The Chief Customer Officer Podcast

Jay and Jeff swap real examples of building custom AI tools instead of buying software — a $1K brand video that beat $750K agency quotes, a content planner built in 35 minutes, and a renewal digest that surfaces customer context weekly with zero manual effort. Plus: why the CS platform category is stalling, and the difference between a service blueprint and a customer journey.KEY TAKEAWAYSAI collapses the agency middle layer: A CEO got $500K–$750K quotes for a brand video, spent $1K in AI credits instead, and got 70% of what he wanted in a weekend. The agencies only offered 10–20% discounts when asked to use AI themselves.Fewer people = faster output: The Mythical Man-Month principle applied to AI: every person you add to a project adds communication overhead. The real superpower of these tools is reducing the number of people in the middle of a problem.Build your content system, not just content: Jeff built an AI-powered content planner in 35 minutes — 9 posts/week, post-type by day, 40-idea backlog, status tracking, and a draft button that fires his LinkedIn writing skill. MVP in a morning.CS cockpit over CS platform: The question isn't which CS tool to buy — it's whether you can build exactly what your team needs. Jeff's team built a weekly renewal digest from support tickets, emails, Slack, and call recordings in one day.The CS platform category is stalling: The layer of workflows that's truly common across software companies is thinner than vendors want to admit. Domain-specific, product-specific nuance is where the real work lives — and that can't be bought off the shelf.Service blueprint vs. customer journey: Service blueprint = what you need to do to interact with a company. Customer journey = how customers mature and get value. CEOs want to hear about the journey. CSMs need to stop confusing the two.CHAPTERS00:01 - Raleigh, end of quarter, baby incoming02:43 - $750K agency quote vs. $1K AI video build05:15 - Building exactly what you need: Jay's HubSpot pipeline dashboard07:28 - The Mythical Man-Month and reducing communication layers with AI09:15 - Paying for expertise, not pixel-pushing10:24 - Jeff's AI content planner: built in 35 minutes on Cowork15:56 - How the draft button and LinkedIn skill work together17:46 - Hosting the planner: Cowork vs. Claude Code21:12 - The CS cockpit idea: custom workflow hub for CSM teams24:50 - Junction's interactive onboarding demo environment25:53 - Why the CS platform category is stalling31:41 - Service blueprint vs. customer journey33:28 - Team AI day: CS team builds a weekly renewal digest35:51 - "You just described Staircase AI" — and built it in a day37:44 - Why PlanHat over HubSpot40:59 - Slack account pulse bot: ping for an AI-written account summaryYour 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 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 conferenceAbout 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

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 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

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

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 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

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