"Physical" AI – artificial intelligence embodied in devices like robots, drones and self-driving cars – could be nearing a tipping point, thanks to recent advancements in large language models and agentic AI. The convergence of physical and agentic AI is giving machines the ability to sense their environment, make decisions and take action.

Practical business applications are already emerging. They're a major focus of research and development at SAP as the ERP market leader investigates how smart robots and other physical AI devices can work with enterprise applications to make businesses more intelligent and automated.

In this episode, we examine trends in physical and agentic AI, how they're transforming industrial automation, and the risks and challenges of implementation.

Featuring: Yaad Oren, Global Head of Research and Innovation at SAP and Managing Director of SAP Labs U.S.

In today's episode, we'll also cover:

• The role of software in physical AI's emergence as a serious business tool.
• Why 2026 represents a tipping point in physical AI's capabilities.
• Examples from SAP Labs.

References:

• Smarter robots: Agentic and physical AI converge in business
• Physical AI explained: Everything you need to know
• SAP article about physical AI partnerships

To learn more about enterprise applications, check out Search ERP.

Real-world deployments of agentic AI have so far been limited in scope, despite strong interest in using the technology to automate many of the business processes now handled by people.

One reason for the slow deployment of agents is the challenge of multi-agent orchestration: the ability of AI agents to communicate with each other and coordinate their activities across enterprise applications, workflows and even corporate firewalls.

There is growing recognition that developing a framework for multi-agent orchestration is essential for deploying agents on a large scale across the entire organization.

In this episode, we explore the main elements of a multi-agent framework, the problems it is meant to address, who is responsible for developing it and where to find ready-made frameworks and tools.

Featuring: Peter Hesse, Partner, 10Pearls

In today's episode, we'll also cover:

• Why the tendency of agents to work in harmony can make them less resilient when their scale expands.
• How a framework can support AI transparency and traceability.
• Using "policy as code" to enforce AI consistency and trust.

References:

• AI agent frameworks: A guide to evaluating agentic platforms
• Real-world agentic AI examples and use cases
• 10Pearls blog post on building enterprise AI agent frameworks

To learn more about enterprise applications, check out Search ERP.

Direct materials sourcing is a critical process in product design, engineering and manufacturing. That was never more apparent in 2017 when Tesla began to ship the Model 3, its first electric vehicle meant to be affordable for middle-income buyers.

The company had set two ambitious and unprecedented goals: building an EV for a base price of $35,000 and completing the development cycle in three years, half the typical turnaround in the automotive industry. The compressed timeline put tremendous pressure on employees to source quality parts economically and on time while keeping up with the tight design and production schedules.

But Tesla was working at a disadvantage. Much of its sourcing and procurement was still done manually, and an IT gap existed between the product lifecycle management system, where engineering and design were managed, and the ERP.

In this episode, we explore the impact inefficient procurement can have on profit margins, how a direct materials sourcing platform can close the technology gap for manufacturers, and the role played by AI.

Featuring: Spencer Penn, Co-founder and CEO, LightSource

In today's episode, we'll also cover:

• Why mastering the bill of materials is so important in sourcing's financial impact.
• How LightSource works, who uses it and where it fits in the product lifecycle.
• Lessons learned from Penn's experiences managing engineering finance at Tesla.

References:

• LightSource on procurement's overreliance on email and spreadsheets
• Tesla misses Q3 goals due to "production bottlenecks"
• Automotive supply chains can benefit from sourcing alliances: Here's why.

To learn more about enterprise applications, check out Search ERP.