Driving Operational Excellence in Manufacturing with Practical AI

The companies winning today aren't planning more; they're executing faster and adapting continuously.

That was the central insight from my recent conversation with Mickey, Co-Founder and CEO of Next Plus, on the AI in Manufacturing podcast. We explored why the traditional approach to manufacturing execution is fundamentally misaligned with today's operational reality, and what leading manufacturers are doing about it.

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What Assumptions Are Built Into Legacy MES Platforms?

Traditional Manufacturing Execution Systems were designed for a world that no longer exists.

Think about the assumptions baked into conventional MES platforms: stable product lines that run for years, operators who stay with a company for decades, sufficient time and budget for 18-24 month implementation cycles, and a workforce comfortable navigating complex digital interfaces while simultaneously running production.

These assumptions made sense when a facility might produce the same SKU for five years straight. They made sense when your most experienced operator had been on the floor for thirty years and could train every new hire personally.

But that's not the manufacturing world we live in anymore.

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How Has the Manufacturing Workforce Changed?

The shift has been dramatic. Markets now demand flexibility that would have been unthinkable a decade ago—the ability to switch from 1.5-liter bottles to 1-liter bottles overnight, or pivot an entire production line based on a customer call that morning. Low volume, high mix production has moved from exception to expectation.

Meanwhile, the workforce equation has inverted completely. Workers now stay two to three years, not twenty. The tribal knowledge that took decades to accumulate is retiring faster than organizations can capture it. And new operators arrive expecting technology that works like their smartphone, not software that requires three weeks of classroom training.

This disconnect between legacy systems and operational reality creates cascading problems that compound over time.

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Why Do MES Implementations Fail to Deliver ROI?

When I talk with manufacturing leaders about their digital transformation challenges, the same patterns emerge repeatedly.

First, there's the frontline intelligence gap. Your operators see problems developing before any sensor detects them. They know which machine settings actually work versus what the documentation says. They've developed workarounds that keep production flowing. But traditional systems can't capture this operational intelligence at scale—the interfaces are too rigid, the workflows too disruptive.

Then there's the ROI delay problem. Organizations invest in capable platforms, but the value gets buried under implementation complexity. Basic systems take two years to deploy. By the time they're operational, the requirements have shifted.

Perhaps most frustrating is what I call pilot purgatory—digital initiatives that demonstrate clear value in controlled tests but never achieve production scale. The gap between "this works" and "this works everywhere" proves insurmountable.

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What Is an AI-Native MES and How Is It Different?

This is why leading manufacturers are rethinking execution from the ground up. Not digitizing existing processes, but reimagining what's possible when you design for today's workforce and operational reality.

The distinction matters. Digitizing a paper-based process gives you a digital version of paper—same rigid structure, same training burden, same disconnect from how work actually happens. An AI-native approach starts with different questions: How do people actually solve problems on the floor? How can technology amplify human capability rather than constrain it? How do we capture knowledge in the flow of work rather than as a separate administrative task?

What does this look like in practice? Mickey shared several examples that illustrate the shift.

Consider standard operating procedures. Traditional approach: an engineer spends days documenting a process, creating screenshots and written instructions that become outdated within months. AI-native approach: record a video of the process, let AI generate the SOP automatically, update it by recording the new method. Engineering time drops by 80%, and documentation actually reflects current practice.

Or think about troubleshooting. Traditional approach: operators search through manuals or wait for a supervisor when something goes wrong. AI-native approach: the system surfaces relevant guidance from historical fault data, learning from every resolution and making that knowledge available to every operator instantly.

The interface design philosophy differs fundamentally as well. Rather than forcing operators to navigate dropdown menus and complex forms, AI-powered interfaces enable natural interaction—voice input, conversational queries, guidance that adapts to context. This isn't about making technology "easier to use." It's about making technology that actually gets used.

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How Fast Can AI-Native MES Platforms Be Deployed?

One aspect of Mickey's approach that resonated strongly was the emphasis on deployment velocity. Traditional MES implementations measure timelines in years. This new generation of platforms measures in weeks.

This isn't just about faster time-to-value, though that matters. It's about maintaining alignment between your systems and your operations. When your production requirements can shift quarterly, a two-year deployment cycle means you're implementing yesterday's solution. Rapid deployment enables continuous adaptation—start simple, learn from actual usage, expand based on real needs rather than projected requirements.

The scalability model also differs significantly. Connecting thousands of machines no longer requires lengthy custom integrations for each one. Modern architectures enable standardized connectivity that scales without proportional complexity growth.

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How Should Manufacturing Leaders Approach MES Modernization?

If you're responsible for manufacturing operations or digital transformation, the implications are significant.

The question isn't whether to modernize execution systems—that's inevitable. The question is whether you'll approach it as a technology upgrade or a fundamental rethinking of how frontline work gets done.

Technology upgrades tend to preserve existing constraints. You get faster versions of the same rigid processes, digital versions of the same training burdens, scalable versions of the same disconnects between system design and operational reality.

Rethinking execution means starting from workforce reality: short tenure, diverse experience levels, variable production requirements, and the need to capture and deploy operational intelligence continuously. It means prioritizing deployment velocity over feature completeness, human-centric design over comprehensive functionality, and adaptability over standardization.

The manufacturers I see succeeding with this approach share a common characteristic: they've stopped asking "how do we implement MES?" and started asking "how do we enable our frontline to execute with excellence in today's environment?"

That shift in framing changes everything that follows.

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