Top 7 AI Use Cases in MES Modernization for 2026

In those days, manufacturing execution system, MES, modernization meant replacing the UI, adding dashboards, and connecting to ERP. In 2026, businesses will be turning MES into a decision engine. It won’t only record what happened on the shop floor; it will also forecast what will happen next and suggest (or initiate automatically) the optimal action.

At Devox Software, MES modernization is a common request. Through years of modernization, we’ve figured out the most optimal way to modernize “from the inside out,” turning AI from a separate analytics initiative into part of the production workflow. This article is based on this practice and delves into the peculiarities of MES modernization use cases that make the most sense for updating MES in 2026.

What does MES mean?

The software layer that performs manufacturing activities every day is called the manufacturing execution system, MES. It serves in the middle between the Enterprise Resource Planning system and the shop floor.

In real life, MES makes schedules and sends work to lines, gives operators instructions, keeps track of WIP and genealogy (what materials went into what batches/serials), records quality checks and deviations, downtime and causes, and makes audit- ready production documents. MES is like a digital nervous system that keeps an eye on, keeps track of, records, and manages the entire manufacturing process.

Manufacturers are putting a lot of money into AI, and what used to be pilots is soon becoming the most important thing to do since “analyze later” costs too much when downtime and quality losses go up. In a modern factory, MES is the hub that brings together all data so that production, quality, maintenance, and compliance all have the same operational truth.

Traditional MES Systems vs. AI-Powered MES Systems

Traditional MES is mostly built on rules. It monitors events, ensures adherence to established workflows, and documents the outcomes. It can tell you “what happened” and “did we follow the process,” but as things change, it usually has to rely on pre-set logic, fixed thresholds, and human interpretation to figure out what to do.

An AI-driven manufacturing execution system, MES, is a totally different thing. It moves away from documentation and toward decision assistance and decision automation. Not only does it keep track of downtime, but it also predicts failures and suggests ways to avoid them.

We’ve prepared a brief comparison table below.

As you can see, the main difference is that AI-driven MES becomes closed-loop because it can perceive what’s going on, understand it, and suggest actions. That earned MES modernization a big score for speed, quality, and resilience.

Top 7 AI Use Cases in MES Modernization for 2026

As we’ve figured out above, an AI-enabled manufacturing execution system, MES, turns that execution data into real-time predictions and next-best actions on the shop floor. Let’s break down how businesses can leverage these features.

Predictive Maintenance

Most factories already have automation and real-time alert systems. The modernization leap here is putting AI predictions directly into MES execution. How does it work? When the risk of failure reaches a certain level, MES automatically creates a maintenance order, reserves parts, schedules a safe micro-stop, and updates the entire history for reliability learning.

Since unscheduled downtime conceals the highest hidden cost, MES modernization brings a positive return on investment (ROI). In 2026, this connection only becomes stronger. With edge inference (low latency), confidence scoring, and tight integration of MES and EAM/CMMS workflows, predictions don’t just track; they take action.

Quality Control

Quality control shouldn’t be an island by itself but integrated into the entire tech ecosystem of the enterprise. In a modern MES, vision models identify problems in real time, automatically tag genealogy/lot history, and send signals back to process control to change parameters, start a pause, or send a product back for rework. They also record every choice for traceability.

Moreover, the feedback loop is what makes the MES modernization valuable: quality signals turn into immediate execution logic instead of weekly reports. To get 2026-ready quality control, you need model monitoring for drift, multi-camera correlation, and a single defect taxonomy inside MES.

Real-Time Rescheduling

Classic MES scheduling often falls apart when things go wrong. For instance, when machines break down, urgent orders come in, materials are late, workers are missing, and so on. AI-driven scheduling uses constraint optimization and learning from the past, including setup times and changeover patterns, to enable replanning almost in real time.

This is a modernizing use case because older MES systems sometimes hardcode rules. Modern architectures, on the contrary, can take in events all the time and change schedules in minutes.

Furthermore, a future-proofness check should include more explainable recommendations that can be explained (like why the schedule changed), a scenario mode (on a what-if basis), and a possibility of human override that teaches the model instead of opposing it.

Anomaly Detection

AI models detect problems like temperature drift, vibration pattern, humidity, and operator step timing early and explain possible causes. Actually, MES is the best place to keep track of context, like orders, recipes, family trees, and reasons for downtime. This way, updating MES means combining event streams to simplify anomaly detection and take action on them.

What is manufacturing execution systems’ “2026-ready”? We recommend relying on causal analysis for the “most likely driver,” not merely correlation, and automatic compilation of deviation records and CAPA tasks when criteria are fulfilled.

AI-Powered Digital Twins

Digital twins typically stop working after they reach the “nice simulation” stage. The practical win in 2026 is to use AI to keep twins constantly calibrated and then perform what-if judgments inside the MES flow: “What happens to OEE, energy, and OTIF if we reroute orders to Line B?”

In particular, Microsoft is clearly putting “industrial AI” around digital threads and agents throughout the manufacturing stack. This is a step toward operations that are always active and focused on making decisions.

Moreover, a “2026-ready” twin is one you can trust because it’s based on real production occurrences and an MES UX that enables supervisors to quickly compare scenarios.

Generative AI Copilots

Generative AI copilots are the fastest-growing “human productivity” layer in MES system manufacturing execution system modernization. They assist with troubleshooting, rapid access to SOPs and changeover instructions, automatic shift handover summaries, and “explain this alarm in my context” across the machine, the batch, and the history. As an example, the Siemens–Microsoft partnership shows that GenAI helpers built for industrial work are becoming more common.

In this case, the 2026-ready layer includes rigorous permissions and audit logs for every recommendation used.

Automated Compliance

Traceability is critical for regulated and high-complexity manufacturing. This includes eBR/eDHR, lot genealogy, deviations, sign-offs, and audit answers. As a step forward, AI can cut down on compliance costs by automatically checking the completeness of records, finding “suspicious” gaps, making structured deviation reports, and writing narratives that are ready for auditors. However, humans will still be in charge of approving these changes.

This use case is even more useful when MES modernization works with ISA-95-style interfaces. This is because compliance evidence covers ERP planning, MES execution, and control systems. Moreover, embedding workflows that are built on exceptions and data models that are “compliance-by-design” instead of PDFs presents a solid preparation for 2026.

How to Prioritize in a 2026 MES Modernization Roadmap

Let’s synthesize the aforesaid enhancements into a comprehensive modernization workflow. These are stepping stones of the manufacturing execution system, MES, modernizartion to your desired results. Begin where MES modernization adds benefit over time:

1. combine event data and context (including orders, recipes, and equipment states),
2. make interfaces the same using ISA-95 patterns where they make sense,
3. create one closed-loop use case that affects execution (not just BI), and
4. apply the same pattern to all lines and plants.

If you want a simple sequence that works in most factories, then it is as follows:

1. Start with predictive maintenance and anomaly detection (quick ROI, uses existing signals),
2. Move to vision quality (big quality wins, needs data discipline),
3. Add scheduling optimization (highest leverage, hardest change management),
4. Implement copilots and compliance automation as multipliers across teams.

Rockwell’s 2025 findings show that this trend is continuing: quality, workforce restrictions, cybersecurity, and AI investment are all going up at the same time.

Wrapping Up

In 2026, modernizing the manufacturing execution system, MES, isn’t only about getting new screens or relocating MES to the cloud. The major change is that MES is no longer just a way to keep track of production; it’s now a way to improve production.

When updating MES, focus on use cases that complete the loop. That’s when AI stops being “analytics” and starts being things like throughput, uptime, and yield that can be measured. With Devox Software, you can always be sure about the results. We invest to keep modernization ROI as high as possible to deliver real changes for your business.