AI seems likely to transform more jobs than it eliminates, despite well-founded fears of job loss as companies increasingly adopt AI automation, lay off workers and move others into AI-centric roles.

That probably means the best response for workers is learning to use AI to their individual advantage, but in ways that align sufficiently with the goals of their organization, rather than resist AI entirely.

In this episode, we explore ways to use AI to automate mundane tasks and boost productivity while developing the innate human skills that are likely to endure through AI's future advancements.

Featuring: Sharon Gai, AI speaker and futurist, author of How to Do More with Less: Future-Proofing Yourself in an AI-driven Economy.

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

• How to identify the right tasks to turn over to AI.
• Why being an AI "beekeeper" is better than being a worker bee.
• Who is responsible for upskilling employees.
• Whether agentic AI standards and technology are mature enough.

References:

• AI job losses: Transformation expected, not mass layoffs
• AI upskilling strategies that center workers, not tech
• Sharon Gai website

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

"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.