Kellie Snyder, Chief Customer Officer at LinkSquares, sat down with Josh Schahter 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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

Subscribe now

🎯 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.

Subscribe now

🎯 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 🧠