Enterprise manufacturing intelligence (EMI) is software that pulls data from machines, MES, ERP, quality and maintenance systems across every plant, puts it in context, and turns it into decisions people can act on. In 2026 the definition has stretched: EMI no longer stops at dashboards, it increasingly includes AI models and agents that recommend or take actions under human approval.
Updated October 2026
What changed in 2026
- Agents moved onto the shop floor agenda. At Hannover Messe 2026, Siemens presented the Eigen Engineering Agent for TIA Portal, tested with more than 100 companies, and Schneider Electric showed its Industrial Copilot built with Microsoft Azure AI.
- Copilots became agents. Siemens added agents that run whole tasks to its Industrial Copilot family in May 2025, across design, engineering, operations and maintenance.
- Partners built agents on hyperscaler stacks. Microsoft showcased Aras's Variant BOM Agent and Resilinc supply chain agents at the show.
- Real-time analytics and twins joined the data platform. Fabric Real-Time Intelligence went GA in Nov 2024, and Microsoft previewed a digital twin builder inside it (see Azure Digital Twins vs Fabric).
- Manufacturers are investing, but adoption is uneven. In Deloitte's 2025 survey of 600 executives (fieldwork Aug–Sep 2024), 29% used AI/ML at facility or network scale and 24% used generative AI.
What EMI is, and what it is not
EMI sits on top of the systems that run a plant. It does not replace them.
| System | Main job | Scope |
|---|---|---|
| SCADA / historian | Control and record machine data | One line or site |
| MES | Execute and track production orders | One site |
| ERP | Plan, buy, ship, account | Company |
| EMI | Combine all of the above, add context, compare and decide | Many sites, the whole enterprise |
The "enterprise" part matters. A single-plant dashboard is reporting. EMI compares lines, sites and suppliers on the same definitions, so a plant manager in one country and a COO at head office see the same OEE number.
The EMI data stack in 2026
Modern EMI is built in layers. Each one has well-known options.
- Connect. Read PLCs, historians and MES through edge connectors (for example Litmus, Kepware, Siemens Industrial Edge, Azure IoT Operations).
- Contextualise. Map raw tags to assets, products and orders, often through a unified namespace on MQTT with Sparkplug.
- Store and model. Land data in a lakehouse or real-time store. Common choices are Fabric Real-Time Intelligence, Databricks (which launched a manufacturing lakehouse in April 2023) and Snowflake (whose Manufacturing Data Cloud also launched in April 2023).
- Analyse. OEE, yield, energy, scrap, schedule adherence, predictive models.
- Decide and act. Dashboards, alerts, and now AI agents that draft work orders, suggest setpoint changes or answer questions in plain language.
Use cases that pay back
The old version of this post listed eight use cases. They still apply. Here is what each looks like in 2026.
| Use case | What EMI does | Where AI fits now |
|---|---|---|
| Production monitoring | Live OEE and downtime by line, site, shift | Agent explains the top loss and drafts the shift report |
| Predictive maintenance | Combines vibration, temperature and work-order history | Model predicts failure; agent schedules the job in the CMMS |
| Quality | Links defects to machine settings and materials | Vision models inspect at the edge; root-cause suggestions |
| Supply chain | Joins supplier, inventory and production data | Supply-risk agents flag shortages early |
| Energy | Tracks energy per unit by asset | Optimises run schedules against tariffs |
| Planning and scheduling | Feeds real capacity into schedules | Re-plans when a line goes down |
| Process optimisation | Finds the settings behind the best runs | Recommends setpoints for operator approval |
| Training | Captures expert knowledge in models and procedures | Copilots answer "how do I" questions at the machine |
Deloitte respondents reported up to 20% gains in production output and employee productivity and up to 15% unlocked capacity from smart manufacturing programmes (Deloitte, 2025). Treat these as self-reported ranges, not a forecast for your plant.
Why edge matters for EMI
EMI used to be a cloud or head-office project. Three things pull part of it to the edge:
- Speed. Quality and safety decisions need answers in milliseconds, at the line.
- Resilience. A plant must keep running when the WAN drops.
- Data volume. Video and high-rate vibration data are too large to ship raw.
The practical split: run inference and time-critical rules at each site (see edge data management), and run cross-site comparison, model training and planning centrally.
Governance: the step most EMI projects skip
Once AI can recommend or take actions, you need rules for it.
- Shared definitions. One formula for OEE, scrap and downtime across sites.
- Human approval. Decide which actions an agent may take alone and which need sign-off.
- Audit trail. Log the data, model version and reasoning behind each automated decision.
- Change control. Treat model and agent updates like PLC changes: tested, staged and reversible.
How to start
- Pick one KPI that leadership already cares about, such as OEE or scrap rate.
- Agree its definition across two or three sites.
- Connect the data for those sites and publish one trusted dashboard.
- Add one predictive model or agent to that KPI, with a human in the loop.
- Measure against a baseline, then scale to more sites.
Where it's heading: 2026 → 2030
| Period | Enterprise manufacturing intelligence |
|---|---|
| Around 2020 | Plant dashboards on historians and MES; cross-site comparison done in spreadsheets |
| 2026 (now) | Lakehouse and real-time platforms; UNS at leading sites; copilots and first agents shown at Hannover Messe 2026; 29% use AI/ML at scale (Deloitte) |
| By 2028 (expected) | Agent marketplaces expected to mature (Siemens has announced one for Xcelerator, ARC); digital twins expected to become a standard EMI layer |
| By 2030 (expected) | Agents, part of a wider shift to physical AI, expected to handle routine scheduling and maintenance decisions within approved limits, with humans owning exceptions |
How NexaStack helps
NexaStack deploys, updates and monitors AI models and agents across plants and edge sites from one place. It runs inference next to the line, keeps model versions consistent across sites, and records every automated decision so EMI actions stay governed and auditable.
Frequently asked questions
What is MI in production?
MI stands for manufacturing intelligence. It is the use of production data from machines, MES and ERP to measure performance and improve decisions. EMI applies it across an enterprise.
What are some examples of intelligent manufacturing?
Predictive maintenance on critical assets, vision-based quality inspection, real-time OEE across plants, energy optimisation and AI agents that re-plan schedules when a line goes down.
What does enterprise intelligence mean?
It means combining data from across a company into shared, trusted metrics so leaders can compare and decide. In manufacturing, that means plants, suppliers and products on one set of definitions.
What is the difference between EMI and MES?
MES runs and tracks production at one site. EMI pulls data from MES and other systems across many sites to analyse, compare and guide decisions.
What is an intelligent enterprise?
An organisation that uses connected data and AI to sense what is happening, decide quickly and act, with clear rules on what is automated and what stays with people.
Put one manufacturing KPI on AI
Pick one KPI such as OEE or scrap, and we will show how NexaStack runs governed models and agents against it across your sites. Book a 30-minute manufacturing AI pilot call →
