Manufacturing | Live Webinar | Aug 27th, | 1 PM ET | 10.30 PM IST

Agentic Operations: The Next Phase of Industry 4.0 is Not on the Shop Floor

A conversation on agentic AI, agentic workflows, and back-office automation, through the lens of Industry 4.0.
Alan Hester

Alan Hester

President of Nividous

Alan Hester

Nicholas Galbincea

Manager of Commodity & Supply Chain Modeling, Metallus

Manufacturers have spent a decade making the plant floor smarter. Sensors, connected assets, and predictive maintenance now catch a failing pump before it stops a line. The back office rarely got the same treatment.

Invoices still pass through three people and three systems. Reporting arrives once a month, long after the moment to act has passed. The data exists, but it is scattered and disconnected, so the slowest part of running the business is getting information ready to make a decision.

In this conversation, Nicholas Galbincea of Metallus and Alan Hester of Nividous look at what happens when you apply the Industry 4.0 playbook to business operations, the shift we're calling agentic operations.

We talk through where to start, how a phased approach takes back-office automation from streamlined workflows to agentic workflows that flag their own exceptions, why the missing piece is usually process data rather than more dashboards, and how to bring a workforce along instead of frightening it.

What attendees walk away with

  • A simple way to tell which back-office processes are ready to automate first, and which are not.
  • The crawl, walk, run phasing that takes an operation from streamlined workflows to anomaly detection to agentic AI that acts within defined guardrails.
  • Why business and operational data alone are not enough, and what the missing process-data layer reveals about bottlenecks.
  • A grounded, non-alarmist view of how automation changes roles, and how leaders can quell the fear of the unknown.

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Frequently Asked Questions

Most conversations about Industry 4.0 stop at the plant floor: sensors, connected equipment, and predictive maintenance that catches a failing pump before it stops a line. The fuller definition is data streams that talk to each other and adjust on their own, embedded in a process. That same model applies just as directly to finance, purchasing, and reporting as it does to a pump.

Because the investment went to the plant floor first. A typical invoice still passes through three people and three systems: one parses it, one re-keys it into another system, one calls to approve it. Every handoff is a chance for error and delay, and month-end reporting is often a manual pull from many reports, once a month, well after the moment to act has passed.

Every operation already has business data (ERP, finance, purchasing) and operational data (equipment, IoT sensors). What most are missing is process data: how the work actually flows between those two, how long each step takes, who owns it, and where it stalls. That layer is what turns a pile of numbers into a faster decision.

In three stages. Phase one streamlines the manual handoffs, like the three-person invoice process, so the business stops waiting on them. Phase two adds triggers: an anomaly gets detected and routed to the right person automatically. Later, agentic AI takes on actions directly, within guardrails, rather than only alerting a human. Nobody replaces a 1985 mill overnight, and this approach doesn't ask you to.

Technology has always shifted work, and that isn't new or unique to AI. The choice is to adapt and use existing skills in new ways, or stay static. The real barrier is fear of the unknown, and the fix is education, not force. Leaders shepherd the change; they don't impose it.

Yes. Registration is free, and a recording will be sent to everyone who registers, whether or not they can attend live.

Anyone responsible for how work actually moves through a manufacturing business, operations, finance, purchasing, and IT leaders at manufacturers of any size. The conversation stays practical and avoids industry-specific jargon.