The Chief Customer Officer Podcast

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 TAKEAWAYSBuild 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 GUESTAurelia 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 LinkedInCHAPTERS03:46 - 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 yetAbout 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

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 TAKEAWAYSWe'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."CHAPTERS00:00 - 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

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 TAKEAWAYSStart 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.CHAPTERS00:01 - 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

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 TAKEAWAYSProve 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.CHAPTERS00:01 - 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-upAbout 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

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 TAKEAWAYSAI 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.CHAPTERS00:01 - 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 guestAbout 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

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 TAKEAWAYSLaw 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

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 TAKEAWAYSGet 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. CHAPTERS00:00 - 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 historyAbout 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

What does world-class digital customer experience actually look like? In this episode, Justin Chappell, Head of Digital Strategy at OneTrust, breaks down the evolution of customer success from reactive firefighting to predictive, AI-powered engagement. Whether you're building your first digital CS motion or looking to integrate AI agents into your customer journey, this conversation offers actionable insights.KEY TAKEAWAYSThe CS Evolution Framework: Why moving from reactive → proactive → predictive requires fundamentally different thinking (and why proactive is closer to reactive than you might think)The 3 Pillars of World-Class Digital CX: Personalized, Predictive, and Digital-First—and how to operationalize eachDirty Data Isn't a Blocker: How predictive models can actually help you identify which data matters while learning from what's usableThe Power of Simply Asking: Why the best renewal signal is giving customers three options—Yes, No, or Undecided—and how to act on eachOnboarding That Works: The pilot process that cut first-time-to-value from 44 days to 8 days (and the customer feedback that made it possible)The Future CS Team Structure: New roles like AI Architect, Knowledge & AI Readiness Specialist, and Business Value ArchitectMonetizing Customer Success: How success packages create recurring revenue while delivering differentiated experiencesABOUT OUR GUESTJustin Chappell is the Head of Digital Strategy at OneTrust, where he oversees digital customer experience for 93% of the company's customer base. He's known for keeping things simple and saying what others are thinking.Connect with Justin on LinkedInCHAPTERS01:17 - The Evolution of Customer Success: Reactive to Proactive to Predictive04:11 - What "Predictive" Really Means (And Why It's Different from Proactive)08:44 - Dirty Data Isn't a Blocker: How Predictive Models Actually Work12:09 - The Truth About NPS Response Rates and Vanity Metrics20:49 - Starting from Zero: Implementing AI at OneTrust23:30 - The 3 Pillars of World-Class Digital Customer Experience31:10 - Lessons Learned: Roles, Timing, and Relevance35:39 - Building the Digital Tech Stack: CRM, CSP, and Community42:15 - Customer-Facing AI Agents: The Agentic CSM46:10 - The Future CS Team: AI Architect, BVA & New Roles53:38 - Managing AI Agents Like You'd Manage Your Best CSM59:24 - How AI Changes the CSM Role (Hint: They Can Handle More)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

In this episode, Jeff and Jay discuss the evolving landscape of customer success, the integration of AI tools, and the importance of gross revenue retention. They explore the metrics that define customer success roles, the challenges faced by early-stage companies, and the need for clear compensation structures. The conversation highlights the significance of creating effective customer success plans and the necessity of aligning customer success efforts with overall business goals.TakeawaysAI tools are transforming how we approach customer success. Customer success metrics are often poorly defined across organizations. Gross revenue retention is becoming a critical focus for investors. The structure of customer success teams can impact their effectiveness. Compensation for customer success roles needs to reflect their contributions to revenue. Early-stage companies must prioritize customer success to drive growth. Separation of roles within customer success can lead to better outcomes. Customer success should be a company-wide initiative, not just a team effort. Creating autonomy for customer success teams can enhance performance. Defining clear success plans is essential for customer retention.Chapters00:00 Website Redesign and AI Integration02:50 The Evolution of AI Tools05:28 Customer Success Metrics and Reporting08:18 The Role of Customer Success in Revenue Generation11:09 Compensation Structures in Customer Success14:06 Strategic Decisions in Customer Success16:51 Navigating Growth in Early-Stage Companies26:40 Sales Strategy and Market Share Growth28:21 Customer Success and Implementation Challenges30:06 Specialization in Customer Success Roles32:14 Creating Autonomy and Predictability in Sales34:51 Understanding Private Equity and Its Impact37:37 The Importance of Gross Revenue Retention40:16 The Role of Customer Success in Retention43:07 Innovative Approaches to Customer Success PlansAbout 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

In this episode of The Chief Customer Officer Podcast, Jeff Breunsbach and Jay Nathan delve into the evolving role of AI in branding and customer experience. Jay shares his recent experiences using AI tools like Claude and ChatGPT to refine branding strategies for his companies, Balboa and Greenshoot Innovation. They discuss the importance of context in enterprise AI applications, emphasizing that the effectiveness of AI agents hinges on their understanding of user preferences and business nuances. The conversation transitions into a broader discussion about the future of enterprise software, highlighting the need for reliable systems that can integrate AI capabilities while maintaining deterministic workflows.   The hosts also explore the implications of AI on job security and enterprise software, countering the prevailing narrative of doom and gloom. They argue that rather than replacing jobs, AI can enhance productivity by automating mundane tasks, allowing teams to focus on customer interactions. The episode wraps up with recommendations for AI-related resources and discussions on the potential for AI to personalize customer experiences, ultimately leading to more tailored and effective service delivery.     Chapters  00:00 Introduction and Weekend AI Experiments02:37 The Importance of Context in AI04:51 AI's Impact on Enterprise Software08:57 Balancing Deterministic Workflows with AI11:15 Personalization and Customer Experience16:38 AI in Developer Tools and Integration21:32 Recommended Resources and Closing ThoughtsAbout 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

The death of the coverage model...In this episode of the Chief Customer Officer Podcast, Jay Nathan and Jeff Breunsbach peel back the layers on the practical application of AI in Customer Success. They start with a reality check: before you can build sophisticated AI agents, you often have to do the manual "grunt work" first — like physically reviewing contracts to clean up renewal data. The conversation shifts to a critical debate on organizational structure, specifically the move away from the traditional "Coverage Model" (assigning accounts based on ARR) toward a "Predictive Model" that relies on real-time data signals rather than arbitrary check-ins. Finally, Jay and Jeff geek out on the concept of "Vibe Coding" — using AI to build internal tools and websites without writing a line of code — and discuss why leaders need to get their hands dirty with these tools to truly understand them.Chapters00:00 Introduction & Dropping the ".io" 01:07 The Reality of Renewals: Manual Work Before AI 04:49 Why You Should Hire Ops Before Your Next CSM 09:49 The Death of the Coverage Model 13:49 Erroneous Signals vs. Predictive Data 17:44 The Evangelism Role & Fixing QBRs 25:36 The "Stay Conversation": 3 Questions to Retain Your Team 27:42 "Vibe Coding" & Building Software with AI 33:25 How Jeff Built the New Website (Without Code) 42:34 Preview: Sierra AI & Next WeekAbout 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

In this episode of the Chief Customer Officer Podcast, Jay Nathan and Jeff Breunsbach discuss the evolving landscape of customer success, particularly focusing on the impact of AI. They explore the importance of building a robust renewal function, identifying key information for renewals, and the necessity of mapping out processes. The conversation delves into the development of granular AI agents to enhance customer interactions and the balance between workflow and AI capabilities. They also emphasize the significance of understanding customer business contexts and choosing the right tools for effective AI implementation. The episode concludes with thoughts on standardizing AI use across teams and the future directions of AI in customer success.Chapters00:00 Introduction to the Chief Customer Officer Podcast 02:58 The Role of AI in Customer Success 05:58 Building a Renewal Function with AI 08:49 Identifying Key Information for Renewals 12:08 Mapping Out the Renewal Process 15:09 Creating Operational Agents for Efficiency 18:10 The Future of AI in Customer Engagement 22:09 Optimizing Workflow with AI Agents 24:18 Personalization in Customer Interactions 26:11 The Importance of Real-Time Data 28:00 Exploring Tools for AI Integration 30:04 Categories of Agentic Tools 35:50 Empowering Business Users with Agent Development 39:04 Real-World Applications of AI in BusinessAbout 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