Discover shows related to The Chief Customer Officer Podcast, based on actual content similarities. Explore podcasts with similar topics, themes, and formats, backed by real data.
EP006: What We're Actually Building with AI Right Now
Season 1 · Episode 6
Thursday, February 19, 2026 • Duration 38:24
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 issues
00:09 - Getting hands-on with AI: why leadership can't just delegate it
02:48 - The SaaS market landscape: Jason Lemkin's take
08:36 - Why established SaaS companies still have real advantages
12:09 - Point solutions at risk; systems of record are defensible
16:17 - Jeff's product marketing skill in Claude Cowork
20:29 - Democratizing documentation: any CSM can now create the guide
22:48 - Feeding source code as context; building a chatbot on your own codebase
25:28 - QPR, playbook, and onboarding skill ideas
27:19 - Managing token costs and what it means for team budgets
28:38 - Jay's interactive RACI builder built with Claude Code
34:20 - 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.
Jeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io
EP005: Building Predictive Customer Success with AI w/ Justin Chappell
Season 1 · Episode 5
Thursday, February 5, 2026 • Duration 01:04:34
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 TAKEAWAYS
The 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 each
Dirty Data Isn't a Blocker: How predictive models can actually help you identify which data matters while learning from what's usable
The Power of Simply Asking: Why the best renewal signal is giving customers three options—Yes, No, or Undecided—and how to act on each
Onboarding 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 Architect
Monetizing Customer Success: How success packages create recurring revenue while delivering differentiated experiences
ABOUT OUR GUEST
Justin 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.
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.
Chapters
00:00 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 Week
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.
EP004: Consensus on GRR, none on Customer Success
Season 1 · Episode 4
Thursday, January 29, 2026 • Duration 47:10
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.
Takeaways
AI 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.
Chapters
00:00 Website Redesign and AI Integration
02:50 The Evolution of AI Tools
05:28 Customer Success Metrics and Reporting
08:18 The Role of Customer Success in Revenue Generation
11:09 Compensation Structures in Customer Success
14:06 Strategic Decisions in Customer Success
16:51 Navigating Growth in Early-Stage Companies
26:40 Sales Strategy and Market Share Growth
28:21 Customer Success and Implementation Challenges
EP003: Context is King
Episode 3
Friday, January 23, 2026 • Duration 24:47
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 Experiments
02:37 The Importance of Context in AI
04:51 AI's Impact on Enterprise Software
08:57 Balancing Deterministic Workflows with AI
11:15 Personalization and Customer Experience
16:38 AI in Developer Tools and Integration
21:32 Recommended Resources and Closing Thoughts
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.
EP001: Agentic Renewals
Episode 1
Thursday, January 8, 2026 • Duration 38:25
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.
Chapters
00:00 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 Business
EP007: The Wisest Person in your Company You're Not Talking To
Season 1 · Episode 7
Thursday, February 26, 2026 • Duration 49:09
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.
EP009: Sitting Ducks and Fortresses: How to Read the AI Landscape
Season 1 · Episode 9
Thursday, March 12, 2026 • Duration 44:58
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 year
02:34 - The job candidate who built an app to stand out
EP008: Cut Your Own Prices Before Someone Else Does
Season 1 · Episode 8
Thursday, March 5, 2026 • Duration 39:33
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 updates
02:32 - Block's 4,000 layoffs: AI as cause or air cover?
07:32 - Small nimble teams and the Jack Dorsey thesis
EP019: The AI Native Services Playbook w/ Jay Nathan
Season 1 · Episode 19
Thursday, May 28, 2026 • Duration 31:17
Jay Nathan flies solo to break down Emergence Capital's AI Native Services Playbook — what it gets right, where it falls short, and what it completely misses.
Using a recruiting firm as an end-to-end example, Jay walks through the shift from selling software to delivering outcomes, and why the founders who win in this space won't come from SaaS — they'll come from services.
KEY TAKEAWAYS
AI Native Services defined: A business that collapses software and services into a single system, delivering outcomes the customer never has to produce themselves. You sell a result; your company produces it.
The recruiting firm example: Instead of selling recruiting software, you become an AI-native recruiting firm — sourcing, screening, scheduling, and delivering candidates. Pricing shifts from per-seat to per-placement.
Domain credibility over everything: Without deep expertise in your vertical, you start every sales conversation with zero trust. Domain credibility is brand — and it comes first.
Mirage PMF is a real trap: Revenue growth powered by headcount, not AI, is not product-market fit. Watch gross margin — if it's not expanding as you scale, automation isn't doing the work.
Outcome-based pricing is the unlock: AI-native services firms own the delivery, so they own the attribution. Price on results, not hours.
Skip the VC framing: These businesses can generate significant free cash flow without venture capital. Don't let a VC playbook push you into unnatural growth moves.
Continuity beats handoffs: Switching from a "Navy SEAL" pilot team to a steady-state delivery team erodes trust and loses context. Keep the same team; embed a forward-deployed engineer from day one.
Ecosystem position is the moat: The AI alone won't differentiate you. Partnerships, certifications, and community presence inside your vertical will.
CHAPTERS
00:00 - Introduction & Episode Overview
01:56 - What Is an AI Native Services Company?
03:43 - The AI-Native Recruiting Firm Example
CHAPTERS
01:17 - The Evolution of Customer Success: Reactive to Proactive to Predictive
04:11 - What "Predictive" Really Means (And Why It's Different from Proactive)
08:44 - Dirty Data Isn't a Blocker: How Predictive Models Actually Work
12:09 - The Truth About NPS Response Rates and Vanity Metrics
20:49 - Starting from Zero: Implementing AI at OneTrust
23:30 - The 3 Pillars of World-Class Digital Customer Experience
31:10 - Lessons Learned: Roles, Timing, and Relevance
35:39 - Building the Digital Tech Stack: CRM, CSP, and Community
42:15 - Customer-Facing AI Agents: The Agentic CSM
46:10 - The Future CS Team: AI Architect, BVA & New Roles
53:38 - Managing AI Agents Like You'd Manage Your Best CSM
59:24 - 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.
32:14 Creating Autonomy and Predictability in Sales
34:51 Understanding Private Equity and Its Impact
37:37 The Importance of Gross Revenue Retention
40:16 The Role of Customer Success in Retention
43:07 Innovative Approaches to Customer Success Plans
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.
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.
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.
Jeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io
04:16 - Three ways to prove you're AI-first as a candidate
09:01 - 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 Balboa
17:53 - Kyle Lacy's three go-to-market AI mistakes
21:50 - Your call recordings are your best practices
23:27 - Jay builds the AI Vulnerability Matrix live on the call
25:32 - The four quadrants: sitting ducks, protected niches, targets, fortresses
33:19 - What AI-first companies actually look like (Jason Lemkin)
35:00 - The shift from CSM to forward deployed engineer
36:12 - The $700K ARR per employee benchmark
36:40 - Jeff's counterpoint: humans stay in the buying loop longer than we think
41:15 - Agent-to-agent PLG is already happening
45:54 - 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.
Jeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io
09:00 - MCPs explained: querying HubSpot and PlanHat in plain English
15:27 - Token costs and natural language as the new interface
17:08 - Jay's SaaS self-disruption pricing thesis
22:39 - M&A in the AI era and the Palo Alto Networks acquisition playbook
27:12 - Jeff's AI build: deal staging from call transcripts via Fathom and PlanHat
32:37 - Creating hundreds of renewal records in one afternoon with Claude Cowork
39:41 - Customer journey vs. service blueprint — the clearest definition yet
42:10 - 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.
Jeff Breunsbach – Head of Customer Success at Junction and Co-Founder of ChiefCustomerOfficer.io
08:10 - Where Emergence Gets It Right: Domain Credibility
09:41 - Mirage Product-Market Fit
11:14 - Outcome-Based Pricing
13:11 - Pushback: The VC Framing Problem
15:40 - Pushback: Don't Switch Pilot Teams
17:28 - Pushback: The Product Development Trap
19:47 - The Vertical Ecosystem Advantage
23:30 - Connecting AI Native Services to Customer Success
26:02 - The AI Recruiting Firm in 2026: What's Automated Now
28:10 - Recap & What to Take With a Grain of Salt
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.
