In this episode of [Un]Churned, former co-host and Gainsight Strategic Principal CSM Jon Johnson reunites with host Josh Schachter for “216. Why Every CSM Needs Commercial Skills in the Next 18 Months.” They discuss why CSMs need to get closer to revenue, how AI is changing the day-to-day role and the future of enterprise SaaS, and why human relationships still matter as technology takes on more of the work.

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

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🎯 The [Un]Churned Take: The Case for Accountability Over Attribution

A customer renews. Sales points to the relationship they built. Product points to the features customers love. CS points to adoption, engagement, and all the work that went into keeping the account healthy.

Everyone contributed. Everyone wants credit.

On this week’s episode of Un]Churned, “216. Why Every CSM Needs Commercial Skills in the Next 18 Months,” Josh Schachter and former co-host Jon Johnson, now a Strategic Principal CSM at Gainsight, revisit the industry’s long-running debate about commercial ownership.

After 15 years in Customer Success, Jon’s eager to see CSMs take greater responsibility for revenue, and with the shift to commercial and outcomes-based monetization, it’s closer than ever. According to Bloomberg, traditional subscription models, which currently make up 60% of software pricing, could fall to just 30% in the next decade. Meanwhile, outcome-based pricing is expected to rise from 10% to 60%.

As revenue becomes increasingly tied to customer outcomes, CS can stop defending its contribution and start owning its commercial impact.

Moving Beyond Individual Credit

A customer’s decision to renew is influenced by plenty of factors, many of which fall outside a post-sales team’s control. With so many moving parts, drawing a straight line between CS activity and revenue outcomes can be difficult.

If CS is responsible for managing a portfolio of revenue, its value comes from how effectively it manages that business over time. Individual wins and losses are part of the picture, but they aren’t the whole story.

“Whether it’s the CSM did a really good job or the market grew, the seat is the value and the management of that seat is the thing that you’re bringing in.”

^{-Jon Johnson, Gainsight}

You could spend hours trying to calculate how much of a renewal CS influenced, only to end up debating percentages that nobody can confidently prove. Meanwhile, the rest of the business is looking at the number.

Being treated as a commercial function means being comfortable being evaluated like one. That means taking responsibility for portfolio performance and being able to explain the decisions, actions, and circumstances behind the results.

Telling a Different Story Through Accountability

Outcome-based pricing gives CS teams a clearer path to draw that thick, bold line in Sharpie from their efforts to revenue. When customers pay for results, the work of helping them achieve those results becomes directly connected to the business’s bottom line.

It also creates an opportunity to rethink the relationship between CS and Sales. Both teams have a stake in customer growth, but their strengths and responsibilities can complement each other in new ways.

“I’ve got incredible account executives. But also, I want them into the greenfield. That’s the most important thing for them to do … And if they’re spending all their time on my lawn, their lawn’s not going to get the water that it needs.”

^{—Jon Johnson, Gainsight}

It’s really a whole new way to divide and conquer shared growth efforts. Sales can focus on bringing in new business and opening doors to new opportunities, while CS takes greater commercial ownership of the customers already in the portfolio.

With outcome-based pricing, that ownership connects more directly to revenue. CS can use its understanding of customer goals, adoption, and realized value to identify growth opportunities, while Sales brings its commercial expertise to the opportunities that need it.

The companies that get this right will turn CS and Sales into a much more powerful growth engine.

A Bigger Role in Revenue Comes With a Bigger Spotlight

Peter Parker’s uncle once said, “With more growth power comes more commercial responsibility.” Or something like that.

As CS moves closer to the bottom line, it has a bigger opportunity to show executives its impact on revenue. And with that opportunity comes higher expectations for how we measure success.

“You need a spotlight on you occasionally … so that people can see … the actual effort and the outcomes that you’re tracking. And it can’t just be a Kanban board hidden behind a login that nobody can see.”

^{—Jon Johnson, Gainsight}

That spotlight needs to illuminate more than just the final revenue number. A CSM who protects a struggling account from churn may have delivered just as much value as one whose portfolio grew because of favorable market conditions. Understanding the work behind those outcomes helps leaders recognize strong performance and identify where teams need more support.

The next chapter of CS will be defined by the revenue we’re willing to own, not the credit we’re able to claim.

🎧 Listen for These Moments

Josh says Jon was always the fan-favorite [Un]Churned co-host. We’ll let you be the judge. Catch their reunion and hear what else they had to say about:

• Why Jon doesn’t attend every customer meeting anymore: AI helps him stay informed across complex enterprise accounts while spending more time on higher-value conversations.

• The future of headless SaaS: Jon challenges the idea that most enterprise software will become headless within 18 months, pointing to the realities of security, governance, and large-scale organizational change.

• The person behind the business outcome: Jon shares why understanding what makes an individual stakeholder feel successful remains central to building trust, even as AI takes on more work.

🔎 Where to Find the Speakers

• Jon Johnson — LinkedIn: https://www.linkedin.com/in/jonwilliamjohnson/

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

📎 Referenced in This Episode

• Claude — Jon shares how he uses Claude to prioritize his work, catch missed emails, and manage information across his accounts.

• Notion — The shared meeting notes Jon uses to stay informed on customer calls.

• Suno — The AI music-generation platform that sparks a discussion about creativity, authenticity, and Jon’s complicated relationship with generative AI.

• OpenAI — Mentioned during the discussion about headless SaaS and accessing software capabilities through AI tools.

• Salesforce — Referenced as another environment where users could access customer information in a headless SaaS future.

• Pulse Europe — Josh and Jon reminisce about their experiences at Pulse Europe, including Jon’s enthusiastic laughter from the front row.

• Wild Coast — Jon’s music project, which Josh enthusiastically endorses 😉

Wrapping Up

If there's one thing this reunion makes clear, it's that the future of CS is changing fast, but the people behind it still have plenty of stories to tell.

See you next week 🧠

In this episode of [Un]Churned, “215. Your CFO Wants Proof: Is Your AI Paying Off?,” Josh Schachter sits down with Rod, CEO of HelloCCO and author of The Chief Customer Officer Playbook for the AI Era, to separate AI ambition from reality. They dig into what’s holding leaders back, the build-versus-buy debate, how to turn AI investments into measurable results as 2027 approaches, and what the future holds for the CCO and the teams they lead.

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

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🎯 The [Un]Churned Take: The Clock Is Ticking on Experimental AI Budgets

Remember when getting everyone on your team to use AI felt like a huge deal? You introduced new tools, rallied everyone around them, and maybe even had some good examples to share at your next all-hands. Now, priorities are shifting, and this year’s investments need to prove their value.

In this week’s episode of the [Un]Churned podcast, “215. Your CFO Wants Proof: Is Your AI Paying Off?”, Rod Cherkas, CEO of HelloCCO and author of The Chief Customer Officer Playbook for the AI Era, talks about what he’s learned from talking to almost 200 post-sales leaders about their experiences with AI. As companies look ahead to 2027, he’s seeing expectations start to change.

“I think what CFOs are starting to see is sort of the credit card charges for the Anthropic and OpenAI on their invoices and saying, hey, what are we getting out of that?”

_{Rod Cherkas, CEO of HelloCCO}

In the last few years, companies have spent money on AI tools, training, and pilot programs, often with high but sometimes unclear expectations. Many of these investments have made a real difference. Teams are saving time, automating routine tasks, and discovering new ways to get things done.

But as planning for the next fiscal year begins, expectations are rising. It’s no longer enough to just use AI. Now, companies need to show how these investments are improving the business metrics that matter most.

So You Saved Five Hours. Now What?

Imagine your team brings in an AI assistant that saves each CSM five hours a week. That’s a big productivity boost, especially across the whole company. But how are those five hours actually used?

Are CSMs using that extra time to connect more with customers? Are they spotting churn risks sooner or finding new growth opportunities they might have missed before? Or are they just clearing out their inboxes faster?

Making daily work easier is a win, but saving time doesn’t always create real value. If that extra time doesn’t lead to something important for the business, it’ll get harder to defend the investment.

The same idea applies to adoption. Having 90% of your team using AI might look impressive on a dashboard, but it doesn’t really show if your retention strategy is improving.

“It’s gonna be important for leaders to think about how you drive not just the activity, but initiatives that are changing results in the metrics your company cares about.”

_{Rod Cherkas, CEO of HelloCCO}

Your AI Strategy Is Backwards

Since AI became popular, most people have asked, “How can we use AI?”

Along the way, we’ve made some exciting discoveries, but it’s also encouraged teams to collect use cases like trading cards, experimenting with everything from meeting summaries to automated customer communications without always knowing what success should look like.

Now, as we look toward 2027, it’s time to flip the question. What does your business need to achieve next year? Is it better retention, more growth, or lower costs? Where can AI really help you reach those goals?

“I’m doing work with clients now, as we’re thinking about the 2027 planning process, to think about the four or five most important metrics in your organization. And then how can you align your AI initiatives around one or two of them to actually move the needle.”

^{Rod Cherkas, CEO of HelloCCO}

The most advanced AI project isn’t always the most valuable. Sometimes, simply automating a complicated sales-to-post-sales handoff can improve the customer experience more than a complex tool that no one really uses.

From Experimentation to Expectation

The AI experiments we’ve done so far have set us up for what’s next. We’ve learned what AI can do, how to use it in our operations, and how to test new ideas as they emerge.

But as we look to the future, expectations are growing along with the opportunities. As we plan the next phase of our AI strategies, we need to make sure our investments lead to real results. Not just more productivity, but true impact on the bottom line.

Not every experiment will be a big success, and not every result will show up in your data right away. That’s just part of the process.

Real progress happens when you know your goals and track the impact as you go. Your CFO isn’t looking for another AI assistant demo. They want to see what new opportunities it can create for your business.

🎧 Listen for These Moments

From rethinking your tech stack to reshaping your team’s roles, retention leaders have some big decisions ahead. Here are a few moments worth tuning in for.

• The unexpected roadblock to AI transformation: Why some of the biggest barriers to AI adoption are coming from leadership, not the front lines, and what executives need to do differently.

• The build-versus-buy reality check: Just because you can build your own AI-powered solution doesn’t mean you should. Rod breaks down the hidden complexities of maintaining homegrown technology.

• The future of the CCO (and the CSM): Will AI shrink retention teams or make their work more valuable? Rod shares his predictions for how roles, responsibilities, and customer relationships will evolve.

🔎 Where to Find the Speakers

• Rod Cherkas — https://www.linkedin.com/in/rodcherkas/

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

📎 Referenced in This Episode

• The Chief Customer Officer Playbook for the AI Era — Rod Cherkas’s latest book on leading post-sales organizations through AI transformation.

• HelloCCO — Rod’s consulting and advisory business.

• AI Post-Sales Council — The community Rod founded to help post-sales leaders navigate AI adoption and transformation.

• David Karp — Billtrust’s SVP of CS and former [Un]Churned guest (check out his episode here). Referenced for his work automating sales-to-post-sales handoffs with AI.

• Abbas Haider Ali — Atlassian’s CCO and former [Un]Churned guest (check out his episode here). Referenced for his approach to measuring the efficiency and financial impact of AI-powered support.

• Marketo — Discussed as an example of how technology reshaped an entire generation of marketing leaders.

Wrapping Up

The AI experimentation era gave everyone permission to play, but 2027 is the year someone checks the scoreboard.

See you next week 🧠

This week, Mike, EVP of Global Renewals at Salesforce, joined host Josh Schachter at Dreamforce for the [Un]Churned episode, “214. How Salesforce Cut Customer Attrition From 20% to 8%.” Mike has spent more than 17 years at Salesforce and now leads a team of about 900 people, roughly 700 of them renewal managers. They discussed the attrition council that brings product, legal, and finance into retention planning, a long-tail renewal journey where one customer click updates the forecast, and what happened when the first version of the renewals agent missed contracts and open deals and the team stopped trusting it.

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

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🎯 The [Un]Churned Take: The Best Retention Tool at Salesforce Might Be a Calendar Invite

Salesforce created its attrition council a couple of years ago, and it meets monthly with senior leaders from across the company, including product, legal, and finance. The agenda rotates through trends across the business, new motions in the customer success organization, and better ways to predict attrition, and Mike Marguglio, EVP of Global Renewals at Salesforce, was clear that the council is one of a number of steps taken along the way. Product has an especially natural interest in it, since product leaders want to know which features customers aren’t consuming and where attrition is starting to tick up.

“Everybody in this organization plays a role in customer health and customer retention.”
_{Mike Marguglio, EVP, Global Renewals, Salesforce}

Salesforce also pushed to align incentives so that sales, success, and renewals are all measured on customer retention and growth, and Mike described joint metrics of success that extend to how those teams are compensated. His line was that a dollar of existing business is just as important as a dollar of new business, which tends to land differently once the compensation plan agrees with it.

Seeing Every Contract, Including the Ones That Aren’t Due Yet

The foundation Mike keeps returning to is visibility. When the team first started, renewal opportunities were loaded three to six months out, which left limited visibility into what a customer had, what they were consuming, and what risk sat on the account by the time the clock started. They changed that so every contract is visible regardless of renewal date, which makes it possible to open an early renewal conversation with a customer whose term runs another three years, and to walk into any renewal already knowing the terms and pricing.

Forecasting builds on that base. Mike’s team blends an attrition model drawing on 27 years of historical trends by product, segment, and industry with validation from renewal managers. He also uses AI to forecast risk 12 to 24 months out and wants to extend that view to FY28, which feeds capacity planning, segmentation, and conversations with sales and customer success about which accounts are good candidates for upgrades.

A Renewals Journey for the Long Tail

For customers below a given spend (Mike used $25,000 a year as an example), many contracts default to auto-renew, and customers can still add or reduce at renewal. Salesforce built a renewals journey on its marketing cloud, which came from the ExactTarget acquisition, that sends notifications at 90, 60, and 45 days and pulls contacts, dates, and amounts from the CRM. Mike admitted that automating this was a little scary at first, given how much has to go to the right people on the right cadence.

A customer who confirms with a click moves the opportunity stage and flows straight into the forecast, and a customer with a question triggers a task for a renewal manager. This year, that second path is getting an agent, so customers get a faster response than the old callback within a day or two, with a human still in the loop and the history carried over. The segment represents over 200,000 transactions annually, and the goal is to put renewal managers’ time on accounts with more risk or more upside.

Agents That Had to Earn Their Seat

Mike described three agents.

A renewals agent handles the forensics, which used to take hours or half a day, and assembles contracts, pricing, open opportunities, cases, invoices, outages, and the history of the last renewal.

An agent called Ask Renewals sits in Slack and answers questions about end-of-life dates, renewal timing, and price caps for the more than 15,000 AEs the team supports.

The third is the long-tail agent above.

The forensics agent paid off in ways Mike found memorable, such as pulling every concession on a customer dating back to 2009, including courtesy licenses, contract modifications, and payment terms. It can also surface that a prior renewal took many cycles and ran right up against the contract end date, which is useful to know before the next one starts.

“The team starts to pull back from that a little bit because they don’t trust what the output is.”
_{Mike Marguglio, EVP, Global Renewals, Salesforce}

That quote is about the first version, which missed contracts and open deals, and adoption dipped accordingly. Salesforce now watches how often the forensics view is used on each renewal and whether people start with Ask Renewals, with the stated aim of finding enablement gaps, and it comes up in mid-year check-ins too. The early warning model has been treated the same way, since it occasionally labeled healthy accounts as at risk and has been adjusted as products and pricing changed.

Renewal Rate Is the Headline, Though Not the Whole Story

Renewal rate is the metric Mike wants to optimize above the rest, and he also tracks leakage inside the contract term, which can come from bad debt write-offs or products customers committed to for only part of a multi-year agreement. Alongside it sit cost to renew, on-time renewals, expansion, pricing, term length, billing frequency, and compliance.

Those conversations are getting more interesting as the portfolio mix changes. Customers might hold traditional licenses, newer consumption products, and maintenance contracts inherited through acquisitions, which makes renewal a natural moment to rebalance toward what they need, for example fewer licenses and more consumption. Josh framed the renewal manager’s job as optimizing the whole portfolio and not each product in isolation, and Mike agreed. His stated aim is a renewal that becomes a non-event, since customers who realize value often restructure, upgrade, or expand before the contract date arrives.

🎧 Listen for These Moments

The full episode covers how Salesforce runs renewals at scale. Here are three moments to listen for:

• The 2009 concessions pull: The renewals agent surfaced every courtesy license, contract modification, and payment-term change on a customer since 2009, a history Mike considers unlikely to have been built by hand, and Josh offers “renewal detective” as a name.

• The Slackbot personality profile: Mike describes asking everyone at customer and prospect dinners to have Slackbot write a personality profile, then reading the results aloud, and he finds them both impressive and a little unnerving.

• The click that updates the forecast: How a customer confirming a renewal flows through to the opportunity stage, and what happens when they have a question instead.

🔎 Where to Find the Speakers

• Mike Marguglio — https://www.linkedin.com/in/michael-marguglio-209895/

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

📎 Referenced in This Episode

• Salesforce: the world's leading cloud-based Customer Relationship Management (CRM) platform where Mike has worked for 17+ years

• Dreamforce: Salesforce’s annual conference where the episode was recorded

• Slack and Slackbot: the home of Ask Renewals and the source of the mid-year performance summaries

Wrapping Up

Salesforce found that moving a retention number takes a few unglamorous ingredients, namely a standing meeting, shared incentives, contracts visible from day one, and agents that earned the team’s trust, and the teams who combine them tend to find the renewal date becomes far less eventful.

See you next week 🧠

AJ Bruno, founder and CEO of QuotaPath, joined host Josh Schachter for this week’s [Un]Churned episode, “213. “We Will Die”: Why QuotaPath Is Betting Everything on AI-Native Services.” They cover how QuotaPath is moving from selling seats to running customers’ commissions for them, why outcome-based pricing has to be earned before it can be charged, and what an AINS pivot looks like when the company still has to hit plan this quarter.

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

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🎯 The [Un]Churned Take: You Can’t Price on Outcomes If You Don’t Know How to Prove Them

Most vendors see outcome-based pricing as just a pricing choice. They pick a metric, set a number, and announce it. But the real test comes later, when the customer gets the first invoice and tries to match the amount to what they saw happen. Outcome pricing succeeds or fails at that moment.

AJ Bruno, founder and CEO of QuotaPath, used to sell commission software by the seat. Now, he is exploring outcome-based pricing, starting with a service that manages commissions for customers.

In his [Un]Churned episode, 213. “We Will Die”: Why QuotaPath Is Betting Everything on AI-Native Services, AJ compares the experience to a fancy dinner.

You get this itty bitty steak, and then you get the bill, and you’re like, what did I just pay for?

An outcome-based invoice creates the same feeling when customers cannot see how the result was achieved. The vendor needs to show everything clearly before asking for payment.

Name the Variables Before You Start

Outcome-based pricing is becoming more common, and many examples are appearing in the SaaS industry. Intercom charges $0.99 for each outcome with its Fin support agent and clearly explains what counts as a billable outcome.

Other outcomes may need more steps and more proof than resolving a ticket. For example, QuotaPath is looking at outcome-based pricing for customer commissions, which involves factors like playbooks, outbound and inbound efforts, and sales team quality.

In cases like these, vendors that set terms and expectations up front will find it easier to prove against them in the long run.

You have to come in with this hypothesis and say, here’s where you are, here are the three things. This is the control group.

A customer who agrees to these terms from the start can connect every dollar on the invoice to something they saw happen.

Earn the Right to Look Inside

Showing the full picture requires access. A vendor can’t compare a customer to a control group without seeing the customer’s data and tech stack, and customers only share that with vendors they trust.

You’re handing over the keys to some of your data and tech stack because you trust us.

Starting with a flat fee builds the foundation for a trusted partnership. It gives the vendor a chance to prove their value and earn deeper access, while giving the customer time to feel confident in sharing sensitive data. Once trust and results are established, both sides can consider outcome-based pricing.

Why This Can’t Wait

The shift in pricing has already begun. Support leaders can now buy an AI agent based on outcomes. Commission software is becoming a service that manages commissions. Renewals are next, with Gainsight handling the process from start to finish with people involved.

Each change replaces a tool that just tracks work with a vendor that delivers results. The move to AI-native services makes seat-based software seem less valuable. For software vendors, doing nothing is now the riskiest choice. As AJ put it:

If we do not make this move, we will be a forgotten SaaS tool like everyone else.

It’s time to rethink what you sell. Focus on the results your customers expect, and ask if they could link those results to your work today. Vendors who can show every step will be the ones customers trust to charge for it.

🎧 Listen for These Moments

If you’re familiarizing yourself with AI-native services, or are already deep into building out your own AINS strategy, here are three moments in this episode worth pressing play for.

1. The tool-versus-result pitch: AJ's reframe of what QuotaPath sells, from a commission tracker priced in the tens of dollars to a service tied to taking a customer's revenue growth from 20% to 30%, and the buyer's honest reply: "I don't believe you."

2. The tiger team approach: how QuotaPath started the service at the beginning of this year, with a small team, a few design partners, and humans kept in the loop for the work that isn't binary.

3. The three-word answer: Josh asks AJ to finish two sentences about where QuotaPath is headed, and one answer is over almost before it starts.

🔎 Where to Find the Speakers

• AJ Bruno — https://www.linkedin.com/in/ajbruno3/

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

📎 Referenced in This Episode

• QuotaPath: AJ’s commission and compensation platform, which is launching an AI-native service to run customers’ commissions end to end.

• Top Line: Pavilion’s go-to-market revenue podcast, which AJ co-hosts.

• TrendKite (Acquired by Cision): the company AJ co-founded before QuotaPath, where the commission headaches that inspired QuotaPath started

Wrapping Up

Pricing used to be a guess about value. Now it can be a measurement. What would you measure first?

See you next week 🧠

In this episode of [Un]Churned, Grant Clarke, EVP and General Manager of Gainsight’s Atlas business, talks with host Josh Schachter for “212. Can AI Really Manage Your Entire Renewal Book?“ They discuss what Atlas is, how AI-native services (AINS) are different from traditional BPO outsourcing, what a renewal rep’s day looks like in the “Atlas cockpit,” and where Atlas is headed after the renewal stage.

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

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🎯 The [Un]Churned Take: AINS Isn’t What Your Agent Can Do. It’s What It Learns.

Every AI agent will eventually face something it can’t handle. The case gets escalated, the ticket quietly closes, and the moment becomes a footnote. But Grant Clarke, EVP and General Manager of Atlas at Gainsight, thinks how an AI-native services (AINS) company handles those moments is critical.

Gainsight’s Atlas product was created to tackle renewals, a process that can involve anywhere from ten to twenty separate steps. More if the customer is unhappy, fewer if they’re already seeing value. Atlas is designed to handle as many of these as possible without human help. But when escalations come up, Grant doubles down.

“Every time we’re going to come up with a new workflow, understand a new way to come at the problem, we’re just training that back into what is going to help the next customer get started faster.”

That ongoing improvement loop is what sets true AINS companies apart. It’s not just about what the system can do today, but how quickly it learns from what it can’t do yet.

The Learning Loop Is the Moat

In the UnChurned episode, “212. Can AI Really Manage Your Entire Renewal Book?”, Grant explained how Atlas works. A team of four, comprised of engineers, product managers, and business analysts sit with renewal reps and watch for moments when the agent hands off a task because it can’t handle it. If the same kind of escalation happens more than once, it triggers a two-week product sprint, and the solution is added back into the agent.

“If they are finding another way that we didn’t already document as part of what Atlas could do, we’re now going to document that.”

This capacity to get smarter with every edge case they encounter unlocks the true potential of AINS. The innovation is in how these systems turn unexpected scenarios into new playbooks, evolving to handle more complexity over time. For customers, that means AINS adapts and grows to meet tomorrow’s challenges, making the partnership more valuable with every iteration.

“If we take those actions and go from only 20% of those actions being Atlas-led to 55% of those actions being Atlas-led, that’s margin accretion.”

A New Lens for Assessing AI Partners

When a vendor pitches an agentic renewal, support, or CS solution, the real question isn’t “what can your agent do?” Instead, ask “what did your agent get wrong last quarter, and what changed in your product because of it?” If the answer is that a person quietly fixed it and nothing else changed, you’re just buying labor with an AI marketing layer. But if they can point to a specific update that now helps the next hundred customers, that’s a sign of something lasting.

The real advantage isn’t just one impressive agent response. It’s whether the system keeps improving from the work people still do, every time.

🎧 Listen for These Moments

Grant talks through his experience with Atlas so far, from defining the ideal customer to explaining how the renewal cockpit works for his reps each morning. Here are three highlights:

1. The 10-to-25 action inventory: Grant’s framework for how many separate steps are in a single renewal, and why that number is the real unit of work Atlas aims to automate.

2. The data hygiene answer: Grant’s straightforward view on why “the D word” isn’t a dealbreaker, and what Atlas really needs from a customer’s data before it can get started.

3. The “Mirage PMF” concept: Grant references Jake Saper’s AINS Playbook and the trap of taking on every eager early customer, even when it dilutes the autonomous leverage you’re trying to build..

🔎 Where to Find the Speakers

• Grant Clarke — https://www.linkedin.com/in/grantclarke/

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

📎 Referenced in This Episode

• Jake Saper’s [Un]Churned episode — 277. Why Domain Credibility Matters More Than Ever

• The AINS Playbook — Emergence Capital's framework, including the "Mirage PMF" concept Grant references directly

• ServiceSource — Grant’s employer for 15 years prior to Dropbox and Gainsight

• Dropbox — Grant’s prior role in B2B/product-led growth

Wrapping Up

The future belongs to those who keep building, learning, and improving.

See you next week 🧠

This week, Notion’s Christina Parra (Head of Outcomes Architects), Kellie Woodin (Commercial Outcomes Architect), and Ariel Risman (Outcomes Architect) joined host Josh Schachter for the [Un]Churned episode, “211. Notion’s Outcomes Architects on Why Today’s CSMs Are Builders” They discussed how Notion merged its renewal manager and CSM motions into one commercial function, what it looks like to run workshops with 190 people across an entire customer org, and how a four-stage AI maturity model shapes every account plan they build.

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

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🎯 The [Un]Churned Take: Usage-Based Pricing Is Here, and It’s Changing Everything

The SaaS equivalent of the classic grandparent adage, “back in my day, we walked 15 miles through the snow,” is quickly becoming, “back in my day, we priced everything per seat.” And that era is fading fast. Gartner projects that by 2030, 40% of SaaS vendors will offer usage- or outcome-based pricing. Bloomberg expects outcome-based pricing alone to jump from about 10% to 60% of the market in the same window.

This shift from seats to consumption and outcomes fundamentally changes renewal conversations. Instead of ending with a number, it sparks a broader dialogue about value delivered, real business outcomes, and how a customer’s needs are evolving over time.

Selling Work, Not Software

When outcomes drive both goals and pricing, what does that mean for Customer Success? Notion, an AI workspace for teams and agents, tackled this by rethinking what they’re paid for and changing how their teams work with customers.

“We’re not selling software, we’re selling work.”
— Kellie Woodin, Commercial Outcomes Architect, Notion

As vendors start charging separately for post-sales work, premium support, professional services, and agentic workflows, these services are no longer bundled into a license. When “the work” becomes the product, a drop in usage isn’t just a loss to fix. Instead, it shows the workflow no longer fits the customer’s needs and is a chance to help them build something new.

“Previously, if they had some kind of contraction in seats, the best we could do is try to get them back up. But now if there is a little bit of contraction here, maybe we talk about some workflows where we can get some custom agents, increase credit usage, and then we can make up some revenue there.”
— Kellie Woodin, Commercial Outcomes Architect, Notion

The CSM Role Is Evolving

As companies move from selling software licenses to offering tailored services and measurable results, Customer Success teams can’t rely on the old generalist approach. They need to rethink how roles are set up and who drives value. With AI handling much of the routine work, the CSM role is changing. It now requires more specialization, strategic thinking, and a new way of working with customers.

Most companies have responded by having some CSMs focus on renewals and commercial talks, while others handle adoption and technical issues. Notion took a different approach. Their outcomes architect role covers both workflows and numbers, turning the traditional CSM job into a value-realization powerhouse.

“What stands out about CSMs that are successful now is curiosity, agency, urgency, and a lot of that is baked into our values. It’s not necessarily about having a ton of experience. It’s about being really willing to learn and having that hunger and then trying it out for yourself.”
— Christina Parra, Head of Outcomes Architects, Notion

Leaving “Back in My Day” Behind

The move to consumption-based pricing gives customer teams a chance to redefine their impact. As technology changes roles and opens new possibilities, the most successful organizations will face change with curiosity and a willingness to reinvent themselves.

Whether you use a specialized team structure or a model like Notion’s, where one role shapes the outcome, what really matters is building stronger partnerships and working toward a shared idea of value. Consumption-based pricing hasn’t made CS obsolete, but it’s made the old CSM role unrecognizable.

🎧 Listen for These Moments

The full episode dives into how Notion’s outcomes architects run their workshops and manage their commercial work each day. Here are three moments to listen for:

1. The AI maturity model: Christina’s four-stage framework (thought partner, personal assistant, teammate, system) that Notion uses to diagnose where a customer actually is before recommending a workflow.

2. The 190-person workshop: Ariel shares how one big AI session brought together revenue, HR, and engineering, and how these workshops help Notion grow.

3. The feature-request pipeline: a multi-agent system that listens to Gong calls and Slack messages, checks Salesforce, and logs requests automatically.

🔎 Where to Find the Speakers

• Christina Parra — LinkedIn: https://www.linkedin.com/in/christina-wills/

• Kellie Woodin — LinkedIn: https://www.linkedin.com/in/kelliewoodin/

• Ariel Risman — LinkedIn: https://www.linkedin.com/in/ariel-risman/

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

📎 Referenced in This Episode

• Notion — AI workspace for teams and agents; Christina, Kellie, and Ariel’s employer

• Gong — conversation intelligence platform that feeds Notion’s automated feature-request intake system

• Salesforce — CRM that Notion’s feature-request agents check against to confirm customer status

Wrapping Up

“Back in my day” is officially yesterday, and the teams ready to reinvent themselves will define what comes next.

See you next week 🧠

Rebecca Nerad, VP of Customer Success at Advantive, joined host Josh Schachter on this week’s [Un]Churned episode, “210. Owning Retention & Standardizing CS Across 17 Acquisitions.” They discussed how she organizes CS across four acquired business lines while managing one retention number for all of them, what she checks during due diligence before a deal closes, and the one part of the customer experience she standardizes.

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

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🎯 The [Un]Churned Take: Standardize the Signal, Not the Playbook

After your fifth, tenth, or even seventeenth acquisition, the instinct is to standardize everything. You stamp the same onboarding flow, health score, and CS playbook onto every business you bought. It feels like you’re gaining control over a complicated tangle of processes, but Rebecca Nerad, VP of CS at Advantive, says that urge to simplify is exactly what breaks an acquired customer base.

After leading CS teams through 33 acquisitions throughout her career in supply chain and operations software (17 at Advantive and 16 at e2Open), Rebecca knows a thing or two about how standardization attempts play out.

“Some of the companies we’ve acquired have had very different ways of engaging with their customers, and that’s what’s hard when you bring them into a larger model.”

Now, she only focuses on standardizing one thing: how a risk gets noticed.

The Low-Tech Solution for a High-Tech Problem

Advantive, an AI platform for specialty manufacturing and distribution, is organized into four lines of business. Each line has its own general manager owning sales, support, and professional services for a completely different customer base, from major packaging accounts to long-tail distributors. Rebecca owns the renewal number across all four. Instead of imposing a unified customer journey, she introduced a universal, deliberately low-tech method to identify issues as soon as they arise.

“It’s intentionally simple in being able to log an at-risk ticket. This is where a human is actually saying, ‘Hey, I heard a customer say this.’”

Each ticket is tagged with enough product and reason-code details so Rebecca can spot trends later, across businesses in automotive, packaging, food and beverage, and more. This is the one system that must work the same way, no matter which acquired company a customer is from.

One Signal, Many Solutions

After a ticket is logged, Rebecca’s teams stop standardizing their approach. Top-tier accounts continue to receive quarterly business reviews. Long-tail customers, often from smaller companies without a dedicated CS team, get a lighter, more scalable outreach model.

“It’s less about it being one line of business versus the other and more about how do we segment to be able to serve those customers appropriately.”

Keeping what works for the CSMs and their customers is most important. Rebecca’s CSMs are organized by business line, not by product. For example, a CSM from an acquired company who knows the automotive industry remains the expert in that area, rather than being moved into a general role. About half of Rebecca’s team came from companies Advantive acquired. Their local knowledge is too valuable to be lost for the sake of standardization.

The Art of Selective Standardization

Rebecca’s approach applies beyond post-acquisition integration. It offers a lesson for any CS leader facing complexity. Whether you’re merging teams, launching new products, or managing a diverse customer base, try not to standardize everything.

Instead, focus on what really needs to be consistent, such as risk signals, and let expert knowledge and local differences guide the rest. The best CS organizations understand that choosing what not to standardize is just as important as what they do standardize.

🎧 Listen for These Moments

Whether you’re scaling a tool, scaling a skill, or just scaling your own honesty about where your team is falling short, listen out for these three moments in Rebecca’s episode:

1. The objection-handling agent: how Rebecca trained an AI tool on 18 months of renewal desk recordings to guide CSMs through negotiation, and her candid admission that she still can’t prove what it’s worth.

2. The empathy coach: how Airspeed, an AI-native platform, is helping Rebecca scale one-on-one empathy coaching across a team spread across four lines of business.

3. The segmentation grade: Rebecca’s honest self-assessment of how well Advantive is actually serving its long-tail customers, and where she says the gap still is.

🔎 Where to Find the Speakers

• Rebecca Nerad — LinkedIn: https://www.linkedin.com/in/rebeccanerad/

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

📎 Referenced in This Episode

• Advantive — provider of mission-critical software for specialty manufacturing and distribution businesses, and Rebecca’s current company

• e2Open — connected supply chain software platform where Rebecca led customer success through 16 acquisitions before joining Advantive

• InSequence — Advantive’s most recent acquisition, a provider of automotive sequencing and manufacturing execution system (MES) software

• Airspeed (formerly Glyphic) — the AI conversation intelligence tool Rebecca’s team uses to gauge customer sentiment and coach CSMs on empathy at scale

Wrapping Up

When in doubt, standardize less and listen more.

See you next week 🧠

Bob London, a strategic discovery expert, speaker, and coach, joined host Josh Schachter for this week’s [Un]Churned episode, “209. Stop Asking “Would You Renew?” Ask This Instead.” They covered the mindset shift from product-pushing to curiosity-led listening, the specific questions that open customers up at different stages of the lifecycle, and how leaders can reinforce the behavior change after the training ends.

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

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🎯 The [Un]Churned Take: “Would You Renew Tomorrow?” Is the Wrong Question to Ask

When you ask a customer if they’d renew tomorrow, their answer is usually just what they think you want to hear.

A customer who hasn’t spoken up in six months might say yes. One who’s been raising issues might say probably. But neither answer reveals what they’re really thinking, how their priorities have changed, or what might catch their interest from a competitor. “Would you renew tomorrow?” is a question makes vendors feel better, but it doesn’t get to the customer’s real thoughts.

Bob London, a coach, advisor, and Chief Listening Officer, has a different approach. Rather than asking customers to judge the relationship, focus on understanding their world.

From Easy Answers to Honest Ones

The question Bob recommends instead is this:

“If one of our competitors reached out to you tomorrow, on a scale of one to ten, how would you react? A one means you’d ignore it completely. A ten means you’d get back to them for any reason.”

After 3,000 conversations, Bob says the average answer is about a seven. Most Customer Success teams worry when they hear that, but the real value comes from digging deeper and finding out why the customer answered that way.

Chances are, it’s not because they actually want to switch. Customers see competitor outreach as a way to get information. They attend conferences, read emails, and want to stay informed. A seven just means they’re watching the market, not that they’re unhappy with you. Bob coaches his clients to use this follow-up:

“Is there anything a competitor might bring up that would be a real head-turner for you—something you haven’t heard anyone solve yet?”

That’s when you see the real gap between where the customer is now and where their curiosity has led them. Maybe it’s a feature that was promised but never delivered, or a workflow no one has solved yet. They aren’t looking to leave. They’re just waiting, sometimes anxiously, to see if their vendor will keep up.

The Leadership Implication: Begin in the Customer’s World

Bob’s competitor question is part of his broader coaching techniques around questions that focus on the customer’s world, not the vendor’s.

In this week’s UnChurned episode, “209. Stop Asking “Would You Renew?” Ask This Instead ft. Bob London”, he shared another example.

“If I could sneak into your last executive leadership meeting, what do you think the big topic of conversation would be?”

Customers don’t push back on these kinds of questions. They pay more attention because they see you care about what’s really happening at their company, not just checking off a renewal task. Adnan Rahman, who brought Bob in to work with the Paycor CS team, shared in his article, “The Unexpected Outcome of Paycor’s Value Realization Playbook”:

“For years I told my teams to be more strategic. It was a mistake.”

The problem wasn’t the goal itself, but that “be more strategic” isn’t specific enough. Bob points out that teams need a clear path, starting with broad, open questions about the customer’s world, and then staying focused there instead of shifting back to your own agenda.

Be a Good Listener, Even If AI Is Listening Too

Most of us have an AI notetaker humming along in the background of our meetings these days. But while AI might change how we capture and analyze conversations, it doesn’t mean we can stop listening altogether.

Technology shows us words and data, but listening intently, asking the right questions, and focusing on what matters to whoever is in the room with you is where your human-ness shines. You may uncover risks hiding behind the inflection in someone’s voice, or the unmet needs that show clear as day in the long pause they take to respond. In a world where AI makes data more accessible, it’s our willingness to ask, listen, and learn that turns customer relationships into lasting partnerships.

🎧 Listen for These Moments

If your team is struggling to move beyond adoption stats and product updates, the full episode gives you the playbook. Three moments to listen for:

1. The lifecycle question map: which questions work at kickoff versus renewal, and why the right question at the wrong moment falls flat.

2. The scene-painting technique: how framing questions around a specific moment in the customer's world (a board meeting, a finance email) unlocks answers that generic questions never get.

3. The RAD coaching GPT: how Bob built a call assessment tool that produces a column-by-column scorecard for behavior change at scale.

🔎 Where to Find the Speakers

• Bob London — LinkedIn: https://www.linkedin.com/in/boblondon/

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

📎 Referenced in This Episode

• Paycor — HR software company where Adnan Raman leads global CS; the team Bob worked with on strategic conversation training

• Adnan Raman’s [Un]Churned episode — the prior episode where Adnan described the value realization transformation that led him to Bob

• JMI Equity — the private equity firm whose event introduced Bob to Jay Nathan and the CS world

• Jay Nathan — the CS operating expert at JMI who put Bob on stage and helped him understand the CS space

• Dale Carnegie — referenced by Bob as the original source of the “to be interesting, be interested” principle underlying his methodology

Wrapping Up

The right question changes everything. You’d be surprised how many customers are just waiting to be asked.

See you next week 🧠

Andy Mowat, founder and CEO of Whispered, joined host Josh Schachter for this week’s [Un]Churned episode, “208. 80% of VP Roles Are Never Posted. Here’s How to Find Them” They covered how the senior executive job market actually works, why most VP and C-level roles are never posted, and what it takes to navigate the hidden job market at the CCO level.

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: Your CS Skills Are a Secret Weapon in Today’s Job Market

AI companies have crossed the threshold from fledgling disruptors to mature, market-shaping organizations. In recent years, they moved quickly to gather customers, build their brands, and launch new features. This rush created high demand for CMOs, CROs, and engineering leaders, while CS leadership was less prioritized. Now, as these companies enter a new phase, the focus is shifting. In the first year, customers are onboarding. In the second, they decide if the product met their needs. By the third year, retention and churn start to look like a very real risk.

Data is beginning to highlight this trend as well. According to ChartMogul’s 2025 SaaS Retention Report, median gross revenue retention across AI-native companies sits at 40%—compared to 63% for traditional B2B SaaS.

Andy Mowat is watching that gap close and sees a reckoning ahead.

“A lot of these AI companies are in year two, and they’re about to face it.”

Throughout his career, Andy has led RevOps at four unicorn companies, advised companies and founders on GTM strategy and technology, and built his own companies. His latest venture, Whispered, is an executive career platform based on the insight that top senior roles are rarely posted publicly. With a career that’s intersected executive search and go-to-market, he has a unique perspective on where the job market is headed.

“I’m starting to see churn become a bigger and bigger issue with a lot of these high-flying companies.”

When that happens, the need for CS leadership will become urgent, quickly.

The C-Level Job Market Is Mostly Invisible

Posting senior roles publicly can come with a slew of issues for the businesses making these big moves. It signals leadership changes to competitors, overwhelms recruiters with too many unqualified applicants, and sometimes creates instability before a replacement is found.

“At the director level, 75 to 80% are posted, but you flip that to 20% posted at the VP level and at the C level virtually nothing is posted.”

Andy’s watched how the market operates differently outside of the job boards. Retained search firms don’t post jobs because their fees rely on managing the process. VC and PE talent partners connect companies with recruiters, and many opportunities come through warm introductions that only become job leads once they are already in motion.

“Do not apply through the front door. It just doesn’t work.”

CS leaders who rely on traditional job searches, like updating their LinkedIn profiles, applying to postings, and checking their inboxes, only reach a small part of the market. There are other strategies at play, and your experience in CS has equipped you to execute them whether you realize it or not.

The Skills You’ve Built Will Power Your Job Search

Andy lays out a set of must-have skills for thriving in the AI era. The good news? You’ve already been sharpening these abilities every day.

Tapping Into Relationships

You already know that lasting client relationships are built through trust and personal connections. In your job search, the same holds true. Instead of applying through the front door, connect with peers or recent hires inside your target company. Just like nurturing stakeholders within an account, these conversations can open doors that applications alone can’t.

Following the Funnel

As a CS leader, you’ve guided customers through the stages of renewal or expansion. The steps you use to navigate complex engagements work the same way as your job search.

“There’s the recruiter call, which is kind of a qualification step…Then there’s like the hiring manager call, which is effectively discovery. And so you have to approach that hiring manager. If you engage the hiring manager, they’re gonna make sure you get pulled through the process. So that’s like the critical stage of the funnel.”

Turning Targets Into Opportunities

You know how powerful a clear account list is when mapping expansion plans. The same principle applies to your job search. Targeting specific companies (Andy recommends 20-40) gives your network a concrete way to help you. When someone hears a company name, they’re much more likely to make a connection on your behalf.

Leveraging Peer Networks

Customer Success thrives on creating value through connection and collaboration. In your search, use the same approach. Reach out to people who have the job you want and ask them to pass along opportunities that aren’t a fit for them. It’s similar to gathering referrals. Low lift for them, high impact for you.

Searching or Not, Your Strategy Should Be Consistent

Not all CS leaders reading this are actively looking for a new job. Still, the hidden job market favors those who build relationships, keep a target list, and grow their network before they need anything.

When companies come looking, the first CS leaders they’ll call are the ones who never disappeared from view and have built genuine connections ahead of the curve.

🎧 Listen for These Moments

If you're in an active search (or think you might be in the near future) the full episode goes behind-the-scenes of the executive search process. Three moments to listen for:

1. The Google group of 340 talent partners: how VC and PE talent partners communicate behind the scenes, and why moving too fast in that network can surface your search before you’re ready.

2. The comp conversation timing: when to bring up compensation in the process, and why roughly half of all posted comp numbers are negotiable even when they look fixed.

3. The “head of” signal: what to look for in senior job postings that indicates a company is open on title and level.

🔎 Where to Find the Speakers

• Andy Mowat — LinkedIn: https://www.linkedin.com/in/amowat/

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

📎 Referenced in This Episode

• Whispered — Andy’s executive career platform connecting senior leaders to unposted roles through a trusted peer network

• Gated — Andy’s previous company, which filtered cold email access through charitable giving

• Carta — equity management platform where Andy ran RevOps and used the target list strategy to land the role

• Zapier — referenced as an example of a target company Andy used to illustrate the specificity strategy with recruiters

Wrapping Up

Every connection you nurture today is a door opening for tomorrow.

See you next week 🧠

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.

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

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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.

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

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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 🧠

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 🧠

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

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

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

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

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

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

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

The Same Inflection Is Happening in Customer Success

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

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

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

What Yash’s Ecosystem Decision Reveals

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

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

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

The Leaders Who Move First

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

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

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find the Speakers

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

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

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

📎 Referenced in This Episode

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

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

Wrapping Up

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

See you next week 🧠

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Trust Layer AI Can’t Build for You

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

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

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find the Speakers

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

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

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

📎 Referenced in This Episode

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

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

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

Wrapping Up

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

See you next week 🧠

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

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

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

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

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

The Role AI Just Took From Your Team

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

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

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

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

What “High-Touch” Actually Means Now

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

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

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

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

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

Raising the Bar for Human Impact

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find Jean

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

📎 Referenced in This Episode

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

• Gainsight — platform Unit4 uses to deliver digital success plans

Wrapping Up

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

See you next week 🧠

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

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

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

Subscribe now

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

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

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

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

What Kellie Found When She Looked Back

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

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

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

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

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

What’s Slipping Through the Cracks?

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

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

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

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

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find Kellie

• LinkedIn: linkedin.com/in/kelliesnyder

📎 Referenced in This Episode

• LinkSquares — AI-native contract lifecycle management platform

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

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

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

Wrapping Up

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

See you next week 🧠