AI Product Kitchen

Join us in the AI Product Kitchen this week as we talk to Ilan Frank, Chief Product Officer at Checkr. We explore the realities of building AI products in highly regulated industries, the counterintuitive challenges of AI implementation, and why most product teams are still in the early innings of the AI transformation. Ilan brings a wealth of experience from his previous roles as VP of Product at Slack (where he launched Slack Connect) and Head of Product at Airtable, giving him unique insights into scaling AI product organisations across different contexts.Learn about the practical challenges and unexpected discoveries that come with deploying AI in mission-critical applications, as Ilan shares honest insights on moving beyond the ChatGPT magic to building AI features that solve real customer problems.In this episode, we dive into:The 30-Inning Game of AI Product Development: Why most teams are only in inning 2 or 3 of AI adoption, what separates teams shipping AI products successfully, and the long journey ahead to truly AI-native organisations.Hiring More Humans Because of AI: The counterintuitive reality of how Checkr actually increased headcount in the short term due to AI implementation, requiring specialists to validate AI outputs.Building AI Products in Highly Regulated Industries: The critical importance of guardrails, compliance considerations, and why "we're wrong only 1% of the time" isn't acceptable when employment decisions are on the line.From Hack Day Brilliance to Production Reality: How a promising AI feature required careful reconsideration to ensure regulatory compliance, and the gap between AI demos and production-ready products.Data Moats and AI Strategy: How Checkr is transforming from a background check API into a comprehensive people intelligence platform, leveraging their unique data collection infrastructure to build sustainable competitive advantages in the AI era.Here's how to connect with us:Find Ilan on LinkedIn⁠Follow Matt on LinedIn⁠ and ⁠X⁠Learn more about CheckrAnd make sure to check out Sauce AI⁠ and follow us on LinkedIn⁠!Timestamps:00:00 - Introduction to Ilan Frank and the 30-Inning Game of AI01:02 - What Separates Successful AI Product Teams: Overcoming Fear01:51 - Checkr's First AI Product: Charge Classification Success03:00 - The Journey from Manual to AI-Powered Background Checks04:36 - Customer-Driven Innovation: Speed vs Accuracy Trade-offs06:18 - The Second AI Product: Charge Explainer Development07:20 - When AI Features Go Wrong: Legal Compliance Challenges09:05 - Identifying Problems Through Customer Feedback10:18 - Building AI in Highly Regulated Industries: Zero Error Tolerance11:11 - Human-in-the-Loop: Why AI Increased Hiring at Checkr12:01 - Operations Specialists: The New AI Quality Assurance Role12:42 - Future of AI Confidence: When Human Review Won't Be Needed13:33 - Checkr's Evolution: From API to Comprehensive Platform15:58 - The Data Advantage: Building Moats in Background Checks16:52 - TAM Analysis: Expanding Beyond Background Checks17:43 - AI Implementation Challenges: Magic vs Production Reality20:11 - Hiring AI Product Managers: Skills and Imagination Over Experience22:28 - Why AI Hasn't Transformed Product Management Yet25:04 - Designing AI-Native Organizations for the Future27:27 - The Next Five Years: Automation vs Human Oversight32:08 - Competitive Threats: Startups vs Incumbents in AI33:01 - Slack Connect and Enterprise Product Lessons39:34 - Airtable's AI Strategy: Builders vs End-Users Decision42:55 - Product Leader Advice: Just Do It and Focus on Pain Points44:21 - Internal AI Tools: Design, Research, and Product Management46:09 - The Unseen Pain: What AI Can't Yet Discover48:09 - Product Beliefs: Being an AI Skeptic While Embracing AI48:39 - Looking Ahead: Building the Human Data Graph

Join us in the AI Product Kitchen this week as we talk to Nan Yu, Head of Product at Linear. In this episode, we talk about how Linear is transforming product development by turning AI agents into first-class team members and revolutionising how the highest-performing tech teams organise their work. Nan leads product at Linear, the modern issue tracking platform trusted by companies like OpenAI, Scale AI, and Ramp.Learn about Linear's unique approach to AI product development, from replacing manual taxonomies with intelligent systems to deploying synthetic actors that participate in software workflows just like human colleagues. In this episode, we dive into:Agents as First-Class Team Members: How Linear is building AI agents that can be assigned tasks, review code, and communicate through the platform like human teammatesThe Death of Manual Taxonomy: Why AI will make current approaches to organising backlogs, labelling issues, and categorising product ideas feel "obviously archaic" within five years.Diligence on Tap: How AI unlocks true product management by handling the consistent, repetitive work that currently requires large teams, freeing humans to focus on strategy and product taste rather than mundane tasks.Low-Cost AI Experimentation: Linear's bottom-up approach to AI development, running multiple prototype experiments simultaneously and scaling the winners based on real usage patterns from their own team.Counter-Positioning Against Giants: How Linear successfully competed with industry incumbents like Jira and Asana by focusing obsessively on the individual contributor experience over middle management’s reporting needs.Here's how to connect with us:Find Nan on LinkedIn⁠ and XFollow Matt on LinedIn⁠ and ⁠X⁠Learn more about LinearAnd make sure to check out Sauce AI⁠ and follow us on LinkedIn⁠!Timestamps:00:00 - Introduction and Guest Welcome00:28 - What Will Feel Archaic in Five Years01:00 - Building Better Products vs Shipping More Features02:05 - Decision Making and Product Clarity03:07 - High-Performing vs Low-Performing Product Teams04:23 - The Problem with Traditional Backlogs07:12 - Understanding Taxonomies and Organization Systems09:03 - The Journey from Old to New Ways of Thinking10:27 - Customer Behavioral Shifts and On-Ramps12:42 - Linear's Engagement Strategy: Distinct Issue Creators15:16 - AI Strategy Part 1: Sharpening Existing Data17:02 - AI Strategy Part 2: Synthetic Actors and Agents18:42 - Which Workflows Will AI Take Over First20:26 - Barriers to AI Adoption in Development21:57 - Building AI Products: Augmentation vs Replacement23:58 - Measuring Success with AI Products25:10 - Business Model Evolution with AI26:26 - Low-Cost AI Experimentation Process28:01 - AI Project Examples: Winners and Failures30:15 - Integrating AI Without Compromising Simplicity32:32 - ROI and Impact of AI Products33:17 - Future Impact on Product Team Structure37:00 - Starting with Customer Problems in AI Development39:46 - Customer Development and Beta Testing Process41:27 - Focusing on Individual Contributors vs Buyers44:15 - Effective Customer Interview Techniques

Join us in the AI Product Kitchen this week as we talk to Eilon Reshef, Co-Founder and Chief Product Officer of Gong. We dive into topics like building AI products before the current boom and Gong’s journey creating the leading revenue AI platform that's transformed how sales teams operate.Eilon shares fascinating insights from Gong's 10-year journey, starting when customers were genuinely scared of AI technology, through to building a multi-billion dollar platform that processes millions of sales conversations. As one of the pioneers who saw AI's potential back in 2015 (he even bought NVIDIA stock!), Eilon offers unique perspectives on what it takes to build world-class AI products that customers actually love.In this episode, we go deep on:Building AI Before It Was Cool: How Gong pioneered conversation intelligence when buyers were skeptical of AI, and the strategies they used to overcome early market resistance.The Design Partner Philosophy: Eilon's extreme approach to customer collaboration, where every PM works with a dozen design partners to iterate rapidly and ensure product-market fit before launch - his "secret sauce" for building AI products that provide real value.AI Augmentation vs Replacement: Why Gong deliberately chose to augment rather than replace salespeople, and Eilon's contrarian view on why the obsession with AI SDRs and replacement technology misses the bigger opportunity.From Single Feature to AI Platform: The strategic journey from simple call recording to a comprehensive revenue orchestration platform, including lessons on when to expand beyond your initial wedge and how to build defensible moats in AI.Measuring AI Product ROI: Practical approaches to demonstrating value from AI products, from talk ratio insights that became viral LinkedIn posts to building multiple value propositions tailored to different stakeholders (productivity, predictability, growth).Here's how to connect with us:Find Eilon on LinkedInFollow Matt on LinedIn and XLearn more about GongAnd make sure to check out Sauce AI and follow us on LinkedIn!Timestamps:00:00 - Introducing Eilon Reshef 02:15 - Building AI Before It Was Hot04:45 - Early AI Conviction and NVIDIA Investment07:20 - The Inefficiency Problem in Sales Organizations09:50 - Meeting Bots and Customer Fear12:30 - Overcoming Early AI Skepticism15:10 - Creating the Conversation Intelligence Category18:25 - Category Creation Strategy and Naming21:40 - Climbing Everest: Lessons and Mistakes24:20 - The Power of Design Partners27:45 - Design Partner Execution and Rituals31:10 - Talk Ratio: The First Breakthrough Insight34:30 - Big Brother Problem and Seller Value Creation38:15 - One-Click Call Sharing Product Loop41:00 - Expanding Beyond Single Product44:20 - Measuring AI Product ROI47:35 - Killing Products: Talk Tracks Feature50:45 - Augmentation vs Replacement Philosophy54:10 - Revenue Orchestration Platform Vision57:25 - Building Product Moats in AI Era

This episode, Vrushali Paunikar - Chief Product Officer at Carta, joins us in the AI Product Kitchen. We talk about Carta's transformation from cap table management to becoming the ERP for private capital, and how they're leveraging AI to revolutionize financial systems stuck in the 80s and 90s. With nearly a decade at Carta, Vrushali has led product at the company through its journey from being rejected by almost every Series A investor to now powering over half of all VC-backed companies and managing over $2.5 trillion in equity.In this episode, we dive into:AI for Document Intelligence: How Carta evolved from early, unsuccessful AI experiments with legal documents to now extracting critical data and automating financial workflows using advanced AI models.AI Agents in Finance: Exploring how AI agents can orchestrate complex financial processes and resolve failing health checks automatically, transforming how private capital finance teams operate.Reimagining UX for AI: The challenges of designing user experiences for AI-powered workflows, from asynchronous processes to potential chatbot interfaces and the future of finance software.The ERP Vision for Private Capital: How connecting disparate systems through an ERP platform can eliminate the current practice of manually verifying numbers across systems, freeing finance teams from just "making sure the math is doing math correctly."Incumbent Advantages vs. Startup Opportunities: Why data-rich incumbents like Carta have an edge in AI, while AI-native startups can differentiate through new design paradigms and aggressive integration strategies.Here's how to connect with us:Find Vrushali on ⁠⁠⁠LinkedIn⁠⁠⁠⁠ and ⁠⁠⁠X⁠⁠⁠⁠Follow Matt on ⁠⁠⁠LinkedIn⁠⁠⁠⁠ and ⁠⁠⁠X⁠⁠⁠Learn more about CartaAnd make sure to check out ⁠⁠⁠Sauce AI⁠⁠ and follow us on ⁠⁠LinkedIn⁠⁠!⁠Timestamps00:00 - Introduction to Vrushali, CPO at Carta01:15 - Carta's origin story: Rejected by every Series A investor03:30 - Business model innovation: Changing the payer from law firms to companies05:45 - Tackling "market too small”, objections in startups07:20 - The pivot from liquidity marketplace to infrastructure platform09:40 - Learning from product-market fit failure in private market liquidity12:15 - Systems thinking approach to identifying new opportunities14:30 - The "startup of startups" org structure at Carta16:10 - From building new products to connecting the platform17:45 - "Never Enter Data Twice" as a product philosophy19:20 - Early AI experiments with legal document processing21:30 - How AI is accelerating innovation at Carta today23:40 - Executing on AI bets: From thesis to experimentation25:15 - AI agents for orchestrating financial workflows27:50 - Augmentation vs replacement debate in AI29:30 - User experience challenges with AI-powered workflows32:10 - AI's impact on design paradigms and information delivery34:20 - Startup advantages in an AI-first world36:45 - Using data to build opinionated products and decision guides38:15 - The future of private capital finance: Beyond "making sure numbers tie up"40:30 - CFOs evolving from back office to strategic operators

Join us in the AI Product Kitchen this week as we talk to Jeff Seibert, CEO and co-founder of Digits. In this episode, we dive into topics like Jeff's time as Head of Consumer Product at Twitter and his mission to revolutionise the accounting industry with Digits, the first end-to-end accounting platform of the AI era.In this episode, we go deep on:In-house AI models vs off-the-shelf solutions: the importance of developing in-house AI models tailored to specific business needs, rather than relying solely on off-the-shelf solutions.Optimising user experience with AI: Creating an intuitive user experience where AI works seamlessly in the background, allowing users to focus on understanding their business rather than managing tedious accounting tasks.Product moats in the AI era: How AI can create durable competitive advantageCommon mistakes in AI product development: The pitfalls of putting AI at the forefront of the product narrative without addressing the core problem first.Lessons from Twitter and AI opportunities: Missed opportunities to leverage AI and the need for a broader mindset in recognizing AI's potential in product development.Here's how to connect with us:Find Jeff on ⁠⁠LinkedIn⁠⁠⁠, ⁠⁠X⁠⁠⁠ and on his websiteFollow Matt on ⁠⁠LinkedIn⁠⁠⁠ and ⁠⁠X⁠⁠Learn more about DigitsAnd make sure to check out ⁠⁠Sauce AI⁠ and follow us on ⁠LinkedIn⁠!⁠Timestamps00:00 - Introduction to Jeff Seibert and Digits00:33 - Jeff's Journey and the Launch of Digits01:42 - The Vision Behind Digits and AI in Accounting03:11 - The Decision to Build in Stealth Mode05:42 - The Importance of Product Quality and Timing06:46 - The Evolution of Accounting Software08:26 - Building AI Models for Accounting10:39 - Defining Success: What is "Good Enough"?12:18 - In-House Models vs. Off-the-Shelf Solutions13:36 - User Experience and Automation in Digits15:40 - Augmenting Accountants, Not Replacing Them17:03 - Common Mistakes in AI Product Development18:36 - Differentiating True Innovation from Noise19:36 - Sustaining Competitive Advantage in AI21:52 - Unique Team Structures and Agile Practices24:12 - The Role of Product Managers in Digits25:15 - Go-to-Market Strategy and Launching Products26:38 - Lessons from Twitter and AI Opportunities27:52 - Reflections on Apple and Product Design Principles30:25 - The Importance of Product Obsession32:32 - Prioritization Strategies for Product Development34:32 - Future of Accounting and Automation

Join us for our very first episode of AI Product Kitchen from Sauce, where we welcome Rachel Wolan - Chief Product Officer at Webflow. With over a decade of experience in the AI space, Rachel shares her journey from launching her first AI product at Talkdesk to leading the development of multiple AI products at Webflow.In this episode, we discuss the intersection of AI and Product, exploring how AI can supercharge Product teams and enhance user experiences. Come take a deep dive into:The evolution of AI in product development: Understanding how AI has transformed from machine learning-based products to generative AI solutions.The importance of solving customer problems: Rachel emphasizes that regardless of advancements in AI, the core focus must always be on addressing user needs effectively.Differentiating between leading and following in the AI space: Insights on when to innovate and when to enhance existing products with AI capabilities.Building a strong AI team: Strategies for upskilling existing teams and integrating AI fluency across product management, design, and engineering.The future of Product in an AI world: Predictions on how AI will reshape the product lifecycle, including the roles of QA, prompt engineering, and user experience design. Here's how to connect with us:Find Rachel on ⁠LinkedIn⁠⁠ and ⁠X⁠⁠Follow Matt on ⁠LinkedIn⁠⁠ and ⁠⁠X⁠⁠And make sure to check out ⁠Sauce AI and follow us on LinkedIn!⁠ Episode timestamps00:00 - Introduction to Rachel Wolan00:37 - Rachel's AI Journey: A Decade of Experience01:18 - The Evolution of AI Product Development03:31 - Leading vs. Following in the AI Space05:25 - Webflow's Initial Product Launch Strategy06:51 - Transitioning to Multi-Product Offerings08:37 - Building the First AI Team at Webflow10:34 - Upskilling Teams for AI Fluency12:11 - Investing in AI: Strategic Considerations14:57 - Trends Reshaping Product Development17:22 - Adapting Product Organization Structures18:24 - Measuring Product Signal and Success20:06 - Evaluating ROI on AI Investments21:54 - Inspirations and Influencers in AI23:10 - The Role of Human Augmentation in AI25:25 - Designing Unique AI Experiences27:03 - Go-to-Market Strategy for SMBs and Enterprises30:48 - Prioritizing Features for Diverse User Personas32:18 - The Concept of PM as GM39:49 - Hiring PMs from Adjacent Functions41:49 - Surprises in Building AI Products42:42 - User Experience Challenges in AI43:51 - Concerns and Excitement about AI's Future44:56 - Future Predictions for Webflow Users46:11 - Closing Remarks and Reflections

Jiaona Zhang (JZ) is the Chief Product Officer at Linktree, who pioneered the 'link-in-bio' category and is one of the world's fastest growing product companies. JZ was previously the Senior VP Product at Webflow, VP Product at WeWork and Head of Product at Airbnb. JZ is also a Lecturer at Stanford University and a Program Creator at Reforge.