“This is my eighth ICC, and this is by far the best.” I asked a customer this morning how he was enjoying this year’s edition of the annual Inductive Automation Ignition Community Conference.
I concur completely (although I’ve attended more than eight). The Harris Center in Folsom had long since proved to be too small to house the community’s growth. Executives agreed to move the venue to the SAFE Credit Union Convention Center in Sacramento. They planned for doubling the size from 800 to 1,600, but the organizers told me that doing this the first time encompassed so many unknowns that they couldn’t breathe easily until half-way through the first day. This one reminded me of the automation company user groups I once attended.
I’m writing this in the hallway. The energy from conversations certainly keeps my energy up.
Part of the attraction this year emanates from the ability to house more breakout sessions. New also this year are Walker Reynold’s Prove It! Sessions. I sat in one with HighByte where the task is to prove to the audience that their solution really works.
Colby Clegg and Carl Gould ICC 2025
It helps when company executives have something good to say. And CEO Colby Clegg and CTO Carl Gould certainly rocked the conference with the huge advances in the latest Ignition Release—8.3.
The original vision I heard some 23 years ago focused on building an HMI/SCADA using IT-friendly technology from the ground up. Oh, and coming from a background as an integrator for another software company, the second focus concerned the pricing model designed to make the software more affordable and pricing more transparent.
Reading through just the bullet points I have below shows how far “IT-friendly” has come. The foundation for Ignition ties even more deeply into technologies familiar to all IT developers. With the hit of the show saved for previews of coming attractions when Colby and Carl announced coming in a few months—Model Context Protocol (MCP), thought of like an API for Agentic AI. MCP is so new and powerful that Inductive Automation may be beating IT developers to the game.
Inductive Automation Releases Ignition 8.3
Ignition 8.3 is such a comprehensive update that I’m surprised that it isn’t 9.0. Inductive says it provides tools for building solutions in SCADA, IIoT, MES, HMI, and more. It’s so much more that if I were an analyst paid $50,000 to do things like this, I’d give the category a new name.
This update (actually a significant technology foundation update) to Ignition 8.3 delivers major advancements in data processing efficiency, security, management, and development speed in order to elevate operational technology (OT) to modern IT standards and meet the speed and scalability needs of modern enterprises. These updates continue in the vision of the founder I first heard 22 years ago about developing OT software with the latest IT-friendly technology.
“Ignition has disrupted the industry and defined a new paradigm in industrial enterprise integration. With Version 8.3, we have completed our long-standing vision to create the world’s most powerful, most open, and most flexible application development platform,” says Colby Clegg, CEO of Inductive Automation and co-creator of Ignition.
Key Ignition 8.3 features include:
The new Industrial Historian Solution Suite, which includes the Historian Core Module, the SQL Historian Module, and a new Historian API for custom historian implementations.
The new Event Streams Module, which enables mapping and directing of event-driven data by creating a communication pipeline between various sources (such as tag changes or Kafka topics) and handlers (like scripts or database tables).
Perspective Module improvements, including a new Drawing Editor with native vector illustration tools, a form generator, and an offline mode for data entry and storage.
The redesigned Ignition Gateway, featuring a faster, more powerful web interface, integrated search capabilities, enhanced customization and visual organization, and consolidated configuration and diagnostic tools.
Next-level security that aligns with modern IT standards, including a new Secrets Management system, and Google Protobuf for faster, more secure communication between clients and gateways.
Long-Term Support (LTS), receiving regular updates and enhancements through the next five years.
Ignition 8.3 also features enhanced store-and-forward capabilities, improved enterprise deployment management, built-in REST API, new Gateway deployment mode, version control and collaboration with Git, simplified containerization, and much more.
Ignition Solution Suites, which include collections of Ignition modules and optional support plans that provide Upgrade Protection. Ignition Solution Suites simplify the Ignition purchasing process so that users can buy and deploy the precise solution they need more quickly. Five Solution Suites are currently available, and they align Ignition’s capabilities with common industrial use cases: The Application Building Suite, The Industrial Historian Suite, The DataOps Operations Suite, The Alarm Management Suite, and The Enterprise Integration Suite.
Every year the company recognizes significant and unique applications developed with Ignition. This year the company also recognized outstanding efforts in educating students about industrial automation with the Educational Engagement Firebrand Award, and honored contributions to the Ignition user community with the Community Impact Firebrand Award.
2025 Ignition Firebrand Award Winners:
Concera (End user: Sibanye-Stillwater)
Insight Engineering (End user: Haymes Paint)
SAGE Group (End user: Sydney Airport Corporation)
ASE Global
National Renewable Energy Lab
2025 Community Impact Firebrand Award Winner: Nick Minchin, Senior System Engineer at SAGE Automation, who dedicated much time to answering questions and helping people on the Ignition Forums.
2025 Educational Engagement Firebrand Award Winner: HebronSoft / Hebron IT Academy. These students from the Ukraine developed a process of custom fabricating prosthetic hands for soldiers who lost limbs in the war.
This news concerns unstructured data management. I wrote about Datadobi several times in 2021 and 2022. Not much since. They have released a new version of their StorageMAP, its “heterogeneous unstructured data management solution.”
StorageMAP 7.3 enables organizations to create policy-driven workflows, act on data more precisely, and migrate between S3-compatible platforms while maintaining compliance.
StorageMAP 7.3 introduces policy-driven workflows that allow administrators to define tasks executed by its workflow engine in response to specific triggers, such as a time schedule. A “dry run” feature facilitates reviewing the scope of a policy before full execution.
These new workflows support a wide range of use cases, including periodic automated archival, creating data pipelines to feed GenAI applications, identifying and relocating non-business-related data to a quarantine area, and more. Once policies are published, StorageMAP runs the workflows on schedule without requiring manual supervision.
In addition, StorageMAP 7.3 adds support for granular file-level deletes. Administrators can identify files that match specific criteria and save them as input to a targeted delete job, which StorageMAP will execute. Each delete job generates a report that documents the job’s details and outcome.
This functionality addresses situations where a coarse-grained directory-level deletion is not possible due to the presence of both relevant and disposable data. By enabling precise file selection, StorageMAP ensures that administrators can apply accurate and effective deletion policies.
Object migration enhancements
StorageMAP 7.3 also enhances its core object migration functionality by supporting the migration of locked objects between S3-compatible storage systems. This allows compliant data stored in a Write Once Read Many (WORM) format to be relocated across different vendor platforms while retaining its retention date and legal holds.
To support cost and performance objectives, the solution includes the ability to select the S3 storage class during object migration or replication. By specifying the desired storage class at the time of the job, organizations can avoid unnecessary post-migration lifecycle policies and ensure data is written directly to the appropriate tier.
I recently wrote an article for my website about technology complexity within industrial technology. Engineering managers have stood at conferences pleading with the standards and technology developers to find ways to simplify interfaces and connectivity.
OPC Foundation keeps adding layers of companion specifications. ODVA members listened to engineers who need help implementing EtherNet/IP (or just ethernet networks) and proceeded to ignore the plea. Paul Miller, an analyst at Forrester reported from a survey where 90% of executives reported data problems from their digital transformation. 71% reported measurement related data problems.
Mattias Stenberg, head of the new software company spinning out from Hexagon called Octave, reported from another survey his group has performed that only one in five executives thought they were getting any value from digital transformation.
The Vice President of Product Development of the company where I worked in the 1970s (back in the day before layers of vice presidents) offered a job to me to leave manufacturing and become his data manager. He was prescient. 45 years later, companies are still trying to manage data. Solutions have become more complex, technology has advance exponentially, yet we still have problems gathering, refining, contextualizing, and using data.
These thoughts were generated from the Hexagon Live Global Conference I attended this week in Las Vegas. I have a lot of trouble wrapping my head around just who Hexagon is. Evidently, I’m not alone. But the company is making it easier by splitting off four groups into Octave.
The simplest definition, yet also most definitive, came from Ola Rollén Hexagon Chairman recounting the company’s 25-year history of growth. “Hexagon is the world’s most sophisticated measuring tape.” Indeed, several of my interviews delved into the world of accurately measuring the very large and the very small. This year’s slogan, “When it has to be done right.”
The new ATS800 laser tracker can easily capture complex shapes with up to micron precision. The company released Autonomous Metrology Suite, software developed on its cloud-based Nexus platform that is designed to transform quality control across manufacturing industries worldwide. By removing all coding from coordinate measuring machine (CMM) workflows, it helps manufacturers speed up critical R&D and manufacturing processes as experienced metrologists become harder to find.
Hexagon and several partners are solving what has been an intractable and troubling problem—data locked into paper-based formats such as pdf files. Several demonstrated the ability to read text and pdf documents that are unstructured data, use a form of AI to tag the data, and then extract to a useable database. This is truly a great advance. Several workforce solutions designed to give companies the ability to attract younger workers into technical positions were demonstrated on the show floor.
Stenberg talked of another problem executives cited—data silos that prevent people from using data to make good decisions. I have been writing about solutions designed to break through data silos for 25 years. I’m beginning to wonder if it is not a technology problem. Perhaps it’s a people problem.
The Aras ACE2025 Community Event in Boston closed two weeks ago. It has taken me that long to wrap my head around everything I learned. Normally there are many really important-sounding words that sound so enlightening at the time, yet when I sit to write I find no substance. In this case, there was so much substance that I have trouble filtering to the most important themes.
Let’s say that not only were the expected buzz words in evidence but the underlying concepts were demonstrably in use. Aras is a PLM (product lifecycle management) developer. They are solving problems that I had in the late 70s while working at a manufacturer. Mainly, how to make usable sense from all the engineering data.
The principle phrase of the week was digital thread. They are all about the digital thread. Companies were also using Large Language Model (LLM) technology trained on their own data. Agentic AI rears its head and will become even more important with use. (See my interview with John Harrington of HighByte for more on Agentic AI.)
Customer presentations that showcase actual use cases provide reality to the theory.
I sat in a presentation by the sensor manufacturer Sick. They have applied AI to unstructured data turning them into useful structured data. Using Aras PLM, they have realized better speed to market finding product data via natural language query. They have instances of development times cut from 3 years to 6 months.
Another customer presentation came from Denso. Engineers find the digital thread from PLM as a tool for collaboration. The connected flow of data ensures continuity from design to manufacturing to operations. Inconsistent data hurts the business. PLM is the heart of their digital strategy with the BOM as centerpiece. Once again an example of someone actually using GenerativeAI trained on their data to fill in gaps.
The highlight of customer applications came from my half-hour discussion with Tetsuya Kato, Manager of the Technical Management Group from SkyDrive in Japan—the Flying Car company. OK, it’s not the Toyota in your driveway suddenly flying to the store. But it’s close. Check out the goodies on their website.
He was hired to bring order to the product information system. In other words, to develop a better Manufacturing Bill of Materials (MBOM). They were using Team Center PLM with a system brought in by a consulting engineering firm. The system had many problems, was taking too long to implement, and forced SkyDrive to change its systems to fit the software.
Kato brought in Aras Connector to bring engineering data from Team Center to the Aras PLM platform. He started the project in September, showed results in two months, and moved all the data in eight months. The Aras solution had all the features necessary for their manufacturing data with the additional benefit of flexibility to allow them to make the system work for them instead of the other way around.
Chief Technology Officer Rob McAveney asks “What if…”
McAveney noted Aras has 25 years of asking what if…
2001 What if PLM could be flexible, webnative platform?
2005 What if PLM applications were built to work together? Integrated data now called digital thread.
2011 what if impact analysis were an interactive experience? Wizard style digital thread.
2014 what if visual collaboration was available to everyone?
2021 What if a SaaS delivery model came without compromise?
2025 What if we could extend reach of the digital thread? Take advantage of Aras Effect, open, reachability; Aras Portals, apps product data platform, composable PLM apps, low code environment?
The digital thread + AI = Connected Intelligence:
The three areas of Connected Intelligence include:
Discover—conversation about data
Enrich—connect more data and people business
Amplify—maximize impact
Pursuing all three together
Discover—natural language search, content synthesis, machine learning, text to SQL (natural language prompt to query; what if guided tour how to set effectivity conditions to sync multiple changes (context aware help), then phase in changes with confidence, eliminate rework and supply chain, what if assess global supply of a sourced component before submitting a change request, avoid wasting time on changes; what if you could ask AI assistant to ID common factors while root cause analysis, persistent quality issues become a thing of the past.
Enrich—entity recognition, contextual reasoning, topic modeling, deep learning, what if missing of inconsistent links in digital thread could be easily identified and corrected, patterns, downstream analytics, stop wasting effort on redoing work, what if requirements could be automatically identified and ingested from reliable external data sources, then see next level requirements traceability with dynamic requirements, what if factory floor data could be linked to quality planning parameters, planning for feedback loop.
Amplify—agentic AI, surrogate modeling, generative engineering, reinforcement learning, what if engineer-to-order business could be transformed by leveraging all your past engineering work to create a common variability model, engineer shift for individual customer projects to improving full product line.
I’m not talking about the Johnny Rivers theme for a late Sixties Saturday afternoon spy TV show. We’re talking software agents. Some may be secret, but none are men.
I once had an annual meeting with the CTO of a large automation company where I shared (non-privileged) information I’d gathered about the market while trying to learn what technologies I should be watching for.
With artificial intelligence (AI) and Large Language Models (LLMs) grabbing the spotlight at center stage, I’m watching for what technologies will make something useful from all the hype.
I’m looking for a return to the spotlight of these little pieces of software called agents. John Harrington, Co-Founder and Chief Product Officer at HighByte, an industrial software company, believes in 5 or so years from now, LLMs won’t be the game-changer in manufacturing that many expect. Instead, Agentic AI is set to have a far bigger impact.
So, John and I had a brief conversation just before my last trip. It was timely due to the nature of my trip—to a software conference where LLMs and AgenticAI would be important topics—and not just in theory.
From Harrington, “Agentic AI is revolutionizing the tech industry by addressing AI’s biggest limitation—making decisions that are more human like. AI agents are yet another application that analyzes and turns large amounts of data into actionable next steps, but this time they promise it will be different.”
He told me that Agentic AI will become more “human-like” going beyond LLMs. HighByte started up as an Industrial DataOps play at a time when I was just hearing about DataOps from the IT companies I followed. I told the startup team that they were entering a good niche. They have been doing well since then. They extended DataOps with Namespace work and now LLMs and agents.
“AI agents can enhance data operations by providing greater structure, but their success depends on analyzing contextualized data. Without proper context, the data they process lacks the depth needed for accurate insights and decision-making,” added Harrington.
Take an example. An agent can be a way to contextualize data, model an asset. Working with an LLM trained on data specific to the application, it can ask the LLM to scan the namespace to see if there are other assets in the database. HighByte’s can work through OPC, and also works with Ignition from Inductive Automation or the Pi database. It looks for patterns and can propose options as the engineer goes in to configure the application.
Not shy in his forecast, Harrington says the future is agents. They can affect and act on data. They can reach out to a control engineer, operator, quality group. It’s a targeted AI tool focused on one small thing. Perhaps there’s a maintenance agent, or one for OEE, or line quality on a work cell. Don’t think of a monolithic code in the cloud. Rather, think of smaller routines that could even work together helping business like Jarvis in Iron Man. Data is food for these agents, and HighByte’s business is data.
I’ve been impressed with HighByte’s growth and sustainability. Also that they’ve managed to remain independent for so long. Usually software companies want to build fast and sell fast. Watch for more progress as HighByte marries agentic AI with data.
HighByte seems to be making more of a splash lately with its DataOps plus Universal Namespace developments. Several years ago I spotted DataOps as an important technology application. Usage still grows.
This news from HighByte concern the ability to build custom APIs.
HighByte released HighByte Intelligence Hub version 4.1 with Callable Pipelines, enabling users to build custom APIs for their operations and industrial data sources. Callable Pipelines, together with the REST Data Server, functionally serve as an “Industrial Data API Builder” that allows users to build custom API endpoints to interact with industrial data. In addition to Pipelines enhancements, version 4.1 delivers native integration with Aspen InfoPlus.21, support for the new Amazon S3 Tables service, granular audit logging for regulated industries, and seamless event capture from file-based data sources.
HighByte Intelligence Hub is an Industrial DataOps solution that contextualizes and standardizes industrial data from diverse sources at the edge to help bridge the gap between OT and IT systems, networks, and teams. HighByte leads the evolving Industrial DataOps market with the most complete solution to optimize the orchestration of usable industrial data across the enterprise. Highly regulated industries like life sciences and oil & gas have become the company’s fastest growing markets in the last twelve months.
The latest release also introduces Pipeline Debug, allowing users to test and troubleshoot individual pipeline stages without impacting the pipeline connected systems. Furthermore, the File connector has received major enhancements, including support for directory reads, SFTP, and character set decoding. The File connector is complemented with a set of new Pipeline stages to parse and write different file formats and extract and process file metadata. Together, the File connector and Pipelines create a holistic and seamless approach to advanced file processing for industry.
HighByte Intelligence Hub version 4.1 is now commercially available. All new features and capabilities introduced in version 4.1 are included in standard pricing.
