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Artwork for Beyond The Pilot: Enterprise AI in Action

Beyond The Pilot: Enterprise AI in Action

VentureBeat
Generative AI
Cybersecurity
Napkin AI
Artificial Intelligence
AI Design
Graphic Design
Powerpoint
Cloud Migration
Intuit
Paypal
AI Infrastructure
Visa
Ebay
Data Centers
Data Center Strategy
Modernization
Verizon
Renewable Energy
Sustainability
Fedex

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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Artwork for Beyond The Pilot: Enterprise AI in Action

Latest Episodes

Major SaaS companies including Salesforce, Intuit, and ServiceNow saw stock drops of 45-50% as enterprises shift from bloated software suites to personalized AI agents that users can control directly. Microsoft just capitulated this week, opening Cop... more

LangChain told employees they cannot install OpenClaw on company laptops due to "massive security risk" — yet this unhinged approach is exactly what makes it work. Harrison Chase unpacks why OpenClaw succeeds where AutoGPT failed, and why context eng... more

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 episod... more

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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 S... more

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Recent Guests

Harrison Chase
Co-founder and CEO of LangChain
LangChain
Episode: LangChain: What OpenClaw Got Right (And Why Enterprises Can't Have It)
Min Chen
Senior Vice President and Chief AI Officer of LexisNexis
LexisNexis
Episode: LexisNexis on Why Standard RAG Fails in Law
Johan Gerber
EVP of Security Solutions at MasterCard with a background in law enforcement
MasterCard
Episode: Mastercard's 160 Billion Transactions: AI's Biggest Test
Chris Mertz
SVP of Data Science at MasterCard, responsible for building AI models
MasterCard
Episode: Mastercard's 160 Billion Transactions: AI's Biggest Test
Erran Berger
VP of Product Engineering at LinkedIn
LinkedIn
Episode: Inside LinkedIn’s AI Engineering Playbook
Amjad Masad
CEO of Replit, known for his innovative solutions in AI and software development
Replit
Episode: Most enterprise AI agents are Slop - here’s why they fail
Pranav Pathak
Leads product AI development at Booking.com
Booking.com
Episode: How Booking.com Boosted Agent Accuracy 2x with Mini LLMs with Pranav Pathak
Ryan Nystrom
Leads the AI team at Notion, formerly involved in product development at Instagram.
Notion
Episode: Beyond the Pilot - NOTION - Unpacking Ryan Nystrom's AI Journey: From Challenges to Custom Agents
Pramod Sharma
CEO of Napkin AI, which generates graphics from text inputs
Napkin AI
Episode: AI CHAT: The Future of AI Agents: How Napkin AI Is Redefining Design

Host

Matt Marshall
Co-host, Founder and Editor-in-Chief of VentureBeat, who engages with industry leaders on practical applications of AI.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#51
United States/News/Tech News
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#60
Canada/News/Tech News
Apple Podcasts
#217
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Apple Podcasts
#237
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Apple Podcasts
#243
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Apple Podcasts
#250
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Apple Podcasts
#25
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Apple Podcasts
#27
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Apple Podcasts
#34
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#44
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#55
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#60
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#61
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#76
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#91
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#116
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#126
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#130
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#135
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Apple Podcasts
#179
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Talking Points

Recent interactions between the hosts and their guests.

LangChain: What OpenClaw Got Right (And Why Enterprises Can't Have It)
Q: How has LangSmith addressed the challenge of observability and monitoring for enterprise-grade agents, especially given unbounded inputs and spikes in model behavior?
LangSmith centers on agent traces as the foundation for observability. It enables regression testing with evals, a debugging playground, and scalable analysis via proxies like an LLM to inspect vast trace data. This approach helps enterprises understand agent behavior, catch issues, and optimize performance across millions of traces, which is essential for reliable production use.
LangChain: What OpenClaw Got Right (And Why Enterprises Can't Have It)
Q: What is context engineering, and how is it being done differently now than with early systems like AutoGPT?
Context engineering is about ensuring the LLM sees the right information at the right time by carefully constructing the system prompt, tool definitions, and how traces are presented. Today, harnesses leverage file systems and dynamic prompts to manage long contexts, allowing models to decide when to collapse context and how to structure tool calls, which improves reliability over earlier approaches like AutoGPT that lacked such integrated context-management.
LexisNexis on Why Standard RAG Fails in Law
Q: Can you give us an example of a query where a semantically similar answer like RAG was dangerously wrong because it lacked the legal authority?
Min Chen discusses how RAG's initial implementations did not guarantee authoritative answers due to the nature of pure semantic search and how they improved the architecture.
Mastercard's 160 Billion Transactions: AI's Biggest Test
Q: How do you build and organize teams to deploy AI?
It's crucial to integrate data scientists across different business verticals to unlock the data's full potential for AI applications.
Mastercard's 160 Billion Transactions: AI's Biggest Test
Q: How does thinking like a criminal change the way you design an enterprise AI security strategy?
Understanding criminal behavior helps shape technology and product development strategies to better prevent fraud.

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Frequently Asked Questions About This Podcast

What is This Podcast about and what kind of topics does it cover?

Conversations in this series offer a glimpse into the pragmatic aspects of artificial intelligence as shared by key business executives. Rather than focusing on theoretical applications, the discussions aim to illuminate real-world implementations of AI across various sectors. Topics frequently include the challenges of scaling AI, the intricacies of organizational design required for successful integration, and tangible lessons learned from both successes and failures. Insights from thought leaders in technology aim to break down the complexities of AI deployment, covering insights on infrastructure and return on investment (ROI). What makes this series particularly noteworthy is its commitment to showcasing concrete results and actionable... more

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this podcast launched 6 months ago and published 25 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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Recent guests on this podcast include:

1. Harrison Chase
2. Min Chen
3. Johan Gerber
4. Chris Mertz
5. Erran Berger
6. Amjad Masad
7. Pranav Pathak
8. Ryan Nystrom

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