The Chief Customer Officer Podcast

Jay Nathan & Jeff Breunsbach
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Conversations about digital experience and AI. 

  1. 1D AGO

    EP013: Building AI Infrastructure That Actually Works

    Jay Nathan goes solo to share what his team at Balboa is actually building—a call intelligence system, Claude-powered skills, and a company operating system called Balboa OS. He also shares the top themes from 90 days of SaaS conversations: broken CSM ratios, the AI sidekick-to-agent gap, messy data, and why onboarding is still the highest-leverage retention moment. KEY TAKEAWAYS - Call Intelligence Over Raw Transcripts: The real value comes from extracting transcripts into a structured, security-layered database with semantic search—so the entire team can query insights across all conversations, not just their own. - The Context Harness Problem: Giving everyone Claude or ChatGPT isn’t enough. Without a centralized context repository, every team member operates in isolation. The winners are building shared knowledge infrastructure. - Balboa OS: A central folder of Markdown files documenting everything—roles, processes, the employee handbook, team roster. It auto-distributes to every teammate and connects to Claude via Claude Cowork. - Autonomous Agents That Self-Improve: Jay has an agent scanning call transcripts to update skills, surface best practices, and build a company wiki from thousands of calls—without anyone having to write a document. - CSM Ratios Are Breaking: Companies are drawing lines at $100K–$250K ARR before assigning a dedicated CSM. The answer is digital CS and one-to-many engagement models that scale what CSMs know. - The Agentic CX Loop: Leading teams are building autonomous loops that sense signals, decide on actions, execute, and learn—continuously improving how they engage customers without manual intervention. - Data Mess Is the Real NRR Problem: At Pendo’s conference, nearly every SaaS leader said the same thing: our data is a mess. A simple product tenant-to-CRM mapping would put 90% of companies in a fundamentally better position. - Onboarding Still Wins Retention: Jay’s team helped a $300M company improve retention over 10 points just by fixing a broken onboarding program. With consumption-based models, activation is now a direct revenue trigger. CHAPTERS - 00:00 - Intro (solo episode, Jeff on vacation) - 01:27 - The Balboa call intelligence system - 06:34 - MCP server and centralizing AI context for the team - 08:06 - Claude skills + call data in practice - 09:22 - Autonomous agents and the self-improving wiki - 11:21 - Balboa OS: the company operating system - 16:50 - 90 days of SaaS industry insights - 17:34 - Theme 1: CSM ratios are broken - 20:54 - Theme 2: AI sidekick vs. AI agent - 21:45 - The autonomous agentic customer journey loop - 24:12 - Theme 3: Data mess as the core NRR limiter - 29:36 - Theme 4: Onboarding as the highest-leverage retention moment - 30:08 - The $300M onboarding case study - 35:06 - Wrap-up and Pulse conference About the Show: Chief Customer Officer Podcast is a show about real strategies for customer-led growth in the AI era—from leaders actually executing, not just talking about it. Your Hosts: - Jay Nathan – CEO of Balboa Solutions and Co-Founder of ChiefCustomerOfficer.io - Jeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io

    32 min

  2. APR 9

    EP012: How CarParts.com Replaced Their Entire Support Stack w/ Aurelia Pollet

    Aurelia Pollet, VP of Customer Experience at CarParts.com, led a small team that built a fully custom AI chatbot and ticketing system from scratch in three months. She shares how they did it, what went wrong when their AI agent Harper made promises it couldn't keep, and why the real value of AI isn't cost-cutting — it's creativity that was never possible before. KEY TAKEAWAYS Build vs. buy is a real question: Off-the-shelf tools were bloated with features they didn't need and missing the ones they did — building from scratch gave them full control at lower cost.Start with one small thing: The chatbot led to the ticketing system, which led to email automation. One use case compounds into the next.Guardrails are the hardest part: Their agent Harper promised customers she could cancel orders — she couldn't. Too few guardrails and AI goes rogue; too many and you've just built a robot.AI enables proactive CX: With 50,000 orders/week, proactive outreach was impossible before. Now they're building toward monitoring every order and surfacing problems before customers call.Insight without action is noise: Aurelia doesn't want to be the "Chief Complaint Officer." Her job is to tie customer insights to company strategy and act — not just report.Maintaining AI is a full-time job: Agents need ongoing updates just like human employees — new policies, new products, new questions. The build is the easy part. ABOUT OUR GUEST Aurelia Pollet is the VP of Customer Experience at CarParts.com, a publicly traded e-commerce retailer shipping ~50,000 orders/week. A mechanical engineer by training, she spent 15 years in luxury goods before moving through B2B and nonprofit roles. She led CarParts.com's AI-native rebuild of its entire support infrastructure. Connect with Aurelia on LinkedIn CHAPTERS 03:46 - Welcome and introduction05:42 - Aurelia's background: from mechanical engineer to VP of CX07:05 - What "end-to-end CX" means at CarParts.com10:09 - Adding value at scale: the win-win framework12:26 - Building an AI chatbot from scratch in 3 months14:34 - From chatbot to full AI ticketing system16:51 - How AI frees humans for empathy and complex cases20:08 - Gathering customer insight across the journey24:50 - Insight as action (avoiding the "Chief Complaint Officer" trap)26:50 - The build vs. buy decision32:57 - Hard lessons: Harper's promises and the guardrail problem38:54 - What's next: stabilizing and scaling the AI stack43:23 - Advice for leaders who haven't started yet About the Show: Chief Customer Officer Podcast is a show about real strategies for customer-led growth in the AI era—from leaders actually executing, not just talking about it. Your Hosts: Jay Nathan – CEO of Balboa Solutions and Co-Founder of ChiefCustomerOfficer.ioJeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io

    36 min

  3. APR 2

    EP011: Team-Wide AI Adoption

    Jay Nathan and Jeff Breunsbach break down what 50+ CS leaders are actually doing with AI — and what most teams are still getting wrong. Jeff shares fresh takeaways from a Planhat-organized event in Boston, and Jay goes deep on the Claude skills his team has built to scale coaching, standardize deliverables, and rethink how prep calls even work. No hype, just real strategies in the field. KEY TAKEAWAYS We're still in early adopter territory: Jeff attended an event with 60 CS leaders and walked away realizing that even among leaders who talk about AI daily, widespread team adoption is far behind what the tech echo chamber suggests. The majority of companies are still evaluating — not executing.You don't know your processes as well as you think: Before you can automate or augment a workflow with AI, you have to actually know what that workflow is. Jay and Jeff agree: most teams haven't documented or visualized their core processes, which is the real bottleneck to AI leverage.Top-down mandates don't drive AI adoption: "Just use AI" from the CEO doesn't work. Real adoption requires demonstrating behaviors at every layer of the org, giving teams specific problems to focus on, and creating regular show-and-tell moments where people can see and iterate on each other's work.Call transcripts are your most underutilized asset: Jay calls Fathom call recordings "the most valuable thing we have in our company." Claude skills as a team force multiplier: Jay built an executive readout coaching skill in Claude that replicates his own feedback patterns, so every team member starts their deck review at a higher baseline. The future CSM is a technical account manager: Multiple CS leaders at Jeff's event flagged this shift — the CSM role is evolving toward one that requires real technical curiosity. Forward deployed engineering is closer than you think: Jay makes the case that giving CSMs a local build of your product — and the ability to generate a feature-based pull request from a customer conversation — is entirely possible today. Build a center of excellence, not just a mandate: Sustainable AI adoption at the team level needs a few dedicated people vetting standard tools, scheduling engagement touchpoints, and measuring outcomes — not just an org-wide Slack message to "go use AI." CHAPTERS 00:00 - Intro & Jay's flight back from Seattle01:29 - Jeff's takeaways from a Planhat AI event in Boston03:15 - Crossing the chasm: where is AI adoption actually at?06:59 - We don't know our processes as well as we think09:21 - Driving team-wide AI adoption: what actually works11:43 - Measuring AI impact: KPIs and the multi-attribution problem17:47 - Individual vs. team AI adoption — why standardization matters18:39 - Jay's executive readout coaching skill in Claude22:52 - Jeff's product marketing skill: Notion to branded one-pager26:28 - Call transcripts as the ultimate knowledge source27:15 - Automating product feedback with Fathom + Linear PRDs29:42 - Forward deployed engineering and voice of customer at scale33:28 - The future CSM is technically fluent35:07 - What to look for when hiring CSMs right now39:17 - Building a center of excellence for AI adoption42:00 - Jay's website glossary and partner portal (show & tell)46:08 - Wrap up

    41 min

  4. MAR 19

    EP010: You Can't AI Your Way Out of Bad Data

    Jay Nathan and Jeff Breunsbach get into the foundational data problem most SaaS teams are ignoring — and why it's about to become a crisis. If your data is siloed, your AI agents will be siloed too. Plus: Jay introduces "Company as Code," a markdown-in-GitHub system for building personalized outreach and landing pages at scale — and why the same playbook applies directly to customer success. KEY TAKEAWAYS Start with a customer master record: Before any AI agent can help you, you need a deduplicated view of your customer outside your CRM. Jay spoke with companies from 150 to 3,500 people this week — they all have the same problem.Company as Code: Jay is documenting personas, ICP definitions, and outreach styles as markdown files in a GitHub repo — one canonical version that every AI tool pulls from, updated by anyone on the team via pull request.Siloed AI = siloed data with a new wrapper: If your sales AI only sees sales data and CS's AI only sees CS data, you've rebuilt your silos with an AI layer on top. Fix the data first.Personalization is now just token costs: Custom landing pages, personalized emails, interactive surveys — what used to require massive investment is now a few tokens.Existing customers need outbound too: Just because someone's a customer doesn't mean they understand your product. Use the same personalization playbook to re-engage and educate your base.ABM belongs in Customer Success: Jay shares the "Spreading the FLU" story — a field-level understanding program that educated every influencer at top accounts. CS teams should run the same play.AI-powered upsell prioritization: Use Fathom recordings, emails, and Slack signals to surface the top 10–20 customers most ready for a new product — before sending a rep in cold.The CSP question is getting louder: "It's 2026. Do you need a customer success platform anymore?" Jay is hosting a webinar with two practitioners who built in opposite directions. CHAPTERS 00:01 - Weekend updates03:45 - Unifying customer data at Junction with BigQuery and Metabase07:00 - Building a customer master record outside your CRM10:10 - Why siloed AI is just siloed data with a new wrapper11:48 - Company as Code: markdown files in GitHub as a single source of truth14:45 - Personalized landing pages for prospects — and customers19:00 - The risk side: when custom-built tools fall short23:45 - Using Claude Cowork to research private companies via public comparables27:00 - Applying outbound personalization to your existing customer base29:00 - AI-powered upsell prioritization using call recordings and signals33:00 - Spreading the FLU: account-based marketing applied to CS35:00 - QPR as an interactive landing page for top accounts36:45 - Webinar preview: Do you still need a CSP in 2026? About the Show: Chief Customer Officer Podcast is a show about real strategies for customer-led growth in the AI era—from leaders actually executing, not just talking about it. Your Hosts: Jay Nathan – CEO of Balboa Solutions and Co-Founder of ChiefCustomerOfficer.ioJeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io

    35 min

  5. MAR 12

    EP009: Sitting Ducks and Fortresses: How to Read the AI Landscape

    Jay and Jeff are back with another hosts-only episode — and Jay builds an AI vulnerability matrix live on the call. They cover how to stand out as an AI-first job candidate, the three biggest AI go-to-market mistakes leaders are making right now, and the heated debate over whether the CSM role is really being replaced or just transformed. KEY TAKEAWAYS Prove you're AI-first before the interview: The best candidates aren't submitting resumes — they're building things. A candidate for Jay's team built an app in Lovable and sent it unprompted. That's how you get noticed. Loom videos, written walkthroughs, anything that shows you've done the work.Centralize AI where it touches systems of record; let everything else run organically: MCP-connected tools that touch your CRM or customer data need governance and controls. Departmental tools like Gamma don't. The mistake is treating all AI adoption the same.The three go-to-market AI mistakes: Automating bad processes at scale, building on generic best practices instead of your own call data, and prioritizing internal efficiency over the buyer experience. These aren't new problems — AI just makes them impossible to ignore.Know which quadrant you're in: Jay's AI Vulnerability Matrix maps companies by solution complexity vs. replicability. Sitting ducks (low complexity, easy to replicate) need to move fast. Fortresses (high complexity, hard to replicate) have time. Knowing your quadrant should drive your entire strategy.The $700K ARR per employee benchmark: AI-native companies are hitting $700K–$1M+ ARR per employee. The old benchmark was ~$200K. If you're still staffing like it's 2019, a competitor is already disrupting you.Humans stay in the CS loop — but the role changes: Agent-to-agent purchasing will happen first for simple products. For complex enterprise software, the relationship still matters. But the job shifts: less task management, more being so embedded in the customer's business that you know their next move before they do. CHAPTERS 00:01 - Welcome + why Balboa runs a February fiscal year02:34 - The job candidate who built an app to stand out04:16 - Three ways to prove you're AI-first as a candidate09:01 - Kyle Norton: centralize AI adoption or let it happen organically?12:16 - Why you need both — and Jay's internal AI show-and-tell at Balboa17:53 - Kyle Lacy's three go-to-market AI mistakes21:50 - Your call recordings are your best practices23:27 - Jay builds the AI Vulnerability Matrix live on the call25:32 - The four quadrants: sitting ducks, protected niches, targets, fortresses33:19 - What AI-first companies actually look like (Jason Lemkin)35:00 - The shift from CSM to forward deployed engineer36:12 - The $700K ARR per employee benchmark36:40 - Jeff's counterpoint: humans stay in the buying loop longer than we think41:15 - Agent-to-agent PLG is already happening45:54 - Wrap-up About the Show: Chief Customer Officer Podcast is a show about real strategies for customer-led growth in the AI era—from leaders actually executing, not just talking about it. Your Hosts: Jay Nathan – CEO of Balboa Solutions and Co-Founder of ChiefCustomerOfficer.ioJeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io

    45 min

  6. MAR 5

    EP008: Cut Your Own Prices Before Someone Else Does

    No guest this week — just Jay and Jeff getting into the weeds on what they're actually building with AI right now. From Block's layoffs (and why AI is mostly "air cover" for over-hiring) to MCPs, deal staging from call transcripts, and creating hundreds of renewal records in a single afternoon — this one is all about practitioners doing the work, not just talking about it. KEY TAKEAWAYS AI layoffs are mostly "air cover": The Block cuts are less about AI replacing workers and more about correcting COVID-era over-hiring. Companies are using AI as PR cover for reductions that should have happened years ago.MCPs are the new API layer for CS teams: MCP servers let leaders query and update tools like HubSpot and PlanHat in plain English — no developer required. Jeff rebuilt his entire field mapping strategy between two platforms in a single Sunday afternoon using Claude Cowork.AI deal staging removes subjectivity from the pipeline: By running Fathom call transcripts through an AI model with defined stage criteria, Jeff's team gets consistent deal staging — notes, next action, and stage movement all updated automatically. CSMs validate rather than enter.You can build a renewal pipeline from scratch in an afternoon: Jeff used Claude Cowork and the HubSpot MCP to auto-generate hundreds of renewal records with ARR, products, and close dates — work that would have taken weeks manually.SaaS incumbents need a self-disruption pricing strategy: If AI is driving down the cost to build software, someone will undercut you on price. The winners will do it to themselves first — like Adobe's "swallowing the fish" move to SaaS. Your gross margin is someone else's opportunity.Customer journey ≠ service blueprint: The service blueprint is how you deliver your service. The customer journey is the customer's path to value — change management, adoption, transformation. Keeping them separate is the future of CS platform design. CHAPTERS 00:01 - Welcome and personal updates02:32 - Block's 4,000 layoffs: AI as cause or air cover?07:32 - Small nimble teams and the Jack Dorsey thesis09:00 - MCPs explained: querying HubSpot and PlanHat in plain English15:27 - Token costs and natural language as the new interface17:08 - Jay's SaaS self-disruption pricing thesis22:39 - M&A in the AI era and the Palo Alto Networks acquisition playbook27:12 - Jeff's AI build: deal staging from call transcripts via Fathom and PlanHat32:37 - Creating hundreds of renewal records in one afternoon with Claude Cowork39:41 - Customer journey vs. service blueprint — the clearest definition yet42:10 - Wrap-up and preview of an upcoming guest About the Show: Chief Customer Officer Podcast is a show about real strategies for customer-led growth in the AI era—from leaders actually executing, not just talking about it. Your Hosts: Jay Nathan – CEO of Balboa Solutions and Co-Founder of ChiefCustomerOfficer.ioJeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io

    40 min

  7. FEB 26

    EP007: The Wisest Person in your Company You're Not Talking To

    Jon Olson, Chief Legal Officer at Blackbaud, joins Jay and Jeff to flip the script on what legal teams are actually for. Jon argues that the law department's job isn't to say no — it's to pave the road so the business can move fast. Then the conversation turns to AI: how Blackbaud built an AI council to accelerate adoption, why law is uniquely suited for disruption by LLMs, and what it looks like when AI stops being a cost-saver and starts driving top-line revenue. KEY TAKEAWAYS Law is a revenue function: Jon reframes what legal teams are for — not compliance gatekeepers, but deal enablers whose job is to help the business move forward. If there's no revenue, every other legal issue becomes irrelevant.The "ultimate backstage pass": Legal touches 80–90% of company accomplishments every quarter — commercial deals, international expansion, M&A, partnerships. That cross-functional visibility makes CLOs uniquely positioned to see the whole business.Say "yes, but" not "no": Jon coaches his team to never just say no. Instead, surface the risks, propose a path forward, and find the creative structure that gets the deal done. The Uber example: every legal concern was real, but the right answer wasn't "stick with taxis."The Minto Pyramid in practice: Jeff shares a communication framework — lead with the conclusion (what the customer wants), then support it. Jon applies the same inverted pyramid when coaching law students: state your position first, then build the argument underneath. Both are fighting the instinct to bury the lead.Blackbaud's AI council as a speed accelerator: When ChatGPT arrived, Blackbaud didn't lock AI down — they built a structured intake process. The result: use cases flow in faster, cross-functional ideas get shared, and the company moves more rapidly because guardrails are clear.AI in law today vs. tomorrow: Today, AI can redline a 100-page contract against a standard playbook in 2–3 minutes instead of hours. Tomorrow, it will analyze thousands of historical contracts, surface negotiation patterns, and flag when a jurisdiction is no longer favorable — moving from paralegal to institutional memory to expert system.AI's real P&L opportunity is top-line, not just cost savings: Most companies are capturing efficiency gains. The bigger prize is revenue growth — using AI to find underpriced customers, tailor offerings, enable dynamic pricing, and unlock new value from existing contract data.Jevons Paradox and AI: New technology creates more demand, not less. The calculator didn't reduce accountants — there are more now than ever. Jon believes AI will expand productivity, create new categories of work, and ultimately generate more jobs, even as individual tasks get automated. About the Show: Chief Customer Officer Podcast is a show about real strategies for customer-led growth in the AI era—from leaders actually executing, not just talking about it. Your Hosts: Jay Nathan – CEO of Balboa Solutions and Co-Founder of ChiefCustomerOfficer.ioJeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io

    49 min

  8. FEB 19

    EP006: What We're Actually Building with AI Right Now

    Jay and Jeff are back — and this week it's just the two of them. No guest, no slides. Just a real conversation about what they've each been building with Claude Code and Claude Cowork over the past few weeks, and what it's revealing about the future of software, teams, and CS. Plus a take on Jason Lemkin's SaaS doom post — and why they're not totally buying the narrative. KEY TAKEAWAYS Get Hands-On or You Won't Get It: If you're in a leadership role and not personally using AI tools like Claude Code or Cursor, you're not fully grasping what's happening right now. Pay the $150 for a month. Just do it.Enabling Your Team Is a Different Problem: Discovering an AI tool, getting excited about it, then "schlepping it over the fence" to your team isn't enabling them — it's abdicating. Change management still applies. Help them get on the curve.Established SaaS Companies Aren't Dead: Brand trust, enterprise security certifications, and multi-year contracts with durable cash flow give companies like Salesforce and Workday real staying power. The question is whether they'll actually act on it.Point Solutions Are Most at Risk: CFOs will start asking whether they need a $40K screen recording tool when it can be vibe-coded in an afternoon. Systems of record with billing, forecasting, and compliance dependencies are much harder to rip out.The Juice Is Now Worth the Squeeze: AI lets teams do things they always wanted to do but couldn't justify. Jay built an interactive RACI builder and a conversational applicant tracking system — on a weekend, without writing a line of code.It's a Context Problem: The real moat isn't the AI model — it's who can pull together all the Slack threads, call transcripts, emails, and product usage data into one accessible place. That's where the advantage lives.Skills Democratize Expertise Across the Team: When any CSM can run the product marketing skill and generate a feature guide on their own, you've removed a bottleneck — not a person. Speed compounds when the whole team can move.Token Costs Need a Line Item in Your Budget: Some engineering teams are already capping AI spend at $100K per engineer per year. CHAPTERS 00:00 - Intro and technical issues00:09 - Getting hands-on with AI: why leadership can't just delegate it02:48 - The SaaS market landscape: Jason Lemkin's take08:36 - Why established SaaS companies still have real advantages12:09 - Point solutions at risk; systems of record are defensible16:17 - Jeff's product marketing skill in Claude Cowork20:29 - Democratizing documentation: any CSM can now create the guide22:48 - Feeding source code as context; building a chatbot on your own codebase25:28 - QPR, playbook, and onboarding skill ideas27:19 - Managing token costs and what it means for team budgets28:38 - Jay's interactive RACI builder built with Claude Code34:20 - Balboa GPT: client-contextual AI with shared conversation history About the Show: Chief Customer Officer Podcast is a show about real strategies for customer-led growth in the AI era—from leaders actually executing, not just talking about it. Your Hosts: Jay Nathan – CEO of Balboa Solutions and Co-Founder of ChiefCustomerOfficer.ioJeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io

    38 min
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About

Conversations about digital experience and AI. 

Information

  • Creator
    Jay Nathan & Jeff Breunsbach
  • Years Active
    2024 - 2026
  • Episodes
    13
  • Rating
    Clean
  • Copyright
    © 2026 Jay Nathan & Jeff Breunsbach
  • Show Website
    The Chief Customer Officer Podcast

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