The manufacturing sector is currently navigating a massive transition from static dashboards to “Agentic AI”, systems that can perceive, reason, and act autonomously. However, as highlighted by industry leaders during the sessions and discussions at IIoT World Days 2025, AI is only as effective as the data feeding it.
Success in 2026 requires a diverse ecosystem of vendors working together. No single platform “does it all.” Instead, forward-thinking manufacturers are building “best-of-breed” stacks that combine connectivity, data management, analytics, and cybersecurity.
Based on expert insights from the 2025 editions of IIoT World Days, here are 27 platforms enabling the smart factory of the future.
I. Predictive & Prescriptive AI Platforms
These platforms utilize advanced algorithms to predict failures, optimize processes, and guide human workers.
| Platform | Core Strength | Key Industrial Application |
|---|---|---|
| Infinite Uptime | Reliability as a Service | Uses a “layered intelligent stack” to deliver physics-based anomaly detection. |
| Arch Systems | Legacy Retrofits | Extracts data from older machines to perform automated root cause analysis via GenAI. |
| Retrocausal | Manual Assembly Optimization | Uses computer vision to identify worker mistakes in real-time, reducing scrap. |
| GausML | “Small Data” Optimization | Optimizes machine parameters (like laser cutting) without needing massive data lakes. |
| Guidewheel | OEE Virtualization | Uses clip-on sensors to monitor legacy machine power/rate without deep PLC integration. |
| AI Dash | Grid Resilience | Combines satellite imagery and AI to predict power outages caused by vegetation. |
II. Data Infrastructure & Management (The Backbone)
Before Industrial AI can operate, data must be collected, contextualized, and stored. These platforms form the “plumbing” of the smart factory.
- Litmus: An industrial edge platform that connects to legacy PLCs (even from the 70s) to normalize data before it hits the cloud.
- HighByte: An Industrial DataOps solution that solves the “context gap” by converting cryptic PLC tags into standardized asset models.
- InfluxData (InfluxDB): A time-series database built to handle high-velocity sensor data for real-time monitoring.
- Snowflake: The “Industrial Data Cloud” that merges IT (ERP) and OT (Machine) data. Its Cortex AI allows users to query manuals using natural language.
- CrateDB: A real-time database capable of ingesting millions of data points instantly, perfect for smart grid load balancing.
- Cirrus Link: Provides the MQTT and Sparkplug infrastructure to create a Unified Namespace (UNS), the single source of truth for the enterprise.
- Databricks: Used for training custom AI models with full data lineage and governance—critical for industrial trust.
- Cumulocity (Software AG): Employs a “medallion architecture” to refine raw IoT signals into business-ready insights.
- Cybus: An IIoT middleware that acts as a “data sovereignty” layer, giving factories granular control over what data leaves the plant.
- Gantner Instruments: Specializes in high-frequency data acquisition (kHz level) for battery testing and grid monitoring.
- Adlib Software: Transforms “dark data” (PDFs, CAD drawings) into structured, AI-ready formats for LLM training.
III. Enterprise & Automation Systems
These platforms execute production and are increasingly embedding AI to move from execution to autonomous optimization.
- Plex (Rockwell Automation): Integrates MES and quality control with AI agents that correlate OEE drops with specific machine health metrics.
- Siemens (Digital Industries): Focuses on Industrial Grade AI, utilizing LLMs and knowledge graphs to ensure AI-generated PLC code is deterministic and reliable.
- Critical Manufacturing: An MES provider that uses data platforms to create a cohesive digital twin of the entire production line.
- Oracle Maintenance Cloud: Integrating “Agentic AI” to automatically generate work orders and surface supply chain anomalies.
- Inductive Automation (Ignition): The industry-standard SCADA/IIoT hub for real-time visualization and interacting with the Unified Namespace.
- AVEVA (PI System): The leader in historical data management, providing the decades-long baseline required for long-term predictive models.
IV. OT Security & Asset Visibility
Connecting the shop floor to the cloud increases the attack surface. These platforms ensure the data pipeline remains secure.
Expert Note: Security is no longer an “add-on”; it is a prerequisite for any AI initiative involving cloud connectivity.
- OPSWAT: Secures the “air gap” by using kiosks and data diodes to sanitize USB drives before they enter the OT environment.
- Armis: Provides total asset intelligence by passively monitoring the network to identify every connected device and its vulnerabilities.
- Fortinet: Delivers essential network segmentation to ensure that while data flows out for AI analysis, control systems remain protected.
- Keyfactor: Manages digital identities (PKI) for machines, preventing unauthorized devices from compromising data integrity.
Disclosure Note
Most of the companies listed above were official sponsors of IIoT World Days 2025. In other instances, the platforms were featured via expert speakers representing their respective organizations during panel discussions.
Building Your Industrial AI Stack: Frequently Asked Questions
Q1: How do I choose between a “Copilot” and “Agentic AI” for my factory?
A: The choice depends on the desired level of autonomy. A Copilot (like those from Siemens) is best for engineering tasks where a human must review code or documentation before execution. Agentic AI (featured in Oracle Maintenance or Plex) is better for high-velocity operational workflows, such as automatically adjusting a production schedule or ordering a spare part, where the system “reasons” through a problem and takes action within pre-set guardrails.
Q2: What is the fastest way to connect legacy (Brownfield) machines to an AI stack?
The most efficient approach identified at IIoT World Days 2025 is “Non-Invasive Connectivity.” Instead of a costly “rip-and-replace” of old controllers, platforms like Guidewheel use clip-on sensors to monitor power draw, while Litmus and Arch Systems use protocol converters to “speak” to 1980s-era PLCs. This allows you to extract data without risking the uptime of the original machine.
Q3: How can I implement AI if I don’t have “Big Data”?
A: You should look for “Small Data” AI solutions like GausML. These platforms are specifically designed for manufacturing processes—such as injection molding or laser cutting—where you may only have a few dozen high-quality data points rather than millions. They use physics-based models to optimize machine parameters rapidly without requiring years of historical logs.
Q4: How does “Industrial DataOps” solve the problem of “Garbage In, Garbage Out”?
A: Industrial DataOps (e.g., HighByte) provides the “context” that AI models require. Raw data from a PLC might look like Tag_101 = 45.5. A DataOps platform cleans and labels this so the AI knows it means Boiler_3_Temperature = 45.5°C. Without this contextualization at the edge, AI models cannot accurately reason or plan.
Q5: What is “Agentic AI” and how does it differ from traditional Industrial AI?
A: Agentic AI represents a shift from reactive dashboards to autonomous digital “agents” that can perceive, reason, and act. While traditional AI predicts a failure (Predictive Maintenance), Agentic AI can independently identify the root cause, check inventory for parts via the ERP, and automatically generate a work order in the Maintenance Cloud. It acts as a “digital co-worker” rather than just a visualization tool.
Q6: What role does “Dark Data” play in training Industrial LLMs?
A: Up to 80% of industrial knowledge is trapped in “dark data”—PDF manuals, CAD drawings, and maintenance notes. Platforms like Adlib Software use AI to structure this unstructured data, allowing Large Language Models (LLMs) to answer technical questions and assist operators with high accuracy, a process known as Retrieval-Augmented Generation (RAG).
Author: Carolina Rudinschi, based on the insights shared at IIoT World Days 2025.
