PodcastsNotíciasBeyond The Pilot: Enterprise AI in Action

Beyond The Pilot: Enterprise AI in Action

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Beyond The Pilot: Enterprise AI in Action
Último episódio

23 episódios

  • Beyond The Pilot: Enterprise AI in Action

    LexisNexis on Why Standard RAG Fails in Law

    18/02/2026 | 35min
    On February 2nd, a single plugin wiped nearly $800 billion off the enterprise software market. Wall Street is terrified that AI agents are about to eat the legal industry's lunch. But LexisNexis isn't scared—they're building the moat.

    In this episode of Beyond the Pilot, Min Chen (Chief AI Officer, LexisNexis) reveals the sophisticated architecture they built to counter the "LLM wrapper" revolution. Moving beyond standard RAG, Min breaks down their move to "GraphRAG", their deployment of Agentic workflows (using Planner and Reflection agents), and why they created a proprietary "Usefulness Score" because standard accuracy metrics weren't good enough for lawyers.

    AI Gets Real Here. No theory, just the execution roadmap for deploying AI in a zero-error environment.

    In this episode, we cover:


    The "Dangerous RAG" Problem: Why semantic search fails in professional domains (retrieving "relevant" but overruled cases) and how "Point of Law" knowledge graphs fix it.


    The "Usefulness" Metric: The 8 sub-metrics LexisNexis uses (including Authority, Comprehensiveness, and Fluency) to grade AI quality.


    Agentic ROI: How deploying a "Planner Agent" to break down complex questions increased answer usefulness by 20%.


    The "Reflection Agent": Using a secondary agent to critique and refine drafts in real-time.


    Hallucination Detection: Why you should never rely on an LLM to judge its own hallucinations (and the deterministic code they use instead).

    ⏱️ TIMESTAMPS

    00:00 - Intro: The $800 Billion AI Threat to Legal Tech

    02:18 - Min Chen’s Journey: From Feature Engineering to Chief AI Officer

    05:55 - Why Standard RAG Fails in Law (and How GraphRAG Fixes It)

    10:40 - "Accuracy" is a Vanity Metric: The 8-Point Usefulness Score

    14:20 - The "Auto-Eval" Framework: Human-in-the-Loop at Scale

    16:40 - The Secret Sauce: Don't Use LLMs to Detect Hallucinations

    21:15 - Agentic AI: How "Planner Agents" Drove a 20% Gain

    22:00 - The "Reflection Agent": Self-Critique Loops for Drafting

    30:30 - Distillation: Balancing Cost, Speed, and Quality

    32:45 - Min’s Advice: Don't Build the Product First (Build the Metrics)

    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

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    Spotify: https://open.spotify.com/show/4Zti73yb4hmiTNa7pEYls4

    YouTube: https://www.youtube.com/VentureBeat

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  • Beyond The Pilot: Enterprise AI in Action

    Mastercard's 160 Billion Transactions: AI's Biggest Test

    04/02/2026 | 55min
    While most of the world is still running GenAI pilots, Mastercard is running AI inference on 160 billion transactions a year—with a hard latency limit of 50 milliseconds per score.

    In this episode of Beyond the Pilot, Johan Gerber (EVP of Security Solutions) and Chris Merz (SVP of Data Science) open the hood on one of the world's largest production AI systems: Decision Intelligence Pro. They reveal how they moved beyond legacy rules engines to build Recurrent Neural Networks (RNNs) that act as "inverse recommenders"—predicting legitimate behavior faster than the blink of an eye.

    AI Gets Real Here. This isn't just about defense. Johan and Chris detail how they are taking the fight to criminals by leveraging Generative AI to engage scammers with "honeypots," expose mule accounts, and map fraud networks globally.

    In this episode, we cover:


    The 50ms Inference Challenge: How Mastercard optimized their RNNs to score transactions at a peak rate of 70,000 per second.


    "Scamming the Scammers": How GenAI agents are being used to automate honeypot conversations and extract mule account data.


    The "Inverse Recommender" Architecture: Why Mastercard treats fraud detection as a recommendation problem (predicting the next likely merchant).


    Org Design for Scale: The "Data Science Engineering Requirements Document" (DSERD) Chris used to align four separate engineering teams.


    The Hybrid Infrastructure: Why moving to Databricks and the cloud was necessary to cut innovation cycles from months to hours.

    🚀 CHAPTERS

    00:00 - Intro: 160 Billion Transactions & 50ms Decisions

    02:08 - Thinking Like a Criminal: Johan’s Law Enforcement Background

    06:22 - Org Design: Why AI is the "Middle Lane" of Engineering

    11:00 - The Scale: 70k Transactions Per Second

    15:47 - Decision Intelligence Pro: The "Inverse Recommender" RNN

    23:00 - The "Lego Block" Strategy: Aligning Data Science & Engineering

    33:00 - Infrastructure: Why Cloud/Databricks was Non-Negotiable

    37:00 - GenAI Offensive: Threat Hunting & "Scamming the Scammers"

    46:40 - "Honeypots" and Detecting Mule Accounts

    52:00 - Advice for Technical Leaders: Talent & Prioritization

    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

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    Subscribe to VentureBeat: 

       /  @VentureBeat  

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    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

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  • Beyond The Pilot: Enterprise AI in Action

    Inside LinkedIn’s AI Engineering Playbook

    21/01/2026 | 40min
    While the rest of the industry chases massive models, LinkedIn quietly achieved a major engineering breakthrough by going small.

    In this episode of Beyond the Pilot, Erran Berger (VP of Product Engineering, LinkedIn) opens the "cookbook" on how they distilled massive 7B parameter models down to ultra-efficient 600M parameter "student" models—scaling AI to 1.2 billion users without breaking the bank.

    AI Gets Real Here. This isn't theory. Erran details the exact architecture, the "Multi-Teacher" distillation process, and the organizational shift that forced Product Managers to write evals instead of specs.

    In this episode, we cover:


    The Distillation Pipeline: How to train a 7B "Teacher" and distill it to a 1.7B intermediate and 0.6B "Student" for production.


    Synthetic Data Strategy: Using GPT-4 to generate the "Golden Dataset" for training.


    Multi-Teacher Architecture: Why they separated "Product Policy" and "Click Prediction" into different teacher models to solve alignment issues.


    10x Efficiency Hacks: Specific techniques (Pruning, Quantization, Context Compression) that slashed latency.


    Org Design: Why the "Eval First" culture is the new requirement for AI engineering teams.

    🚀 CHAPTERS

    00:00 - Intro: LinkedIn's Massive "Small Model" Feat

    04:00 - Why Commercial Models Failed at LinkedIn Scale

    08:00 - The "Product Policy" Funnel & Synthetic Data Generation

    12:00 - The Pipeline: 7B → 1.7B → 600M Parameters

    19:00 - The "Multi-Teacher" Breakthrough (Relevance vs. Clicks)

    23:00 - How They Achieved 10x Latency Reduction (Pruning/Compression)

    31:00 - Changing the Culture: Why PMs Must Write Evals

    35:00 - The "Bright Green Matrix": Measuring Success & Future Roadmap

    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

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    Subscribe to VentureBeat: 

       /  @VentureBeat  

    .

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    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

    #EnterpriseAI #LLMDistillation #LinkedInEngineering #SmallLanguageModels #AIArchitecture #TechLeadership

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  • Beyond The Pilot: Enterprise AI in Action

    Most enterprise AI agents are Slop - here’s why they fail

    07/01/2026 | 1h 3min
    The "TAM" for AI Agents isn't software. And there is a $10 Trillion opportunity.

    In this episode, Replit CEO Amjad Masad reveals why 99% of today's enterprise AI agents are just "Slop"—unreliable, generic toys that fail in production. We dive deep into the engineering reality of building autonomous agents that actually work, moving beyond simple chatbots to systems that can navigate the messy reality of enterprise infrastructure.

    Amjad breaks down Replit’s "Computer Use" hack that makes agents 10x cheaper than generic models, explains why "Vibe Coding" is the future of the C-Suite, and issues a warning to technical leaders: If you want to ship fast in the AI era, you need to kill your product roadmap.

    In this episode, we cover:


    The "Slop" Problem: Why most LLM outputs are generic and how to inject "taste" back into software.


    The Computer Use "Hack": How Replit built a programmatic verifier loop that outperforms vision-based models.


    Vibe Coding: Why non-technical domain experts (HR, Sales, Marketing) will build the next generation of enterprise software.


    The $10T Market: Why the Junior Developer role is disappearing and being replaced by the "Manager of Agents."

    🚀 CHAPTERS

    0:00 - Intro: Why most AI Agents are "Toys"

    03:02 - The only 2 AI use cases making money right now

    06:00 - The "Crappy Product" Strategy (Shipping fast)

    10:00 - What is "AI Slop"? (And how to fix it)

    14:30 - The "Deleted Database" Incident: Solving Reliability

    18:00 - The "Squishy" Divide: Why Marketing Agents fail

    21:45 - Vibe Coding in the Enterprise

    26:00 - Model Wars: Claude Opus vs. Gemini vs. OpenAI

    28:10 - The "Computer Use" Hack (10x Cheaper, 3x Faster)

    36:00 - Why Product Roadmaps are Dead

    43:00 - Replit is the #1 Software Vendor (Ramp Data)

    49:00 - The Unit Economics of Agents (Token Costs vs. Value)

    53:00 - Open Source vs. Closed: The "Cathedral of Bazaars"

    59:00 - The $10 Trillion Opportunity: Replacing Labor

    🔗 CONNECT WITH US

    Subscribe to our Newsletters for technical breakdowns: https://venturebeat.com/newsletters

    Visit VentureBeat: Venturebeat.com

    #AgenticAI #Replit #VibeCoding #EnterpriseAI #LLM #SoftwareEngineering #FutureOfWork #AmjadMasad #ArtificialIntelligence #DevOps

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    Subscribe to VentureBeat: 

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    Apple: https://podcasts.apple.com/us/podcast/venturebeat/id1839285239

    Spotify: https://open.spotify.com/show/4Zti73yb4hmiTNa7pEYls4

    YouTube: https://www.youtube.com/VentureBeat

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  • Beyond The Pilot: Enterprise AI in Action

    How JPMorgan Engineered a 30K AI Agent Economy

    17/12/2025 | 46min
    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

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    Subscribe to VentureBeat: 

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    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_

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Sobre Beyond The Pilot: Enterprise AI in Action

AI gets real here. On “Beyond the Pilot,” top business execs share what actually happens after the AI proof of concept — from infrastructure and org design to wins, failures, and ROI. Not theory, but deep dives into how they scaled AI that works.
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