by Gary Mintchell | Mar 19, 2026 | Business, News, Software
I thought something like this would happen when asset management firm TPG scarfed up some castoff small software divisions of larger companies. They’ve brought GE Vernova’s former Proficy business and PTC’s former Kepware and ThingWorx businesses together into a new company.
From the press release (which must mention AI to meet today’s standards), Velotic will provide new levels of AI-driven manufacturing efficiency, productivity, and data visibility.
The company will be led by Brian Shepherd (CEO) and James Heppelmann (Executive Chairman). Shepherd was formerly at Rockwell Automation and PTC. Heppelmann led PTC.
My observation is that this company will have a tough go competing against Inductive Automation (yes, they sponsor me, but don’t tell me what to write, and I like their continual innovation). Then there are established companies such as AVEVA (Wonderware, etc.) and Rockwell Automation (FactoryTalk etc.).
This will be interesting to watch.
Velotic today announced its launch as a leading independent industrial software company, uniting multiple trusted platforms to advance a new era for industrial and manufacturing technology. The formation of Velotic coincides with the closings of TPG’s previously announced acquisitions of Proficy, the former manufacturing software business of GE Vernova, and PTC’s former industrial connectivity and Internet of Things (IoT) businesses. Backed by TPG, Velotic delivers a leading suite of data-driven solutions focused on improving processes by unlocking efficiency, enhancing productivity, and providing visibility across complex data and industrial operations.
The obligatory marketing justification geared not to you, the prospect or user, but to market analysts.
Velotic is purpose-built to meet the rapidly evolving productivity and data needs of manufacturing operators across the globe with a focus on creating next-generation, AI-powered industrial and manufacturing software solutions. By bringing together Proficy’s automation and production management expertise with Kepware’s industrial connectivity leadership and ThingWorx’s best-in-class industrial data and analytics applications, Velotic will provide customers with greater visibility, unparalleled insight, and the robust data and AI capabilities needed to produce and compete in today’s complex manufacturing environment.
Status.
Based in the Boston area, Velotic has more than $300 million of revenue and serves customers across manufacturing, oil & gas, utilities, and infrastructure. Proficy, Kepware, and ThingWorx will remain as distinct product lines within the broader Velotic portfolio, now operating under one mission and platform.
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by Gary Mintchell | Mar 9, 2026 | Generative AI, Robots, Software
I walked into my local Starbucks this morning for my usual Doppio Espresso with cinnamon powder. I told my barista I was about to listen to a press conference on “physical AI.” “What do you think that is?” I asked her. “I don’t know. Maybe something like robots?” she countered. She saved me doing a deep dive with my buddy Claude.
The press conference was with ABB Robotics and NVIDIA announcing an expansion (for a fee) of ABB’s RobotStudio software to incorporate AI models establishing a new product called RobotStudio HyperReality coming to a computer near you in a few months.
- ABB Robotics integrates NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy
- New RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly and reliably manufacturers can scale production, reducing costs by up to 40% and accelerating time-to-market by 50%
- Full range and breadth of industrial applications, with real-world pilot being conducted by Foxconn in consumer electronics assembly
- At NVIDIA GTC, the robotic workforce company WORKR will showcase how it’s using the solution to help manufacturers across the U.S. address critical labor shortages
The collaboration focuses on combining ABB Robotics’ software programming, design and simulation suite, RobotStudio, with the physically accurate simulation power of NVIDIA Omniverse libraries to close technology’s long-standing ‘sim-to-real’ gap. Developers can simulate robots in digital twins and generate synthetic data to train their physical AI models, enabling businesses of all types and sizes to deploy AI-driven robotics for various industrial workflows.
Called RobotStudio HyperReality, the resulting physically accurate simulations and foundation models are endlessly optimized with real-world data feedback continuously improving the system. These models can be used to train any number of ABB robots, anywhere in the world, with the reliability and accuracy demanded by industry.
The long-standing deficit between simulation accuracy and real-world lighting, materials and environments is known as the ‘sim-to-real’ gap. For decades, this gap has limited the ability of manufacturers to design and develop advanced manufacturing processes in the virtual world.
By integrating NVIDIA Omniverse libraries into RobotStudio, ABB Robotics will deliver unprecedented robotics simulation and synthetic data generation capabilities that will allow intelligent robots to bridge this gap with up to 99 percent accuracy. ABB is the only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near perfect correlation between simulation and real world performance. Combined with ABB Robotics’ Absolute Accuracy technology, which reduces positioning errors from 8–15 mm to around 0.5 mm, ABB delivers unmatched precision in both virtual and physical environments, making it suited to high-precision industrial-grade applications.
ABB Robotics is also assessing the potential to integrate the NVIDIA Jetson edge computing plat-form into its Omnicore controller to achieve real-time AI inference at the edge for its extensive robot portfolio. Today’s announcement builds upon ABB Robotics’ long-standing work with NVIDIA, including the previous integration of NVIDIA Jetson into ABB Robotics’ VSLAM autonomous mobile robots as well as the development of gigawatt-scale AI data centers.
RobotStudio HyperReality will serve industrial clients at any scale, across a breadth of industries and applications, with select customers already testing its capabilities ahead of a full release to ABB Robotics’ 60,000 RobotStudio customers worldwide in the second half of 2026.
Foxconn, the world’s largest electronics contract manufacturer, is piloting the first joint use case in consumer electronics assembly. Automating the assembly of a tiny piece in consumer electronics is challenging, as multiple device variants require different production methods and the delicate metal structure requires precise pick-and-place and assembly control, as well as fine-tuned setup, often demanding additional debugging time and engineering resources. Using RobotStudio HyperReality, Foxconn’s assembly robots are trained virtually, using synthetic data to perfect multiple real-world production processes in various scenarios, before moving them to the production line with 99 percent accuracy. By optimizing production lines virtually, Foxconn will reduce set-up times and costs by eliminating physical training and tests, and accelerate time-to-market for consumer electronics.
WORKR, a California based robotic workforce company that delivers robotic manufacturing solu-tions to industry, is extending the reach of this technology to small and medium manufacturers across the United States. At NVIDIA GTC 2026 (March 16-19, San Jose, CA), WORKR will demonstrate AI- powered robotic systems built on ABB technology, trained with synthetic data using NVIDIA Omniverse libraries, and deployed without operators needing to know any program-ming. By combining ABB’s industrial grade robotics with its proprietary WorkrCore™ AI platform, the company is helping manufacturers address critical labor shortages with its robotic workforce that can learn new tasks in minutes and be operated by anyone.
by Gary Mintchell | Feb 19, 2026 | Productivity, Software, Technology
I reflected recently on the changers in programming since my first experiences around 1977.
Everything back then was text based. You typed everything line-by-line. I started with BASIC and assembler. And also RPG on an IBM minicomputer. Went to C and C++ and then picked up Java in the early 90s.
Then I discovered integrated development environments (IDE), such as eclipse for Java. Then the IDE for C#. At that point, I was thinking, “this isn’t programming. There’s so much built in that you don’t even have to type.”
I try to forget the horrible experience of Ladder Diagram on a PLC.
(Oh, I should note that I was never a professional programmer. Fortunately, I had other roles.)
Lately, automation suppliers have been adding CoPilot to their programming interfaces.
Why this reflection on migration? I’ve been reading mass media and social media angst about the end of programmers with things like Vibe Coding and Claude Code.
Programming automation has been a constant for decades. They all served to make programmers better and faster and better able to tackle tougher problems.
Even with AI, someone must have the ideas of what needs to be developed, do the thinking about approaching the problem, and make the decisions for the best application.
We’re only going to see better applications solving harder problems. Those who lose their jobs will be those who cannot adapt.
New people? They will just think it’s the only way.
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by Gary Mintchell | Feb 9, 2026 | Automation, Process Control, Software, Standards
The Open Process Automation Forum has been building a standard of standards to promote open and interoperable technology for process automation. PLCOpen has been at the forefront of international standards promulgation as the organization behind IEC 61131. This latter organization has instituted a Working Group to create IEC61131 process automation standard and certifications for application engineers to efficiently deploy PLC, DCS, and open platform controls in process industry applications.
I’ve been following and promoting open and interoperability for decades. This should be a useful step forward.
Bill Lydon sent this explanation of the background and current status of programming standards.
The cost of programming process automation and control continues to grow and is a significant part of project costs. Each supplier having unique function blocks that do not follow a single worldwide standard increases training, application development costs, and project profit risk. PLCopen standardization and modular methodology lowers training time, project development costs, and lowers project cost overruns risk.
This further expands the base of PLCopen standards that enable No-Code/Low-Code industrial automation programming across vendor platforms including industrial computers. This will include incorporation of the function blocks defined in the O-PAS standard into a new PLCopen standard.
The new PLCopen Process Functions standards and certification make it easier for application engineers to deploy PLC, DCS, and open platform controls in process applications.
Working Group Goal
The PLCopen Process Industry Working Group goal is accelerating the convergence of discrete and process control & automation into harmonized PLC, DCS and open platform system architectures to achieve industrial business digitalization.
Today there are a diverse number of ways to program applications for process control and automation. The goal is to develop PLCopen function block standards for process control functions. Function Blocks are encapsulations of variables, parameters and their processing algorithms. Similar standardization has been done with PLCopen standards developed for motion control, safety, fluid power, XML Program Interchange, and OPC UA.
He notes process control applications being done using PLCs. I actually sold a PLC to a chemical plant engineer, who used it to control one of his processes. That was in 1995. So, while unusual, not unheard of.
Today many process control applications are being done using PLCs (Programmable Logic Controllers) since the capabilities of these devices is far beyond original 1970s relay replacement applications. The emerging use of industrial edge computers with IEC 611 31 runtime software engines is another segment that benefits from the results of the PLCopen Process Industry Working Group.
PLCopen Background
PLCopen has been successful defining IEC 61131 functions and certifications used widely throughout industry worldwide increasing engineering efficiency, quality and empowering a wider number of people in motion control, fluid power, safety, and other functions. The standards define common inputs outputs and behaviors with vendor certifying conformance to accomplish the functions or additional features.
PLCopen Standards
- Logic – The PLCopen basis is provided by the world wide standard IEC 61131, and especially Part 3 – Programming Languages.
- Motion Control – Creating reusable, hardware independent Motion Control applications via IEC 61131-3 and PLCopen Function Blocks including Fluid Power.
- Safety -PLCopen Safety integrates safety functionality into the IEC 61131-3 development environments. Meets IEC 61508 & related standards.
- Communication – PLCopen and OPC Foundation combine their technologies to a platform and manufacturer-independent information and communication architecture.
- XML Exchange – PLCopen added independent XML schemes to IEC 61131-3
Movements including Industry 4.0, Industrial Internet of Things, The Open Process Automation Forum, and Smart Manufacturing are creating a drive for more standards. IEC 61131-3 along with PLCopen extensions and certifications are well established in discrete and hybrid applications and with the addition of OPC Function blocks is already part of the newer Industry 4.0 and Industrial Internet of Things offerings.
Working Group
As part of our ongoing efforts to drive standardization and interoperability in industrial automation PLCopen will start a new workgroup exploring the incorporation of the function blocks we have developed for the O-PAS standard into a new PLCopen standard.
The O-PAS (Open Process Automation Standard) is an open, interoperable, and vendor-neutral standard developed by the Open Process Automation Forum (OPAF) to enable flexible and modular process automation systems. It is designed to replace traditional, proprietary DCS’ with a standards-based, plug-and-play architecture, allowing components from different vendors to work seamlessly together. O-PAS is based on existing industry standards, such as (among others) IEC 61131 & IEC 61499.
Part 6.4 of the O-PAS defines a set of standard function blocks to ensure interoperability, consistency, and comparability across different process automation systems. These FBs provide a reference model with standardized inputs, outputs, and behaviors. By establishing a uniform function block framework, part 6.4 supports modular automation, making it easier to adopt open, vendor-independent control solutions. PLCopen helped creating several pre-defined function blocks for part 6.4 of the O-PAS standard.
In order to standardizing these function blocks within PLCopen we are starting a new workgroup to create a new PLCopen standard for the process automation.
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by Gary Mintchell | Feb 4, 2026 | Generative AI, News, Process Control, Security, Software
This news came last week. Just as I was contemplating the business model of cybersecurity firms following another acquisition, this news of a new company launch with a unique take on security. This company will be interesting to watch. The news comes from Amsterdam concerning the launch of a company called Indurex. Naturally they have AI in their product offering and manage to work in an older term—cyber-physical systems.
The quick take: An AI-powered, human-in-the-loop platform that brings together process safety and cybersecurity, turning complex signals into trusted decisions for resilient critical infrastructure.
Indurex, a pioneering artificial intelligence (AI) and cyber-physical systems (CPS) security company, announced on January 27 its official launch to help protect critical infrastructure, smart manufacturing, and connected industrial operations. The company’s mission is to deliver robust, adaptive security solutions that safeguard both the physical and digital worlds as they increasingly converge.
Founded by a team of seasoned experts in operational technology (OT), cybersecurity, and process safety systems, Indurex enters the market at a decisive time. Operators across energy, utilities, and manufacturing sectors face mounting challenges from IT-OT convergence, cyber sabotage, and cascading system failures — putting both process safety and cybersecurity integrity under increasing pressure and exposing essential assets to unprecedented risk. Traditional tools, designed for isolated IT networks or legacy control systems, can no longer assure the level of operational, safety, and cyber integrity required in today’s highly connected industrial environments.
Industrial organisations continue to face a critical gap between process safety and cybersecurity, which are managed in disconnected silos. Existing tools generate high volumes of alerts without sufficient industrial or engineering context, leading to alert fatigue and a limited ability to assess real operational and safety impact. At the same time, a new class of AI-enabled and cyber-physical threats is emerging — capable of exploiting process behaviour, safety dependencies, and human workflows. Detecting and stopping these threats requires AI-native technologies designed for industrial systems, combined with human-in-the-loop intelligence to ensure explainability, trust, and effective decision-making.
Indurex bridges this gap with an AI-native, interoperable platform that unifies engineering context and cybersecurity intelligence — an approach the company defines as Engineering Cyber Intelligence.
This delivers measurable returns across three dimensions:
- Operational Excellence & Safety Integrity: Fewer trips and faster recovery through unified situational awareness and continuous assurance of Safety Integrity Functions (SIF)
- Cyber Resilience: Contextualized detection and response across digital and physical domains, aligned with operational and safety impact
- Cost & Compliance: Automated reporting and defensible evidence of risk, control maturity, and safety integrity across critical systems
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by Gary Mintchell | Jan 30, 2026 | Generative AI, Manufacturing IT, Software
I touched on this concept reporting from the Ignition Community Conference last September. It’s where I was sitting beside this excitable “influencer” who was overjoyed at the announcement from Inductive Automation that MCP was coming to Ignition sometime in 2026 and darn near put a big bruise on my thigh hitting me in his excitement.
This blog post on the Inductive Automation website, What Is MCP? Understanding the Model Context Protocol, explains MCP for Ignition coming this year.
Our company is working on an MCP Module for Ignition that will be released later in 2026. MCP is a very new technology on the scene, so you shouldn’t feel bad if you’re asking yourself, ‘Cool, but what exactly is MCP?’ In this blog post, we’ll give you a quick overview of what MCP does so you can start thinking of exciting ways to use the new module once it’s released.
As AI continues to evolve, one of the biggest limitations holding it back from widespread real-world adoption is its isolation. Large language models (LLMs) are powerful, but they are typically trained on a fixed dataset and are unable to access or act on real-time information.
The Model Context Protocol (MCP) breaks down that barrier. Introduced by Anthropic in November 2024 as an open standard protocol, MCP creates a standardized two-way communication bridge between AI systems and external tools, applications, and data sources. It extends LLMs with the ability to interact with enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, databases, APIs, and external developer tools. You can think of it as a universal plug that allows LLMs to connect seamlessly with information outside of their training data.
Traditional LLMs are limited in two critical ways: they are static and isolated. This means that once an LLM is trained, its knowledge is frozen in time, and it cannot access external tools or databases unless you build custom integrations. MCP solves both of these problems by turning LLMs into dynamic agents. Through MCP, AI systems can query real-time data, update records, and trigger workflows.
For example, an enterprise assistant built with MCP could answer questions about project timelines, check your Google Calendar, update a ticketing system, query metrics, update internal systems, book events, or send an email within the same conversation. In creative fields, MCP-enabled AIs could write code and deploy it to production environments or generate 3D designs and send them directly to a printer.
Simply put, MCP increases LLM utility and automation by enabling it to perform a wide range of actions that would be impossible without extensive custom engineering.
One of the most important advantages of MCP is that it significantly reduces the hallucinations or inaccuracies that LLMs often generate by allowing models to access authoritative, real-time sources like your databases and APIs. This ensures that your LLMs’ outputs are more grounded in reality rather than relying on probabilistic text generation.
Additionally, unlike proprietary integrations that lock AI applications into a specific tool or vendor ecosystem, MCP is an open standard, which enables developers to share pre-built MCP server frameworks. This allows AI systems to evolve over time, and provides the critical benefit of solving the N x M problem of integration, where N (AI models) and M (tools) require N x M number of custom connectors. MCP provides a consistent grammar and communication protocol, standardizing the interface and allowing a single tool to be shared across models via a plug-and-play architecture. This makes it easier to reuse components, accelerates development, and fosters open collaboration across vendors and platforms without rewriting application logic, positioning MCP as foundational infrastructure rather than a short-lived integration layer.
MCP uses a client-server architecture. The AI application acts as the MCP host, while MCP clients serve as bridges to external systems and tools. These clients handle session management, parsing, reconnection, and translation of user requests into MCP’s structured format. Each MCP client communicates with a unique MCP server, which connects to external databases, APIs, and web services, enabling it to execute tool functions, fetch data, or provide prompts.
MCP servers expose three core primitives: resources, tools, and prompts. Resources provide read-only access to data sources like databases or files; tools perform actions, such as making API calls or triggering workflows; and prompts are reusable templates that set the structure for how the LLM communicates with tools and data. MCP uses these primitives as structured, declarative interfaces rather than allowing the LLM to issue arbitrary API calls. This streamlines the AI by shielding it from low-level system complexity, ensuring that it invokes well-defined actions with clearly scoped inputs and outputs.
MCP can be deployed in many ways to align with the needs of different environments and industries:
- Local servers for privacy-sensitive and high-speed offline tasks
- Remote servers for cloud-based, shared services
- Managed servers for scalability and operational simplicity
- Self-hosted servers for compliance, control, on-premise, or legacy environments
Using AI with MCP is very simple from the user’s perspective. You prompt your LLM as you normally would, and the MCP-connected system handles the rest. For example, if you ask, “Build me a report,” the AI host initiates a tool discovery process by querying the MCP server. It retrieves a list of available tools, selects the appropriate one, and calls the function with the necessary parameters.
If your system needs a real-time update, such as a tool becoming unavailable, the MCP server can push a notification to the client without waiting for a new prompt. Once the tool completes its task, MCP integrates the results into the AI’s response or uses them to trigger the next action in a multi-step workflow.
This orchestration model makes MCP ideal for building advanced AI agents capable of reasoning with live data, executing actions across systems, and adapting dynamically as tools and environments change.
MCP represents a foundational shift in how AI connects to systems. It transforms LLMs from static knowledge engines into intelligent, action-capable systems. As adoption grows, MCP is poised to become a core part of modern software infrastructure, powering a new generation of agentic and adaptive AI applications.
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