Making the value of technology clear
How are manufacturers managing their manufacturing operations?
Tech-Clarity invites you to participate in a research study on “level 3” manufacturing operations software for production facilities. What business value are companies gaining from software at this level? How is MES/MOM/MOS evolving? What impact is AI having on this landscape?
We will also use the results to report on best practices to maximize business value from MOS and AI. Please take 10-15 minutes to fill out this short survey. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, eligible respondents will be entered into a drawing for one of twenty $25 Amazon gift cards. See the survey for eligibility details.
Take the survey now to share your perspective!
Please feel free to forward this survey to others you feel have an opinion about US-based fabs to share. Individual responses will be kept confidential.
Thank you for your support. Please check out our Active Research page for additional Tech-Clarity survey opportunities.
[post_title] => What is the State of Manufacturing Operations Software (MOS) in the Age of AI?
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How can manufacturers gain confidence in moving to autonomous operations? Combining the agility of SaaS with the reliability of edge at the plantwide level is an emerging approach for MES. This eBook walks through why MES is core to moving from automation to autonomy. It discusses the challenges of gaining the resilience between the plant floor and SaaS applications. It also explains edge-to-cloud architectures and their benefits.
Please enjoy an overview of our findings, below. For the full research, please visit our sponsor, Rockwell Automation.
Beyond normal data access, autonomous operations need resilience. 24/7 autonomous operations need seamless, low-latency data access and data management across IT and OT. Still, most companies, even Top Performers, rate their capabilities for the seamless movement of operational data from collection to analysis between ‘Not at all’ and ‘OK’. AI intensifies the pressure to improve industrial data movement and governance. Our research shows that the #1 issue for AI success is a lack of data readiness.
OEMs spend significant time and effort pulling together data to meet Integrated Product Support (IPS) requirements. Creating and aggregating product sustainment data is inefficient due to disconnected data and processes. How can PLM help OEMs and suppliers develop and capture high quality IPS data to meet standards like SAE GEIA-0007C without creating redundancy and excess cost? How does this approach support a closed loop between engineering and sustainment? We explore three levels of Product Support Data Management (PSDM) maturity ranging from manual approaches to an integral approach leveraging the PLM data model.
Please enjoy an overview of our findings, below. For the full research, please visit our sponsor, Siemens.
The Critical Role of IPS in Sustainment
The best designed equipment doesn’t fulfill mission objectives if it’s not operational. Mission readiness relies on well-maintained equipment. That may be obvious. But keeping aircraft flying or vehicles rolling can’t come at any cost. Operators must be able to sustain their fleets with an optimal balance of cost and risk.
Service data from the OEM is essential to maintaining this balance. IPS (Integrated Product Support) is essential for providing operators with the information they need to sustain equipment across both military and commercial fleets. For defense contractors, of course, IPS is a mandatory, contractual obligation.
PSDM: Improving IPS with PLM
Today, OEMs and their suppliers are meeting IPS demands through brute force and significant manual effort. Today’s processes are inefficient and costly. This is true for initial IPS database development and delivery, but even more so in the field as equipment is updated through ECOs and MRO activities.
PLM-enabled IPS, or PSDM (Product Support Data Management), provides the support data management processes required for the generation and storage of the data required for standards such as SAE GEIA-0007C.
PLM offers the opportunity to streamline IPS development by connecting data across the digital thread from design through service and improve processes with AI. But managing product support data in a PLM context can do more than just increase efficiency. It can improve sustainment processes and data.
PSDM Maturity
PSDM can bring engineering and logistics data – and engineers – closer together to better design for sustainment and close the loop on service issues. For commercial operations, it can also help drive service profitability.
We see three levels of increasing maturity and value available from PSDM:
Drive Higher Strategic Value
Striving for Integral PSDM
Achieving the highest level of maturity could be viewed as a journey starting with added efficiency using PLM and then better managing configuration and changes to extend the value. Then, OEMs could strive to create a collaborative, closed-loop environment to further extend the value. There may be opportunities, however, for a program to leap multiple levels of maturity at one time without passing through lower levels of maturity. In the end, collaborative, closed loop PSDM is what will differentiate a winning OEM from a compliant one. It moves the conversation from simply delivering a logistics product database to creating a comprehensive digital twin from engineering to service. This value is achievable with PLM and promises significant new value ranging from efficiency to increased service revenue, where applicable.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor, Siemens.
If you have difficulty obtaining a copy of the research, please contact us.
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Rick Franzosa and I recently had the opportunity to attend the Proficy Accelerate User Conference. This customer conference, already planned for the Proficy® from Velotic™ community, was the first since the formation of Velotic. Less than two months into being a new company, the event showcased not only the latest from Proficy but also the Kepware® from Velotic™ and ThingWorx® from Velotic™ suites in demo booths. Implementation and reseller partners were also there in force.
Brian Shepherd, Velotic Software CEO, kicked off the event by explaining their clarity of purpose: helping producers “do the right thing right.” Their tag line is “Build Brighter.” He pointed out that it has 1,200 employees across 27 countries, over 150 partners, and $350M in revenue. Making a fresh start across all three product suites is crucial, as both GE Vernova and PTC had other main focus areas.
So far, investor TPG appears to be ready to invest aggressively in Velotic’s people, processes, and technology. The technology investments are aimed at improving each product and integrating them effectively to make them easier to use and deploy. They will also be infusing AI throughout the products for both augmented and autonomous operations. He hinted at an interest in growing the functional footprint to serve other problems as well.
Several customer speakers from the Food industry highlighted their successes using Proficy Smart Factory Cloud MES.
Perhaps the biggest news at the event was the unveiling of Proficy Data Hub, due out in full production release in calendar Q3 of 2026. Data Hub is the foundation for industrial DataOps and data fabric. While the Proficy Operations Hub delivers the user experience and visualization, the Data Hub creates a unified namespace (UNS) around an asset model. The Data Hub composable framework is intended to help customers achieve faster time-to-value and quicker decision-making.
Brian Johnson, product manager for both Data Hub and Historian, points out that this overarching data model or fabric spanning all Proficy products is a long-anticipated advancement. Each Proficy product (Historian, SCADA, MES) has its own data model, and the Data Hub pulls them all into a single model. This enables users to better leverage all of them without needing to know where the data resides.
Since the Velotic Proficy portfolio includes a Historian, SCADA, and MES, and they integrate with ERP and other systems, there is already context and governance to a certain extent. The Data Hub becomes a single, back-end source to integrate data from across the plant and the enterprise. Companies can connect just once and reuse those. MCP helps to abstract the user experience from source understanding. We expect the ThingWorx ThingModel to play a role in the Data Hub as well, but that’s still in the works.
Historian is the default time-series data storage system for the portfolio. This year, they are also launching a Hyper Scale Cloud Historian that runs natively on AWS. This scalable microservices historian will enable one billion samples per minute. This is three orders of magnitude faster than the current version, and will be useful in industries such as the electric grid. AI is also coming into Historian for natural language queries.
Velotic is weaving AI into the Proficy Smart Factory MES and the entire Plant Apps suite. What we saw in action was the natural language interface. The MES context helps deliver good data for AI to leverage. The demonstration also showed how the new Data Hub can pull data from many sources to answer complex questions through that natural language interface.
There is also a major industry-specific release in the works for discrete assembly, Proficy for Assembly Operations. Until now, the automotive industry has primarily used level 2 Proficy products. This MES is being co-developed with major automotive customers to meet their specific requirements. In an automotive environment, MES does not actually ‘execute’. Execution occurs at the SCADA or PLC level. For this reason, level 2 and level 3 systems are tightly linked to perform their functions. Proficy for Assembly Operations is designed to address the high-speed, complex-model-mix discrete market, using a model-driven approach to configuration, execution, and data collection, with a cloud-native, 100% web-based UI.
Velotic demonstrated chat-based capabilities with Proficy Smart Factory MES, and have a series of other AI enhancements, including a GraphQL API that enables AI through Model Context Protocol (MCP), a natural language chat interface just like the frontier models, multi-tenant, multi-user Role Based Access Control (RBAC). Velotic is also planning to include conversation history and Retrieval-Augmented Generation (RAG) stores to house additional searchable context. These capabilities are targeted for release in August 2026.
Velotic is coming together in a way that matches its name – with velocity and agentic capabilities across the portfolio. Velotic branding and websites should be ready by the end of the year. Seeing some of the people from each of the three product camps and previously two companies come together at this event was fun. We are reassured that the new company, with its sole focus on these industrial software products, will continue to innovate and invest, both in the near term and in the future.
We appreciate the opportunity Velotic provided to host Julie Fraser and Rick Franzosa at this event. Thanks to Carver Conway for arranging our meetings and keeping us analysts on track. Special thanks to Proficy VP of Operations Jeff Bartoletti and CMO Nicole Rowe for your openness. It was a delight to spend time in person with Stephen Pavlosky, Prasad Pai, Brian Johnson, and Joe Gerstl from the product management team, as well as Phaedra Martin from industry marketing.
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We recently had a briefing with Lytica, which has long brought unprecedented visibility to electronic component sourcing for its customers. The big news is that they have developed AI agents to leverage the data about purchase prices and transactions from their growing customer base. Sourcing is challenging, but having actual data from across the industry is Lytica’s competitive advantage, which they offer in a win-win approach with customers.
Materials sourcing is always about making tradeoffs. Wise procurement professionals know this, but don’t always have all of the data they need to see where they might not be getting the best deal, even if it’s the lowest price. Lytica is proclaiming itself the first agentic AI system to balance procurement cost, supply risk, design decisions, and scale needs. Procurement costs are the company’s original focus, and that continues to expand.
With the speed of change in this industry and geopolitical tensions, the way manufacturers procure for each of their production sites is evolving. Parts shortages due to the huge boom in AI and data center needs are only one angle; every industry sector and chip class has its own tensions. Finding the balance, Lytica argues, is all about having the data available to see what’s happening around the industry at any given time.
SupplyLens™ Pro is Lytica’s electronic component market intelligence platform, built on real customer transactional data and increasingly delivered through AI-powered workflows. Its ML engine learns from real customer transactions involving electronic components, helping buyers negotiate more effectively and review supplier proposals with confidence.
How? The platform anonymizes, secures, and aggregates customer data from each OEM or EMS, creating a current electronic component purchasing data set that we have not seen elsewhere. Lytica calls this a digital twin of the electronics marketplace. Using anonymized market intelligence on what components are being bought, from which supplier channels, and at what prices, Lytica offers benchmarking for OEMs and EMS providers. If you know Lytica, this network effect — or give-to-get data platform — is not new.
Customers are global brands you’d recognize. They showed dozens of the biggest names in electronics, telecommunications, medical devices, industrial equipment, defense, and household hard goods. Using data from those sources, they claim they can typically achieve 10% savings for a new customer. So, customers get big enough benefits to justify trusting Lytica with their component procurement data.
Each of the four solutions is composed of multiple modules that the company has built over time. Negotiator is the data for procurement professionals. They can use Validator for quotes, Mitigator to review risks and options, and Accelerator for design-to-source collaboration, which is often the starting point for sourcing challenges.
What’s new is the set of AI Agents Lytica has designed to leverage their industrywide dataset further. The important point is not simply that Lytica has added agents; it is that those agents operate on a proprietary, continuously refreshed market intelligence foundation. The first agent, Neo, was announced on April 30, 2026.
Neo is an agent designed to support procurement professionals in their negotiations. It guides a buyer through four phases:
The other agents were in a demo but have not yet been announced. They include
LISA – Lytica Intelligent Sourcing Agent – is the starting point for a procurement professional’s day. This personal analyst prioritizes what the person needs to do and explains why it is prioritized, with coordination across the other dedicated specialized AI agents.
DESI – The design agent supports product design and development engineers by flagging potential risks and high-impact components. It also recommends ways to mitigate them while maintaining design integrity.
RICK – The risk agent is focused on risk mitigation throughout the process.
So, the Lytica balancing act is still gaining strength. Adding agents and building out more strength in the core solution sets as a result of that and other enhancements.
Lytica has built strong trust and eye-popping testimonials from major customers. One reports 10-20% savings, for example. Trust building began long ago, when the company was founded by Ken Bradley, who last served as CPO at Nortel for 3 decades in the 70s-2000s. Since then, the company has collected data in a private way that still serves the entire community. With such strong customer logos, prospects start to relax and believe the savings and opportunities are worth any potential risk from sharing the data. Trust is not just a sales enabler for Lytica; it is part of the product architecture. The more customers contribute anonymized transactional data, the stronger the market intelligence becomes for the entire community.
We have rarely seen customers willing to share their data with a software provider, but in Lytica’s case, it appears to be working for everyone. Data that enables benchmarking, along with internal and supplier collaboration, can make a huge difference in margins for many companies in these markets. At Tech-Clarity, we cover the product from concept and design through the entire lifecycle into manufacturing, and Lytica’s vision sees some of the same needs we have identified for better supplier and internal collaboration.
Electronic component sourcing is becoming a category of its own. No wonder; supply chain volatility has been an ongoing reality since the dawn of the electronics market and semiconductor chips. As more products use electronic components, the need for and value of sourcing these effectively increase. Having collected data on its SaaS platform for years, Lytica is well-positioned to grow.
We are grateful to Gerry Abbey for arranging the briefing, and to Lytica CEO Martin Sendyk and VP Product Shawn Bradley for briefing us on the vision and solutions. We look forward to following Lytica’s progress in the market!
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How far have manufacturers come in connecting the product digital thread from design through manufacturing? How mature is the integration of data in the two primary systems supporting engineering and production? We interviewed over 200 large manufacturers to answer those questions and many more.
Please enjoy an overview of our findings, below. For the full research, please visit our sponsor, Kalypso.
Investing in PLM-MES Integration
Today’s manufacturers need to rapidly bring high-quality products to market despite rising product complexity. One way they can do this is by improving the quality and timeliness of their digital thread data and enabling better collaboration between engineering and manufacturing. The challenges and impacts of a disconnected product digital thread caused by poor PLM – MES integration hampers that ability.
Mixed Messages on the Status Quo
Our survey of over 200 complex, discrete manufacturers with revenues greater than $500 million that have implemented both Product Lifecycle Management (PLM) and Manufacturing Execution Systems (MES) systems shows relatively low integration maturity. Only about one in five companies in our study demonstrates truly mature PLM-MES integration across the areas assessed. Very few have adopted the advanced practices needed to fully connect the digital thread from engineering through manufacturing.
The results, however, suggest that “average” respondents have adopted more advanced processes than one might expect. We believe this is because the survey participants reflect larger, more advanced companies based on the targeted industries, company size, and level of system adoption. Based on our experience, this audience is more likely to have adopted advanced practices than the average manufacturer.
Clear Opportunity for Improvement
Despite the somewhat optimistic state of the average respondents, the survey points to clear room for improvement. Manufacturers that have integrated MES are achieving the product quality and time-to-market advantages they seek, among other valuable benefits. PLM – MES integration yields benefits even when companies don’t achieve the highest level of maturity. But manufacturers still have a long way to go to create a closed-loop, model-based digital thread between engineering and manufacturing.
Room for Improvement
Despite sharing optimistic levels of maturity and integration across the different aspects of PLM – MES integration, manufacturers reported relatively low maturity in the one process examined in more detail, engineering change management. Although most companies say they have at least somewhat integrated engineering and manufacturing data, over three-quarters still need to run reports or manually look up information for change impact analysis.
This example shows that the vast majority of manufacturers can continue to improve value by adopting more mature practices. PLM – MES data integration should continue to be a high-priority investment for manufacturers.
Further, this report focused on complex, discrete manufacturers with over $500 million in annual revenue. This sample likely represents an advanced set of manufacturers, and smaller companies likely have less mature practices. These companies can follow the examples and best practices adopted by these larger manufacturers.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor, Kalypso.
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Integrating PLM with MES
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How strong is your foundation for Industrial DataOps, Smart Manufacturing, and plant-level AI? Are you considering an AI stack, or separate layers from leading proven providers?
Join this webinar on June 18 at 10am Eastern time, 16:00 CET, to get new ideas about how to evaluate and choose the right industrial connectivity approach for long-term success.
AI is moving fast, and yet deciding on the best approach for connectivity needs careful consideration. To ensure IT and OT data are connected, extracted, normalized and secured, manufacturers need to make crucial architectural decisions. While companies may choose differently, it’s important to understand the tradeoffs that are often overlooked at this level.
Connectivity is the starting point for data quality, governance, and trust. Those, in turn, can enable agility to change, resilience for unexpected challenges, and data that’s always ready for the next decision. We will discuss the pros and cons of an all-in-one industrial stack vs. a best-of-breed connectivity approach.
Join Tech-Clarity’s Julie Fraser and Velotic Kepware’s Emily Griffin for this discussion packed with important topics and pragmatic insights. Julie will share some of Tech-Clarity’s new Industrial Connectivity Buyer’s Guide and Emily will share about her years working with customers and what the move into Velotic might mean. Don’t leave your data connectivity to chance - make the right decision for your business. Come to this webinar discussion with your questions ready!
[post_title] => Webinar: The Connectivity Choice: All-in-One Stacks vs. Best-of-Breed Platforms
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We recently spoke with Athena Technology Solutions and its partner, LLM at SCALE.AI, about the launch of a new offering: FabOrchestrator.AI, their agentic AI foundry for manufacturing. This multi-faceted platform streamlines everyday tasks across the plant.
As the name suggests, this new offering is focused on orchestrating agents for a semiconductor fab or other manufacturing facility. Athena, as a major implementation partner for leading discrete and mixed-mode MES solutions, understands the current realities in manufacturing. They see that every plant is being asked to do more with less, struggling to keep up with manual reporting and repetitive tasks, and seeking to make sense of data across plant and enterprise systems with different data models. The focus for FabOrchestrator.AI is to transform manufacturing data into intelligent decisions, enabling streamlined processes for implementing MES and running the plant(s).
Athena calls FabOrchestrator.AI a foundry because it blends an array of existing systems into a cohesive operations intelligence platform. The platform has a five-layer AI stack, spanning data connectivity, data access, intelligence, and the user experience, with feedback loops to users and systems at each layer. It incorporates security and governance at all layers and can output in interactive dashboards, text-based summaries, visualizations, or actionable insights.
The system’s four major capabilities are:
Together, these are designed to reduce the effort needed for MES implementation, rollout, and support. FabOrchestrator.AI is also intended to reduce routine support tickets coming into engineering and deliver stronger, more complete, and real-time intelligence to support manufacturers’ decision-making.
Athena’s long history working as a partner with both Siemens Opcenter and Critical Manufacturing MES means they see what manufacturers are doing. They know where the weaknesses and challenges lie, and have released FabOrchestrator.AI to supplement these environments. They have customers in the Americas, APAC, and EMEA, primarily making semiconductors, electronics, medical devices, solar panels, and clean energy, including batteries.
LLM at Scale AI is an enterprise-grade agentic AI platform specializing in factory automation, multi-agent orchestration, and large language models (LLMs). Their customers include JTC, CBRE, JLL, Cushman & Wakefield, Johnson Controls, and the State of California. Starting from this proven platform, Athena adds its deep industry knowledge to create a central intelligence platform for manufacturing.
This is a notable addition to the crowded AI-for-MES market. With Athena’s strong understanding of leading comprehensive MES systems, we expect customers will be guided to get their data ready to leverage their data more effectively. Athena has a good reputation with its partners, which speaks well.
This seems a good use of agentic AI. The combination of speeding up workflows, unifying systems, and automating decisions has a good chance of delivering strong value quickly. Athena has published a straightforward pricing approach. As a seasoned system integrator, we know they will support customers through the entire project lifecycle, and those implementations should run shorter when leveraging FabOrchestrator.AI.
Thank you to industry luminary and long-time friend Maryanne Steidinger for introducing us and setting up the briefing. Thank you, Senthil Ranganathan, founder and CEO of Athena, and Jothi Periasamy, Chief Agentic AI Architect at LLM at Scale AI, for explaining the offering you have launched. We look forward to following its progress in the market.
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Tech-Clarity invites you to participate in a research study on the state of the semiconductor market in buying and connecting systems from sub-fab to fab to enterprise. The survey looks at technology investments for brownfield and greenfield fabs. We will also use the results to report on the state of the market in semiconductor and identify best practices. Please take 10 minutes to fill out this short survey. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, eligible respondents will be entered into a drawing for one of twenty $25 Amazon gift cards. See the survey for eligibility details.*
Take the survey now to share your perspective!
Please feel free to forward this survey to others you feel have an opinion about US-based fabs to share. Individual responses will be kept confidential.
Thank you for your support. Please check out our Active Research page for additional Tech-Clarity survey opportunities.
[post_title] => How Does Semiconductor Data Flow in US Fabs from Automation to MES and the Enterprise?
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How can aligning product data in PLM with the aftermarket drive business value? Too often, PLM data is kept within Engineering, leading to disconnects between rich design data and downstream functions like service. Aftermarket support demands accurate product information at the product configuration level, and PLM is the source. How can manufacturers create a cohesive, connected digital thread of product data from engineering through to the aftermarket?
PTC’s Justin Lloyd moderates an informative panel featuring research from Tech-Clarity President and Digital Transformation Analyst Jim Brown and AGCO’s Global PLM Manager for Aftermarket, Tim Mulso to uncover the opportunities and discuss industry progress toward a connected aftermarket. The panel will tackle questions including:
How is process manufacturing taking advantage of the AI opportunity? We analyzed over 250 companies in the chemical, food, animal nutrition, and engineering industries to understand their plans and progress. We found broad plans, varied readiness, and a small number of companies who have made substantial progress beyond proofs of concept and pilots.
Please see a summary of the chemical report, below. You can access all three reports, AI in the Chemicals Industry, AI in Food and Animal Nutrition, and AI for Engineering in addition to a related webinar here.
Thank you for the opportunity to conduct this research to find out the “no bull, no hype” truth on your behalf, Datacor.
Please enjoy the summary* below. For the full research, please visit our Datacor (registration required).
Tech-Clarity spoke with Datacor customers and surveyed over 250 companies with under $300 million in revenue to understand how chemical companies are approaching AI, where they’re finding success, and what’s holding others back.
The findings tell a clear story: chemical manufacturers and distributors are making meaningful progress with AI and doing so more quickly than many might expect.
It was exciting to attend my first Aras Corporation ACE User Conference with Jim Brown. The conference was held in Miami, which provided a welcome relief from Boston's long, cold winter. More importantly, it was great to learn more about Aras' strategy and products, especially from the Aras leadership team, including their new CEO, Leon Lauritsen. Leon came to Aras with the Minerva PLM acquisition, where he led EMEA sales. Prior to becoming the Aras CEO, he headed the Aras global sales team. His PLM experience, product understanding, and leadership should provide a steady hand as Aras continues to execute on its SaaS and AI strategy.
Our key takeaway is that Aras is building on its core strength as a flexible, extensible, and scalable digital thread platform. The company is moving quickly toward an AI-augmented, agent-based model on a governed, data-centric PLM foundation. We are continually impressed by the level and openness of their community engagement, which Aras considers critical to defining and prioritizing its product roadmap, especially regarding AI. This was evident during the event itself, where discussions consistently centered on what’s working, what could be improved, and how Aras can better support its users.
In his opening remarks, Leon highlighted that much of the pressure facing manufacturers today comes from rising complexity—across products, regulations, supply chains, and digital systems—combined with the expectation to do more with the same resources. While AI inevitably entered the discussion, we agree with his point that its effectiveness depends on the quality and control of underlying data, reinforcing the continued importance of a strong PLM foundation.
Leon noted that with constant change as the only certainty, flexibility is essential. He believes Aras is well-equipped to address uncertainty with its adaptable, extensible data model and scalable capabilities in workflow management, governance, integration, and performance. We also noted flexibility as a key Aras differentiator in our Aras ACE 2023 Insight, writing, “While most vendors tout standardization and de-customization, Aras encourages customers to leverage the flexibility built into the platform to modify Aras Innovator to support their unique needs.”
At last year's event, they introduced Aras InnovatorEdge, and since then, they have made significant progress. Aras InnovatorEdge is built on three pillars:
Edge API – Governed Data Gateway to securely expose PLM data and digital thread services to external applications and agents. The Edge API works with both Aras SaaS and On-Prem infrastructure. The API exposes only the necessary data segments for specific use cases, ensuring controlled access.
Edge Builder – SaaS-based ecosystem for building, deploying, and operating task-based apps with native Aras Innovator connectivity. Edge Builder includes a pre-integrated UI framework, a comprehensive component library, and secure authentication. Additional capabilities provided by Edge Builder will further enhance this model, reinforcing the idea that PLM is no longer a monolithic system but a platform that can be extended and adapted at the edges.
Edge AI – A suite of services for building, running, and governing specialized agentic workflows and AI services across the PLM ecosystem. (Aras noted this is coming in Q3, 2026)
Edge Apps are specialized applications developed using Edge Builder —either built internally or through prebuilt solution accelerators developed by Aras and its partners for industries such as medical devices and high-tech electronics. A no-code Agent Builder allows business users to create and deploy their own agents. This enables rapid development of agent-driven workflows directly within the PLM environment, supported by templates and guided configuration.
Aras introduced their own industry-specific Edge Apps, each targeting a specific use case and pre-configured with data models, workflows, and UI aligned to a given vertical. These include Aras Digital Manufacturing Engineering, Medical Devices, and High-Tech Electronics apps.
Aras introduced dependency graphs that extend beyond simple connections to explicitly identify and model relationships across the digital thread. By mapping these dependencies—even across disparate systems—organizations can automate tasks that were previously manual and often neglected. This unfolds in phases: first building the dependency map, then proposing changes, and finally evaluating downstream impact. The result is a more intelligent, responsive PLM system that can adapt quickly as complexity grows, with many of these capabilities already available in Aras and others rolling out in the near term.
Rob McAveney, Aras CTO, shared a look into the future of Enterprise Software UX. Historically, enterprise software—and PLM in particular—has been built for humans sitting in front of screens, navigating form-based systems and structured workflows. That model is now being challenged. According to Gartner, by 2029, a majority of software will be designed primarily for AI agents, with human interaction becoming secondary. That shift has major implications for PLM: systems will need to support not just human users, but intelligent agents that operate within defined boundaries of governance, security, transparency, and observability. In this model, humans don’t disappear—they guide and oversee—but much of the execution moves to AI. In our industry discussions, API-first architectures are often referred to as “headless” solutions.
Rob noted there are 5 trends shaping Enterprise SW UX:
What emerges is a deconstruction of the traditional PLM application hub into a more flexible, agent-driven framework. Work is broken into different kinds —discovering data, enriching it, and amplifying it into decisions and innovation— under an agentic framework. How work gets done now includes conversational interfaces, task-based assistants, autonomous agents, monitoring systems, and collaborative tools. Just as importantly, work is no longer confined to a desktop; it spans mobile devices, tablets, and mobile apps. This “redelegation” happens incrementally: starting with natural language interfaces and chatbots, then evolving into task assistants and monitoring agents that operate more independently. Capabilities like intent-based discovery (from on-demand insights to predictive and even “invisible” discovery), AI-augmented enrichment (such as interpreting requirements documents and feeding structured data into the digital thread), and AI-guided operations begin to reshape how PLM delivers value.
Rob provided some practical examples of how this may unfold through targeted micro-experiences rather than wholesale system replacement. For example, a simple problem report can start with a photo, with AI automatically populating details and refining them via voice input. A requirements companion app can compare documents against PLM data, identify relationships, and even ingest updates without the user ever entering a traditional PLM interface. Engineers working in CAD can be alerted to issues by monitoring agents, with problems detected “invisibly” and surfaced through conversational prompts.
We believe the path forward is iterative: define the work, determine how and where it should happen, ensure trust, and rapidly deliver a minimum viable product. With a foundation built for adaptability, platforms like Aras Innovator are positioned to support this transition, enabling organizations to move quickly as the shift toward agent-driven PLM accelerates over the next couple of years.
Microsoft is an important Aras partner and customer. At last year's event, Microsoft shared examples of using AI to extend the value of PLM as a tool to improve people's efficiency. (see our Aras ACE 2025 Insight). A lot has changed in a year. This year, they shared that Microsoft has an AI-first approach to its business, including its PLM environment (OnePDM), connecting Aras and Microsoft agents. In a multi-agent ECO scenario spanning PLM, engineering, and supply chain systems, Microsoft replaced brittle integrations with agent-to-agent (A2A) communication, allowing agents to interpret data, negotiate across systems, and handle inconsistencies that would typically break traditional integrations. This approach reduced change cycle times from 8 weeks to 2, aiming to reach 2 days, while achieving ~82% accuracy and strong evaluation scores for output quality, task alignment, and business value.
Microsoft found that smaller, task-specific agents—each aligned to a clear function—perform better than monolithic models, and that selecting the right model for each task (e.g., reasoning vs. image recognition) improves outcomes. As they expand toward fully agent-driven PLM workflows, the emphasis is on adaptability, governance, and iterative improvement, recognizing that agent performance will evolve over time with use and refinement.
Aras has always stood apart from traditional PLM vendors—both conceptually and technically—which is a big part of its value and why it’s worth evaluating alongside more traditional players. That differentiation continues as Aras strengthens its digital thread capabilities while layering in AI, all without losing focus on its core strengths of flexibility and extensibility.
Under Leon Lauritsen, the company is sharpening that direction through a clear strategy built on market disruption, customer obsession, and people. The approach is to double down on core PLM capabilities, reinforce its unique, adaptable architecture, and extend it with AI—enabling organizations to learn, evolve, and act at speed.
Thank you, Leon Lauritsen, Rob McAveney, and Igal Kaptsan, for the business and product updates, and to Josh Epstein, Jason Kasper, Kylie Ochab, and others for insightful conversations and help along the way.
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What are many manufacturers missing to succeed in digital transformation, smart manufacturing, and successful artificial intelligence (AI) initiatives? A common foundation for reliably getting the right data and information to the right place at the right time. In short, what’s often missing is enterprise-grade industrial connectivity.
Normalizing, organizing, and making available the massive amounts of data from both automation and information systems is not straightforward. Discerning which systems can deliver it is crucial to a manufacturer’s short-and long-term success. In this Buyer’s Guide, we discuss essential evaluation criteria for an industrial connectivity foundation that’s flexible and future-ready.
Please enjoy the summary* below. For the full research, please visit our sponsor Kepware (formerly PTC, now Velotic), (registration required).
Standardizing on industrial connectivity across an enterprise also requires options for using and managing it at the enterprise level. Increasingly, manufacturers are using the industrial edge to ensure data capture and processing close to the devices. This approach helps keep latency low for high-performance operations, data, and intelligence. Containers at the edge also simplify deployment and re-deployment.
What do asset-intensive businesses need to manage the supply chain uncertainty they face? IFS believes it’s better warehouse software, and completed its acquisition of Softeon last month. Operating as a standalone company rebranded as IFS Softeon, the company is staying intact while gaining the backing of a credible enterprise software giant with strong AI and a cloud platform that adds value to Softeon customers and opens opportunities to expand IFS customers’ capabilities.
IFS’ Industrial X AI focus “for mission-critical applications where you can’t afford to get it wrong. IFS drew a significant distinction for what’s required in these settings compared to back-office applications. This is what they call Applied AI, which is in context, in reality, auditable, domain aware, works offline, mindful of safety, and keeps people safe doing actual work,” according to Jim Brown’s Insight about this.
Now, IFS says, “Building upon IFS Softeon’s warehouse execution engine, IFS.ai Logistics extends intelligence across transport planning, shipment execution, freight audit, and network optimization.” This could be a powerful approach to supply chain intelligence.
A warehouse is certainly a mission-critical use case. With automation and robotics, many forms of AI are coming into play. As part of IFS, Softeon now has even more AI resources and can leverage a strong global marketing and sales presence. At the same time, IFS customers will be able to expand visibility into their warehouse operations with confidence as the software moves to the IFS Cloud. The focus is on offering an end-to-end supply chain intelligence platform.
Softeon is best known for warehouse management software (WMS), coupled with warehouse execution software (WES) to manage automated facilities effectively. It also offers yard management (YMS) for dock loading and unloading, as well as for companies with yard spaces outside the warehouse walls. Distributed Order Management (DOM) rounds out the main suite.
Softeon serves third-party logistics (3PL) as well as time-sensitive food and beverage, electronics, and other manufacturing and retail warehouses. Some of the fastest-moving and most complex warehouse operations are core target markets for this 25-year-old company.
IFS Softeon also has AI tools and specialized “enablement tools” to improve speed, fit, and maintainability of its implementations. Softeon introduced Softeon AI Layer (SAIL) in 2025, with:
This was all before the IFS acquisition, so we expect strong AI capabilities to continue to emerge rapidly. The expansion mentioned above of IFS.ai Logistics is early indication of the momentum that’s building.
We believe this is a strong combination of companies that support their customers effectively. We also saw IFS Softeon at MODEX, and the booth had a great buzz, so we may not be the only ones excited.
IFS explored the market for this acquisition and chose Softeon for its obsession with customer choice and experience. As we warned in our previous work on WMS, forcing “best practices” on a warehouse operation can often result in radical sub-optimization and customer dissatisfaction.
We were delighted to be invited to the briefing with Lance Olmsted, Michael Catalino, and Mark Frampton. Special shout-out to Michael for meeting me at the MODEX show for some face time!
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Why do audits continue to find problems after appropriate root cause analysis (RCA) and corrective and preventative actions (CAPA) processes are complete? MetaFloor AI argues that there is a missing capability to capture and consistently reuse causal data for process intelligence. Their AI-based platform uses a causal graph with AI to not only capture process standards and details of actual events, improvement processes, and outcomes, but also store and leverage them as codified institutional knowledge.
The platform this young company has developed has many capabilities:
It is one thing to have formal processes for customer complaint handling, return materials authorization, and process non-conformance; that’s common. It is rare to be confident that the desired outcomes are complete. It is nearly unheard of to achieve that level of certainty in a rapid, automated fashion. This is where a causal graph and very precise process definitions come into play.
MetaFloor AI founders argue: “Standards-bound operational work is not fundamentally a document problem and not merely a generic workflow problem. It is a problem of preserving valid causal structure under uncertainty, coordination cost, and partial visibility. I don’t have the space to explain it all here, but the founders’ vision and fully documented formulas for typed causal governance are convincing. With this approach, it becomes clear when a case can safely be closed or when there are other dependencies to resolve.
While every manufacturer might face these issues, MetaFloor AI’s initial customers are in the electronics industry. The company is also focused on aerospace & defense (A&D), automotive, and medical devices. These industries have both industry regulations and standards, as well as company-specific standards for production processes. MetaFloor AI’s custom workflows are designed for each industry, and they anticipate an 80% fit once they have created the workflow for a few customers in that industry and the model is trained.
Users tend to be process engineers, quality engineers, operational excellence, or continuous improvement (CI) professionals. These are typically the employees tasked with day-to-day incident management. The AI can pull together knowledge and past experience from all these people and projects, identifying where a current situation might be similar to past events.
The MetaFloor AI platform is designed to be self-serve. A customer can sign up and without consulting or integration up front, start with an incident to record. The system will prompt about that event and also request previous RCA reports. Building the knowledge base happens incrementally with each problem the team brings to the system, providing an always value-adding, low-friction adoption path.
To encourage use, MetaFloor AI has bundled everything into a monthly price. For $499/month, a company can get a manager seat with all three system layers and 100 events per month. The second user is free to further encourage companies to learn and spur greater use and compliance easily and quickly.
The company was founded in the fall of 2025 and is just getting off the ground. However, the idea took hold earlier. The founding team spent months refining the thesis and validating the problem with operators in regulated manufacturing before MetaFloor AI was officially launched. The founders each bring particular strengths to this venture. This is the third startup CEO and commercial leader Anup Mehta has founded; previous ones include DeepEdge and Clarice Technologies. Sridhar Perepa is COO and has worked in engineering across the electronics, life sciences, transportation, and aeronautics sectors. Arun CS Kumar is Head of AI and Product, and also heads AI for DeepEdge; he has a PhD in AI/computer vision and a background in perception engineering for autonomous driving.
Closing the loop to ensure process improvements take place is not easy, but this AI- and industry-based approach holds great promise. The platform’s deep capabilities, combined with industry focus, bundled, cost-effective pricing, and NL interaction, bode well for growing adoption.
Thank you, Anup, Arun, and Sridhar, for briefing me on your breakthrough concept and sharing your white paper on Typed Causal Governance. I look forward to following MetaFloor AI’s progress in the market!
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Where are manufacturers focusing their AI initiatives? We surveyed over 250 manufacturers to find out, and we’re sharing a preview of the findings in an upcoming webinar on April 28th.
What is the current state of AI readiness from a data, organizational, and technical perspective? Are manufacutrers prioritizing the plant, R&D, engineering, the front office, or the backoffice? What are they trying to improve, and how well are they achieving the value they’re looking for?
Datacor asked Tech-Clarity to get down to the real truth about plans, approaches, and progress for AI in the process industries. Datacor Chief AI Officer, Sundar Kuppuswamy, will hold a fireside chat with the report author, Jim Brown, to understand the state of AI in process manufacturing. Sundar will also share insights from his customer experiences.
Join the webinar to learn more and get your questions answered. After the webinar we will be releasing three industry-specific reports based on this data, with details on:
Does PLM drive better outcomes in new product development?
Tech-Clarity invites you to participate in a research study on using PLM to support new product development across engineering and product development teams. Please take 10 minutes to fill out this short survey. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, eligible respondents will be entered into a drawing for one of twenty $25 Amazon gift cards. See the survey for eligibility details.
Take the survey now to share your perspective!
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Don’t miss this event achieving product data-driven digital maturity in the age of AI!
Digital maturity is no longer just about adopting new tools. For design and manufacturing organizations, it is about building the strategy, culture, and connected data foundation needed to make better decisions across the product lifecycle — and to prepare for the next wave of AI-enabled transformation.
Join Jim Brown, President of Tech-Clarity, and Bassanio Peters, Senior Market Development Manager of Design & Manufacturing at Autodesk, for a LinkedIn Live conversation on what it takes to become a more data-driven organization. They’ll discuss how manufacturers can evaluate where they are today, identify the organizational and process gaps holding them back, and create a practical roadmap for advancing digital maturity.
This lively discussion will explore why product data is becoming more critical as a business asset, how AI is raising the stakes for data quality and accessibility, and why connected, contextualized information is essential for faster, more confident decision-making. Jim and Bassanio will also discuss how leading organizations are improving visibility and alignment across teams as they modernize the way product information moves through the enterprise.
Attendees will come away with a higher-level understanding of how to:
Don’t miss this event achieving product data-driven digital maturity in the age of AI! Digital maturity is no longer just about adopting new tools. For design and manufacturing organizations, it is about building the strategy, culture, and connected data foundation needed to make better decisions across the product lifecycle — and to prepare for the…
MESA and Tech-Clarity Open Survey on The Business Value and Evolution of MES and AI Knowledge Sharing Opportunity: Complete a survey to get the results and learn from other manufacturers Phoenix, AZ, and Media, PA, USA, June 23, 2026 – The Manufacturing Enterprise Solutions Association International (MESA) and Tech-Clarity, Inc. are inviting responses to a…
How are manufacturers managing their manufacturing operations? Tech-Clarity invites you to participate in a research study on “level 3” manufacturing operations software for production facilities. What business value are companies gaining from software at this level? How is MES/MOM/MOS evolving? What impact is AI having on this landscape? We will also use the results to…
How can manufacturers gain confidence in moving to autonomous operations? Combining the agility of SaaS with the reliability of edge at the plantwide level is an emerging approach for MES. This eBook walks through why MES is core to moving from automation to autonomy. It discusses the challenges of gaining the resilience between the plant…
OEMs spend significant time and effort pulling together data to meet Integrated Product Support (IPS) requirements. Creating and aggregating product sustainment data is inefficient due to disconnected data and processes. How can PLM help OEMs and suppliers develop and capture high quality IPS data to meet standards like SAE GEIA-0007C without creating redundancy and excess…
Rick Franzosa and I recently had the opportunity to attend the Proficy Accelerate User Conference. This customer conference, already planned for the Proficy® from Velotic™ community, was the first since the formation of Velotic. Less than two months into being a new company, the event showcased not only the latest from Proficy but also the…
We recently had a briefing with Lytica, which has long brought unprecedented visibility to electronic component sourcing for its customers. The big news is that they have developed AI agents to leverage the data about purchase prices and transactions from their growing customer base. Sourcing is challenging, but having actual data from across the industry…
Five sponsors support research into the value and evolution of commercial plant-level software, including AI Phoenix, AZ, and Media, PA, USA, May 29, 2026 – The Manufacturing Enterprise Solutions Association International (MESA) and Tech-Clarity, Inc. have added sponsors to the 2026 research program. The two additions joining to support The Business Value and Evolution of…
How far have manufacturers come in connecting the product digital thread from design through manufacturing? How mature is the integration of data in the two primary systems supporting engineering and production? We interviewed over 200 large manufacturers to answer those questions and many more. Please enjoy an overview of our findings, below. For the full…
How strong is your foundation for Industrial DataOps, Smart Manufacturing, and plant-level AI? Are you considering an AI stack, or separate layers from leading proven providers? Join this webinar on June 18 at 10am Eastern time, 16:00 CET, to get new ideas about how to evaluate and choose the right industrial connectivity approach for long-term…
We recently spoke with Athena Technology Solutions and its partner, LLM at SCALE.AI, about the launch of a new offering: FabOrchestrator.AI, their agentic AI foundry for manufacturing. This multi-faceted platform streamlines everyday tasks across the plant. Why FabOrchestrator.AI? As the name suggests, this new offering is focused on orchestrating agents for a semiconductor fab or…
How well does data flow across semiconductor fabs from sub-fab and fab automation to MES and up to the enterprise? Tech-Clarity invites you to participate in a research study on the state of the semiconductor market in buying and connecting systems from sub-fab to fab to enterprise. The survey looks at technology investments for brownfield…
How can aligning product data in PLM with the aftermarket drive business value? Too often, PLM data is kept within Engineering, leading to disconnects between rich design data and downstream functions like service. Aftermarket support demands accurate product information at the product configuration level, and PLM is the source. How can manufacturers create a cohesive,…
How is process manufacturing taking advantage of the AI opportunity? We analyzed over 250 companies in the chemical, food, animal nutrition, and engineering industries to understand their plans and progress. We found broad plans, varied readiness, and a small number of companies who have made substantial progress beyond proofs of concept and pilots. Please see…
It was exciting to attend my first Aras Corporation ACE User Conference with Jim Brown. The conference was held in Miami, which provided a welcome relief from Boston’s long, cold winter. More importantly, it was great to learn more about Aras’ strategy and products, especially from the Aras leadership team, including their new CEO, Leon…
What are many manufacturers missing to succeed in digital transformation, smart manufacturing, and successful artificial intelligence (AI) initiatives? A common foundation for reliably getting the right data and information to the right place at the right time. In short, what’s often missing is enterprise-grade industrial connectivity. Normalizing, organizing, and making available the massive amounts of…
What do asset-intensive businesses need to manage the supply chain uncertainty they face? IFS believes it’s better warehouse software, and completed its acquisition of Softeon last month. Operating as a standalone company rebranded as IFS Softeon, the company is staying intact while gaining the backing of a credible enterprise software giant with strong AI…
Why do audits continue to find problems after appropriate root cause analysis (RCA) and corrective and preventative actions (CAPA) processes are complete? MetaFloor AI argues that there is a missing capability to capture and consistently reuse causal data for process intelligence. Their AI-based platform uses a causal graph with AI to not only capture process…
Where are manufacturers focusing their AI initiatives? We surveyed over 250 manufacturers to find out, and we’re sharing a preview of the findings in an upcoming webinar on April 28th. What is the current state of AI readiness from a data, organizational, and technical perspective? Are manufacutrers prioritizing the plant, R&D, engineering, the front office,…
Does PLM drive better outcomes in new product development? Tech-Clarity invites you to participate in a research study on using PLM to support new product development across engineering and product development teams. Please take 10 minutes to fill out this short survey. As a thank you, we will send you a copy of the report…
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