Making the value of technology clear
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.
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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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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!
Please feel free to forward this survey to others you feel have an opinion 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.
During a closed-door introduction with ReilAI Corporation, I began to believe agentic AI might actually be ready for operations. I am delighted I got a peek at a new approach to making it easier to understand what’s happening in complex, ever-changing manufacturing and supply chain operations. This young company was founded on the belief that agentic AI holds the key to understanding and fully leveraging diverse data to inform the actions of a system in motion.
In this call, they showed me (representing Tech-Clarity, Inc.) and a select few others their Governed Agentic Execution Layer (GAEL), intended to bridge between data infrastructure and execution systems. Most exciting, it does so without displacing people or systems already operating in the plant, company, and ecosystem.
One of the curious points that ReilAI founder Joanne Friedman points out is that manufacturers measure the return on investment or return on invested capital for a $50,000 piece of equipment, but not for the $5-6M in data infrastructure that keeps the entire facility running – or not.
Data infrastructure is crucial to execution success. They point out three areas where an execution time gap exists today; these are how long it takes to:
In previous research (A New Era of Continuous Improvement), I’ve pointed out that time matters; it is one thing we cannot replace or regain. As most of us know, AI can save quite a bit of time. It can also capture and leverage knowledge, which is crucial as much of our experienced workforce nears retirement. So, a platform that leverages a knowledge and context graphs as well as agentic AI could help close those gaps.
A manufacturer adopting GAEL can keep their current workflows or change them up front. Agentic AI choreography enables agents to run in parallel or in swarms. This goes beyond prescriptive orchestration to enable adaptation as conditions change, which is essential for ongoing execution. The design focuses on extensibility, observability, and explainability.
At the foundation of this new platform, and the first word in its name, is governed. Governance for swarms of AI agents is crucial. Key principles of this design is observability, traceability, and flexibility. The starting use cases are manufacturing and supply chain, which are inherently fast-moving, multi-disciplinary, and challenging.
The platform is also designed for collaboration. I mentioned a knowledge graph for context. This architecture is not unique to GAEL, but essential for many current systems to be execution-ready. Graphs can create meaning and context from otherwise fragmented data. GAEL also has context graphs and judgement layers. So context might include user intent, perspective, experience, expertise, authority, and security. With agents for nearly any role or discipline, the data appears with this full, deep context, ready for action.
The question is how to ensure the agents will generate great results. Friedman points out that no executive will just trust AI agents to work autonomously. Agents must earn trust. So, ReilAI has developed a training pathway for the agents.
Given that the founders are deep manufacturing and supply chain experts, they have built agents for specific roles with some starting knowledge:
The platform has built-in “governed trust paths” that make sense for manufacturers. These start with physical safety, move to compliance, and also include margin protection logic. Until all of these are satisfied, even a “Master” level agent cannot autonomously execute its commands.
If you know Joanne Friedman, you have likely seen her work on Return on Data (RoD). She and I were recently on a podcast together discussing this topic. Her equation for RoD is:
[Sunk Data Capital] X [Contextual Intelligence] = [EBITDA Expansion]
The returns can be in top-line or bottom-line; in cost or revenue enhancements. Most manufacturers store tremendous amounts of data and pay for infrastructure and cloud services. The question is, how much of that data is delivering value, and how much?
One aspect of that we’ve also written about is expanding beyond production to link multiple companies in an ecosystem. The ReilAI vision is that each industrial facility (plant, warehouse, distribution center) can leverage GAEL to become an intelligent smart node within a coherent system. They say, “As more partners integrate, predictive power and execution value compound exponentially.”
Agentic AI may be ready for industrial environments. To date, news about challenges in scaling and trusting AI in industrial settings abounds. We hear that investments may or may not pay off. The team at ReilAI has me thinking that might be news of the past, not the future or even the present. If GAEL can deliver everything in their vision, it will be a powerful platform to enhance operational execution. Governed agents ranked by expertise, leveraging existing people and data, seems like a good path forward.
Thank you, Joanne Friedman and Matthew Funderburg, for inviting us to this fascinating early look at your innovation at ReilAI Corporation. We look forward to following your progress in the market.
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We had the chance to spend some time with PTC leaders recently to discuss their strategy. We’ve followed PTC for over two decades and watched them transform numerous times. They’re currently in a new era under the leadership of CEO Neil Barua , who took over the CEO role several years ago after serving as the CEO of ServiceMax. Our impression from the conversation is that the PTC strategy feels refreshingly focused and leverages their core strengths. We think customers will respond very well to this approach.
Stepping back, what are PTC’s core strengths? PTC started with parametric CAD and has remained strong in the design world, currently with Creo, Onshape, and Creo Elements Direct. They were also one of the earliest PLM companies with their Windchill platform. They have deep roots in these spaces and have continued to innovate and invest over time.
But PTC has also been forward-thinking and pushed the boundaries of what a “PLM” suite should offer. They have consistently looked more broadly beyond engineering, supporting what today we’re all calling the digital thread. Examples of that include expanding to ALM to support product software, Arbortext for product documentation, and Servigistics for SLM. Product data is PTC’s core strength, and their current strategy has “shifted exclusively to product data value.” Given the increasing value of structured data to fuel AI initiatives, we believe this is a solid strategy.
The new vision PTC unveiled is IPL, or “Intelligent Product Lifecycle.” Following on their digital thread history, it covers the full product lifecycle from engineering, through manufacturing, operations, service, and sustainment. PTC says IPL is “Powered by product data, fueled by AI.” The strategy leverages both PTC’s broad product portfolio and a belief in openness to connect to product data in adjacent and even competitive systems following an OSLC and other standards-based approaches. An example of openness is Windchill, which has always put a high priority on supporting multiCAD environments. Their open, enterprise-level approach is highly valuable as manufacturers continue to streamline operations and remove product development friction.
PTC is also increasing investment in their design capabilities, what they’re calling Integrated Product Engineering, or IPE. The IPE approach consists of orchestration and collaboration across design disciplines. PTC supports this with a collection of the right pieces for today’s complex, software-defined products including the Codebeamer ALM solution to support software-defined products. This gives PTC both mechanical and software design, and they partner with leading ECAD vendors.
PTC’s strategy extends further down the digital thread and product lifecycle to manufacturing, where they support manufacturing process planning. From there it extends to the service lifecycle and end-of-life. These areas follow the PTC strategy to focus where product data drives value. For example, PTC can offer configuration-specific work instructions for field service or MRO based on the as-designed, as-manufactured, and as-maintained product structures.
PTC will continue to focus on five markets they feel they can best service because they have what they call “whole product” needs, including software-defined products, safety-critical / regulated industries, serviceable / circular products, and high rates of engineering change. The industries they focus on are:
Product data also supports PTC’s “Applied AI” strategy. It’s a practical strategy to help their customers gain new value through high value, achievable use cases. PTC has already delivered AI through existing capabilities like shape recognition and topology optimization in CAD. Now, they are using AI in ALM to validate and improve requirements and draft test requirements. In field service, they plan to leverage AI’s ability to gather data from disparate systems to streamline field service through generative data aggregation. PTC will surely extend these capabilities, and we look forward to learning more as they progress. From what I learned from a recent PTC AI in Focus webinar I joined, PTC has been making progress and delivering on a holistic, practical AI strategy.
We’re also looking forward to learning more about a new PTC product, Asset360. Asset360 is a product twin that serves as the data hub for physical assets in the field. The Product Twin Is PLM-connected and includes fielded asset configuration and activity data.
PTC also made a strategic decision to divest Kepware and ThingWorx. The capabilities were intended to further PTC’s “smart connected products” strategy, but their IoT capabilities gained traction on factory equipment around the product being manufactured, rather than on the products themselves. Divesting Kepware and ThingWorx closes that chapter and allows PTC to focus more squarely on product-centric capabilities versus manufacturing asset-centric functionality.
Lastly, it’s important to mention PTC’s SaaS strategy with "Plus" offerings for Creo, Windchill, and Codebeamer. This is in addition to cloud-native solutions Onshape and Arena. Today, PTC focuses Onshape on smaller companies and Arena on fast-moving products like electronics, what we would call supply-chain-centric versus engineering-centric manufacturers. They also have their FlexPLM solution for footwear and apparel. These solutions aren’t being force-fit into the IPL strategy, which allows PTC to focus on their core capabilities.
We expect a continued positive reaction to the focus from their customers and we’re excited to follow their progress. Thank you to PTC's Danaya Ostine, Dan Kerns, and Dave Duncan for your time sharing your vision with our analyst team. Thank you to Tech-Clarity's Michelle Boucher, Howie Markson, and Julie Fraser for joining the briefing and providing input to this post.
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How can food and other recipe-based manufacturers accelerate their innovation without wasting time and compounding their compliance risks? Startup Prodeen offers AI agents and playbooks designed to turn the tide, and early customers are finding benefits. These early customers are global food and beverage companies. Initial users are in regulatory affairs, quality, and food safety, with a view to R&D, innovation, supply chain, and procurement, as well as other recipe-based industries in the future.
The Prodeen team sees a SaaS paradox: more tools but less intelligence. A significant disconnect is between internal master data and external intelligence. They say 60% of regulatory time is wasted fighting this “compliance crisis.” So, they set out to develop agentic AI not for governance, but for operating across tasks and discipline siloes.
They set out to complement the existing systems of record and data governance, such as PLM and ERP, as well as the many point solutions for specific roles or tasks. Like Prodeen, we have also seen that existing systems often reinforce the siloes for each discipline in a company.
Our State of PLM in CPG research shows that the majority of CPG companies do not feel their PLM is prepared to meet their future needs. Replacing PLM is difficult, but getting more value from it by using AI may be a better approach. Prodeen’s solution aims to orchestrate across PLM, ERP, and point SaaS systems. Worth noting: this platform is not only for analyzing the mountains of regulatory, R&D, recipe, ingredients, and operational data, but also for executing on what they see in a governed yet rapid and agile manner.
The Prodeen agentic AI platform includes several capabilities, each focused on a common industry need.
Fascinating also is that they recognize the potential for rapidly escalating costs and tokens; they are focused on engineering a framework by taking a multi model approach or allowing companies to bring their own model to make this sustainable for a large company with its many ingredients, suppliers, and regions while complying with regulations.
Prodeen’s strategy is to leverage every customer engagement to create reusable compliance workflow automation. The vision is that each new customer and use case will compound value across all customers. Examples might include conformance documentation and label simulation. With this approach, Prodeen can quickly engineer specific workflow templates into the product. The user can not only enter chat, but also use a framework to execute a process, such as editing recipes or build dashboard for risk assessment. The flywheel does not use the customer’s data, but the needs and priorities across customers. In practice, the system that listens to what the customers say during sales, demos, and use, and enables Prodeem with a 2-person team to develop one or two new playbooks per week. As they prove this out, they can quickly scale into new functionality. This is also important since AI tools and regulations change regularly.
Though the company is young, the founders have deep experience in recipe-based product lifecycle management and solutions for batch process manufacturers. The founders, Nicola Colombo, Tye B., and Jakub Janoštík, worked together most recently at SGS DigiComply. Nicola was also a co-founder of Selerant, which became Trace One, and his father owned a flavor house.
They know how to run and scale a software company, and their entire careers have been focused on recipe-based industries and the challenges they pose. The AI agentic platform poses new challenges for them, but their core competencies have already been proven.
While Prodeen is young, we see great promise in a system that has such deep industry expertise at its core and aims to orchestrate workflows that leverage existing systems. The promise of greater clarity on regulatory needs as they change, coordinated across disciplines, can begin to alleviate some of the challenges of recipe-based companies.
They have recently won some big-name customers by getting up and running quickly to add value. One large company saw results and went from PoC to production with Prodeen in less than three months. We know how thorny recipe-based compliance and innovation can be, and look forward to watching Prodeen’s progress in the market.
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How can manufacturers meet customer traceability requirements faster and easier, with a higher level of reliability? Arcstone Advanced MES would argue that using the customer’s choice of LLM AI tool to access the real-time MES and supply chain data in their solution is the answer. Apparently, quite a few automotive components and food and beverage companies would agree, as Arcstone has been growing worldwide in these industries. Arcstone has also added an AI governance tool and a studio for building and governing apps. We recently caught up with founder Willson Deng to learn the latest.
The concept behind Arcstone’s offering is that MES at every level is the missing link to achieve real-time, end-to-end supply chain visibility. The company’s stated mission is to provide complete manufacturing transparency across the entire supply chain. CEO Willson Deng states: “By digitalizing and integrating manufacturing operations from the shop floor right to the hands of consumers, we aim to enable a more responsive, responsible, and sustainable manufacturing ecosystem for us all.”
This company offers both MES and supply chain software, aiming to enable even the smallest suppliers to deliver accurate manufacturing data into their ecosystem. We had our first briefing with Arcstone a few years ago; that insight goes into the concept in more detail, lists the product elements, and shows them in a graphic.
The company serves many industries, but two in particular have adopted this multi-tier supply chain via MES approach. Precision-engineered automotive parts and components, as well as food and beverage products, have driven excellent worldwide growth for Arcstone.
Both industries face significant risk from faulty materials in their products and are thus regulated accordingly. Automotive parts and food and beverage are highly competitive. They can both capture more or higher-margin revenue with better traceability.
Every conversation about software these days touches on AI. Arcstone’s focus is not on creating new tools, but on ensuring companies have protection when accessing actual manufacturing data from their software. Operations people spend plenty of time seeking data, and Arcstone created an MCP interface to enable any LLM or third-party system to read and interpret data across their own enterprise and their suppliers’, customers’, and partners’ systems. It has also created a manufacturing assistant agent that makes it easier to leverage data from the plant floor.
Exposing the Arcstone MES and supply chain systems’ data across the ecosystem has also made it easier to create integrations. An MES-to-ERP integration is crucial, and rather than the two or three people and a month it can often take, the new AI approach enables it to be handled by one person in a few days. MCP also cuts time by half or more when customizations are used to help ensure the MES matches operating best practices and is adopted by operators, Arcstone says. The Arcstone ecosystem of system integrators (SIs) is finding that this AI approach enables them to focus on customer success and satisfaction with fewer headaches, too.
In a pragmatic yet visionary way, Arcstone has also recently released two new capabilities to support governance in the age of AI and low-code apps.
Deng is a true visionary and continues to offer new insights. One is that the mindset for MES and supply chain must shift from what it can do to how customers can use it. Today, AI can build code to do specific functions. Yet, the complexity of managing manufacturing data and sharing it across a particular enterprise and its supply chain ecosystem is where the fundamental value now lies. This underlying MES capability enables significant efficiency gains, whereas functional improvements and the addition of AI for specific capabilities typically deliver only incremental improvements.
Thus, the Arcstone roadmap is less focused on functionality than on robustness, reliability, and ease of use for SIs to do what they need to do. Arcstone’s focus on architecture and ease of customization is to satisfy the SIs. The SIs are there to help manufacturers learn to use the system, then hand it over to end customers to manage and maintain, with no support beyond what they might need.
Arcstone’s vision of end-to-end multi-tier supply chain visibility to the factory floors is a strategic dream for most manufacturers. So is the level of sustainability it could enable. In many industries, being wasteful is more cost-efficient than addressing yield issues at the source. However, as regulations change, we expect to see more uptake of this unusual plant-first approach to supply chain resilience.
As commercial LLMs and AI tools mature, their integrate-what-you-choose approach may also serve them and their customers well. Adding arc.ai appears to lower the risk considerably. It can also help corporate IT as they gain AI expertise and new approaches continue to emerge. When combined with arc.studio for governed apps, the picture starts to make sense, particularly for larger enterprises and their ecosystems.
Arcstone’s partner-first philosophy further differentiates them. While many other MES and supply chain providers seek to perform services or ask customers to do it themselves with low-code or AI approaches, this can create competition with SI partners. Arcstone sees the SIs as the expert human touch that customers want and need for implementation, customization, and ongoing 24x7 support worldwide.
We have been impressed by Arcstone's vision and approach for years. We look forward to hearing about what comes next.
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Our research shows that 86% of companies consider environmental sustainability to be critical or important to their long-term business success. Further, our studies show that digital transformation is crucial to achieving it. But how can companies determine the sustainability impact their technology adoption makes to make a business case for new solutions? This eBook explores the relationship between digital transformation and sustainability, and more specifically, how sustainability impact can be credibly measured and used to make business decisions. The eBook uses and shares highlights from eleven credible, audited case studies from real companies that have improved sustainability through technology adoption.
Please enjoy the summary below. For the full research, please visit our sponsor, Dassault Systèmes (registration required).
Sustainability Demands Digital Transformation
Sustainability is good business, and ESG initiatives require supporting technology. More than three-quarters of companies, 82%, report that technology / digital transformation is important or critical to support environmental and social sustainability. Digital transformation and sustainability can go hand-in-hand.
Difficulty Determining Sustainability Value
While most digital transformation initiatives and technology investments are initiated and justified to achieve a financial return on investment (ROI), it’s important to recognize their ESG advantages in addition to their financial benefits. For example, manufacturers may reduce cost by improving production efficiency while also reducing carbon emissions. Similarly, engineers could optimize designs to enhance performance while simultaneously reducing energy usage and waste. These values should be calculated to complement the financial ROI and improve decision-making based on ESG impacts.
Driving Economic and Environmental Advantages
A joint Rockefeller Asset Management-NYU Stern Center for Sustainable Business analysis reports a growing consensus that good corporate management of ESG issues typically results in improved operational metrics such as return on equity (ROE), return on assets (ROA), or stock price. It’s increasingly valuable to determine the sustainability impact of digital transformation more directly associated with the initiative leading to the improvement. It’s even more valuable to estimate this impact in advance, complementing the financial ROI with a strategic sustainability ROI to help justify the investment.
While companies have significant experience deriving financial metrics, they often lack the ability to accurately and holistically measure the ESG impact of their digital transformation efforts on their value chain. This research explores how to properly determine the value, and reviews case studies that show tangible sustainability impact based on a credible, scientific methodology.
How can manufacturers develop a digital thread and unlock the business value necessary to stay competitive?
Today’s manufacturers operate in an environment defined by compressed timelines, increasing product complexity, and heightened customer expectations. Success depends on the ability to move quickly without sacrificing quality, compliance, or profitability. To achieve this, organizations must enable seamless collaboration and smarter decision-making across engineering, manufacturing, quality, and the supply chain.
True operational efficiency comes from connecting people and processes through a single, reliable source of product information. When every team works from accurate, up-to-date product information, organizations reduce errors, eliminate rework, and respond more effectively to change.
A product digital thread makes this possible. Enabled by product lifecycle management (PLM), the digital thread creates a continuous, end-to-end flow of product data across the organization and throughout the product lifecycle. This eBook explores what a PLM-enabled digital thread is, why it matters, and how manufacturers can build one to drive lasting business value.
Please enjoy the summary* below. For the full research, please visit our sponsor, Propel (registration required).
New Product Development is More Complex
For manufacturers, delivering profitable products to the market has become significantly harder. Products are more complex than ever, requiring additional resources with expertise in new disciplines, driving up development costs, and putting profit margins at risk.
The Heightened Impact of External Pressures
Some of this complexity arises from external factors outside a manufacturer’s control. Customers are increasingly demanding, expecting innovative products more quickly than ever before. Competition is coming from all directions. Not only from traditional competitors, but also from new entrants. Our State of Product Development survey found that 56% of manufacturers face competition from adjacent industries, while 52% compete with low-cost or offshore manufacturers.1 Today’s supply chains add to the challenge. In fact, 74% of manufacturers in the survey identified supply chain disruptions or market volatility as a top challenge in product development.2 Beyond that, government and industry regulations are widespread, especially in High Tech and medical technology, demanding strict engineering and quality processes with thorough data collection and management.
Multi-CAD Environment Complicates Design Collaboration
Some of the complexity stems from internal issues. Remember when products were primarily mechanical?
Those days are gone. Now, mechanical, electrical, and software teams all need to work together – and be productive doing it. They must ensure that form, fit, and function all work in harmony while delivering their designs on the same development and launch timeline.
However, each design discipline uses different tools, with product data stored and managed separately or, in the worst case, only on an individual engineer's drives. Managing and accessing product data across multiple design systems, let alone file folders and shared drives, negatively impacts collaboration and reduces productivity.
Where to Start
For some manufacturers, establishing a digital thread may be viewed as out of reach when facing budget, resources, and time constraints. However, manufacturers can establish a digital thread despite these challenges.
Use 80-20 Rule
Applying the 80-20 rule helps focus on the most important and common use cases and workflows first. These deliver the most significant business value without getting bogged down with less common and more complicated edge cases. In short, keep it simple.
Keep Established Workflows
Established workflows need to continue, especially those supporting regulatory requirements, but avoid excessive customization whenever possible. Using out-of-the-box functionality saves implementation time and money, and reduces the need for dedicated IT resources.
Connect Existing Systems
There is no need to start from scratch. A practical approach is to connect existing CAD, PDM, and PLM investments and applications that are working well to create the product digital thread.
Take a Phased Approach
The best path is to take it one step at a time. Since data across systems is probably not perfectly aligned, a phased approach to PDM-PLM integration is preferred. Start with a small project, or assembly, to avoid a massive data cleanup upfront. Then add new projects and products as data cleansing progresses.
*This summary is an abbreviated version of the eBook and does not contain the full content. For the full research, please visit our sponsor, Propel (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
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What is the role of AI in Pharma manufacturing? Can highly regulated pharmaceutical companies use AI effectively? What are companies doing to leverage AI without compromising their CGMP-validated processes? Please join this practical, real-world conversation on moving AI in pharma beyond pilots and into meaningful results.
Tech-Clarity’s Julie Fraser joined a panel discussion with Sam Laermans, Global AI Lead at NNIT, Senior IT Leader and Biotech Expert Joseph Ricci, and Kate Porter, Director of Product Management and Research at POMS. Roland Esquivel, POMS VP of Sales and Marketing will moderate the discussion and lend his experience also. This diverse panel will discuss what is already working and where regulatory, quality, IT, and other questions and challenges lie.
The lively discussion touches on these topics:
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We had the chance to spend some time with PTC leaders recently to discuss their strategy. We’ve followed PTC for over two decades and watched them transform numerous times. They’re currently in a new era under the leadership of CEO Neil Barua , who took over the CEO role several years ago after serving as…
How can food and other recipe-based manufacturers accelerate their innovation without wasting time and compounding their compliance risks? Startup Prodeen offers AI agents and playbooks designed to turn the tide, and early customers are finding benefits. These early customers are global food and beverage companies. Initial users are in regulatory affairs, quality, and food safety,…
Global Manufacturing IT Association and Research Firm Tech-Clarity Focus on How Manufacturers Gain Value from MES in the Age of AI Phoenix, AZ, and Media, PA, USA, February 27, 2026 – The Manufacturing Enterprise Solutions Association International (MESA) is working with Tech-Clarity, Inc. on a research program, The Business Value and Evolution of Manufacturing Operations…
How can manufacturers meet customer traceability requirements faster and easier, with a higher level of reliability? Arcstone Advanced MES would argue that using the customer’s choice of LLM AI tool to access the real-time MES and supply chain data in their solution is the answer. Apparently, quite a few automotive components and food and beverage…
Our research shows that 86% of companies consider environmental sustainability to be critical or important to their long-term business success. Further, our studies show that digital transformation is crucial to achieving it. But how can companies determine the sustainability impact their technology adoption makes to make a business case for new solutions? This eBook explores…
How can manufacturers develop a digital thread and unlock the business value necessary to stay competitive? Today’s manufacturers operate in an environment defined by compressed timelines, increasing product complexity, and heightened customer expectations. Success depends on the ability to move quickly without sacrificing quality, compliance, or profitability. To achieve this, organizations must enable seamless collaboration…
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What is the role of AI in Pharma manufacturing? Can highly regulated pharmaceutical companies use AI effectively? What are companies doing to leverage AI without compromising their CGMP-validated processes? Please join this practical, real-world conversation on moving AI in pharma beyond pilots and into meaningful results. Tech-Clarity’s Julie Fraser joined a panel discussion with Sam…
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