On this episode of the [Un]Churned podcast, 207. Why Domain Credibility Matters More Than Ever , Jake Saper, General Partner at Emergence Capital, joins host Josh Schachter to share how the next great software company may not sell software at all. They discuss why per-seat pricing is “definitely dying” while SaaS itself endures, how AI will be used to deliver work rather than merely sell tools, and and what it takes to build a real AINS business that’s not just an AI-wrapped services firm.

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

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🎯 The [Un]Churned Take: SaaS Isn’t Dead. It’s Evolving Into AI-Native Services.

“The death of SaaS.” “How to survive the SaaS-pocalypse.” Obituaries for SaaS have been making headlines in the wake of AI’s constant advancements. Jake Saper, General Partner at Emergence Capital, isn’t buying the hype. He’s too busy creating the next evolution of SaaS. It’s a model he’s called AI-Native Services (AINS).

“I don’t think SaaS is dead. I think the per-seat pricing model is definitely dying, and will go away. But the enduring value that software providers play in certain industries, particularly regulated industries, I think will endure.”

On the [Un]Churned episode, “Why Domain Credibility Matters More Than Ever”, Jake Saper shared his viewpoint that customers never wanted software for its own sake. They bought software because they wanted an outcome. As AI can perform more of the underlying work, the value proposition shifts from selling a tool to delivering results.

Don’t Sell the Fish Pole, Sell the Fish

An AINS company uses AI to deliver an existing service better, faster, and more cheaply than the incumbent, while pricing based on results rather than seats. The key distinction from everything else being marketed right now is that AINS companies don’t sell software for customers to operate. They perform the service themselves.

“We’re now in a world where AI can do much of the work itself. And so selling a tool doesn’t make as much sense anymore. We don’t want to sell the fishing pole. We want to sell the fish.”

Not to be confused with professional services, AINS companies stake their claim on carrying out the work, not just deploying the software. Domain credibility plays a key role here. A true AINS company doesn’t just wrap AI around human labor. It provides a credible path to AI doing the majority of the work, rising leverage per service employee, and ultimately software-like margins. It also requires a difficult cultural transition, as AINS companies need to build a technology platform and a high-quality services operation at the same time.

What AINS Looks Like IRL

Early examples of AINS are in industries where the incumbent service is expensive, slow, and rely heavily on human involvement.

Mechanical Orchard helps large enterprises move legacy software to the cloud through extensive mainframe migrations. Their AINS offering takes the load off customers’ IT teams by bringing migration in-house and using AI to speed up the process. Now, instead of, “Here’s our software, we’ll work with your IT team to migrate your mainframe,” Mechanical Orchard can say, “You need your mainframe migrated. Let us take care of it. Not only can we do it faster with our software and AI, but we’re experts in this field.”

Hanover Park, another one of Jake’s portcos, uses AINS to automate the complex, repetitive tasks involved in fund administration. By replacing manual spreadsheet checks, and email workflows with AI-driven agents, they now deliver a service where AI handles the prep and their expert accountants review the work.

Jake also pointed to Gainsight as the “first major SaaS company” taking part in the AINS transition, moving from selling software that helps customers manage their post-sales motion to delivering that motion as a service. The industries vary, but the value proposition remains that AINS providers own the outcome.

“There is a kind of wild future where technology vendors look less like ‘I sell you code.’ Instead, it could look more like ‘I am an insurance carrier, and I am guaranteeing financially some outcome you’re hiring me to perform.”

The “Wild Future” is Closer Than You Think

As AI-Native Services and the technology that surrounds it continue to reshape the tech industry, Customer Success (CS) faces both a mounting challenge and a tremendous opportunity. Instead of a playbook built around driving adoption and renewals, the focus shifts to ensuring customers achieve tangible, measurable outcomes.

The companies exploring AINS are still writing the playbook as they go, but you’ll probably start seeing a lot more fish and fewer fishing poles soon.

🎧 Listen for These Moments

Revenue leaders interested in how AI is changing sales enablement and rep ramp will learn a lot in this episode. Here are three moments to listen for in this week’s episode:

1. The mirage PMF warning: Jake's specific definition of what separates a real AINS business from a professional services firm with a good deck, and the gross margin thresholds that tell you which one you're looking at.

2. Open weight models: why Jake co-authored a letter with NVIDIA and Microsoft supporting open weight models, and what cheaper, customizable tokens mean specifically for AINS businesses trying to hit software-level margins.

3. The Embarcadero walk: the moment Chuck Ganapathi told Jake that Gainsight was making the AINS transition, and why it caught him off guard.

🔎 Where to Find the Speakers

• Jake Saper — LinkedIn: https://www.linkedin.com/in/jakesaper/

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

📎 Referenced in This Episode

• Emergence Capital — venture capital firm focused on early-stage enterprise software and AI Native Services companies

• “The AI Native Services Playbook” — Jake’s definitive guide to the AINS business model

• Mechanical Orchard — AI native service that automates mainframe migrations for large enterprises

• Hanover Park — AI native service providing fund administration for private equity and venture capital firms

• Gainsight — customer retention platform and the first major SaaS company making the transition to AI Native Services

• “The Death of Deloitte” — Jake’s foundational piece on how AI Native Services are disrupting legacy professional services firms

• “Why We Signed an Industry-Wide Letter on Open Weights” — Emergence Capital’s case for open weight models and what they mean for the future of AINS businesses

Wrapping Up

To quote Jake, “Keep AINSing.”

See you next week 🧠

Garrett Marker, Chief Revenue Officer at Brex, joined host Josh Schachter on this week’s [Un]Churned episode, “206. 2 Months of Revenue in 8 Days: How Brex’s CRO Fixes What Breaks at Scale“. They cover how Brex, an AI financial platform, is scaling one of the fastest-growing sales forces in tech without letting rep performance slip, the internally built narrative platform mapping the full buyer journey across sales and post-sale, and why Garrett never raised SDR quotas when AI made his team more productive.

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

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🎯 The [Un]Churned Take: Your Buyer Isn’t Confused. They’re Worried It Won’t Work.

As Chief Revenue Officer, Garrett Marker leads nearly 700 people across sales, Customer Success (CS), RevOps, enablement, and support at Brex. His 2026 mandate: scale one of the fastest-growing salesforces in tech without letting rep performance slip. Some segments are growing 200% year over year; one rebounded from a slump to hit two months of revenue in just eight days, thanks to a course correction. It’s a high bar, but the bigger story is how he’s closing the gap between good and great teams.

Garrett’s core belief is that the biggest gap between good and great revenue teams isn’t process; it’s understanding the buyer’s emotional journey, especially after the deal is signed.

"The core difference oftentimes between top sellers that do 10x better than everybody else and the sellers that are not doing as well is their understanding of the buyer's journey and their ability to then better do active listening."

Pattern Recognition Over Process

Garrett’s strategy goes beyond discovery questions or objection frameworks. It’s about empathy. The seller needs to internalize the emotional situation a buyer is in well enough to respond to what’s actually happening in the room, not what the script says should happen next.

That classic scenario, where a rep asks a set question, gets an answer, and then asks the next set question that has nothing to do with what the buyer just said, leaves a weak foundation for the sale to stand on. The rep may have done their research and preparation, but what they missed is the story underneath the process.

How Brex Closed the Gap

To address this, Garrett built what he calls a narrative platform. Unlike typical productivity tools focused on individual reps—think better notes, CRM hygiene, call prompts—this platform is designed for the entire customer journey. It pulls in data from sales, CS, support, and implementation, focusing less on closing deals and more on whether the customer is thriving two years later. For CS leaders, this means measuring and coaching for ecosystem fit and long-term value, not just initial adoption.

That’s a different design philosophy and it leads to a different set of questions. Not “what should I say next on this call?” but “what does this customer need to be true twelve months after close?”

Inviting Your Buyers to Trust You

Brex increased the share of customers who close their books on the platform by 20% in a year—not because the product changed, but because CS shifted from product-centric to ecosystem-centric conversations. Instead of explaining features, the team started mapping Brex into the CFO’s existing workflow—NetSuite, QBO, Rillet, and beyond. Adoption rose because anxiety dropped: buyers saw how Brex actually worked in their world.

The buyer isn’t confused about your product. They’re anxious about whether it’ll work in their unique environment. Miss this, and you risk stalled deals and churn. When CS and revenue teams address the real source of buyer anxiety, the entire conversation and the outcome changes.

Asking “what are the reasons you won’t move forward?” is an invitation for trust. Objections exist whether you surface them or not. Those who address them open the door for stronger relationships and deeper understanding.

🎧 Listen for These Moments

Revenue leaders interested in how AI is changing sales enablement and rep ramp will learn a lot in this episode. Here are three moments to listen for in this week’s episode:

1. The eight-day recovery: how one slumping segment turned around fast, and the AI-assisted competitive diagnosis that made it possible

2. The narrative platform: why Brex built something the market doesn’t offer, and what data it pulls that most seller tools ignore entirely

3. The TAP program: how SDRs earn on-the-spot promotion to AE by closing real deals first, and why Garrett never raised their quotas when AI made them more productive

🔎 Where to Find the Speakers

• Garrett Marker — LinkedIn: https://www.linkedin.com/in/gmarker/

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

📎 Referenced in This Episode

• Brex — financial platform for business spend, expense management, and agentic finance products

• RepVue — sales rep review platform

Wrapping Up

Imagine if every customer felt total confidence that your solution fit seamlessly into their world. Not just at close, but a year later. That’s the kind of trust that drives both growth and retention.

See you next week 🧠

On this week’s episode of the [Un]Churned podcast, “205. Headless AI vs. CSP: Where CS Insights Should Live,” Mark Vovsi, Senior Director of CS Operations (GTM Automation & AI) at Proofpoint, sits down with host Josh Schachter to walk through what actually got built, in what order, and why. They also cover Proofpoint's four-stage AI maturity build, why generic AI tools fall short for CS work without a data connection, and the debate every CS leader is about to have about where AI-driven insights should actually live.

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

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🎯 The [Un]Churned Take: Why Your AI-Powered Health Scores Are Gathering Dust

The customer health score has been the golden child of Customer Success for over a decade. We’ve made countless investments as an industry to achieve more accurate scoring through product telemetry, support data, and eventually by incorporating conversation signals. Scores were getting better, but churn still slipped through the cracks.

Health scores may be useful for an aggregate view of your entire customer base, but what about the CSM that has to take action? A CSM who logs in and sees seventeen accounts flagged yellow has information, but they likely lack clarity on which of those seventeen actually needs attention today, why, and what specifically to do about it. The score became part of the noise. By itself, it’s just a measure. What matters is what happens next.

When AI Enters the Equation

Mark Vovsi, Senior Director of CS Operations (GTM Automation & AI) at Proofpoint, focused his first two years on building the data foundation that his early churn detection AI tool would eventually read. This included assigned accounts, a functioning CSP, clean data, and digital motion.

“You can’t do anything in digital, you can’t launch a new CSP, you can’t automate anything—you’ve got to organize [data].”

What he’s building now is an early warning system that pulls together structured telemetry, unstructured conversational data, human-reported signals, and ML-based prediction through a partnership with QuadSci. The output is a risk summary for the account, including the key issues driving it and recommended next steps, generated and organized before the CSM opens the account. They receive a narrative brief rather than a dashboard to interpret.

Most CS orgs have invested heavily in improving the prediction. But the real shift that Mark realized came from designing an output with the CSM’s workday in mind.

Surfacing Risk and Acting On It Are Two Different Problems

Surfacing risk is a data and modeling problem. Acting on it is a product and workflow problem. Mark’s build at Proofpoint went through foundation first, then role-based AI assistants with customer data access, then auto-loaded success plans, and now the early warning system. Each layer required the one before it to be stable.

Customer Success has always sought to detect churn risks early. With AI, we now have unprecedented access to data for predicting those risks. But more data isn’t the answer on its own.

The real value comes from building systems that translate signals into clear, actionable steps, empowering CSMs to respond effectively.

🎧 Listen for These Moments

For any CS leader thinking through what an AI transformation actually looks like in sequence, this one is worth the full listen. Here are three moments to listen for in this week’s episode:

• The success plan architecture: Mark describes how Proofpoint went from CSMs who didn’t know how to create a success plan to auto-loading product-specific plans for every customer, every quarter. Who owns the templates, how the guardrails get built, and why it’s not just a time saver.

• Why Amazon Q beat ChatGPT for post-sales: Learn why Mark thinks generic AI tools fall short for CS work, and what “connected to the data” actually has to mean before a role-based assistant becomes useful.

• The 12-month vision: Hear what a successful early warning system looks like from the CSM’s perspective a year from now, and why Mark thinks CS has finally caught up to a promise it’s been making for fifteen years.

🔎 Where to Find the Speakers

• Mark Vovsi — LinkedIn: https://www.linkedin.com/in/vovsi/

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

📎 Referenced in This Episode

• Proofpoint — enterprise cybersecurity platform

• Amazon Q — AWS’s AI assistant for business, used by Proofpoint’s post-sales team as their connected, role-based AI tool

• QuadSci — predictive analytics platform powering the ML layer of Proofpoint’s early warning system

• Gainsight MCP — connects the entire Gainsight platform to any MCP-enabled LLM

Wrapping Up

Mark started with nothing and built a system that can see churn coming from a year away. What future are you looking to build?

See you next week 🧠

Models keep getting better. Inference keeps getting cheaper. And yet organizations are hitting token cost ceilings with little attributable ROI to show for it.

The bottleneck is not intelligence. Most organizations just haven’t built the layer where the value actually lives. There are two common problems that stand between AI investment and the value it delivers:

The first is that raw intelligence doesn’t automatically translate into economic value. The system that can handle real-world scenarios reliably, decides how much reasoning a task deserves, and when to involve a human, that’s the bottleneck. Not the model.

The second is that better models increase in demand. They unlock new uses. More workflows become viable. More decisions become worth augmenting. More customers become worth reaching. The demand for intelligence is effectively infinite.

These two problems point to the same answer. Think of the foundation model as the brain—general purpose, powerful, expensive to run at scale. Economic value comes not from the brain itself, but from the neural pathways that encode specific skills. Those pathways are built through messy iterations in the real world. They improve through repetition. They get extremely efficient at the task.

Models are now becoming an input. The system and harness around it is becoming the product. That shift has significant implications for Customer Success leaders trying to make sense of their AI roadmaps, and for understanding why some organizations are compounding advantage right now while others are still waiting for the technology to deliver.

Acquiring the Skill

I started as a data scientist. Eight years and several AI paradigm shifts later, I’m Director of AI Product at Gainsight, building Atlas, our autonomous agent platform that manages entire customer lifecycles. This journey has shaped the way I think about the gap between AI capability and AI value.

Building production agents is much harder than building good demos. The value capture lies in the last mile, and the last mile is where most organizations underestimate the work.

Traditional software engineering is about expressing logic. You define the rules upfront and the system runs on it. Agent engineering is about governing judgment. The rules don’t cover everything and the edge cases only show up in production. LLMs have jagged capability profiles. They can perform brilliantly on one task and fail unpredictably on an adjacent one.

My team experienced this firsthand while implementing intelligent outreach for an Atlas customer. A “Do Not Contact” designation had been entered into a middle name field as a workaround. It had always been handled implicitly by the humans who knew to look for it. When translating emails into gendered languages, a similar problem surfaced. Templates needed to be gender-neutral in ways that had never been formally specified.

These are edge cases in our data that nobody anticipated and weren’t visible until the agent hit them.

The second problem is different in kind: institutional knowledge that was never written down because it never needed to be. The full set of reply categories—what the system should do when a customer says they’ve already renewed, or that the wrong person was contacted, or that they want to escalate, existed almost entirely in people’s heads and in years of email threads. Humans handled it implicitly. The agent couldn’t.

Your team carries an enormous amount of undocumented operational knowledge. Deploying AI forces that knowledge to the surface in a process that’s neither fast nor clean. What looks like an AI problem is often a knowledge problem. What looks like a failed agent is often an organization meeting itself for the first time.

Codifying the Pattern

A key limitation of LLMs is that they cannot learn on the job. They don’t fail, correct, and improve the way a practitioner does. The neural pathway doesn’t strengthen on its own. That’s what the system above the model has to do.

The “Do Not Contact” case I mentioned earlier is a good example of how this works in practice. The fix wasn’t a single patch. We separated contact name cleaning into its own agent, reviewed every eval failure in that category, and tightened prompt instructions with targeted examples.

Issues caught during human review went into judge prompts and validation datasets. That specific case required the cleaning agent to flag contacts as unusable rather than attempt to clean them. It was a decision that required rethinking task decomposition entirely and only became clear after seeing the failure in production.

If you want a closer look at how we actually build and stress-test the evaluation layer behind Atlas agents, we wrote about it here: Before the Agent Ships: The Human Layer Behind Gainsight’s Agent Evaluation Process. For more weekly insights on AI, retention, and Customer Success, subscribe to the [Un]Churned Substack.

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Currently the learning process requires careful distillation of observed issues and fixing them across the system. Our engineers use AI every step of the way, but it’s inefficient and requires supervision. Learning harnesses will improve over time, but I expect there will remain a system above the model responsible for turning experience into capability.

Neural pathways don’t form from a single exposure. They strengthen through repetition, through failure, through correction. You can’t just read a book on riding a bike, hop on, and compete in a triathlon. You fall, correct, fall again, and eventually your body internalizes something your brain couldn’t have calculated upfront. Once it clicks, it’s automatic and nearly impossible to unlearn.

This process produces the raw material for operational improvement today and training substrate for more specialized, efficient models tomorrow. Organizations deploying agents now and instrumenting them well are accumulating a moat most haven’t fully recognized yet.

Executing Efficiently

Once the skills are acquired and the learning system is in place, the optimization problem shifts. The goal is no longer capability. It’s efficiency, which is another defining characteristic of neural pathways.

The demand for intelligence is effectively infinite. The answer is spending it deliberately. Not every task deserves frontier reasoning. Not every task needs a language model at all. As domain-specific systems mature, fine-tuning smaller models on your own deployment data becomes standard practice. The encoded knowledge accumulated through deployment is what makes that specialization possible, and adds another layer of defensibility to this layer.

The Pathway Is the Product

That’s the real opportunity above the model. Not building a better brain, but building a better system for putting it to work.

The current gap between AI investment and attributable ROI is not evidence that the technology is overhyped. Intelligence isn’t the bottleneck for a majority of economic tasks anymore. The system that makes it useful is still being built, and the value it unlocks will be larger than what came before.

The assertions in this article are meant for the next year or two. Or months. Or days. Beyond that, we’re all guessing. We might end up with a Technocore negotiating in the Datasphere while humans go on pilgrimages. IYKYK. Until then, there’s plenty of interesting work to be done above the model.

Shantan Reddy is Director of Product, AI at Gainsight, where he leads Atlas, Gainsight's autonomous agent platform for Customer Success. Outside work, he plays racquet sports and reads too much sci-fi.

🔗 Connect with Shantan here on LinkedIn.

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.

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

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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.

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

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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.

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

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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.

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

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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 🧠

Fifteen years ago, I took my first Customer Success (CS) job at a small marketing analytics startup called Invoca. It was early enough in the AI timeline that the most exciting thing happening in machine intelligence was IBM’s Watson.

The promise behind it, at least in the world I was working in, was that it would take enormous amounts of unstructured marketing data and make it useful for the average business user. No data analyst or technical engineer standing between you and the insight. Just ask it a question and get an answer. Today that promise is mostly true. Claude, GPT, and Gemini aren’t the early Watson. The leap in capability is real.

But what gets lost in every breathless LinkedIn post about AI transformation is this: the part that makes it work is still you.

Early LLMs learned from everything. Every Reddit thread, every bot-generated forum post, every confidently wrong hot take ever published to the internet. Garbage in, industrial-scale garbage out. That hasn’t gone away. These models still don’t know what truth is. They know what’s probable, and that’s why what you feed them matters more than anything else.

I think about it like this: an LLM is more like a helpful little alien you’ve sent to the grocery store. It wants to help, but doesn’t know the difference between an egg and an onion. The way it learns that an egg has a hard shell and comes in a carton, while an onion has a papery skin and lives on the shelf—that’s the context you give it. Without that context, it’s guessing. And it will guess confidently.

Today I’m a Principal Customer Success Manager (CSM) at Gainsight (the company that wrote—and continues to write—a lot of the CS playbook) managing a strategic book of business in the eight figures. My accounts are some of the largest enterprise customers in the world. And the biggest names on my list are asking the exact same question as the scrappiest startup CSM I know.

Not “should we.” Not “is this real.” But, “How do I stay relevant in this new ecosystem?”

Let’s Be Honest, The Job’s Changed

The job has changed, but not just in the ways you see on your LinkedIn feed.

The manual execution, like writing the Timeline entry in Gainsight, formatting the internal Slack update, chasing Call-to-Action (CTA) hygiene across fifteen accounts—that’s moving. What’s staying with you is the context. You know the customer meeting last Tuesday was tense even though the notes look clean. Or the executive sponsor just changed and the health score hasn’t caught up yet. You decide which flag is noise and which one you need to have a call about at 8 am.

The system can draft the update, but it cannot know what the update should mean.

The human didn’t get removed from the loop. The human got moved upstream. Away from the manual execution and into the place where judgment lives.

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What Moving Upstream Looks Like

This concept of “moving upstream” really came into focus when I inherited a new book of business. There had been about eight weeks of overlap with the previous CSM. During that transition period, ownership was technically shared, but in practice it was occasionally unclear who was responsible for what.

I had a status meeting with my customers in two hours. The kind where you’re expected to walk in knowing what’s outstanding, what’s been completed, what’s next, and why it matters. The kind where “let me follow up on that” four times in forty minutes tells the customer exactly where they stand.

I made a lovely pour over cup of coffee, threw some waffles in the toaster, and opened Claude.

What I have connected gives me the full account picture:

• Gmail tells me what was said after the meeting.

• Calendar tells me what meetings actually happened.

• Gainsight tells me what made it onto the record.

• Slack tells me what the internal conversation really was.

• Notion tells me what the team knew going in.

• Linear fills in the product gaps.

In the past, I was the one bouncing between all of those, assembling the picture manually. On a good day that took a few hours, but a real audit would take most of a day. The work was never the writing, it was the gathering.

I ran a monthly account audit skill I’d built across my full connector stack. It pulled thirty days of activity, cross-referenced what had been posted to Timeline, and surfaced the gaps.

Eleven touchpoints. Twenty minutes.

Claude brought each one to me with a recommendation and an approval step. It knew where each one belonged, such as which updates needed to go to Timeline, which ones were tied to specific Success Plan actions, which ones needed to be logged as milestones. It wasn’t treating Gainsight as one big bucket. It understood the architecture of the tool and routed accordingly.

For five or six of the suggested updates it stopped entirely: I’m missing context here. I don’t see a transcript for this meeting. I don’t see a follow-up email. Can you fill this in? And in one case it went further: it looks like this project needs a follow-up task—want me to add it to your dashboard?

Closing the Gap, Not Just Surfacing It

I read every update, catching what was wrong or incomplete and deciding what went out under my name. For someone still learning the Gainsight platform and figuring out when something is a milestone versus a Success Plan action versus a Cockpit task, it wasn’t just doing the work. It was showing me how the work was supposed to be done. It led me to the ‘how’ instead of just carrying out the action.

By the time I got on that call two hours later, I knew exactly where we were. Not because I’d spent the morning buried in tabs, but because I’d spent twenty minutes on the part that actually required me.

The meeting was productive. With the insights from Claude, we were able to talk about what was next, not where we were.

What the AI Couldn’t Know

A lot of the AI conversation assumes that if the machine can do the task, the human becomes less important. The update gets written, the entry gets posted, the gap gets closed, and your job shrinks a little each time.

That’s wrong.

When I was sitting in that Claude approval queue, I wasn’t proofreading. I was the only person in that loop who knew certain things had already happened. A decision made in a meeting that had nothing to do with the account name. A conversation that lived in a Slack DM that never mentioned the customer. Details settled offline, in the hallway, over a call that wasn’t logged anywhere the system could find.

The alien can only see what it has access to. It doesn’t know what it doesn’t know. Without a human in that seat who actually knows the account, the people, and the history, those gaps don’t get caught. The updates appear complete and the Timeline seems clean, but somewhere downstream, something breaks because a conversation that was closed in a hallway got reopened in a customer meeting.

What I didn’t expect was what catching those things would feel like.

I came into this role carrying real doubt about whether I could provide value this early. Would I know enough? Were my relationships deep enough? Then I sat in that approval queue and I knew things the system didn’t. I could give direction, close loops, and look at a suggested update and say, “No, that’s already been handled, here’s what actually happened.”

The approval process didn’t just improve the output. It showed me I was already adding value I couldn’t see yet.

Finding My Place in the Loop

I spent time early on thinking the goal was to get the workflow right and build something complete enough that it would just run. I understand now more than ever that the workflow is never done. It reflects what you know in the present. The context you fed it and the data you connected today. The moment you stop tending it, it starts drifting from the truth.

The skill is just a template. That’s the most important thing I can tell you about building any of this.

What I’ve actually gotten better at isn’t building workflows. It’s the thing only a human can do: notice what’s happening and provide that unique context. Remembering the cell phone call or offhand conversation that was never caught.I’m more deliberate now about capturing those moments because I know the system can’t get there without me.

That’s the job now. Not knowing every answer before the customer asks. It’s always been “I don’t know, let me find out.” What’s changed is I have a very capable alien helping me find out faster, so I can spend more time on the part that I can make a valuable impact on.

What Leadership Needs to Understand

Telling your team to “be confident” isn’t a strategy,and right now, a lot of leaders are doing exactly that. They’re handing CSMs a growing book, an expanding tool set, or a ship that adds a new deck every quarter, and saying, “trust the process.”

But the process is still being built in real time by the people you’re asking to be confident while they build it.

The most valuable thing you can do right now isn’t roll out another tool. It’s to create the space for your team to be honest about what’s working and what isn’t. Not in a QBR slide, but in a real conversation. Because the CSMs who are figuring this out aren’t doing it in your dashboards. They’re doing it at 7 am in a personal Claude window before a customer call. And that work deserves to be seen, supported, and built into the actual process.

Retire the things the tools are replacing. Give people permission to experiment without the fear of getting it wrong. Ask whether the load actually got lighter or just changed shape.

The alien needs context to work. So do the people managing it.

You’ve Got This. Because You’ve Always Had This.

The grocery list got bigger. There are features you haven’t learned yet, integrations you didn’t ask for, and customers who will ask you something tomorrow that you won’t know the answer to.

That’s always been part of the CSM role.

“I don’t know, but I can find out” has always been the skill. The curiosity, the relationships, the ability to read a room and know what a customer actually needs—that was always you, not the tools.

The alien may help you manage a bigger list, but you need to keep it fed by staying in the loop. Don’t forget who’s actually running the ship.

Jon Johnson is a CSM through and through—which apparently includes co-hosting 100+ podcast episodes with Josh Schachter, co-founding a newsletter, publishing a poetry collection called Oaxaca for Sushi, and making records in a home studio between account reviews. He works in Customer Success the same way he does everything else: like someone who has something to say and too many formats to say it in.

🔗 Connect with Jon here on LinkedIn.

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.

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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 🧠

From the day your SaaS solution launches, it’s already drifting from what your customer actually needs. Sometimes their leaders know it. Sometimes nobody does. But it’s in the data and a strategy shift is coming, and your onboarding cycle wasn’t built for it.

You do your best, you train the customer admins, you train your own CSMs, but eventually the instance drifts. Sometimes it’s minor stuff: tech debt, automations running in the dark and going nowhere. Sometimes it’s major: the exec sponsor left and you’re unmoored. How do you get it back? Do you even know how big a problem it is for you?

I Hadn’t Written Code Since 2006

I came to this question from an unusual angle. My background is in GTM, not engineering and definitely not software development. When I stepped into a new role leading Gainsight’s GTM org’s AI strategy, I hadn’t written a line of code since a Java Programming class in 2006. I got to this work not because I was the obvious person to build something, but because I was deep enough in the business to know what was worth building.

When I started the role, we were torn between surveying the org and trying to sort and solve all the problems at once, and just jumping in and solving one problem end-to-end. We’d heard that this was the right approach, but we didn’t know which use case was going to have the biggest bottom-line impact, nor which we could deliver quickly. Luckily I was not the only person trying to solve this. Josh Schachter had just joined Gainsight and had a mandate to do exactly that: find an AI use case for the CS team and solve it.

Asking the Right Questions

We asked our partner, Method Garage,and they simply asked the CSMs. Actual conversations with your internal customers! Embarrassed to say we hadn’t started there. I was focused on scraping use cases out of Slack posts and surveys, but the real answer was in our top CSMs heads. Of course, the problem was our pre-AI answer to the problem I described above: customer instances drift from their goals, and CSMs were logging in and evaluating configurations one feature at a time, taking 8 to 12 human hours to get from there to a reasonable presentation on what was right, what was wrong, and where to start on bringing the instance into alignment with the customer’s goals.

The decision to focus on Instance Reviews as the solvable use case came out of a simple question our partners posed early in the process: if we could build software to help our own CSMs, what would it do? The answer was immediate and unanimous. I joined the project a bit later as the SME on the configurations the tool would need to analyze, and this turned out to be exactly the right entry point.

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The Foundation Already Exists

Method Garage proposed a design, and then the crucial question landed: who is managing these internal builds today? Who will build it, host it, run AppSec, authenticate users?

We didn’t know. We’d heard rumors that some teams had built things like this, but no real answer. Then we found it: Anoosh, who leads operations for our Solutions Consultants, had already done the hard work.

In July 2025, well before I started building anything, Anoosh had worked with engineers, developers, and IT across Gainsight to stand up a secure, compliant software stack. He built it originally for Solutions Consultant to generate bespoke data for community demo orgs. He’d also built an RFP response generator that matched dozens of questions to a library of hundreds of answers. In doing so, he’d gotten IT and engineering approval, established real web hosting (internal only), and created something that almost no other team at Gainsight had: infrastructure that was actually ready to build on.

He hadn’t built it for us, but it was there. Without it, our time to value would have stretched into quarters.

If you’re reading this and wondering whether you already have something like this inside your company, my advice: find your Anoosh before you find your use case. Look for the person who has already fought the AppSec and hosting battles on behalf of a different team. That foundation is usually closer than you think, and it’s what lets a non-engineer like me ship production software in 26 days.

Vibe Coding as a Subject Matter Expert (SME)

Josh asked me to drop our careful analysis of use cases and infrastructure and, starting February 1st, build the instance review agent on Anoosh’s stack. I had a Product Requirements Document (PRD) and an existing repository. I kid you not, I hadn’t written a line of code since 2006. I’d been using Claude for about two months, but never Claude Code. I didn’t have the first clue what an IDE was.

From there, I climbed my way up the learning curve. I spent the first week orienting myself by learning enough vocabulary to ask the right questions, making mistakes that Claude Code helped me walk back, figuring out the difference between understanding what needed to be built and being able to describe it clearly enough for a model to build it. That gap is where the SME really makes an impact.

Claude Chat was my PM. Claude Code was my engineer. I was the SME who knew what “correct” looked like.

By February 26th, the Instance Review agent was deployed on production hardware and ready for use with customers. I built, tested, got it vetted by CSMs, and deployed in time to demo at our Revenue Kickoff, in less than a month from the first time I had opened VS Code. We delivered the first instance review from this internally built software to a very kind and open-minded customer’s ops team only three weeks after I started building.

Did I learn React or Python? No. But I shipped production software with both.

If You’re Thinking About Building

Today 27 CSMs have shared instance reviews with more than 50 customers. The effort has paid for itself already in time savings alone.

Our Professional Services team is now building on this; the Instance Review agent is becoming part of how we systematically detect and close that drift before it becomes a renewal conversation. You can’t buy this as a product. You could hire someone to build it, but they’d need your SMEs in the room anyway. When the expertise already lives inside your company, AI closes the gap between knowing what to build and being able to build it.

If you’re an SME thinking about taking the same leap, here’s what I’d tell you:

Find the infrastructure first. Don’t start with the use case. Start by asking whether someone in your org has already done the AppSec, hosting, and IT approval work. That stack is your launchpad.

The SME work is the real work. I didn’t need to become an engineer. I needed to be specific enough about what “good” looked like that Claude Code could build toward it. Your domain expertise is the input that makes the output worth anything.

Ship something, then improve it. I had a working agent in three weeks. While it wasn’t perfect, feedback from real CSMs on real customer instances is what made the next version better. You won’t get that from a PRD.

What did you decide to build instead of buying? Or anything you regretted building?

Steven Doty is a Senior Program Manager of Gainsight Systems at Gainsight, where he leads AI strategy and builds the tools his GTM team actually uses. He came to this work from ops, not engineering: eleven years at Rosetta Stone, where he was one of Gainsight’s earliest customers, owned the company’s NRR reporting process, and won employee of the year for it.

Kendra McClanahan, now his manager, did his onboarding session in 2015. He picked up Gainsight that day and hasn’t been able to put it down since. He lives in the Shenandoah Valley of Virginia, where he restores native plant habitat and wages a losing war against deer, rabbits, and his own Claude token usage.

Connect with Steven here on LinkedIn.

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 🧠

The thing about explorers is that they’re never actually the first to see something. They’re the first to make sense of a discovery for the age in which it’s found.

Take National Treasure, for example. Nic Cage’s character isn’t just following a trail of clues. He’s the descendant of the people who encoded that knowledge in the first place. He spent his life learning the history that ultimately led him to find the treasure hidden by his ancestors.

Why are we using a National Treasure metaphor? Well, this year, Gainsight’s Pulse 2026 conference had an exploration theme that extended far beyond the thrilling keynote intro video or the fun graphics lining the hallways.

The people who mapped Customer Success (CS) and post-sales strategy in the first place are still active practitioners and leaders, and they were at Pulse 2026. These were the folks who built CS before it had a name and figured out by trial and error what retention actually requires, who are now watching AI surface everything they spent 15 years proving.

The real story of Pulse 2026 is that the map-makers are still in the room, and they’re the ones who know exactly where to point the tools. Here’s how the lessons of the past are paving the way for post-sales evolution in the age of AI.

1. The People Who Proved CSMs Work Are Proving AI Does Too.

In 2010, Frank Auger split HubSpot’s customer base into two cohorts just to prove that Customer Success Managers (CSMs) moved the churn number. He didn’t have a playbook or benchmarks. He had a hypothesis to prove and a controlled experiment with which to do so. This willingness to dive into the unknown and make discoveries through trial and error is how the post-sales discipline was built.

We’re seeing this instinct come into play as AI adoption takes off in CS and revenue teams. The people who built the function aren’t waiting for definitive frameworks before they act. Brad Casemore and the PartsSource team hypothesized that AI works best after the operating system is in place. So they spent 18 months building a closed-loop intelligence system before even touching agentic capabilities. Only once that foundation was solid did they layer in the Atlas renewal agent for their long tail.

Their methods mirror Frank’s—understand the signal, build the play, measure it, then scale. Teams like CivicPlus are running the same kind of experiments with their conversation intelligence. The stakes may be higher, but the tools in our belt make us faster and the CS leaders paving the way aren’t waiting for permission to figure out how to use them.

2. We Know the Signals. Now We Can Act on Them.

Over the years, CSMs have been knowledge gatherers, carrying customer conversations and signals that didn’t fit into platforms. Extreme politeness masking a negative sentiment. Offhanded comments that show that a customer was, in fact, well informed of a competitor’s pricing and capabilities. These are some of the examples Bob London (Bob London LLC) shared in his session on critical listening. In the past, these signals didn’t make it to the health score and because this knowledge lived in the CSM’s head, once they left, so did those insights.

Now, we can marry the CSM’s well-honed skill of identifying these signals, with AI’s ability to capture them at scale. The context that used to live in one CSM’s memory can now be surfaced, queried, and extended across an entire book of business. As Christine Storm (Securonix) said in her panel, “No CSM has the time to do the archaeological digging necessary to go through Confluence, Jira, support tickets, the Gainsight stuff. There are so many sources of information and so many people that have the information in their head.”

Post-sales veterans know these signals intimately, and they’re the ones who will chart the course to operationalizing them at scale.

3. We’re Building and Buying.

The concept of build vs. buy has plagued post-sales teams since the early days of SaaS. With the decision to build comes flexibility and the need for internal upkeep, while choosing to buy comes with speed and institutional knowledge, but product constraints. For fifteen years, post-sales leaders have been navigating that tradeoff by stitching together point solutions, waiting on roadmap queues, and making peace with the gap between what the software did and what the business needed.

This discussion showed up everywhere from the mainstage to the hallways this year at Pulse. In his keynote, Gainsight CEO Chuck Ganapathi offered a new way of thinking: “You’re forced to choose between the freedom, and the pain, of vibe coding something from scratch; or the shackles of inflexible software and living with its limits. At Gainsight, we think that framing is fundamentally wrong. Because the future is not build or buy. It’s both.”

IBM’s VP of Customer Success Kim Humphrey put the practitioner version of that argument on stage, saying, “We absolutely could have built everything ourselves. The real question wasn’t can we build, it was where do we uniquely differentiate versus where it makes sense to accelerate.” In this case, IBM used Gainsight as the CS foundation and built differentiated workflows on top using watsonx.

Knowing what can be built and what needs to be bought is what post-sales veterans have either been thinking about themselves, or guiding their customers through. What Pulse 2026 surfaced is that the tradeoff itself is now optional. The post-sales teams best positioned to take advantage of that are the ones who spent the last decade knowing exactly what they’d build if they ever got the chance.

4. We always bet on human connection. AI is proving us right.

Customer Success was built on a thesis that most of the enterprise software world perceived as a “soft skill.” We’ve since proved that this act of building the relationship between a vendor and a customer and nurturing it carefully over time was itself a business asset. The pioneers of the discipline staked the whole function on that belief, but often without the data to back it up at board level.

This year, Chuck closed out Pulse 2026 by citing a behavioral economist who asks, “In a world where AI makes more things easier to produce, what becomes scarce? Human connection.” While Pulse 2026 showed the myriad ways AI is shaping post-sales strategies, it also shone a light on the discoveries we’ve made about where the human-in-the-loop needs to stay, well, in the loop.

What actually got handed to AI across every demo, case study, and panel at Pulse were aspects like:

• handoff summaries

• meeting prep

• EBR decks

• risk detection

• renewal motions

• support deflection

Every single thing that moved to an agent was administrative, analytical, or retrieval-based. Across sessions like Driving Meaningful AI Adoption: Turning Experiments into Real Impact in Post-Sales, Afraid of AI? Your Team Isn’t, and The Fast Track to Value: How Cribl CS Orchestrates Digital, Human, and Agentic AI, the teams describing their AI deployments all arrived at the same goal in almost identical language: free up the humans for the work that actually requires them.

Through the process of elimination, AI has proven the original Customer Success bet. Every time an agent takes something off the to-do list, what remains is the relationship building and judgement calls that still require a real-life, actual human.

The Exploration Continues.

There’s a lot of discussion about the old world versus the new, and as AI touches every corner of our work and life, it’s hard not to feel like we’re operating in an entirely different environment. What Pulse 2026 showed us is that while the technological change is real and rapid, post-sales strategy is still true to its roots.

The people who got us here will help move us forward, and alongside the next generation of practitioners and thought leaders, together we’ll make sense of what Customer Success means in an AI-native world. To see all the sessions from Pulse 2026, explore the Pulse Library.

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.

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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.

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 🧠

Customers are getting harder to earn and easier to lose. With more choices and lower switching costs, customers expect real value and have little patience for relationships that don’t deliver. Now more than ever, Customer Success (CS) teams are responsible for proving that value all the time, not just at renewal.

The response from most CS organizations has been to push their teams toward more strategic, outcome-driven work. But without a clear definition of what strategic work actually looks like, teams default to what they know: reactive problem-solving, relationship maintenance, and last-minute saves. These CS heroics at scale are unsustainable for ambitious retention numbers and for the people doing the work.

So if you asked your team to be more strategic and nothing has changed in their work, can you identify why?

The problem is that “be more strategic” is a destination, not a direction. Your Customer Success Managers (CSMs) want to get there, but without a defined framework for what “strategic” actually looks like, the directive is just pressure.

Paycor is a human capital management (HCM) platform that helps small and mid-sized businesses streamline payroll, HR, recruiting, and workforce management. Adnan Rahman, Head of Customer and Partner Success, and Alan Schaffer, Sr. Manager of Customer Enablement, built a value realization playbook to help their CSMs drive customer outcomes strategically and at scale.

Here’s how they did it and the unexpected impact it had on their team.

🎧 Hear Adnan on the [Un]Churned Podcast

Why Success Plans Weren’t Working

Before the playbook, Paycor’s success plans were, as Alan put it, “not very meaningful, impactful, or consistent.” Their customers were using their tools, but didn’t have a shared idea of success.

One CSM might count a resolved support issue as a win. Another might record a completed training. Someone else might mention an executive check-in that led nowhere. All of these went into the “success plan,” but they weren’t aligned, and the real business outcomes Paycor could deliver weren’t being tracked.

Paycor delivers value in many ways, but without alignment on the core outcomes that matter most to customers, it’s difficult to consistently reinforce that value or scale it across the organization. What they saw in practice was that each function defined “value” through the lens of their own role:

• Sales often framed value around the initial business case: cost savings, consolidation of systems, or improving hiring outcomes.

• CSMs tended to anchor on activity-based wins: resolving issues, completing trainings, or maintaining a strong relationship with the primary contact.

• Support and Services teams focused on responsiveness and issue resolution as indicators of value delivered.

• Finance or procurement stakeholders often viewed value through cost comparisons, such as what was paid versus a competitor or staying within budget.

• Operational teams sometimes defined value in terms of usage or throughput, like the number of payrolls processed or transactions completed.

All of these are valid signals of value, but they weren’t aligned to a shared definition of business outcomes. Without that agreement, their current success plan strategy could never be consistent at scale.

With over 30,000 customer relationships to manage, scale mattered at Paycor. A framework that depends on individual CSM judgment or style doesn’t hold at that volume. Whatever they built needed to be repeatable and easy enough for a CSM to pick up and run with, regardless of tenure, territory, or personal instincts about what strategic work looks like.

Adnan and Alan started here. They needed to define value before they could deliver it. What they didn’t fully anticipate was how much that clarity would change the nature of the CSM role.

Building the Playbook from the Outside In

Their development process started with deep research. Alan and his team talked to sellers, solutions engineers, learning, and development staff and customers. They reviewed call transcripts and asked questions like:

• What matters most to customers?

• What worries them during the sales process?

• What issues keep coming up after they start using the product?

Defining the Outcomes

The goal wasn’t to map every possible value Paycor could deliver. That list would have been too long to be useful. The goal was to identify the few categories that mattered most and that the entire organization could get behind. “You want to keep things somewhat simple,” Adnan says. “There are a lot of things you could pick. How do we keep teams focused on, let’s say, five main things, and get real agreement on what those things are?”

So that’s exactly what they did. They boiled down research and focused their value framework on five core areas where their customers typically seek transformation:

Each area connects to specific business outcomes and KPIs, making it easier to tailor conversations to what matters most to each customer.

Establishing the Metrics

To make the value framework actionable, each outcome area was paired with a defined set of metrics that help customers measure progress and validate results. These metrics give CSMs and customers a shared way to quantify success and anchor conversations in outcomes rather than activity. Here are some examples:

1. Attracting Talent

• Number of applicants per role

• Time to fill (often targeting a 30–40% reduction)

• Offer acceptance rate

2. Retaining Talent

• % of employees with completed development plans

• Frequency of manager check-ins and 1:1s

• Reduction in voluntary turnover

• Employee Net Promoter Score (eNPS)

3. Administering Benefits

• % of employees enrolled by enrollment deadline

• Benefits participation and utilization rates

• Time saved during open enrollment

4. Optimizing Workforce

• Forecast accuracy (scheduled vs. actual labor)

• Manager satisfaction with workforce planning tools

5. Simplifying Back Office

• % of workflows automated

• Admin and manager system usage rates

• Payroll error rate

The value framework is embedded directly into Quarterly Business Reviews (QBRs), serving as the foundation for how CSMs structure the conversation—aligning on goals, tracking progress, and demonstrating measurable outcomes over time. These metrics also act as conversation starters, helping customers think more strategically about what success looks like and how to measure it.

“To help CSMs connect customer goals to tangible results, we partnered closely with our product team to map each outcome to the Paycor capabilities that enable it. For example, a customer focused on retaining top talent would align to talent development solutions, with success measured through improvements in engagement, retention, and employee growth.” - Alan Schaffer

This structure ensures that every QBR clearly connects three things: the customer’s stated goals, the actions taken, and the measurable impact achieved.

Growing Cross-Functional Alignment

Getting cross-functional agreement on the categories was a challenge in itself. When you’re building something that sellers, CSMs, solutions engineers, and learning teams all need to use consistently, you’re building a shared language. This requires the kind of multi-stakeholder interviews that most teams skip because they feel slow. Alan’s view: you can’t shortcut it. The consistency you get on the back end is only possible if you do the alignment work on the front end.

One of the biggest learnings was how much this work influenced teams beyond Customer Success. What started as a CS initiative quickly made its way upstream into the sales process. The value framework and success planning approach are now being introduced on prospect calls, to clearly outline the outcomes buyers can expect and the structured partnership they’ll receive post-sale.

“That wasn’t part of the original plan. We set out to improve how CSMs drive value after the sale, but it ended up reshaping how we position value before the deal is even closed.”
- Alan Schaffer

They were surprised at how powerful the alignment between Sales and CS became. Instead of Sales, CS, and customers having slightly different definitions of success, Paycor now has a shared language from the first conversation. It reinforces a simple idea early: Customer Success isn’t something that happens later. It’s the foundation of the partnership from day one.

How the Playbook Gets Used

With the playbook developed, it was time to set it in motion. It operates in a cyclical structure.

A CSM opens with a strategic conversation rooted in two open-ended questions:

• What are your goals for the next six to twelve months?

• What problems are you trying to solve?

Using the insights from that conversation, the CSM creates a success plan. This shared document connects customer goals to specific outcome categories, with metrics attached.

From there, the CSM becomes a curator. If a customer has flagged hiring velocity as a priority, they’re receiving relevant content, such as articles, webinars, and product guidance, specifically tied to that goal. Instead of generic Paycor communications, customers receive targeted communications that ladder up to their desired outcomes.

At the QBR the loop closes. The CSM comes back to what was agreed, shows what moved, and gets the customer to verify. “You mentioned you wanted to reduce your time-to-hire. Using this product, you cut it by 50%.” Once you’ve documented an outcome, you can reset and go again. New goals. New metrics. Another cycle.

One surprising outcome was the increased level of executive engagement. Paycor’s CSMs work primarily with payroll administrators, HR directors, and VP-level contacts. CHROs and CFOs aren’t usually in the room. But when the conversation shifts from “here’s what the platform can do” to “here’s the measurable improvement your organization achieved,” executives start showing up. The playbook created a reason for them to do so.

The Unexpected Win: Boosting Team Morale

The Human Capital Management (HCM) industry isn’t for the faint of heart. As Adnan describes it, it’s “relentless and thankless, especially at year-end.” Customers are under a lot of stress, issues come fast, and there is a constant pull toward reactive firefighting. The work left his team with no way to feel they were winning, and inevitably, burnout followed.

The value realization framework changed this energy completely. When CSMs have a clear idea of what strategic work looks like and the tools to have those conversations, they get back something they were missing: the feeling that they’re making progress. Alan calls it “a renewed sense of focus and energy.” Confidence went up and the stress of heroics went down.

Before launching, Adnan surveyed the team. The high-performing CSMs wanted this. Not because it made their jobs easier, but because it gave them a framework for the conversations they already thought they should be having. They wanted to be thought leaders for their customers, but never had the structure to do it.

That’s the piece that’s easy to miss when you think about value frameworks as customer-facing tools. The CSMs who’d been operating in a transactional mode were doing it because the work was set up that way. When you give them a defined path to something more strategic, the identity shift follows. How a customer sees their CSM changes. How the CSM sees themselves changes. Paycor went from being simply a vendor to a strategic partner, and the CSM was the champion leading the way.

Alan and Adnan made two investments that were critical to the overall success of their playbook rollout.

Investing in Enablement

Paycor worked with Bob London to teach CSMs how to ask questions that move the conversation from “let’s talk about your issues” to “let’s talk about your business.”

These enablement sessions reinforced a critical behavior shift: great customer conversations start with understanding, not solving. CSMs were trained to ask open-ended, strategic questions and resist the urge to immediately jump into solutions.

“As Nick Mehta once shared with my team, the greatest asset a CSM can have is to be curious. Ask questions to understand. For many on the team, this was a reset. It gave them permission to slow down, listen deeply, and focus on the customer’s broader business context instead of just product usage or immediate issues. As a result, conversations became more strategic, insights were richer, and CSMs felt more confident leading discussions with higher-level stakeholders.” - Adnan Rahman

This training was essential. The playbook showed CSMs what to discuss and the training made it actionable, showing them how to have impactful value conversations. If you want to hear Adnan expand on the curiosity mindset and what it actually takes to coach a team toward strategic conversations, check out his episode of the [Un]Churned podcast.

Investing in Talent Clarity

Once strategic work is defined, it’s easy to see who’s having those conversations and who isn’t. Coaching became more focused. Gong call reviews targeted the right moments. The playbook made it clear who was adapting to the new model and who wasn’t. Not everyone made the transition, but that clarity was valuable too.

Together, these investments ensured the playbook didn’t just exist on paper. The team had the skills to execute it and a clear picture of what execution actually looked like. With the human side of the rollout on solid footing, the question became, “How do you deliver this level of strategic engagement across tens of thousands of customer relationships?”

The Digital Layer

The next step is figuring out how to scale the value playbook through digital channels and AI-assisted outreach. With 30,000 relationships, not every customer can have a dedicated CSM.

As a customer moves through the sales cycle, key information about their goals and context follows them into the post-sale relationship. For customers without a CSM, AI-driven campaigns send targeted content based on their outcome categories using the same logic a CSM would use in a one-on-one conversation, but now at scale.

For customers with a CSM, digital campaigns work alongside the human relationship. As Alan says, it arrives “on a silver platter.” The CSM doesn’t have to search for context; it comes to them. Using Gainsight’s Journey Orchestrator, they’ve built signal-driven journeys based on key customer parameters. These journeys allow them to engage customers directly with targeted, outcome-aligned content, while simultaneously creating coordinated calls to action for CSMs.

Example 1: Maximize Growth Opportunities

These journeys identify customers at critical growth moments and deliver timely, value-added content, tailored support, and relevant expansion pathways. At the same time, Gainsight surfaces these signals to the CSM as a Call to Action (CTA), equipping them with the context they need to engage in a more strategic, informed way.

Example 2: Deliver Client Education

These journeys focus on client education to accelerate onboarding and time-to-value. New users are enrolled in targeted programs that deliver role-specific training and certification content, helping them get up to speed quickly and begin realizing value early in their lifecycle. CTAs are also sent to CSMs, prompting them to follow-up on new administrator certifications to ensure successful adoption.

Alan and Adnan built the playbook first, tested it with real CSMs, and are now using digital tools to scale what works. That’s the right way to do it. If you automate before you know what you’re delivering, you just get more volume, not more value.

Once You Define Value, You’re Accountable to It

The hard truth about building a playbook like this is that once it’s in place, you can’t ignore what it shows. You see which customers are having strategic conversations and which aren’t. You see which CSMs use the model and which are still reactive. You see which parts of the organization are aligned and which aren’t.

Paycor set out to solve a customer value problem. Along the way, they also solved a team clarity problem. The playbook didn’t just change how CSMs talk to customers, it changed how leaders can see, develop, and hold the CS team accountable for more than just managing tickets.

That’s what this kind of initiative really asks: be willing to see what you build. Not every organization is ready for that. But organizations that are ready often find their CSMs were ready all along.

About This Article

This article draws from a detailed conversation with Adnan Rahman, Head of Customer and Partner Success, and Alan Schaffer, Sr. Manager of Customer Enablement, both at Paycor, on how they developed a value realization framework that changed both customer outcomes and team morale on Paycor’s CS organization.

Adnan also joined the [Un]Churned podcast to talk through how CS teams can lead with business outcomes, and what it actually takes to make that shift stick. Check out his episode, here.

All data referenced here has been sanitized to protect confidential business information.