Beyond The Pilot: Enterprise AI in Action

A QuickBooks customer discovered significant fraud by asking their AI assistant follow-up questions about transaction amounts that didn't add up. This isn't a demo — it's one of 3 million customers now using Intuit's AI agents in production, with 80.5% returning to use them again. Marianna Tessel, EVP and GM of QuickBooks (formerly CTO of Intuit), walks through the architecture decisions behind one of the first enterprise AI deployments at true scale. Intuit's "done-for-you" agents now automate book closing, reconciliation, transaction categorization, and payroll — but the breakthrough came when they realized chatbots alone weren't enough. Businesses wanted human experts integrated directly into AI workflows, creating what Intuit calls the "AI + HI" model (artificial intelligence + human intelligence). The results: invoices paid 5 days faster, 90% more paid in full, 30% reduction in manual work, and 62% of users reporting bookkeeping is easier. Tessel reveals the technical evolution: moving from monolithic agents to a dynamic orchestration layer that routes queries across multiple LLMs (including Intuit's proprietary FinLM built on open-source), 24,000 bank connections, and 600,000 customer attributes. The system now handles proactive anomaly detection, benchmarking against similar businesses, and even nascent vibe coding — all without requiring users to understand they're essentially programming workflows through natural language. She also addresses the "SaaS apocalypse" narrative head-on, explaining why QuickBooks saw 18% growth last quarter while competitors faced market pressure: durable data advantages and customer trust in financial accuracy matter more than ever when AI enters the mix. For enterprise builders navigating agent architecture, data grounding, and human-in-the-loop design, this is a rare look inside a working system serving millions. 🎙️ GUEST: Marianna Tessel | EVP & GM, QuickBooks (Intuit) 🎙️ HOSTS: Matt Marshall | VentureBeat, Sam Witteveen | VentureBeat 00:00 Intro — Customer discovers fraud using QuickBooks AI 03:26 Intuit Intelligence: Agents, BI, and human expertise integration 05:20 First-time AI users and going beyond chatbots 08:02 How Intuit decides which workflows to automate 10:16 Sponsor: Outshift by Cisco 10:38 Human-in-the-loop: When to insert experts vs. full automation 13:00 The AI + HI model: Why customers want human verification 15:24 Human expertise as confidence layer, not just AI check 16:14 Proprietary data advantage: 24K bank connections, 600K attributes 18:39 Benchmarking: "Businesses like me" — using aggregate data for competitive insights 19:52 First-party vs. third-party data strategy 21:38 Addressing the "SaaS apocalypse" narrative — why Intuit grew 18% last quarter 24:39 Proactive AI: Anomaly detection for marketing expense spikes 25:20 Builder perspective: Leaning on LLM orchestration, not use-case-by-use-case builds 27:32 Architecture evolution: From monolithic agents to dynamic tools and skills 29:10 Composite UX: Chat side-by-side with traditional workflows 30:35 Multi-model strategy: Genos platform, FinLM, and model routing 31:16 Vibe coding and actions: Letting users automate without realizing they're coding 32:47 Personalization wave: Memory, persistence, and user-defined workflows 35:08 Docker background and primitives that survive disruption 36:00 Open Claw and agent automation: Real revolution or risky experimentation? #EnterpriseAI #AIAgents #QuickBooks #Intuit #LLMOrchestration #AgenticAI Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more at outshift.cisco.com. About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality. 🔗 CONNECT WITH US Subscribe to our Newsletters for technical breakdowns: https://venturebeat.com/newsletters Visit VentureBeat: Venturebeat.com . . . Subscribe to VentureBeat:     /  @VentureBeat   . . Subscribe to the full podcast here: Apple: https://podcasts.apple.com/us/podcast/venturebeat/id1839285239 Spotify: https://open.spotify.com/show/4Zti73yb4hmiTNa7pEYls4 YouTube: https://www.youtube.com/VentureBeat Learn more about your ad choices. Visit megaphone.fm/adchoices

They unpack how personalized AI agents are reshaping enterprise software and shifting market power. They explore Microsoft’s Copilot move and the changing build-vs-buy decisions. The conversation highlights Zoom’s journey from templates to user-controlled skills and the race to own personal context. Identity, token budgets, multi-user risks, and the rise of agent-centric architectures are examined.

Harrison Chase, Co-founder and CEO of LangChain, leads the team behind the LangChain library and agent frameworks. He explains why OpenClaw succeeded where AutoGPT did not. He breaks down Deep Agents: planning, to-do lists, subagents, file systems, and large system prompts. He outlines LangGraph, LangChain, and harness choices, and why context engineering matters for reliable agent loops.

Min Chen, Chief AI Officer at LexisNexis and longtime leader in legal ML, explains why standard RAG breaks for law and how GraphRAG and point-of-law graphs provide authoritative grounding. She describes their 8-part Usefulness Score, agentic workflows like Planner and Reflection agents, and deterministic checks for hallucination detection. Practical, execution-focused breakdowns of deploying AI in zero-error legal settings.

Chris Mertz, SVP of Data Science at Mastercard who builds large-scale ML systems, and Johan Gerber, EVP of Security Solutions with a law-enforcement background, pull back the curtain on running AI at massive scale. They discuss scoring 160 billion transactions under a 50ms limit, an RNN “inverse recommender,” GenAI honeypots that bait scammers, and org and cloud choices that made it all possible.

Erran Berger, VP of Product Engineering at LinkedIn who led distilling large LLMs into ultra-efficient production models. He reveals how LinkedIn distilled 7B models down to 600M students, the multi-teacher split for policy vs. clicks, synthetic GPT-4 golden datasets, and the 10x latency savings from pruning, quantization, and context compression. He also explains the org shift to eval-first product design.

Amjad Masad, CEO of Replit and a visionary in programming accessibility, unpacks the $10 trillion potential of enterprise AI in a fascinating discussion. He critiques the majority of AI agents as 'Slop,' explaining their generic failures and the need for quality inputs. Masad introduces 'Vibe Coding,' where non-tech experts shape software, and reveals Replit's 'Computer Use' hack, making agent development cheaper and faster. He warns leaders to ditch rigid roadmaps in favor of rapid iteration to thrive in the evolving AI landscape.

Inside the 'Agent Economy': How 30,000 AI Assistants Took Over JPMorgan While most enterprises were scrambling after ChatGPT launched, JPMorgan Chase was already two years ahead. 🚀 In this episode of Beyond the Pilot, we sit down with Derek Waldron, Chief Analytics Officer at JPMorgan Chase, to reveal how the world’s largest bank built an internal AI platform that is now used by 1 in 2 employees daily. Derek shares the contrarian insight that drove their strategy: AI models are commodities; the real moat is connectivity. Learn how they scaled from zero to 250,000+ users, why they empowered employees to build 30,000+ of their own "Personal Agents," and how they are solving the data privacy challenge at an enterprise scale. 🔥 IN THIS EPISODE: The "Super Intelligence" Thought Experiment: Why raw intelligence is useless without enterprise connectivity. The Agent Economy: How JPM enabled non-technical staff to build 30,000 custom AI assistants. The Adoption Playbook: How to break through the "30% wall" and get the majority of your workforce using AI. Build vs. Buy: Why JPM built their own "LLM Suite" instead of waiting for vendors. ⏳ CHAPTERS: 00:00 - Introduction: The JPMorgan AI Story 01:45 - The 3 Core Principles Behind JPM’s Strategy 03:25 - The "Super Intelligence" Thought Experiment 05:00 - Data Privacy: Why JPM Doesn't Train Public Models 06:00 - Viral Adoption: From 0 to 250k Users 09:20 - Evolution of LLM Suite: From RAG to Ecosystem 14:00 - The "Moat" is Connectivity, Not the Model 23:00 - The Agent Economy: 30,000 Employee-Built Assistants 31:00 - Governance & Guardrails for AI Agents 33:00 - Crossing the Chasm: Getting to 60% Adoption 40:00 - The "Product" Mindset: Solving Business Problems First 42:30 - The Future: End-to-End Process Transformation 46:25 - The "Unsolved" Problem Derek Wants to Fix 🙏 SPECIAL THANKS TO OUR SPONSOR: This episode is presented by Outshift by Cisco. Learn more about their work on the Internet of Agents and the open-source Linux Foundation project: 🔗 https://www.agentcy.org 🎙️ GUEST:  Derek Waldron | Chief Analytics Officer, JPMorgan Chase HOSTS: Matt Marshall | VentureBeat Sam Witteveen | VentureBeat #EnterpriseAI #JPMorgan #GenerativeAI #AgenticAI #FinTech #ArtificialIntelligence #Innovation #BeyondThePilot . . . Subscribe to VentureBeat:     /  @VentureBeat   . . Subscribe to the full podcast here: Apple: https://podcasts.apple.com/us/podcast/venturebeat/id1839285239 Spotify: https://open.spotify.com/show/4Zti73yb4hmiTNa7pEYls4 YouTube: https://www.youtube.com/VentureBeat https://www.youtube.com/playlist?list=PLMQoSwszBxm5dCv2bdqGnJ0QAL9n7Ds4_ 🔗 CONNECT WITH VENTUREBEAT: Website: https://venturebeat.com LinkedIn: https://www.linkedin.com/company/venturebeat X (Twitter): https://twitter.com/VentureBeat Newsletter: https://venturebeat.com/newsletters Learn more about your ad choices. Visit megaphone.fm/adchoices

We built AI agents by accident... and it worked. 🤯 In this episode of VentureBeat’s Beyond the Pilot, we go inside the engineering brain of Booking.com with Pranav Pathak (Director of Product Machine Learning). Pranav reveals how they "stumbled" into agentic architectures before the term even existed, how a simple text box revealed a massive missed revenue opportunity (the "Hot Tub" story), and exactly how they stack LLMs, RAG, and Orchestrators to handle millions of travelers without breaking the bank. If you are building Enterprise AI, this is the blueprint for moving from "cool demo" to production scale. 🚀 In this episode, we cover: The "Hot Tub" Revelation: How free-text AI search exposed features customers desperately wanted but couldn't find. Real ROI Metrics: How LLMs drove a 2x increase in topic detection accuracy and freed up 1.5x of agent bandwidth. The Booking.com AI Stack: A full breakdown of their Orchestrator → Moderation → Agent → RAG architecture. Latency vs. Intelligence: Why they don't use GPT-5 for everything and how they decide between small models and big brains. The Memory Problem: How to build AI that remembers user preferences without being "creepy”. 00:00 Introduction to Agentic Architectures 00:30 Meet Pranav Pathak from Booking.com 01:24 Evolution of Travel Recommendations 03:41 Impact of Gen AI on Customer Service 07:29 Building an Effective AI Stack 10:32 Agentic Systems and Best Practices 13:45 Choosing Between Building and Buying AI Solutions 18:51 Evaluating AI Models for Business Use 24:10 Challenges in Human Evaluation 25:06 Recommendation System and Data Utilization 27:26 Innovations in Travel Search 29:04 Journey and Challenges in ML Integration 32:08 Managing Memory and User Data 38:07 Future of Travel Assistance 41:33 Advice for New AI Integrations 43:57 Final Thoughts and Farewell 🔗 LINKS & RESOURCES: OutShift by Cisco (Sponsor): outshift.cisco.com VentureBeat: www.venturebeat.com #ArtificialIntelligence #GenAI #Bookingcom #MachineLearning #AgenticAI #LLM #TechPodcast #EnterpriseAI . . . Subscribe to VentureBeat:     /  @VentureBeat   . . Subscribe to the full podcast here: Apple: https://podcasts.apple.com/us/podcast/venturebeat/id1839285239 Spotify: https://open.spotify.com/show/4Zti73yb4hmiTNa7pEYls4 YouTube: https://www.youtube.com/VentureBeat Learn more about your ad choices. Visit megaphone.fm/adchoices

In our inaugural episode, we sit down with Ryan Nystrom, leader of the AI team at Notion, to pull back the curtain on Notion 3.0. Ryan reveals the journey of integrating powerful AI agents into the productivity platform and draws fascinating parallels between the current AI era and the mobile revolution he witnessed at Instagram. He shares exclusive insights into the development challenges, the critical role of tools, context, and curation, and how custom agents are poised to reshape work. Plus, Ryan offers essential advice for any company diving into the AI space. Learn more about your ad choices. Visit megaphone.fm/adchoices