relaunch can drive change in ways internal efforts often can’t, and what the data showed about why customers were leaving quietly.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: Your Best Processes Deserve a Second Look

These days, most CS teams are wondering, “What new thing should we build with AI?” We’re thinking about new workflows and playbooks. The idea behind this question is that the missing piece is something we haven’t built yet.

But Kellie Snyder’s experience suggests a different question: What have you already built that’s worth revisiting?

Retention matters more than ever. Gainsight’s 2025 CS Index found that 76% of companies now see customer retention as a main revenue metric. It’s getting harder to win new customers, and renewals face more scrutiny. In this climate, not knowing about a broken process can be more costly than before.

What Kellie Found When She Looked Back

LinkSquares is an agentic Contract Lifecycle Management platform serving legal, sales, and procurement teams. When Kellie became CCO eight months ago, she took over a carefully built onboarding process. The existing process was thoughtful and thorough, and made by people who really understood the product. There were detailed demos, full feature walkthroughs, and a real effort to show customers everything the platform offered.

The team who built the process wanted customers to see the full scope of LinkSquares’ capabilities.

What they couldn’t see was how it felt for customers at scale. When Kellie’s team pulled data from nine systems and analyzed it with Claude, the AI revealed a previously hidden pattern: customers were leaving after being shown too much, too quickly.

“We scared some of those customers,” Kellie said. “That was part of our retention problem. It was just too much for them. It was more than what they needed.”

No one set out to design a bad process. It made sense from the inside, but the outside perspective only became clear when AI could review hundreds of accounts at once.

What’s Slipping Through the Cracks?

This story matters for more than just onboarding because it raises questions about every process you’ve built and stopped examining.

CS teams build up processes over the years, like QBR cadences, health score weightings, escalation triggers, and renewal timelines. Each was created by someone with good judgment and real context. Most have never been tested against large-scale data, because until recently, that kind of analysis wasn’t practical. It used to take a data team, a project, and months of work.

Now, it just takes an afternoon and a well-crafted prompt.

CS leaders are rushing to build new AI-native processes, but the smarter move might be to use AI to review what they already have. Not to automate, but to audit. To ask the question the original designers couldn’t answer: does this actually work the way we think it does?

Kellie’s answer changed how LinkSquares onboards every new customer from then on. The real question is what you might discover about your own processes.

🎧 Listen for These Moments

If you're inheriting an org or just trying to keep up with the AI wave, you'll want to hear from Kellie. Here are four moments to listen for in this week’s episode:

1. Why Kellie compares AI to A/B testing — and what her days at Adobe taught her about the gap between what humans think they know and what the data actually shows.

2. How LinkSquares thinks about value realization — why they start identifying customer outcomes in pre-sales, and why they don’t assume those outcomes stay the same over time.

3. The digital-first over-rotation — why Kellie thinks CS went too far, how the new platform gave her cover to pull back, and what rebuilding the human engagement model actually looks like.

🔎 Where to Find Kellie

• LinkedIn: linkedin.com/in/kelliesnyder

📎 Referenced in This Episode

• LinkSquares — AI-native contract lifecycle management platform

• Claude by Anthropic — the AI tool Kellie’s team uses for customer data analysis and internal enablement

• Gong — referenced as a tool for capturing call data and reducing manual CSM data entry

• Snowflake — referenced as the data infrastructure layer for Link Squares’ customer 360 consolidation

Wrapping Up

The processes you trust the most are the ones worth interrogating first. Don’t forget to make time to pull up processes you haven't questioned in the past couple of years. That’s your new starting point.

See you next week 🧠

Monika Saha, Chief Commercial Officer at Articulate, joined host Josh Schachter and co-host Samantha Murray this week on the [Un]Churned podcast for a wide-ranging conversation on what a PLG-native company knows about digital CS that the rest of the industry is still catching up to.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: Before You Go After the Long Tail, Know What You’re Fighting For

The long tail has been in the spotlight lately, and it’s about time.

Last week, Chuck Ganapathi made the case that the 80% of customers most Customer Success teams can’t afford to cover could actually drive the next wave of growth, now that agentic AI makes a real motion against that segment economically viable. This week, Monika Saha, Chief Commercial Officer at Articulate, adds a prerequisite: before teams jump in, they need to determine which churn in their long-tail segment is actually worth fighting.

“Trying to improve churn beyond a certain point is pointless… I think it’s a law of diminishing returns. Trying to improve even 1% on that baseline is not the same as trying to improve 1% on your enterprise segment.”

Before you can impact growth and retention in your long-tail, you need to understand where to focus your efforts.

What Happens When Churn Is Inevitable?

Articulate serves over 125,000 organizations, including 100% of the Fortune 100, and delivers learning content to 133 million users globally. As CCO, Monika’s remit runs from trial to renewal, which means she’s managed the full spectrum: strategic enterprise, mid-market, SMB, and a high-velocity long tail that plays by its own rules.

In a product-led, long-tail segment, some churn is baked into the buying behavior. Small companies try things, credit cards get charged, situations change. An account that churns after seven months wasn’t necessarily a CS failure. It might’ve been exactly the customer that segment attracts.

The problem is when CS orgs treat that baseline as something a better QBR cadence or a more proactive outreach sequence will fix. But if we’re being honest with ourselves, we know it usually won’t. What it will do is burn cycles that belong in your enterprise book, where a 1% retention improvement is worth materially more and actually responds to human effort.

Monika still has a full team and tech stack dedicated to long-tail accounts at Articulate. The mandate is just different: maintain churn through digital motion, community, and in-product engagement. Don’t try to human-CSM your way to a number the segment was never going to give you.

What Good Segmentation Actually Does

Most teams segment their customer base to decide how many humans to assign. Monika describes something more fundamental: segmentation as a permission structure. Once you’ve done it well enough, you know which retention battles are worth fighting, which ones are fixed costs, and where your digital motion needs to cover the gap. Without that clarity, more long-tail coverage just means more effort applied to a baseline that won’t move.

Chuck’s episode last week was focused on how agentic AI finally makes it possible to run a real process for accounts you’ve never been able to staff. Both are true simultaneously. You need the right motion for the segment, and you need to be honest about what “good” looks like within it before you build toward it.

The Long Tail Has Been Waiting for the Right Motion

At Articulate, in-product engagement, community, and education aren’t a budget substitute for CSMs. They’re the motion that fits the segment, and they were built that way from day one because Articulate is a PLG company. A trial converts because the product guides the user to an outcome before anyone from CS picks up the phone.

That instinct carries into post-sale. The right motion for your long tail scales without headcount, responds to product signals, and keeps customers progressing without requiring a human at every touchpoint. It’s not a lighter version of your enterprise motion. It’s a different one entirely.

“The key is knowing where to invest what types of energy. It becomes very clear if you’ve segmented your customer base well.”

Build the right motion for that segment. Then go fight for it.

🎧 Listen for These Moments

As someone who owns marketing, sales, and CS simultaneously, Monika provides a unique GTM perspective you don’t usually get. Here are three moments to listen for in this week’s episode:

1. The customer education cost center debate that reveals sharper and more honest insights than the standard “education drives retention” response.

2. Monika’s QBR exercise for finding AI leverage: each go-to-market leader brings one painful process, shows it live on a recorded video, and the team triages which ones AI should actually fix. It’s a replicable framework that you can put into action quickly.

3. The cross-sell motion at a PLG company: how Articulate turns on product trials for existing customers, instruments them toward specific milestones, and routes the signal to sales only when behavior warrants it.

🔎 Where to Find Monika

• LinkedIn: linkedin.com/in/monikasaha

📎 Referenced in This Episode

• Articulate — AI-powered workplace learning platform

• Skilljar by Gainsight — referenced as a distribution platform and Articulate customer

• What to Do With the 80% of Customers You Can’t Afford to Cover — last week’s [Un]Churned episode with Chuck Ganapathi

Wrapping Up

For a long time, the long tail was the part of the business you managed, not the part you bet on. That mindset is shifting, and we’ll keep you in the know along the way.

See you next week 🧠

In this special [Un]Churned episode, Chuck Ganapathi makes the case that most Customer Success (CS) and revenue leaders didn't choose to ignore their long tail. They just never had enough headcount to do anything different—until now.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: The Accounts You Stopped Chasing Are Your Next Growth Engine

You have an unspoken agreement with your long tail. The accounts are too small to staff, too numerous to ignore, and too unpredictable to forecast. So they go on auto-renewal, churn at 30-40%, and nobody brings it up in the board meeting.

The truth was, teams didn’t have enough headcount to do things differently. Now, agentic AI is changing that. CS organizations that adapt early will gain a real GRR advantage over those still treating long-tail churn as a fixed cost.

The Economics Finally Work

The long tail has always been hard to serve, not because it’s complex, but because of margin. Staffing people to handle hundreds of small accounts never made financial sense. Traditional outsourcing doesn’t fix it either; it just moves the headcount somewhere cheaper without changing the basic math.

Agentic AI changes the cost structure. Agents take care of the scale work like outreach, renewal tracking, and risk flagging, while humans focus on judgment, intervention, and the moments where context matters more than speed. This mix makes it possible to run a real process for accounts that have been ignored for years.

Gainsight is putting this into action with Atlas, the first AI-native renewals services for customer retention. This launch was the anchor of the Pulse 2026 keynote and Chuck gave a rundown during his conversation with Josh. With Atlas, instead of just giving you AI agents and leaving you to handle the outcome, Gainsight runs the long-tail renewal process for you, with agents and humans working together. The kicker: Gainsight only wins if you do. “That wasn’t possible even a year ago,” Chuck said. “Agentic technology wasn’t there. And now it is.”

This flips the long-tail equation on its head. A 10% improvement in long-tail renewals means a two-point increase in overall GRR. For most CS teams, that’s something to bring to the board.

Your Long Tail is Future Pipeline

While the risk of churn is reason enough to act, the potential growth that hides in your smaller accounts is worth considering.

With AI accelerating both the ability to build in-house and the potential for startups to get up and running quickly, gaining and keeping customers is harder than it’s been in years. Your long-tail customers are already in your corner, and keeping them there can provide an untapped source for growth.

Chuck pointed to AI-native companies as a prime example. While they may be small by contract value today, they’re growing faster than almost anyone else in the market. The broader point extends well beyond any one category, though. Small accounts don’t stay small, and the CS org that’s been running a real motion against its long tail will be in a fundamentally different position than the one that’s been hoping for auto-renewal.

Humans and Agents Work Better Together

The Atlas model doesn’t remove humans from the long-tail motion. It changes what they’re doing there. “Agents are probabilistic software and they’re not going to get everything right perfectly,” Chuck shares. This means humans need to be in the mix to handle the judgment calls, like when to intervene, when the agent’s output needs a second look, when a customer situation has context the system can’t read.

Chuck was direct about why humans stay in the picture, emphasizing that “The tolerance for mistakes is very low. We’re talking about real revenue here.” Agents will get things wrong. Having humans in the loop is what makes the system accurate enough to trust with real renewal numbers, and over time, humans and agents make each other better. Humans refine the judgment that agents act on, and agents surface the patterns at a scale no human team could cover alone.

Most CS orgs have spent years treating long-tail churn as the cost of not having enough people. With Atlas, that excuse just got a lot harder to make.

🎧 Listen for These Moments

Chuck and Josh chat the evening before Pulse 2026 for a thirty-minute, candid conversation about retention, agentic AI, and what Gainsight is betting on. Be sure to listen for these moments in the full episode:

1. The Atlas model in plain terms, including what outcome ownership actually means in practice and why Chuck thinks the long tail has been one of the most costly blind spots in CS for years.

2. The build-or-buy false choice, and Chuck’s case for why the best agentic AI strategies aren’t choosing between the two, they’re layering them.

3. Chuck’s take on AI optimism, and how we can use our human judgment to make sure the agents are doing the right thing.

🔎 Where to Find Chuck

• LinkedIn: linkedin.com/in/chuckganapathi

📎 Referenced in This Episode

• Pulse 2026 Keynote

• “Services, the New Software” by Sequoia

• “How to Save Your SaaS Company” by Jake Saper, General Partner at Emergence Capital

Wrapping Up

The long tail has always been worth fighting for. Now there's finally a way to do it. In fact, a lot of things that felt impossible in CS a year ago are starting to look very different. Join the waitlist for everything Gainsight launched at Pulse 2026 here.

See you next week 🧠

Chael Banks, SVP of Customer Success at Okta, joins host Josh Schachter on this episode of [Un]Churned, “Here’s What Happens When CS Teams Cross the Commercial Line.” He breaks down how Okta structures CS across its customer base, why separating advisory from commercial produces better outcomes at scale, and how AI is helping his team maintain a consistent conversation from its smallest accounts to its largest.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: The CS Orgs That Own Maturity Don’t Negotiate Renewals

Most CS orgs can tell you which features a customer has adopted. What matters most to Chael’s team is something different: a customer’s identity security maturity. That’s how far along an organization is in controlling who has access to what, and how securely. Where they sit on that spectrum, where their industry peers sit, and what the gap between the two is costing them is the entire basis of the CS relationship at Okta.

Chael has found that when his team focuses on benchmarks rather than product conversations, customers respond differently and more meaningfully.

When Chael talks about his team’s role, he avoids words like “renewal” or “expansion.” Instead, he focuses on “progression.” Their main goal is to guide customers through a maturity journey, using Okta’s customer data points for accuracy. They look at factors such as phishing resistance, National Institute of Standards and Technology (NIST) compliance, and governance. Each skill links to a result, and every customer is matched to a benchmark. The conversation is about where you are, where your peers are, and what steps you can take next.

The Benchmark Exists Whether They Renew or Not

Chael’s approach works because the benchmark gives customers an important focus that isn’t tied to Okta’s sales cycle. Identity security maturity is a real goal they need to reach, whether they renew or not. Okta’s team is the most credible group to define and track this progress.

That’s the beauty of this approach. If your CS team controls the benchmark, the renewal conversation changes. When customers see you as the most credible guide for their maturity progress, leaving means losing that guidance, not just the software.

Chael shares that his team earns the right to involve a sales rep only after demonstrating that their primary goal is to help customers progress. Trust comes before any commercial opportunity.

You Need the Data to Back It Up

To build a maturity model that’s truly credible, you need scale. Having enough customers across different industries helps define what “good” looks like for a bank, a healthcare provider, or a tech company. Okta’s 20,000-plus customers and billions of daily transactions inform its precise NIST compliance benchmarks for each industry.

This can be a challenge for smaller teams, but the main idea still stands. Focusing CS conversations on outcome benchmarks instead of product features doesn’t require thousands of customers. It just takes defining what “mature” means in your field and creating a way to measure customer progress.

Building AI for Consistency Over Efficiency

The maturity model only works if every conversation lands the same way. A benchmark that a veteran CSM articulates brilliantly, but a newer rep fumbles, is more of a liability than a framework. At Okta, reps range from seasoned industry practitioners to people two years out of school.

Chael’s answer is what he calls “a little thing in your ear”: tools that surface product telemetry and benchmark data in real time, so a rep four years into identity security can have the same conversation as one who has been in it for twenty.

Most AI implementations in CS optimize for efficiency: fewer tasks per rep, faster responses, and automated summaries. That’s a cost question. Instead, Okta is asking, “How do we make sure every customer gets our best rep every time?” If you’re not asking that question, your AI strategy is solving the wrong problem.

🎧 Listen for These Moments

Chael brings a consultant’s specificity to a conversation most CS leaders are having at the philosophical level. Thirty-one minutes, dense with operational detail.

1. The identity maturity model in practice and how Okta maps customer adoption to outcome benchmarks by industry, anchoring every CS conversation to progression rather than product.

2. The AI consolidation problem: Chael’s candid take on what happens when everyone on your team can build something with Claude, and how you govern toward consistency without killing the innovation that’s actually useful.

3. The no-commercials policy explained: not as a philosophical stance but as a structural decision, and the signals from his consulting career that convinced him the trust boundary is worth protecting.

🔎 Where to Find Chael Banks

• LinkedIn: linkedin.com/in/chaelbanks

📎 Referenced in This Episode

• Book: Winning on Purpose by Fred Reichheld

Wrapping Up

Renewal gets easier when your customer has somewhere to go and you're the only one who can take them there.

See you next week 🧠

Fred Reichheld, Bain Fellow and creator of NPS, joins host Josh Schachter on the latest [Un]Churned episode, “187. The Creator of NPS Says It Was Never Built for Surveys” to share what two decades of loyalty economics actually taught him, and why the metric most CS orgs are optimizing isn’t capturing true growth potential.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

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🎯 The [Un]Churned Take: The Referral Engine Is the Only Growth Lever That Compounds

Fred spent decades believing retention was the engine of loyalty economics and referrals were the icing on top. Then he went back and looked at the data more carefully.

On average, 20% of new customers come through referrals, but those 20% generate closer to 80% of profitable growth. He calls it the thing that “blows people’s minds.” But for post-sales leaders, the sharper implication isn't about acquisition. It's about where Customer Success (CS) energy goes. Most programs are built around churn reduction and NPS score management. Fred's argument is that those things capture maybe 10% of the loyalty upside. The real gold mine comes from making customers love you enough to send their colleagues your way.

Referrals Compound In Ways Retention Doesn’t

Referred customers are qualitatively different from customers who came in through marketing. They arrive already trusting you, already understanding what they’re buying, and already pre-disposed to be satisfied. They pay closer to full price and retain longer. Most importantly, they’re more likely to refer someone else and that’s where the exponential math kicks in.

Fred shares that referred customers come from true promoters who are “really acting in their friend’s best interest.” That’s a different psychological contract than a prospect who found you through a paid ad or an outbound sequence. The economics follow the psychology.

Retention is finite. Every customer has a ceiling on how much they can buy from you. The referral engine has no ceiling. It’s the only customer growth mechanism that genuinely multiplies.

And Then AI Walks In

Here’s where it gets urgent for CS leaders specifically. Fred’s view on AI is not that it disrupts loyalty economics; it’s that it accelerates them, hard, in one direction.

AI tools are being used by buyers to cut through paid signals like ads, SEO, and sponsored influencers, and get to what he calls “the truth”: legitimate reviews, peer conversations, and referrals from similar customers.

That's a direct threat to growth strategies built on paid acquisition and survey scores, but for companies that built real referral engines, it’s a direct tailwind. The buyers coming through AI-assisted research are going to find themselves leaning towards the companies with the most credible word-of-mouth. Not whoever spent the most on marketing.

What a Modern Referral System Should Look Like

When a new customer closes, find out how they came in. If a referral played a role, track it. Rank-order which referral was most important. Get that back to the people in your org who created that referenceable relationship. Make it part of their performance record, their bonus, their career trajectory.

At Bain, this was formalized: to make junior partner, you needed at least three C-suite executives who would enthusiastically pitch Bain in a competitive situation. Mid-level partner: six or seven. Senior partner: a dozen. Tracked by name. The requirement was real, not abstract.

Most CS orgs have nothing like this. They have NPS dashboards and churn reports. They might have an advocacy program with points. What they don’t have is a system that connects individual CSMs and CS leaders to the referrals their accounts generate. And one that rewards connection in ways that actually change behavior.

Most CS orgs have never built for this. As AI pushes buyers toward earned trust over paid signals, that's going to show up in the numbers.

🎧 Listen for These Moments

Fred Reichheld has been thinking about loyalty economics for longer than most of us have been in the workforce. This conversation covers the full arc, from why he built NPS in the first place, to why he thinks surveys are over, and what he’s arguing in his next HBR piece.

1. Why Fred thinks 90% of NPS implementations are missing the point, and the specific misuse that he argues destroys the signal entirely.

2. The earned growth framework and how to calculate it without a single survey; all of which live in your CRM already.

3. The Bain referral model in detail and what it would take to build something equivalent in a CS org.

🔎 Where to Find Fred Reichheld

• LinkedIn: linkedin.com/in/fred-reichheld

• Bain profile: bain.com/our-team/fred-reichheld

📎 Referenced in This Episode

• Book: Winning on Purpose by Fred Reichheld

Wrapping Up

Fred leaves CS leaders with this question, so now we’ll pass it along to you: if your team disappeared tomorrow, would your customers still refer you?

See you next week 🧠

Adnan Rahman, Global Head of Customer and Partner Success at Paycor, joins host Josh Schachter on the latest [Un]Churned episode, “186. Why The Best CS Leaders Start With The Boardroom, Not The Product.” He shares how Paycor structures Customer Success (CS) across a 35,000-account base, why discovery questions were crucial, and how he sees AI helping to scale their success.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

Subscribe now and get the [Un]Churned take straight to your inbox.

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🎯 The [Un]Churned Take: Curiosity Is Infrastructure, Not Instinct

Adnan Rahman built a value realization playbook for his team at Paycor, complete with outcome categories, leading and lagging metrics, a cyclical success plan structure, and a Quarterly Business Review (QBR) process. But in building it, he learned something the playbook itself couldn’t fix. You can’t operationalize value delivery if you can’t surface what the customer actually cares about in the first place.

Inspired by a past visit from Nick Mehta, whose parting advice was to “be more curious,” Adnan dug deeper to figure out what that actually looks like in a strategic sense.

His solution was to make curiosity part of the process.

The Simplest Answer Is to Ask More Questions

Health scores reward account stability. Renewal timelines reward contract closings. Escalation queues reward responsiveness. The CSM who keeps things quiet is doing their job by every measure you’ve given them. There’s no metric for the question they never asked.

To help him incorporate curiosity into the value realization framework, Adnan brought in customer discovery expert and coach Bob London. Bob shared a deceptively simple rule for discovery: stop asking questions customers can answer with a yes or no. “Do you have hiring challenges?” gets you nowhere. “Which parts of your hiring process feel inefficient?” gets you a conversation. Same topic, completely different output.

Adnan’s team put that into practice with two questions. The first: if you could sit in on your board meeting right now, what’s the one thing they’re debating? The second, which he calls the churn question: if a competitor called you today, would you answer, and what would you say?

His team was hesitant about the second one, assuming that customers would avoid answering. But the opposite happened. “Customers are very forthcoming when you give them the opportunity to tell you about the relationship and how things stand,” Adnan shared. They had opinions; they just needed someone to ask.

Why Most Frameworks Fail Before They Start

The most critical step leaders can miss when moving towards a value delivery strategy is that they define outcome categories and build frameworks, but assume their team already knows how to help customers clearly articulate priorities in measurable terms. In reality, most CSMs don’t, which isn’t a personality issue, but a training gap that often gets mistaken for a lack of motivation.

When CSMs get stuck in tactical conversations, it’s rarely due to a lack of care. The real issue is that nothing about their environment makes it easier to have strategic conversations instead of reactive ones. With the right questions and structure, that dynamic shifts. Customers see their CSMs differently, and CSMs start to see themselves differently, too.

🎧 Listen for These Moments

This conversation goes places most CS content doesn’t. A few cues worth listening for:

1. The resistance before the results: Adnan’s team didn’t immediately buy in. He talks about what it took to get them there, and why the hesitation was worth pushing through.

2. The long-tail customer problem: How Adnan thinks about AI agents specifically for the accounts that will never have a dedicated CSM, and why he’s sequencing efficiency and insights first before touching automation.

🔎 Where to Find Adnan Rahman

LinkedIn: linkedin.com/in/adnan-rahman

📎 Referenced in This Episode

• Bob London — customer discovery coach and creator of the UBR method

📅 Coming Up

Pulse 2026 — Gainsight’s annual conference is May 27–28 in Las Vegas. CS and AI are converging faster than most organizations are ready for. Come see what leading teams are actually doing. Register at gainsightpulse.com →

New Rules of CS: Expansion — The series continues June 9th with a session on identifying early expansion signals. If your team owns any part of the revenue number, this one’s worth your time. Save your seat →

Wrapping Up

Customer Success is in the middle of an identity shift from vendor contact to strategic advisor. If you feel like your team is still struggling through this metamorphosis, it might be time to do the unglamorous work of discovering what strategic thinking actually looks like. Not just the vision, but also the infrastructure.

See you next week 🧠

Scott Barker, former head of partnerships at Outreach and co-founder of GTM Fund, joined Josh Schachter and co-host Jenny Calvert on the most recent [Un]Churned episode, “185. The AI Acceleration Decade Is Coming for Everyone. Are You Ready?” He’s not here to tell you to take more walks. He’s here to argue that the practices most Customer Success (CS) leaders treat as nice-to-haves are the ones that will determine who’s still standing at the end of this decade.

Listen on YouTube, Spotify, Apple Podcasts, and Gainsight.com.

Subscribe now and get the [Un]Churned take straight to your inbox.

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🎯 The [Un]Churned Take: Slowing Down Is the Only Skill AI Can't Replicate

The Acceleration You Didn’t Sign Up For

Scott spent a decade building what appeared to be a flawless GTM career. He was the youngest director in Outreach’s history, $20M to $250M ARR, $100M under management—and then had a panic attack. The weight of running the wrong operating system at full speed finally took hold. His benchmarks were money and status, and the better he got at hitting them, the less they delivered.

Scott chose his acceleration decade. Nobody is choosing this one. AI is compressing timelines, raising expectations, and making “more with less” a structural condition rather than a budget ask. The Gainsight 2025 CS Index, drawing on data from more than 400 CS professionals, found CS leaders are under pressure to support more customers, adopt AI for scale, and demonstrate measurable revenue impact without proportional headcount growth. Scott looked at that picture from an ashram in India and recognized it immediately. It’s the decade he just survived.

What AI Actually Can’t Do

“What AI can’t do,” Scott argues, “is find meaning in someone’s life.” Scarcity creates value. If AI generates breadth, speed, and synthesis on demand, the things that don’t get cheaper are depth, judgment under uncertainty, and the capacity for original thought.

While constant AI collaboration makes us more productive, it may be lowering our tolerance for slow, generative thinking. CS leaders can’t afford to atrophy this muscle. The role is built on reading customers, holding relationships under pressure, and making judgment calls without a complete picture. None of that gets easier if you’ve outsourced the thinking.

His practical version: pick one hard question—what does success actually mean to you now? what is your team’s real ceiling in the next 18 months?—and spend two weeks exploring it with no deliverable in mind. Explore this for the sole purpose of going deep.

Acceleration Without Integration Breaks People

The most countercultural thing Scott proposes is also the simplest. Treat your attention like a financial asset and pay yourself first. Every day, before Slack and dashboards and renewal prep, a one-hour block with four components: movement, stillness, breath, and solitude. He’s explicit that timing is secondary to consistency. If checking email first removes the friction that’s stopping you from doing the practice, check the email. The goal is sustainability, not an ideal morning routine.

As the pace of AI accelerates, your capacity to absorb that change has to grow with it. CS leaders who invest only in AI adoption without investing in the human capacity to integrate change will see the returns diminish and the burnout accumulate. Investing in AI without investing in yourself is just a faster way to hit the same wall Scott hit.

🎧 Listen for These Moments

This conversation goes places most CS content doesn’t. A few cues worth listening for:

1. The six-hour silence protocol: Scott’s specific, repeatable practice for clearing inputs and recovering the capacity for original thought. More practical and more uncomfortable than it sounds.

2. The attention economy argument: his case that attention may already be worth more than money in an AI-flooded market, and what that implies for where you invest your own.

3. The “old programming” audit: the idea that most leaders are still running a definition of success written by their 22-year-old self, and why that’s worth examining before the next wave of AI pressure lands.

Where to Find Scott Barker

• LinkedIn: linkedin.com/in/ssbarker

• Substack: The Wake Up Call

Referenced in This Episode

• The Wake Up Call: How to Prepare for the Next Decade — Scott’s article that stopped a lot of people in this industry cold. Read the comments too.

• Gainsight 2025 CS Index — Data from 400+ companies on how CS teams are scaling with AI.

📅 Coming Up

Pulse 2026 — Gainsight’s annual conference is May 27–28 in Las Vegas. CS and AI are converging faster than most organizations are ready for. Come see what leading teams are actually doing. Register at gainsightpulse.com →

New Rules of CS: Expansion — The series continues June 9th with a session on identifying early expansion signals. If your team owns any part of the revenue number, this one’s worth your time. Save your seat →

Wrapping Up

If AI keeps making skills cheaper and breadth easier, the thing left to compete on is the quality of your thinking. That only comes from protecting the conditions that make it possible.

See you next week 🧠