The conversation with Matt Gordon covers his journey from early career to founding A3 Analytics, insights from Toyota Research Institute, challenges in implementing machine learning in manufacturing, lessons from early days at Palantir, and the interplay between forward deployed engineers and software engineers. Matt also discusses the challenges in advanced manufacturing and AI adoption, the role of AI in manufacturing, and the balancing of software and services at Palantir. The conversation highlights the Rice Theory of Culture and its impact on manufacturing, as well as the contextualization of data with LLMs. Finally, Matt shares insights into the role of forward deployed engineers and the concept of Data-driven Kaizen.

Takeaways

• Data-driven Kaizen
• Challenges in Manufacturing
• Holistic vs Analytic Thinking

Chapters

• 00:00 Challenges in Advanced Manufacturing and AI Adoption

The conversation explores the impact of AI technology in the field, focusing on productivity enhancement and the importance of data structuring. It delves into customer stories, time savings, quality of data, and the future vision of AI in the physical world, highlighting the adoption of robotics and drones, customer interaction, and the evolution of field operations.

Takeaways

• AI in the field enhances productivity
• Data structuring is crucial for AI technology

Chapters

• 00:00 Introduction and Mic Check
• 05:27 Time Sheets and Billing
• 12:02 Rich Context and AI Interfaces
• 17:33 Customer Interaction and Scheduling

The conversation covers Formant's services, Caleb's role as a forward deployed engineer, the challenges of operational complexity in manufacturing, and the potential of AI in predictive maintenance. It also explores the collaboration between software and services, as well as the issue of data-rich, action-poor environments in advanced manufacturing.

Takeaways

• The role of a forward deployed engineer involves on-site interaction with customers and the translation of customer needs into product features.
• The potential of AI in predictive maintenance offers a new approach to solving long-standing problems in advanced manufacturing.

Chapters

• 00:00 Introduction to Formant's Services
• 09:24 Operational Complexity in Manufacturing Facilities
• 16:20 Collaboration Between Software and Services
• 22:33 Predictive Maintenance in Manufacturing
• 27:37 Data Rich, Action Poor in Advanced Manufacturing

In this episode, Henrik Bennetsen and Fin Elliott pull back the curtain on the "Data-Rich, Action-Poor" reality of modern manufacturing. While the world hears "automation," the truth on the ground is often a "tsunami of alarms"—sometimes over 9,000 per day—that leave operators drowning in noise.

Henrik and Fin discuss how Agentic AI is changing the game by moving beyond simple dashboards to active investigation. We dive into:

• The 9,000 Alarm Problem: Why a single facility's sensor systems can generate more data than humans can realistically process.

• Investigation Blueprints: How Formant captures "tribal knowledge" to train AI agents as specialized procedural masters for your specific site.

• The Junior Technician at Scale: Real-world results from semiconductor pilots where AI identified over 50% of alarms as false or "shelved," saving massive amounts of human labor.

• Chaos to Clarity: Shifting from reactive "firefighting" to a stable, scalable operational flow

Ready to audit your own automation strategy?

Don’t let your next robotics investment collect "robot dust." Visit [Link] to download our Robotics Deployment Checklist—a framework used by Holman and Formant to ensure every machine drives measurable business value from day one.

👉 Explore the Holman-Formant Partnership: Formant.io Learn how to scale your fleet without scaling your headcount. We dive deep into the specific Physical AI workflows mentioned in this episode and how Holman is redefining asset management for the next century.

🚀 Join the Movement: Follow Formant on LinkedIn Don’t get left behind as the industry moves to AI-driven ops. Follow us for daily insights on robot-human collaboration and the software power behind the world’s largest fleets.

🚛 Experience the Legacy: Holman Official Website Visit the home of the 100-year-old leader in automotive and fleet services. To see their specific work in automation, check out the Holman Robotics division.

🔗 Connect with the Experts: Holman on LinkedIn

Stay updated on the latest hardware and service innovations from Joe Foster and the Holman team as they play offense in the robotics space.

#Robotics #PhysicalAI #FleetManagement #Automation #Formant #Holman #IoT #FutureOfWork

Are your operations data-rich but action-poor? Most operational teams spend 40% of their time "firefighting" data alerts. In this episode, Henrik Bennetsen and Fin Elliott show you how to hand that work off to AI Agents.

The Demo |

Watch as we build a "digital brain" for physical AI using Formant Triage. We move away from complex coding and toward "Plain English" instructions, allowing AI to:

• Analyze telemetry spikes (like thermal shock) in real-time.
• Reason through failures using specific business principles.
• Collaborate with human operators through "Human-in-the-Loop" checkpoints.

Key Chapters |

0:00 – The Vision: AI Agents vs. Human Teams 2:15 – Demo: The "Triage" Kanban Board 5:05 – Setting Triggers: From Data to Action 9:30 – Why "Plain English" is the New Coding 13:15 – Human-in-the-Loop: Managing AI Autonomy 23:00 – Cost vs. Intelligence: Choosing the Right AI Model 44:00 – From Rules-Based to Principle-Based Ops

Resources |

👉 Read the Full Breakdown: Learn More

Learn how to scale your fleet without scaling your headcount. We dive deep into the specific AI workflows mentioned in this video on the Formant Blog.

🚀 Join 10k+ Robotics Leaders: Join the Fleet

Don’t get left behind as the industry moves to AI-driven ops. Follow us on LinkedIn for daily insights on the future of robot-human collaboration.

#Robotics #AIAgents #Automation #Formant #IOT #AI

In this conversation, Henrik Bennetsen and Jeff Linnell explore the transformative impact of AI agents on software and robotics. They discuss the evolution of software from traditional models to more dynamic, disposable forms that adapt to user needs. Jeff shares his extensive background in robotics and automation, emphasizing the importance of human factors in technology adoption. The discussion highlights the shift from dashboards to proactive AI agents that can analyze data and take action autonomously. They also delve into the significance of incorporating tribal knowledge into AI systems and the necessity of building trust through transparency and engagement. The conversation concludes with insights on the future of interfaces in AI software and the evolving landscape of business in the age of AI.

Takeaways

• AI agents can take initiative within workflows.
• Software is becoming more disposable and adaptable.
• The evolution of software requires a participatory approach.
• Robotics deployment faces challenges related to human factors.
• AI agents can proactively solve problems before human intervention is needed.
• Data richness is crucial for effective AI and robotics.
• Institutional knowledge is invaluable for operational efficiency.
• Trust in AI is built through engagement and transparency.
• Interfaces should be flexible and user-driven.
• The landscape of business is rapidly changing with AI advancements.

In the new podcast episode, Henrik sit down with our Technical Product Manager, Fin Elliott, to unpack what came out of it, including:

• A prompt-driven robot simulator that lets our team spin up realistic, vertical-specific demos in hours instead of weeks.
• A compensation review tool built by HR + chief of staff using AI-assisted “vibe coding,” no traditional dev team.
• Examples of going from “idea in Slack” → working proof of concept in front of customers in a couple of days.

In this episode, Henrik chats with Richard Anaya, Formant's Head of AI, about his essay, "Physical AI: From Rules to Principles." They explore topics such as why traditional robots often get stuck in a loop with their rigid rules and how we can transform them by using principles rather than just a series of if–then statements. This shift might just be the key to helping machines thrive in the real world! If you're curious about Physical AI—what it means, why it matters, and where things are headed—this episode is a must-listen!

Article can be read here!