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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

Subscribe now

🎯 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.

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

Subscribe now

🎯 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.

Subscribe now

🎯 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.

Subscribe now

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

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

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

When AI Enters the Equation

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

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

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

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

Surfacing Risk and Acting On It Are Two Different Problems

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

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

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find the Speakers

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

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

📎 Referenced in This Episode

• Proofpoint — enterprise cybersecurity platform

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

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

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

Wrapping Up

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

See you next week 🧠

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

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

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

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

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

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

Acquiring the Skill

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

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

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

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

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

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

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

Codifying the Pattern

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

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

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

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

Subscribe now

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

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

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

Executing Efficiently

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

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

The Pathway Is the Product

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

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

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

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

🔗 Connect with Shantan here on LinkedIn.

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

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

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

Subscribe now

🎯 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.

Subscribe now

🎯 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.

Subscribe now

🎯 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.

Subscribe now

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

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

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

Duplication as a Signal, Not a Flaw

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

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

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

Seeing Opportunity in Team Initiative

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

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

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

Don’t Trade Connection for Speed

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

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

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

The Big Picture

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find the Speakers

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

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

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

📎 Referenced in This Episode

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

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

Wrapping Up

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

See you next week 🧠

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

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

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

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

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

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

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

Let’s Be Honest, The Job’s Changed

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

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

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

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

Want more perspectives like this on AI and
retention—straight to your inbox? Subscribe today 👋

Subscribe now

What Moving Upstream Looks Like

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

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

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

What I have connected gives me the full account picture:

• Gmail tells me what was said after the meeting.

• Calendar tells me what meetings actually happened.

• Gainsight tells me what made it onto the record.

• Slack tells me what the internal conversation really was.

• Notion tells me what the team knew going in.

• Linear fills in the product gaps.

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

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

Eleven touchpoints. Twenty minutes.

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

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

Closing the Gap, Not Just Surfacing It

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

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

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

What the AI Couldn’t Know

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

That’s wrong.

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

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

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

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

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

Finding My Place in the Loop

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

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

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

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

What Leadership Needs to Understand

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

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

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

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

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

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

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

That’s always been part of the CSM role.

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

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

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

🔗 Connect with Jon here on LinkedIn.

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

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

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

Subscribe now

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

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

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

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

The Same Inflection Is Happening in Customer Success

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

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

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

What Yash’s Ecosystem Decision Reveals

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

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

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

The Leaders Who Move First

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

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

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find the Speakers

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

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

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

📎 Referenced in This Episode

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

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

Wrapping Up

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

See you next week 🧠

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

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

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

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

Subscribe now

🎯 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.

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

Subscribe now

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

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

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

The Role AI Just Took From Your Team

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

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

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

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

What “High-Touch” Actually Means Now

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

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

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

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

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

Raising the Bar for Human Impact

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

🎧 Listen for These Moments

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

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

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

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

🔎 Where to Find Jean

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

📎 Referenced in This Episode

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

• Gainsight — platform Unit4 uses to deliver digital success plans

Wrapping Up

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

See you next week 🧠

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

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

I Hadn’t Written Code Since 2006

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

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

Asking the Right Questions

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

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

Subscribe now

The Foundation Already Exists

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

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

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

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

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

Vibe Coding as a Subject Matter Expert (SME)

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

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

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

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

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

If You’re Thinking About Building

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

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

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

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

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

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

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

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

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

Connect with Steven here on LinkedIn.

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

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

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 🧠