Applied IntelligenceThe go-to show for business leaders navigating AI adoption in the real world. Host Keith Richman talks with operators, founders, and business leaders who are actually deploying AI—exploring what works, what doesn't, and how to bridge the gap between AI's capability and practical implementation. Smart, grounded, actionable.en2025 Applied IntelligenceTue, 16 Jun 2026 22:35:00 -0000Tue, 16 Jun 2026 22:35:00 -0000https://rss2.flightcast.com/k9g3741qwvu50v1ruonbimu8.xmlFlightcast RSS Feed GeneratorApplied Intelligencehttps://files.flightcast.com/workspaces/hxdsezvybfwvi8wcup9ofkm7/01KFXA52WJNC2B6X16D8BRNPAW/ai_pod_cover_v2.jpghttps://rss2.flightcast.com/k9g3741qwvu50v1ruonbimu8.xmlepisodicKeith RichmanKeith Richmanp.cotter@ledehook.comThe go-to show for business leaders navigating AI adoption in the real world. Host Keith Richman talks with operators, founders, and business leaders who are actually deploying AI—exploring what works, what doesn't, and how to bridge the gap between AI's capability and practical implementation. Smart, grounded, actionable.AppliedIntelligencefalsetech, ai, artificial intelligence, business, management, startups, founder, ceonoThe Hidden Threat of Shipping AI Code FasterAs AI tools like Claude and Codex democratize software development, writing code has never been easier—but deploying reliable code has never been harder. In this episode of Applied Intelligence, host Keith Richman sits down with Pramin Pradeep, founder of Botgage, to explore the hidden dangers of AI-generated "shadow code" and why traditional QA frameworks are breaking under the pressure of hourly release cycles. Pramin breaks down the shift from manual testing to agentic, end-to-end behavioral testing, sharing practical frameworks for budgeting and managing QA in a fast-paced environment. They also discuss his personal AI tech stack, the future of single-screen software interactions, and how businesses can maintain release confidence without breaking the bank. Smart, grounded, and actionable advice for any team building with AI. Chapters 00:00:00 Introduction: The Shadow Code Problem 00:00:50 The Problem-First Approach to Development 00:01:37 The New Era of Code Creation and Testing 00:03:46 From Unit Testing to End-to-End: The Testing Gap 00:05:42 The Speed Problem: Releasing Every Hour 00:09:07 Budget and Time: The QA Investment Question 00:10:12 Shadow Code: Not Bad Code, Dangerous Code 00:10:58 Why AI Can't Just Fix Everything 00:13:15 The 50K Rule: Budgeting for Robust QA 00:15:11 The Future: Single Screen and Agentic Testing 00:16:55 Multi-Model Strategy and Monitoring #AI #SoftwareDevelopment #Coding #QualityAssurance #TechPodcast #BusinessLeaders #ArtificialIntelligence #SoftwareEngineering #AITesting #SaaSflightcast:01KV96MM7FYHQ3CZWJRBX2H3ETTue, 16 Jun 2026 22:35:00 -0000As AI tools like Claude and Codex democratize software development, writing code has never been easier—but deploying reliable code has never been harder. In this episode of Applied Intelligence, host Keith Richman sits down with Pramin Pradeep, founder of Botgage, to explore the hidden dangers of AI-generated "shadow code" and why traditional QA frameworks are breaking under the pressure of hourly release cycles. Pramin breaks down the shift from manual testing to agentic, end-to-end behavioral testing, sharing practical frameworks for budgeting and managing QA in a fast-paced environment. They also discuss his personal AI tech stack, the future of single-screen software interactions, and how businesses can maintain release confidence without breaking the bank. Smart, grounded, and actionable advice for any team building with AI.

Chapters

  • 00:00:00 Introduction: The Shadow Code Problem
  • 00:00:50 The Problem-First Approach to Development
  • 00:01:37 The New Era of Code Creation and Testing
  • 00:03:46 From Unit Testing to End-to-End: The Testing Gap
  • 00:05:42 The Speed Problem: Releasing Every Hour
  • 00:09:07 Budget and Time: The QA Investment Question
  • 00:10:12 Shadow Code: Not Bad Code, Dangerous Code
  • 00:10:58 Why AI Can't Just Fix Everything
  • 00:13:15 The 50K Rule: Budgeting for Robust QA
  • 00:15:11 The Future: Single Screen and Agentic Testing
  • 00:16:55 Multi-Model Strategy and Monitoring

#AI #SoftwareDevelopment #Coding #QualityAssurance #TechPodcast #BusinessLeaders #ArtificialIntelligence #SoftwareEngineering #AITesting #SaaS

]]>The Hidden Threat of Shipping AI Code Fasterfull1115falseAs AI tools like Claude and Codex democratize software development, writing code has never been easier—but deploying reliable code has never been harder. In this episode of Applied Intelligence, host Keith Richman sits down with Pramin Pradeep, founder of Botgage, to explore the hidden dangers of AI-generated "shadow code" and why traditional QA frameworks are breaking under the pressure of hourly release cycles. Pramin breaks down the shift from manual testing to agentic, end-to-end behavioral testing, sharing practical frameworks for budgeting and managing QA in a fast-paced environment. They also discuss his personal AI tech stack, the future of single-screen software interactions, and how businesses can maintain release confidence without breaking the bank. Smart, grounded, and actionable advice for any team building with AI. Chapters 00:00:00 Introduction: The Shadow Code Problem 00:00:50 The Problem-First Approach to Development 00:01:37 The New Era of Code Creation and Testing 00:03:46 From Unit Testing to End-to-End: The Testing Gap 00:05:42 The Speed Problem: Releasing Every Hour 00:09:07 Budget and Time: The QA Investment Question 00:10:12 Shadow Code: Not Bad Code, Dangerous Code 00:10:58 Why AI Can't Just Fix Everything 00:13:15 The 50K Rule: Budgeting for Robust QA 00:15:11 The Future: Single Screen and Agentic Testing 00:16:55 Multi-Model Strategy and Monitoring #AI #SoftwareDevelopment #Coding #QualityAssurance #TechPodcast #BusinessLeaders #ArtificialIntelligence #SoftwareEngineering #AITesting #SaaSThe Truth About AI Employees & Agentic WorkersIn this episode of Applied Intelligence, host Keith Richman talks with Joe Mayberry, Head of AI at SailPoint, about navigating the shift from simple conversational models to the "agentic era." Joe explains why we haven't hit the true AI Industrial Revolution yet and how businesses can practically prepare for a future workforce of autonomous AI coworkers. They dive into the critical need for "agent identity," explaining why AI agents require corporate trust scores, strict governance, and limits just like human employees. Whether you're an enterprise leader managing board expectations or a founder trying to scale, Joe breaks down the actionable steps to build an AI-ready organization, unify siloed corporate data, and safely deploy multi-agent systems. Timestamps 0:00 Introduction: The Agentic Epoch and AI Bosses 0:54 The Agentic Era: Non-Corporeal and Physical Agents 2:28 The Industrial Revolution That Hasn't Happened Yet 5:09 The Three-Step Journey: Data, Enablement, and Governance 9:21 The Complexity Challenge: Big Companies vs. Small Companies 10:00 Agentic Identity: Treating AI Like Humans 11:56 The Dynamic Trust Model for Agents 18:27 Learning from Social Media's Mistakes 20:03 Board Expectations vs. Reality: The AI Strategy Gap 33:09 The Four Essential Board Reports for AI Governance 23:38 Trust as the Competitive Advantage 25:41 The Org Chart of the Future: Human-Directed, Agent-Executed 30:10 Moving Fast vs. Moving Slow: The Startup Advantage 35:43 Joe's Tech Stack: Building on Anthropic Claude 37:36 Scaling the Unscalable: The Future of Personal Expertise 40:30 The Next Generation: Experience vs. Agency in an AI World #ArtificialIntelligence #AI #Business #EnterpriseTech #AIAgents #CyberSecurity #Innovation #FutureOfWork #Technology #Leadershipflightcast:01KSRD9KZ00M1F54GBNV981Z1VThu, 28 May 2026 23:45:00 -0000In this episode of Applied Intelligence, host Keith Richman talks with Joe Mayberry, Head of AI at SailPoint, about navigating the shift from simple conversational models to the "agentic era." Joe explains why we haven't hit the true AI Industrial Revolution yet and how businesses can practically prepare for a future workforce of autonomous AI coworkers. They dive into the critical need for "agent identity," explaining why AI agents require corporate trust scores, strict governance, and limits just like human employees. Whether you're an enterprise leader managing board expectations or a founder trying to scale, Joe breaks down the actionable steps to build an AI-ready organization, unify siloed corporate data, and safely deploy multi-agent systems.

Timestamps

0:00 Introduction: The Agentic Epoch and AI Bosses
0:54 The Agentic Era: Non-Corporeal and Physical Agents
2:28 The Industrial Revolution That Hasn't Happened Yet
5:09 The Three-Step Journey: Data, Enablement, and Governance
9:21 The Complexity Challenge: Big Companies vs. Small Companies
10:00 Agentic Identity: Treating AI Like Humans
11:56 The Dynamic Trust Model for Agents
18:27 Learning from Social Media's Mistakes
20:03 Board Expectations vs. Reality: The AI Strategy Gap
33:09 The Four Essential Board Reports for AI Governance
23:38 Trust as the Competitive Advantage
25:41 The Org Chart of the Future: Human-Directed, Agent-Executed
30:10 Moving Fast vs. Moving Slow: The Startup Advantage
35:43 Joe's Tech Stack: Building on Anthropic Claude
37:36 Scaling the Unscalable: The Future of Personal Expertise
40:30 The Next Generation: Experience vs. Agency in an AI World

#ArtificialIntelligence #AI #Business #EnterpriseTech #AIAgents #CyberSecurity #Innovation #FutureOfWork #Technology #Leadership

]]>The Truth About AI Employees & Agentic Workersfull2544falseIn this episode of Applied Intelligence, host Keith Richman talks with Joe Mayberry, Head of AI at SailPoint, about navigating the shift from simple conversational models to the "agentic era." Joe explains why we haven't hit the true AI Industrial Revolution yet and how businesses can practically prepare for a future workforce of autonomous AI coworkers. They dive into the critical need for "agent identity," explaining why AI agents require corporate trust scores, strict governance, and limits just like human employees. Whether you're an enterprise leader managing board expectations or a founder trying to scale, Joe breaks down the actionable steps to build an AI-ready organization, unify siloed corporate data, and safely deploy multi-agent systems. Timestamps 0:00 Introduction: The Agentic Epoch and AI Bosses 0:54 The Agentic Era: Non-Corporeal and Physical Agents 2:28 The Industrial Revolution That Hasn't Happened Yet 5:09 The Three-Step Journey: Data, Enablement, and Governance 9:21 The Complexity Challenge: Big Companies vs. Small Companies 10:00 Agentic Identity: Treating AI Like Humans 11:56 The Dynamic Trust Model for Agents 18:27 Learning from Social Media's Mistakes 20:03 Board Expectations vs. Reality: The AI Strategy Gap 33:09 The Four Essential Board Reports for AI Governance 23:38 Trust as the Competitive Advantage 25:41 The Org Chart of the Future: Human-Directed, Agent-Executed 30:10 Moving Fast vs. Moving Slow: The Startup Advantage 35:43 Joe's Tech Stack: Building on Anthropic Claude 37:36 Scaling the Unscalable: The Future of Personal Expertise 40:30 The Next Generation: Experience vs. Agency in an AI World #ArtificialIntelligence #AI #Business #EnterpriseTech #AIAgents #CyberSecurity #Innovation #FutureOfWork #Technology #LeadershipSEO is Dead: The Rise of Answer Engine OptimizationIn this episode of Applied Intelligence, Keith Richman sits down with Graphite founder Marcos Ciarrocchi to explore the massive shift from traditional SEO to AI-driven Answer Engine Optimization (AEO). Marcos explains how Large Language Models (LLMs) and AI agents are changing the way users search, and what this means for both publishers and brands. Learn actionable strategies to optimize your brand for AI consensus, leverage "information gain" to stand out, and structure your website data so AI models can easily cite your products. Whether you're trying to influence ChatGPT, Claude, or Google's Gemini, Marcos breaks down how to build topical authority, track AI citations, and capture high-converting referral traffic in the new agentic search era. TImestamps 0:00 Intro 0:52 How LLMs Changed the Search Journey 1:59 Retrieval, Grounding, and Fresh Data 3:15 What AI Search Means for Brands vs. Publishers 4:32 Authority, Trust, and Spam in AI Search 5:43 The New Search Engine Fragmentation 7:28 How ChatGPT Uses Search to Ground Answers 9:24 “Agent Optimization” and the Future of SEO 9:46 Where Brands Should Start Right Now 11:27 Is AI Search Replacing Google Search? 12:38 Why Off-Site Footprint Matters More Now 13:13 Reddit, YouTube, and Unexpected AI Citations 14:28 How to Build Pages AI Models Can Actually Use 15:08 What Happens When an AI Searches for You 17:03 Citation Rate as a New Marketing KPI 18:37 Consensus, Repetition, and Model Recommendations 19:19 The Modern SEO Content Playbook 20:16 Chunking, Context Windows, and AI-Friendly Content 21:35 Making Product Claims Easy for Models to Understand 22:50 Podcasts, Experts, and Information Gain 24:19 Why Unique Perspective Beats Generic Content 25:23 Interactive Content, Calculators, and Product POV 26:36 Why Brand Personality Matters More in AI Search 28:12 Can Smaller Brands Win in AI Search? 28:40 Topical Authority and Finding Your Wedge 31:12 The Long-Tail Opportunity in Personalized AI Search 33:09 Measuring the Business Impact of AI Search 34:01 AI Referral Traffic and Dark Attribution 36:26 Tools for Tracking AI Visibility 38:46 The Future SEO Job Title 40:12 Which Models Should Brands Optimize For? 42:30 Building a Google Flights MCP Travel Tool 44:33 What Agentic Search Means for Travel and Commerce #ArtificialIntelligence #SEO #AEO #DigitalMarketing #ChatGPT #LLM #BusinessStrategy #MarketingTips #SearchEngineOptimization #TechTrendsflightcast:01KS1A4YEFRH29R4YJN1FPN7V3Wed, 20 May 2026 00:07:00 -0000In this episode of Applied Intelligence, Keith Richman sits down with Graphite founder Marcos Ciarrocchi to explore the massive shift from traditional SEO to AI-driven Answer Engine Optimization (AEO). Marcos explains how Large Language Models (LLMs) and AI agents are changing the way users search, and what this means for both publishers and brands. Learn actionable strategies to optimize your brand for AI consensus, leverage "information gain" to stand out, and structure your website data so AI models can easily cite your products. Whether you're trying to influence ChatGPT, Claude, or Google's Gemini, Marcos breaks down how to build topical authority, track AI citations, and capture high-converting referral traffic in the new agentic search era.

TImestamps

0:00 Intro
0:52 How LLMs Changed the Search Journey
1:59 Retrieval, Grounding, and Fresh Data
3:15 What AI Search Means for Brands vs. Publishers
4:32 Authority, Trust, and Spam in AI Search
5:43 The New Search Engine Fragmentation
7:28 How ChatGPT Uses Search to Ground Answers
9:24 “Agent Optimization” and the Future of SEO
9:46 Where Brands Should Start Right Now
11:27 Is AI Search Replacing Google Search?
12:38 Why Off-Site Footprint Matters More Now
13:13 Reddit, YouTube, and Unexpected AI Citations
14:28 How to Build Pages AI Models Can Actually Use
15:08 What Happens When an AI Searches for You
17:03 Citation Rate as a New Marketing KPI
18:37 Consensus, Repetition, and Model Recommendations
19:19 The Modern SEO Content Playbook
20:16 Chunking, Context Windows, and AI-Friendly Content
21:35 Making Product Claims Easy for Models to Understand
22:50 Podcasts, Experts, and Information Gain
24:19 Why Unique Perspective Beats Generic Content
25:23 Interactive Content, Calculators, and Product POV
26:36 Why Brand Personality Matters More in AI Search
28:12 Can Smaller Brands Win in AI Search?
28:40 Topical Authority and Finding Your Wedge
31:12 The Long-Tail Opportunity in Personalized AI Search
33:09 Measuring the Business Impact of AI Search
34:01 AI Referral Traffic and Dark Attribution
36:26 Tools for Tracking AI Visibility
38:46 The Future SEO Job Title
40:12 Which Models Should Brands Optimize For?
42:30 Building a Google Flights MCP Travel Tool
44:33 What Agentic Search Means for Travel and Commerce

#ArtificialIntelligence #SEO #AEO #DigitalMarketing #ChatGPT #LLM #BusinessStrategy #MarketingTips #SearchEngineOptimization #TechTrends

]]>SEO is Dead: The Rise of Answer Engine Optimizationfull2716falseIn this episode of Applied Intelligence, Keith Richman sits down with Graphite founder Marcos Ciarrocchi to explore the massive shift from traditional SEO to AI-driven Answer Engine Optimization (AEO). Marcos explains how Large Language Models (LLMs) and AI agents are changing the way users search, and what this means for both publishers and brands. Learn actionable strategies to optimize your brand for AI consensus, leverage "information gain" to stand out, and structure your website data so AI models can easily cite your products. Whether you're trying to influence ChatGPT, Claude, or Google's Gemini, Marcos breaks down how to build topical authority, track AI citations, and capture high-converting referral traffic in the new agentic search era. TImestamps 0:00 Intro 0:52 How LLMs Changed the Search Journey 1:59 Retrieval, Grounding, and Fresh Data 3:15 What AI Search Means for Brands vs. Publishers 4:32 Authority, Trust, and Spam in AI Search 5:43 The New Search Engine Fragmentation 7:28 How ChatGPT Uses Search to Ground Answers 9:24 “Agent Optimization” and the Future of SEO 9:46 Where Brands Should Start Right Now 11:27 Is AI Search Replacing Google Search? 12:38 Why Off-Site Footprint Matters More Now 13:13 Reddit, YouTube, and Unexpected AI Citations 14:28 How to Build Pages AI Models Can Actually Use 15:08 What Happens When an AI Searches for You 17:03 Citation Rate as a New Marketing KPI 18:37 Consensus, Repetition, and Model Recommendations 19:19 The Modern SEO Content Playbook 20:16 Chunking, Context Windows, and AI-Friendly Content 21:35 Making Product Claims Easy for Models to Understand 22:50 Podcasts, Experts, and Information Gain 24:19 Why Unique Perspective Beats Generic Content 25:23 Interactive Content, Calculators, and Product POV 26:36 Why Brand Personality Matters More in AI Search 28:12 Can Smaller Brands Win in AI Search? 28:40 Topical Authority and Finding Your Wedge 31:12 The Long-Tail Opportunity in Personalized AI Search 33:09 Measuring the Business Impact of AI Search 34:01 AI Referral Traffic and Dark Attribution 36:26 Tools for Tracking AI Visibility 38:46 The Future SEO Job Title 40:12 Which Models Should Brands Optimize For? 42:30 Building a Google Flights MCP Travel Tool 44:33 What Agentic Search Means for Travel and Commerce #ArtificialIntelligence #SEO #AEO #DigitalMarketing #ChatGPT #LLM #BusinessStrategy #MarketingTips #SearchEngineOptimization #TechTrendsProcessing Trillions of Real-World Data Points with AIIn this episode of Applied Intelligence, Keith Richman sits down with Praveen Murugesan, VP of Engineering at Samsara, to explore how AI is transforming physical world operations. Praveen reveals how Samsara leverages massive IoT sensor data to solve complex real-world problems—from catching fleet fuel theft to building smart commercial navigation that dynamically reroutes drivers based on dashcam insights. They discuss the game-changing potential of AI-driven "computer use," how to safely implement internal AI coding tools without breaking production, and why AI is finally democratizing data analysis for non-technical operators. If you want a grounded, actionable look at bridging the gap between AI hype and enterprise-scale deployment, this conversation delivers. Chapters 00:00:00 Introduction: AI at Scale with Samsara 00:00:45 Computer Use: The Most Contrarian AI Take 00:04:52 Empowering Engineers: AI Tools and Guardrails 00:07:49 The Wins: Speed, Prototyping, and Quality 00:10:05 Customer Co-Creation: Solving Fuel Theft with AI 00:15:10 From Sensors to Intelligence: The Fuel Theft Solution 00:16:59 LLMs as Judges: Accelerating Data Labeling 00:19:36 Commercial Navigation: Waze for Enterprise 00:23:24 Predictive Operations: The Future of Strategic Planning 00:26:51 Democratizing Expertise: From Specialists to Superhumans 00:29:36 The New Reality: PMs Shipping Code, Designers in Production 00:30:30 The AI Stack: Blessed Models and Cost Management 00:32:42 The Hidden Value: Physical World Data at Scale 00:35:38 What's Overhyped: Security Fears and the Positive Lens #ArtificialIntelligence #AI #MachineLearning #IoT #EnterpriseAI #DataScience #Engineering #TechPodcast #Innovation #SoftwareDevelopmentflightcast:01KRF2CE5B4084Z0JRD1K1WMPSTue, 12 May 2026 22:30:00 -0000In this episode of Applied Intelligence, Keith Richman sits down with Praveen Murugesan, VP of Engineering at Samsara, to explore how AI is transforming physical world operations. Praveen reveals how Samsara leverages massive IoT sensor data to solve complex real-world problems—from catching fleet fuel theft to building smart commercial navigation that dynamically reroutes drivers based on dashcam insights. They discuss the game-changing potential of AI-driven "computer use," how to safely implement internal AI coding tools without breaking production, and why AI is finally democratizing data analysis for non-technical operators. If you want a grounded, actionable look at bridging the gap between AI hype and enterprise-scale deployment, this conversation delivers.

Chapters

  • 00:00:00 Introduction: AI at Scale with Samsara
  • 00:00:45 Computer Use: The Most Contrarian AI Take
  • 00:04:52 Empowering Engineers: AI Tools and Guardrails
  • 00:07:49 The Wins: Speed, Prototyping, and Quality
  • 00:10:05 Customer Co-Creation: Solving Fuel Theft with AI
  • 00:15:10 From Sensors to Intelligence: The Fuel Theft Solution
  • 00:16:59 LLMs as Judges: Accelerating Data Labeling
  • 00:19:36 Commercial Navigation: Waze for Enterprise
  • 00:23:24 Predictive Operations: The Future of Strategic Planning
  • 00:26:51 Democratizing Expertise: From Specialists to Superhumans
  • 00:29:36 The New Reality: PMs Shipping Code, Designers in Production
  • 00:30:30 The AI Stack: Blessed Models and Cost Management
  • 00:32:42 The Hidden Value: Physical World Data at Scale
  • 00:35:38 What's Overhyped: Security Fears and the Positive Lens

#ArtificialIntelligence #AI #MachineLearning #IoT #EnterpriseAI #DataScience #Engineering #TechPodcast #Innovation #SoftwareDevelopment

]]>Processing Trillions of Real-World Data Points with AIfull2304falseIn this episode of Applied Intelligence, Keith Richman sits down with Praveen Murugesan, VP of Engineering at Samsara, to explore how AI is transforming physical world operations. Praveen reveals how Samsara leverages massive IoT sensor data to solve complex real-world problems—from catching fleet fuel theft to building smart commercial navigation that dynamically reroutes drivers based on dashcam insights. They discuss the game-changing potential of AI-driven "computer use," how to safely implement internal AI coding tools without breaking production, and why AI is finally democratizing data analysis for non-technical operators. If you want a grounded, actionable look at bridging the gap between AI hype and enterprise-scale deployment, this conversation delivers. Chapters 00:00:00 Introduction: AI at Scale with Samsara 00:00:45 Computer Use: The Most Contrarian AI Take 00:04:52 Empowering Engineers: AI Tools and Guardrails 00:07:49 The Wins: Speed, Prototyping, and Quality 00:10:05 Customer Co-Creation: Solving Fuel Theft with AI 00:15:10 From Sensors to Intelligence: The Fuel Theft Solution 00:16:59 LLMs as Judges: Accelerating Data Labeling 00:19:36 Commercial Navigation: Waze for Enterprise 00:23:24 Predictive Operations: The Future of Strategic Planning 00:26:51 Democratizing Expertise: From Specialists to Superhumans 00:29:36 The New Reality: PMs Shipping Code, Designers in Production 00:30:30 The AI Stack: Blessed Models and Cost Management 00:32:42 The Hidden Value: Physical World Data at Scale 00:35:38 What's Overhyped: Security Fears and the Positive Lens #ArtificialIntelligence #AI #MachineLearning #IoT #EnterpriseAI #DataScience #Engineering #TechPodcast #Innovation #SoftwareDevelopmentThe AI Testing Framework Every Business Needs (But Few Use)Keith Richman sits down with Hamel Husain, machine learning engineer and founder of Parlance Labs, to demystify AI evaluations (evals). Hamel breaks down why generic AI testing metrics fall short and how businesses can actually measure, debug, and improve their AI applications in the real world. They explore the pitfalls of simply slapping a chatbot on an existing product, the importance of iterative error analysis, and why starting simple with the most powerful models beats reaching for immediate complexity. Whether you're an executive fielding AI mandates or a developer building the stack, Hamel shares actionable advice on how to stop building the wrong things faster and start deploying AI that truly moves the needle. Chapters 00:00:00 Introduction: Why AI Testing Matters More Than You Think 00:01:52 What Are AI Evals and Why Every Business Needs Them 00:04:03 The Generic Metrics Trap: Why Off-the-Shelf Testing Fails 00:11:57 The Two Biggest Failure Modes in AI Implementation 00:13:37 Moving Fast vs Being Deliberate 00:15:04 The Slop Problem 00:21:24 Guardrails Done Right 00:23:45 Model Selection Strategy 00:27:25 Build vs Buy: When to Use Consulting vs Internal Teams 00:29:40 The Bootcamp Approach 00:31:19 The Million Lines of Code Myth 00:32:46 Embracing Mistakes and the Experimental Mindset 00:34:33 Personal Tech Stack and the OpenClaw Reality Check #ArtificialIntelligence #MachineLearning #AITesting #TechLeadership #SoftwareEngineering #DataScience #OpenAI #ProductManagement #GenerativeAI #AIEvalsflightcast:01KPP5QA66J9853TJY42N3TSXVMon, 20 Apr 2026 22:11:00 -0000Keith Richman sits down with Hamel Husain, machine learning engineer and founder of Parlance Labs, to demystify AI evaluations (evals). Hamel breaks down why generic AI testing metrics fall short and how businesses can actually measure, debug, and improve their AI applications in the real world. They explore the pitfalls of simply slapping a chatbot on an existing product, the importance of iterative error analysis, and why starting simple with the most powerful models beats reaching for immediate complexity. Whether you're an executive fielding AI mandates or a developer building the stack, Hamel shares actionable advice on how to stop building the wrong things faster and start deploying AI that truly moves the needle.

Chapters

  • 00:00:00 Introduction: Why AI Testing Matters More Than You Think
  • 00:01:52 What Are AI Evals and Why Every Business Needs Them
  • 00:04:03 The Generic Metrics Trap: Why Off-the-Shelf Testing Fails
  • 00:11:57 The Two Biggest Failure Modes in AI Implementation
  • 00:13:37 Moving Fast vs Being Deliberate
  • 00:15:04 The Slop Problem
  • 00:21:24 Guardrails Done Right
  • 00:23:45 Model Selection Strategy
  • 00:27:25 Build vs Buy: When to Use Consulting vs Internal Teams
  • 00:29:40 The Bootcamp Approach
  • 00:31:19 The Million Lines of Code Myth
  • 00:32:46 Embracing Mistakes and the Experimental Mindset
  • 00:34:33 Personal Tech Stack and the OpenClaw Reality Check

#ArtificialIntelligence #MachineLearning #AITesting #TechLeadership #SoftwareEngineering #DataScience #OpenAI #ProductManagement #GenerativeAI #AIEvals

]]>The AI Testing Framework Every Business Needs (But Few Use)full2374falseKeith Richman sits down with Hamel Husain, machine learning engineer and founder of Parlance Labs, to demystify AI evaluations (evals). Hamel breaks down why generic AI testing metrics fall short and how businesses can actually measure, debug, and improve their AI applications in the real world. They explore the pitfalls of simply slapping a chatbot on an existing product, the importance of iterative error analysis, and why starting simple with the most powerful models beats reaching for immediate complexity. Whether you're an executive fielding AI mandates or a developer building the stack, Hamel shares actionable advice on how to stop building the wrong things faster and start deploying AI that truly moves the needle. Chapters 00:00:00 Introduction: Why AI Testing Matters More Than You Think 00:01:52 What Are AI Evals and Why Every Business Needs Them 00:04:03 The Generic Metrics Trap: Why Off-the-Shelf Testing Fails 00:11:57 The Two Biggest Failure Modes in AI Implementation 00:13:37 Moving Fast vs Being Deliberate 00:15:04 The Slop Problem 00:21:24 Guardrails Done Right 00:23:45 Model Selection Strategy 00:27:25 Build vs Buy: When to Use Consulting vs Internal Teams 00:29:40 The Bootcamp Approach 00:31:19 The Million Lines of Code Myth 00:32:46 Embracing Mistakes and the Experimental Mindset 00:34:33 Personal Tech Stack and the OpenClaw Reality Check #ArtificialIntelligence #MachineLearning #AITesting #TechLeadership #SoftwareEngineering #DataScience #OpenAI #ProductManagement #GenerativeAI #AIEvalsEmployees as Air Traffic Controllers: Rethinking Work in the AI EraSergio Furio, CEO of Creditas, joins Keith Richman to discuss how AI is fundamentally transforming his Brazilian fintech company. In this episode of Applied Intelligence, Sergio reveals how transitioning from traditional apps to 24/7 AI conversational agents is revolutionizing the customer experience and loan underwriting. He details how Creditas automated 85 complex legal risk assessments for collateralized lending and drastically boosted employee productivity—allowing the company to grow 30% annually while halving its overall headcount. Sergio also shares how AI platforms like Claude, Perplexity, and Notion are shifting company culture, turning traditional managers into hands-on "builders" who can execute end-to-end. If you want to know what real-world AI deployment looks like at a massive scale, this is a must-listen. Chapters 00:00:00 Intro 00:08:51 The AI Factory: Cultural Transformation Over Technology 00:09:28 Personal AI Transformation: From Excel to AI-First Workflows 00:16:26 Employees as Air Traffic Controllers 00:23:07 Liquidity Risk and Intelligent Data Orchestration 00:28:04 Expanding Access: How AI Reduces Costs and Widens the Funnel 00:29:37 Perplexity for Proof of Concepts: Building Agents in Real-Time 00:32:29 Builders Over Managers #AI #ArtificialIntelligence #Fintech #Business #Leadership #MachineLearning #Productivity #Technology #Lending #Startupsflightcast:01KNJ20R53CGE2Z1QXX7FMD18ATue, 07 Apr 2026 15:10:00 -0000Sergio Furio, CEO of Creditas, joins Keith Richman to discuss how AI is fundamentally transforming his Brazilian fintech company. In this episode of Applied Intelligence, Sergio reveals how transitioning from traditional apps to 24/7 AI conversational agents is revolutionizing the customer experience and loan underwriting. He details how Creditas automated 85 complex legal risk assessments for collateralized lending and drastically boosted employee productivity—allowing the company to grow 30% annually while halving its overall headcount. Sergio also shares how AI platforms like Claude, Perplexity, and Notion are shifting company culture, turning traditional managers into hands-on "builders" who can execute end-to-end. If you want to know what real-world AI deployment looks like at a massive scale, this is a must-listen.

Chapters

  • 00:00:00 Intro
  • 00:08:51 The AI Factory: Cultural Transformation Over Technology
  • 00:09:28 Personal AI Transformation: From Excel to AI-First Workflows
  • 00:16:26 Employees as Air Traffic Controllers
  • 00:23:07 Liquidity Risk and Intelligent Data Orchestration
  • 00:28:04 Expanding Access: How AI Reduces Costs and Widens the Funnel
  • 00:29:37 Perplexity for Proof of Concepts: Building Agents in Real-Time
  • 00:32:29 Builders Over Managers


#AI #ArtificialIntelligence #Fintech #Business #Leadership #MachineLearning #Productivity #Technology #Lending #Startups

]]>Employees as Air Traffic Controllers: Rethinking Work in the AI Erafull2065falseSergio Furio, CEO of Creditas, joins Keith Richman to discuss how AI is fundamentally transforming his Brazilian fintech company. In this episode of Applied Intelligence, Sergio reveals how transitioning from traditional apps to 24/7 AI conversational agents is revolutionizing the customer experience and loan underwriting. He details how Creditas automated 85 complex legal risk assessments for collateralized lending and drastically boosted employee productivity—allowing the company to grow 30% annually while halving its overall headcount. Sergio also shares how AI platforms like Claude, Perplexity, and Notion are shifting company culture, turning traditional managers into hands-on "builders" who can execute end-to-end. If you want to know what real-world AI deployment looks like at a massive scale, this is a must-listen. Chapters 00:00:00 Intro 00:08:51 The AI Factory: Cultural Transformation Over Technology 00:09:28 Personal AI Transformation: From Excel to AI-First Workflows 00:16:26 Employees as Air Traffic Controllers 00:23:07 Liquidity Risk and Intelligent Data Orchestration 00:28:04 Expanding Access: How AI Reduces Costs and Widens the Funnel 00:29:37 Perplexity for Proof of Concepts: Building Agents in Real-Time 00:32:29 Builders Over Managers #AI #ArtificialIntelligence #Fintech #Business #Leadership #MachineLearning #Productivity #Technology #Lending #StartupsThe 6-Layer Framework for Scaling AI That Actually WorksKeith Richman sits down with Jeff McMillan, former Head of AI at Morgan Stanley, to unpack the realities of deploying enterprise AI. Jeff shares his journey of partnering with OpenAI in early 2022 and breaks down his six-layer framework for scaling AI effectively—emphasizing why models alone aren't enough. They explore the critical importance of data quality, governance, and the emerging "agentic orchestration" layer that will define the future of corporate AI. Jeff also explains why he believes the "Head of AI" title should be a temporary role and why giving every employee access to AI tools is the best way to drive adoption and reduce risk. If you're a business leader navigating AI integration, this episode delivers practical, battle-tested strategies. Chapters 00:00:00 Introduction: From Morgan Stanley to AI Transformation 00:00:41 The Unlikely Path: From Army Lieutenant to AI Leader 00:03:33 The OpenAI Partnership: First Enterprise Customer 00:06:47 The Six-Layer Enterprise AI Stack 00:08:45 Why Data Quality Still Matters More Than Models 00:09:58 The Agentic Orchestration Layer: The Future of Enterprise AI 00:14:56 MCP Protocol: The Railway Gauge for AI 00:17:22 Build vs Buy: Strategic Decisions for Different Company Sizes 00:23:44 The Ferrari in the Walmart Parking Lot Problem 00:28:13 Measuring AI Success: From Contact Velocity to Revenue 00:32:34 The Three-Year Timeline: Realistic Expectations for AI Transformation 00:36:32 Shadow AI and Enterprise Tool Strategy #ArtificialIntelligence #EnterpriseAI #BusinessLeadership #MachineLearning #TechInnovation #DataAnalytics #GenerativeAI #FutureOfWork #AIStrategy #AgenticAIflightcast:01KMQW0FDD5QASXHKKJ27HHQJDFri, 27 Mar 2026 15:16:00 -0000Keith Richman sits down with Jeff McMillan, former Head of AI at Morgan Stanley, to unpack the realities of deploying enterprise AI. Jeff shares his journey of partnering with OpenAI in early 2022 and breaks down his six-layer framework for scaling AI effectively—emphasizing why models alone aren't enough. They explore the critical importance of data quality, governance, and the emerging "agentic orchestration" layer that will define the future of corporate AI. Jeff also explains why he believes the "Head of AI" title should be a temporary role and why giving every employee access to AI tools is the best way to drive adoption and reduce risk. If you're a business leader navigating AI integration, this episode delivers practical, battle-tested strategies.

Chapters

  • 00:00:00 Introduction: From Morgan Stanley to AI Transformation
  • 00:00:41 The Unlikely Path: From Army Lieutenant to AI Leader
  • 00:03:33 The OpenAI Partnership: First Enterprise Customer
  • 00:06:47 The Six-Layer Enterprise AI Stack
  • 00:08:45 Why Data Quality Still Matters More Than Models
  • 00:09:58 The Agentic Orchestration Layer: The Future of Enterprise AI
  • 00:14:56 MCP Protocol: The Railway Gauge for AI
  • 00:17:22 Build vs Buy: Strategic Decisions for Different Company Sizes
  • 00:23:44 The Ferrari in the Walmart Parking Lot Problem
  • 00:28:13 Measuring AI Success: From Contact Velocity to Revenue
  • 00:32:34 The Three-Year Timeline: Realistic Expectations for AI Transformation
  • 00:36:32 Shadow AI and Enterprise Tool Strategy

#ArtificialIntelligence #EnterpriseAI #BusinessLeadership #MachineLearning #TechInnovation #DataAnalytics #GenerativeAI #FutureOfWork #AIStrategy #AgenticAI

]]>The 6-Layer Framework for Scaling AI That Actually Worksfull2608falseKeith Richman sits down with Jeff McMillan, former Head of AI at Morgan Stanley, to unpack the realities of deploying enterprise AI. Jeff shares his journey of partnering with OpenAI in early 2022 and breaks down his six-layer framework for scaling AI effectively—emphasizing why models alone aren't enough. They explore the critical importance of data quality, governance, and the emerging "agentic orchestration" layer that will define the future of corporate AI. Jeff also explains why he believes the "Head of AI" title should be a temporary role and why giving every employee access to AI tools is the best way to drive adoption and reduce risk. If you're a business leader navigating AI integration, this episode delivers practical, battle-tested strategies. Chapters 00:00:00 Introduction: From Morgan Stanley to AI Transformation 00:00:41 The Unlikely Path: From Army Lieutenant to AI Leader 00:03:33 The OpenAI Partnership: First Enterprise Customer 00:06:47 The Six-Layer Enterprise AI Stack 00:08:45 Why Data Quality Still Matters More Than Models 00:09:58 The Agentic Orchestration Layer: The Future of Enterprise AI 00:14:56 MCP Protocol: The Railway Gauge for AI 00:17:22 Build vs Buy: Strategic Decisions for Different Company Sizes 00:23:44 The Ferrari in the Walmart Parking Lot Problem 00:28:13 Measuring AI Success: From Contact Velocity to Revenue 00:32:34 The Three-Year Timeline: Realistic Expectations for AI Transformation 00:36:32 Shadow AI and Enterprise Tool Strategy #ArtificialIntelligence #EnterpriseAI #BusinessLeadership #MachineLearning #TechInnovation #DataAnalytics #GenerativeAI #FutureOfWork #AIStrategy #AgenticAIThe Argument for Why AI Can’t Replace TeachersIs AI actually helping students learn? In this episode of Applied Intelligence, host Keith Richman sits down with Jason Robinovitz, an education business leader and micro-school founder, to discuss the real-world impact of AI in education. Jason shares why his school prioritizes human connection over screens, how he practically integrates tools like ChatGPT and Claude to empower teachers, and why heavily marketed "two-hour AI schools" might leave kids unprepared for the real world. They also dive into the ethics of AI in college applications and why AI might actually widen the educational gap rather than close it. Whether you're an educator, business owner, or parent navigating technology in the classroom, this conversation offers grounded, actionable insights on deploying AI without losing the human element. Timestamp: Chapters 00:00:00 Intro 00:00:54 The Micro-School Model 00:01:40 The ChatGPT Moment 00:03:46 Detecting AI Usage 00:06:16 Maximizing Human Connection Time 00:07:16 The Alpha School Debate 00:11:24 Ethical AI Usage 00:12:42 The English Major's Revenge 00:15:30 The Future of Tutoring 00:17:53 Why AI May Increase Educational Inequality #AI #Education #EdTech #ArtificialIntelligence #Tutoring #Business #Technology #FutureOfEducation #AppliedIntelligenceflightcast:01KM139Q0QM7KTYT8J78Y4X1REWed, 18 Mar 2026 19:05:00 -0000Is AI actually helping students learn? In this episode of Applied Intelligence, host Keith Richman sits down with Jason Robinovitz, an education business leader and micro-school founder, to discuss the real-world impact of AI in education. Jason shares why his school prioritizes human connection over screens, how he practically integrates tools like ChatGPT and Claude to empower teachers, and why heavily marketed "two-hour AI schools" might leave kids unprepared for the real world. They also dive into the ethics of AI in college applications and why AI might actually widen the educational gap rather than close it. Whether you're an educator, business owner, or parent navigating technology in the classroom, this conversation offers grounded, actionable insights on deploying AI without losing the human element.

Timestamp:

Chapters

  • 00:00:00 Intro
  • 00:00:54 The Micro-School Model
  • 00:01:40 The ChatGPT Moment
  • 00:03:46 Detecting AI Usage
  • 00:06:16 Maximizing Human Connection Time
  • 00:07:16 The Alpha School Debate
  • 00:11:24 Ethical AI Usage
  • 00:12:42 The English Major's Revenge
  • 00:15:30 The Future of Tutoring
  • 00:17:53 Why AI May Increase Educational Inequality

#AI #Education #EdTech #ArtificialIntelligence #Tutoring #Business #Technology #FutureOfEducation #AppliedIntelligence

]]>The Argument for Why AI Can’t Replace Teachersfull1188falseIs AI actually helping students learn? In this episode of Applied Intelligence, host Keith Richman sits down with Jason Robinovitz, an education business leader and micro-school founder, to discuss the real-world impact of AI in education. Jason shares why his school prioritizes human connection over screens, how he practically integrates tools like ChatGPT and Claude to empower teachers, and why heavily marketed "two-hour AI schools" might leave kids unprepared for the real world. They also dive into the ethics of AI in college applications and why AI might actually widen the educational gap rather than close it. Whether you're an educator, business owner, or parent navigating technology in the classroom, this conversation offers grounded, actionable insights on deploying AI without losing the human element. Timestamp: Chapters 00:00:00 Intro 00:00:54 The Micro-School Model 00:01:40 The ChatGPT Moment 00:03:46 Detecting AI Usage 00:06:16 Maximizing Human Connection Time 00:07:16 The Alpha School Debate 00:11:24 Ethical AI Usage 00:12:42 The English Major's Revenge 00:15:30 The Future of Tutoring 00:17:53 Why AI May Increase Educational Inequality #AI #Education #EdTech #ArtificialIntelligence #Tutoring #Business #Technology #FutureOfEducation #AppliedIntelligenceEnterprise AI Is a Mess. Here's the Fix.Brendan Falk joins Keith to dismantle the hype around enterprise AI. While AI can generate software instantly, Falk explains why legacy integrations and edge cases make real-world deployment a massive challenge. He shares his proven framework for prioritizing AI initiatives based on business value versus technical feasibility and offers actionable strategies for upskilling teams. Whether you are evaluating internal tools, looking to automate customer support, or trying to bridge the gap between AI capabilities and actual ROI, this episode provides a grounded roadmap for success. #artificialintelligence #businessstrategy #enterpriseai #nocode #startups #softwareengineering #aws #productivity #techtrends #managementflightcast:01KK0P8HF8FN78BTRXXX8PN4ABFri, 06 Mar 2026 16:00:00 -0000Brendan Falk joins Keith to dismantle the hype around enterprise AI. While AI can generate software instantly, Falk explains why legacy integrations and edge cases make real-world deployment a massive challenge. He shares his proven framework for prioritizing AI initiatives based on business value versus technical feasibility and offers actionable strategies for upskilling teams. Whether you are evaluating internal tools, looking to automate customer support, or trying to bridge the gap between AI capabilities and actual ROI, this episode provides a grounded roadmap for success.

#artificialintelligence #businessstrategy #enterpriseai #nocode #startups #softwareengineering #aws #productivity #techtrends #management

]]>Enterprise AI Is a Mess. Here's the Fix.full3009falseBrendan Falk joins Keith to dismantle the hype around enterprise AI. While AI can generate software instantly, Falk explains why legacy integrations and edge cases make real-world deployment a massive challenge. He shares his proven framework for prioritizing AI initiatives based on business value versus technical feasibility and offers actionable strategies for upskilling teams. Whether you are evaluating internal tools, looking to automate customer support, or trying to bridge the gap between AI capabilities and actual ROI, this episode provides a grounded roadmap for success. #artificialintelligence #businessstrategy #enterpriseai #nocode #startups #softwareengineering #aws #productivity #techtrends #managementWe Help 800k Patients with AI — Here's What Actually WorksNuman CEO Sokratis Papafloratos joins Keith to discuss the delicate balance of deploying AI in a highly regulated healthcare environment. Sokratis shares his unique perspective, having studied neural networks 25 years ago before building a digital health platform that has served 800,000 patients. They dive into why Numan uses a hybrid AI-human model for behavioral coaching while strictly avoiding AI for clinical diagnostics due to safety concerns. Sokratis details their internal tech stack—including Cursor for coding and Intercom’s Fin for support—and explains the critical process of converting unstructured patient data into actionable insights. This is a pragmatic look at how to scale healthcare safely using artificial intelligence. Chapters 00:00:00 Introduction: AI's Promise for Healthcare Access 00:00:38 From Neural Networks to Numan: A 25-Year AI Journey 00:06:11 The ChatGPT Watershed Moment and Healthcare Caution 00:10:56 Hybrid AI-Human Coaching: The Current Model 00:17:32 Democratizing Healthcare Through AI Scale 00:22:52 AI Across the Organization: Coding, Customer Service, and Beyond 00:24:09 Developer Productivity 00:43:11 Security, Data Privacy, and the Healthcare AI Guardrails 00:46:44 Personalized Healthcare Journeys #AI #Healthcare #DigitalHealth #Business #Technology #Startups #MachineLearning #Leadership #Innovation #MedTechflightcast:01KH5NWFKDXH1EAZ64FPA9HVPKWed, 11 Feb 2026 16:00:00 -0000Numan CEO Sokratis Papafloratos joins Keith to discuss the delicate balance of deploying AI in a highly regulated healthcare environment. Sokratis shares his unique perspective, having studied neural networks 25 years ago before building a digital health platform that has served 800,000 patients. They dive into why Numan uses a hybrid AI-human model for behavioral coaching while strictly avoiding AI for clinical diagnostics due to safety concerns. Sokratis details their internal tech stack—including Cursor for coding and Intercom’s Fin for support—and explains the critical process of converting unstructured patient data into actionable insights. This is a pragmatic look at how to scale healthcare safely using artificial intelligence.

Chapters

  • 00:00:00 Introduction: AI's Promise for Healthcare Access
  • 00:00:38 From Neural Networks to Numan: A 25-Year AI Journey
  • 00:06:11 The ChatGPT Watershed Moment and Healthcare Caution
  • 00:10:56 Hybrid AI-Human Coaching: The Current Model
  • 00:17:32 Democratizing Healthcare Through AI Scale
  • 00:22:52 AI Across the Organization: Coding, Customer Service, and Beyond
  • 00:24:09 Developer Productivity
  • 00:43:11 Security, Data Privacy, and the Healthcare AI Guardrails
  • 00:46:44 Personalized Healthcare Journeys

#AI #Healthcare #DigitalHealth #Business #Technology #Startups #MachineLearning #Leadership #Innovation #MedTech

]]>We Help 800k Patients with AI — Here's What Actually Worksfull3089falseNuman CEO Sokratis Papafloratos joins Keith to discuss the delicate balance of deploying AI in a highly regulated healthcare environment. Sokratis shares his unique perspective, having studied neural networks 25 years ago before building a digital health platform that has served 800,000 patients. They dive into why Numan uses a hybrid AI-human model for behavioral coaching while strictly avoiding AI for clinical diagnostics due to safety concerns. Sokratis details their internal tech stack—including Cursor for coding and Intercom’s Fin for support—and explains the critical process of converting unstructured patient data into actionable insights. This is a pragmatic look at how to scale healthcare safely using artificial intelligence. Chapters 00:00:00 Introduction: AI's Promise for Healthcare Access 00:00:38 From Neural Networks to Numan: A 25-Year AI Journey 00:06:11 The ChatGPT Watershed Moment and Healthcare Caution 00:10:56 Hybrid AI-Human Coaching: The Current Model 00:17:32 Democratizing Healthcare Through AI Scale 00:22:52 AI Across the Organization: Coding, Customer Service, and Beyond 00:24:09 Developer Productivity 00:43:11 Security, Data Privacy, and the Healthcare AI Guardrails 00:46:44 Personalized Healthcare Journeys #AI #Healthcare #DigitalHealth #Business #Technology #Startups #MachineLearning #Leadership #Innovation #MedTechHow AI Helped Double Our Revenue with Half the StaffVoi CEO Fredrik Hjelm joins Keith Richman to reveal how the European micro-mobility giant is transitioning into an AI-native organization. Fredrik breaks down his aggressive "build vs. buy" strategy, explaining how Voi reduced legacy SaaS spend by nearly 40% by replacing software with custom LLM tools. The conversation covers the evolution from basic chatbots to autonomous agents, the reality of "Shadow AI," and why AI curiosity is now a hiring requirement. From automating tender bids to replacing junior engineering tasks, this is a masterclass in practical, high-impact AI deployment for operators. #AI #ArtificialIntelligence #BusinessStrategy #CEO #TechTrends #SaaS #Voi #Automation #Leadership #MachineLearning Chapters 00:00:00 Introduction: Hiring for AI Readiness 00:00:30 Voice's Journey: From Hypergrowth to AI-First 00:01:14 The Klarna Moment: Early AI Inspiration 00:05:10 Phase One: The Year of the Chatbot (2023) 00:05:43 Organizational Change Management for AI 00:09:04 Shadow AI and the White-Listing Process 00:11:12 Phase Two: Coding Assistants and Vertical AI Tools 00:13:36 Buy vs Build: Rethinking the SaaS Stack 00:16:47 Mapping Software Products for AI Replacement 00:18:02 Removing Human from the Loop 00:19:01 Top-Down Cross-Functional AI Implementation 00:21:24 The New Software Sales Reality 00:23:50 Cost Savings vs Revenue Growth with AI 00:27:38 The Rise of 100x Engineers 00:30:01 Hiring for AI Aptitude Across All Roles 00:31:13 Domain-Specific AI Tool Stacks 00:32:28 Structured vs Unstructured Data: The AI Challenge 00:35:22 Understanding LLMs in Enterprise Settings 00:37:07 Case Study: Liquidize Marketing Automation 00:38:50 LLM Orchestration and Model Selection 00:40:34 Becoming the World's First AI-Native Micromobility Platform 00:42:13 Constraints: The Key to Safe AI Automation 00:44:05 Organizational Response: From Resistance to Enthusiasm 00:46:54 Creating the Head of AI Role 00:50:42 The Results: Double Revenue, Half the Staff 00:51:41 Frederik's Personal AI Tech Stack 00:56:22 The Biggest Win: Company-Wide AI Adoptionflightcast:01KGKJ139QXRTC859WBMJWQSAXWed, 04 Feb 2026 14:00:00 -0000Voi CEO Fredrik Hjelm joins Keith Richman to reveal how the European micro-mobility giant is transitioning into an AI-native organization. Fredrik breaks down his aggressive "build vs. buy" strategy, explaining how Voi reduced legacy SaaS spend by nearly 40% by replacing software with custom LLM tools. The conversation covers the evolution from basic chatbots to autonomous agents, the reality of "Shadow AI," and why AI curiosity is now a hiring requirement. From automating tender bids to replacing junior engineering tasks, this is a masterclass in practical, high-impact AI deployment for operators.

#AI #ArtificialIntelligence #BusinessStrategy #CEO #TechTrends #SaaS #Voi #Automation #Leadership #MachineLearning

Chapters

  • 00:00:00 Introduction: Hiring for AI Readiness
  • 00:00:30 Voice's Journey: From Hypergrowth to AI-First
  • 00:01:14 The Klarna Moment: Early AI Inspiration
  • 00:05:10 Phase One: The Year of the Chatbot (2023)
  • 00:05:43 Organizational Change Management for AI
  • 00:09:04 Shadow AI and the White-Listing Process
  • 00:11:12 Phase Two: Coding Assistants and Vertical AI Tools
  • 00:13:36 Buy vs Build: Rethinking the SaaS Stack
  • 00:16:47 Mapping Software Products for AI Replacement
  • 00:18:02 Removing Human from the Loop
  • 00:19:01 Top-Down Cross-Functional AI Implementation
  • 00:21:24 The New Software Sales Reality
  • 00:23:50 Cost Savings vs Revenue Growth with AI
  • 00:27:38 The Rise of 100x Engineers
  • 00:30:01 Hiring for AI Aptitude Across All Roles
  • 00:31:13 Domain-Specific AI Tool Stacks
  • 00:32:28 Structured vs Unstructured Data: The AI Challenge
  • 00:35:22 Understanding LLMs in Enterprise Settings
  • 00:37:07 Case Study: Liquidize Marketing Automation
  • 00:38:50 LLM Orchestration and Model Selection
  • 00:40:34 Becoming the World's First AI-Native Micromobility Platform
  • 00:42:13 Constraints: The Key to Safe AI Automation
  • 00:44:05 Organizational Response: From Resistance to Enthusiasm
  • 00:46:54 Creating the Head of AI Role
  • 00:50:42 The Results: Double Revenue, Half the Staff
  • 00:51:41 Frederik's Personal AI Tech Stack
  • 00:56:22 The Biggest Win: Company-Wide AI Adoption

]]>How AI Helped Double Our Revenue with Half the Stafffull3446falseVoi CEO Fredrik Hjelm joins Keith Richman to reveal how the European micro-mobility giant is transitioning into an AI-native organization. Fredrik breaks down his aggressive "build vs. buy" strategy, explaining how Voi reduced legacy SaaS spend by nearly 40% by replacing software with custom LLM tools. The conversation covers the evolution from basic chatbots to autonomous agents, the reality of "Shadow AI," and why AI curiosity is now a hiring requirement. From automating tender bids to replacing junior engineering tasks, this is a masterclass in practical, high-impact AI deployment for operators. #AI #ArtificialIntelligence #BusinessStrategy #CEO #TechTrends #SaaS #Voi #Automation #Leadership #MachineLearning Chapters 00:00:00 Introduction: Hiring for AI Readiness 00:00:30 Voice's Journey: From Hypergrowth to AI-First 00:01:14 The Klarna Moment: Early AI Inspiration 00:05:10 Phase One: The Year of the Chatbot (2023) 00:05:43 Organizational Change Management for AI 00:09:04 Shadow AI and the White-Listing Process 00:11:12 Phase Two: Coding Assistants and Vertical AI Tools 00:13:36 Buy vs Build: Rethinking the SaaS Stack 00:16:47 Mapping Software Products for AI Replacement 00:18:02 Removing Human from the Loop 00:19:01 Top-Down Cross-Functional AI Implementation 00:21:24 The New Software Sales Reality 00:23:50 Cost Savings vs Revenue Growth with AI 00:27:38 The Rise of 100x Engineers 00:30:01 Hiring for AI Aptitude Across All Roles 00:31:13 Domain-Specific AI Tool Stacks 00:32:28 Structured vs Unstructured Data: The AI Challenge 00:35:22 Understanding LLMs in Enterprise Settings 00:37:07 Case Study: Liquidize Marketing Automation 00:38:50 LLM Orchestration and Model Selection 00:40:34 Becoming the World's First AI-Native Micromobility Platform 00:42:13 Constraints: The Key to Safe AI Automation 00:44:05 Organizational Response: From Resistance to Enthusiasm 00:46:54 Creating the Head of AI Role 00:50:42 The Results: Double Revenue, Half the Staff 00:51:41 Frederik's Personal AI Tech Stack 00:56:22 The Biggest Win: Company-Wide AI AdoptionHow We Are Deploying AI: 10x Functionality at 20% the CostAnthony Soohoo, CEO of MoneyGram, joins host Keith Richman to discuss how he is transforming an 80-year-old legacy payments company into an AI-first organization. Anthony shares his practical philosophy on AI adoption: start with a clear problem statement rather than the technology itself, and manually validate workflows before automating them. He explains why enterprise-wide goals must supersede departmental silos and predicts a massive disruption in the enterprise software market where costs drop by 80% while functionality increases tenfold. This episode is a masterclass for leaders looking to navigate the gap between AI hype and real-world operational implementation #AI #BusinessStrategy #DigitalTransformation #FinTech #Leadership #ArtificialIntelligence #TechTrends #CEO #MoneyGram #AppliedIntelligence Chapters 00:00:00 Introduction: AI Transformation at MoneyGram 00:00:40 MoneyGram's Legacy Business and Digital Transformation 00:03:58 Building an AI-First Culture 00:05:56 Starting with Problem Statements, Not Functions 00:09:48 Communicating AI to Employees 00:11:30 Outcome-Based AI Strategy vs Functional Automation 00:14:27 The Future of Enterprise Software 00:15:44 Vetting AI Vendors and Partners 00:21:06 Enterprise Goals vs Departmental Goals 00:25:10 Data Strategy and Infrastructure 00:32:35 Early Wins and Productivity Gains 00:34:25 Hiring for AI Success: Enterprise Mindset 00:36:00 Data Labeling and Training Approaches 00:39:54 Personal AI Tech Stack 00:41:29 Advice for 100M Revenue Companies 00:42:24 The Transformation Opportunityflightcast:01KFXA8J00PBFCJBKHV1QB3Y5EMon, 26 Jan 2026 15:46:00 -0000Anthony Soohoo, CEO of MoneyGram, joins host Keith Richman to discuss how he is transforming an 80-year-old legacy payments company into an AI-first organization. Anthony shares his practical philosophy on AI adoption: start with a clear problem statement rather than the technology itself, and manually validate workflows before automating them. He explains why enterprise-wide goals must supersede departmental silos and predicts a massive disruption in the enterprise software market where costs drop by 80% while functionality increases tenfold. This episode is a masterclass for leaders looking to navigate the gap between AI hype and real-world operational implementation

#AI #BusinessStrategy #DigitalTransformation #FinTech #Leadership #ArtificialIntelligence #TechTrends #CEO #MoneyGram #AppliedIntelligence

Chapters

  • 00:00:00 Introduction: AI Transformation at MoneyGram
  • 00:00:40 MoneyGram's Legacy Business and Digital Transformation
  • 00:03:58 Building an AI-First Culture
  • 00:05:56 Starting with Problem Statements, Not Functions
  • 00:09:48 Communicating AI to Employees
  • 00:11:30 Outcome-Based AI Strategy vs Functional Automation
  • 00:14:27 The Future of Enterprise Software
  • 00:15:44 Vetting AI Vendors and Partners
  • 00:21:06 Enterprise Goals vs Departmental Goals
  • 00:25:10 Data Strategy and Infrastructure
  • 00:32:35 Early Wins and Productivity Gains
  • 00:34:25 Hiring for AI Success: Enterprise Mindset
  • 00:36:00 Data Labeling and Training Approaches
  • 00:39:54 Personal AI Tech Stack
  • 00:41:29 Advice for 100M Revenue Companies
  • 00:42:24 The Transformation Opportunity

]]>How We Are Deploying AI: 10x Functionality at 20% the Costfull2675falseAnthony Soohoo, CEO of MoneyGram, joins host Keith Richman to discuss how he is transforming an 80-year-old legacy payments company into an AI-first organization. Anthony shares his practical philosophy on AI adoption: start with a clear problem statement rather than the technology itself, and manually validate workflows before automating them. He explains why enterprise-wide goals must supersede departmental silos and predicts a massive disruption in the enterprise software market where costs drop by 80% while functionality increases tenfold. This episode is a masterclass for leaders looking to navigate the gap between AI hype and real-world operational implementation #AI #BusinessStrategy #DigitalTransformation #FinTech #Leadership #ArtificialIntelligence #TechTrends #CEO #MoneyGram #AppliedIntelligence Chapters 00:00:00 Introduction: AI Transformation at MoneyGram 00:00:40 MoneyGram's Legacy Business and Digital Transformation 00:03:58 Building an AI-First Culture 00:05:56 Starting with Problem Statements, Not Functions 00:09:48 Communicating AI to Employees 00:11:30 Outcome-Based AI Strategy vs Functional Automation 00:14:27 The Future of Enterprise Software 00:15:44 Vetting AI Vendors and Partners 00:21:06 Enterprise Goals vs Departmental Goals 00:25:10 Data Strategy and Infrastructure 00:32:35 Early Wins and Productivity Gains 00:34:25 Hiring for AI Success: Enterprise Mindset 00:36:00 Data Labeling and Training Approaches 00:39:54 Personal AI Tech Stack 00:41:29 Advice for 100M Revenue Companies 00:42:24 The Transformation Opportunity