Human-centered leadership aims to put people first by prioritizing empathy, inclusiveness and employee development. While the approach has roots in the early 20th century, it is still just gaining a foothold in a world where top-down management remains dominant.

But now artificial intelligence is emerging as an effective tool for amplifying human-centered leadership. At the same time, human-centered approaches are proving to be the most effective way to encourage AI adoption that meets the goals of individual employees and the business as a whole.

In this episode, we explore how an emerging type of "supermanager" blends savvy use of AI with human-centered leadership, and why developing supermanagers is essential in helping employees adapt to the massive changes brought about by AI.

Featuring: Julia Bersin, Director of Research, The Josh Bersin Company

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

• Why redesigning work around AI requires input from employees who are comfortable experimenting with the technology.
• Tips on training employees to become "superworkers" by using AI more effectively.
• AI tools supermanagers use in their own supervisory tasks.

References:

• Career cure for AI phobia: Be a beekeeper, not a worker bee
• When building an AI strategy, don't forget the humans
• Bersin video: The rise of the supermanager

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

Can non-technical workers really use supposedly user-friendly low-code/no-code development tools to write and customize software, especially AI applications, for serious business use?

Indeed they can -- if they're operating under the auspices of a well-planned citizen developer program that sets realistic expectations, establishes clear guardrails, and provides the right programming tools and training. It's also important to have an effective process for deciding which staff-written AI agents and apps should be productized or added to the organization's internal IT architecture.

In this episode, we explore how citizen developers can jumpstart an organization's AI deployment efforts, the essential elements of a program, who needs to be involved and the challenges to expect.

Featuring: Fabien Cros, Chief Data and AI Officer, Ducker Carlisle

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

• How Ducker Carlisle cut operating costs by 3%.
• Whether citizen developer programs change the relationship between the business and IT sides.
• How to decide when a citizen-developed app merits professional development.

References:

• What is citizen development?
• Citizen developers are redefining enterprise AI development
• StackAI low-code tool used by Ducker Carlisle

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

Assessments are valuable hiring tools, but they can be challenging to design and implement. Recruiters and hiring managers use them to evaluate whether a candidate has the skills for a specific job, but also to identify cognitive capabilities and behavioral characteristics that are often better predictors of success.

Hiring assessments are mostly digitized and conducted over computers, but they tend to be reserved for high-volume recruiting for roles that can be encoded in a few reusable assessments. Now artificial intelligence is making assessments feasible for specialized job openings, including executive positions.

In this episode, we explore the role hiring assessments have traditionally played in recruiting, how AI makes assessments easier to develop and deploy, and why the soft science of industrial-organizational psychology (IO) provides a firmer foundation than resumes and interviews.

Featuring: Mike Hudy, Chief Science Officer, Hirevue

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

• How Hirevue's Assessment Builder software works.
• The steps taken to mitigate AI bias in the tool.
• Other ways digital technology is changing IO.

References:

• What is Hirevue?
• Learn about skills-based job descriptions, candidate testing
• Hirevue Assessment Builder

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

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.

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.

Agentic AI is the hottest trend in ERP. It promises to infuse enterprise applications with AI that anticipates users' needs, communicates with them in natural language and handles more of the tedium of working in ERP systems.

SAP is one of the leaders of the push for agentic AI. It has aggressively added special-purpose AI agents and development tools for building custom agents and beefed up its data platforms to accommodate AI's needs.

But in practice, building agentic AI that works across SAP and non-SAP systems is challenging, and it often requires outside help from a consulting firm or system integrator.

In this episode, we explore the practical realities of implementing AI applications on SAP systems, including the design process, development tools and integration challenges.

Featuring: Gianluca Simeone, Vice President, CTIO and GenAI Leader, Capgemini

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

• Keys to successful agentic AI projects.
• Integration tools and standards for SAP AI.
• Whether non-programmers can use low-code development tools to play a meaningful role in agentic AI design.
• The status of multi-agent orchestration protocols.

References:

• SAP pitches role-based Joule assistants as ERP work partners
• Agentic AI explained: Key concepts and enterprise use cases
• Simeone explains procure-to-pay project in SAP video

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