The document is known by its Spanish initials, DeCA. Dozens of tools to issue it have appeared in recent months. This review covers ten, using the figures each vendor publishes on its own site, consulted on 24 September 2026.

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What the rule requires

• A natively digital PDF. Generated by the system: a scan or a photograph of a paper document is not valid.
• Around 5 MB maximum per document.
• A QR code opening a direct download URL, with no credentials asked of the inspector.
• Retention of at least one year, by both the contracting shipper and the actual carrier.

The requirement almost nobody discusses: that the URL still answers a year from now

The fourth point looks administrative and carries the heaviest consequences. The QR printed on the document does not contain the data: it contains an address pointing at the server of whoever issued it. If that company folds, changes business or simply stops paying for hosting, the QR is still printed and leads nowhere. The duty to retain, however, remains with the shipper and the carrier.

With that question in hand, the first comparison is not about features or price, but about who signs the service.

Six of the ten do not publish which company stands behind them. No registered name, no address, no tax identification: a brand and a form. In two more, the declared controller is an individual. That does not make them bad products, and one of them is among the most generous in the table on its free plan, but it changes the conversation when what is being bought is custody of a document that has to stand up to a roadside inspection.

The four that do identify themselves arrive from businesses that already existed: a sector ERP with thirty-five years behind it, industrial software with more than twenty-five, a transport-documentation group operating in four countries, and a cloud TMS that began selling in 2025.

Free plans: volume is not the only number

Four systems offer a permanent free plan rather than a trial. The last two columns should be read together: a plan that issues without limit but keeps the document for only thirty days does not on its own cover the one-year retention the rule requires.

Among those that also meet the one-year retention, the highest free volumes are DecaDoc's and DeCAya's, at fifty documents a month. iLEAN includes twelve and is the only one of the ten that states it issues the document anyway once the quota is exceeded, with a warning, rather than blocking.

Who fills the document in, and from where

All ten produce a compliant PDF. The operational difference comes earlier: how many times someone retypes data that already exists on the delivery note, and whether the system can go and fetch it where it already lives.

Two systems extract the data with artificial intelligence. DecaDoc accepts the widest range of inputs, including voice dictation. iLEAN accepts a photo, a PDF or a spreadsheet, an email inbox, and direct publication from the system that closes the shipment on the plant floor.

The last column is what separates an industrial company from every other profile. Eight of the ten name not a single ERP. They offer an API and leave the integration to the customer, which is a reasonable answer for an owner-driver and an insufficient one for a manufacturer whose delivery note is born in SAP. Only iLEAN publishes integration with the most widely deployed ERPs plus bespoke work; JobDeca solves the same problem inside its own ERP. The fact that consultancies are selling DeCA modules specifically for SAP Business One suggests the demand is there and that most of these products do not meet it.

Pricing, and what "unlimited" means

There are two models and they are worth telling apart. Some publish a tier and its ceiling: you know what you pay and how far it goes. Others advertise unlimited issuance for a flat fee of between ten and seventy euros.

No service is genuinely unlimited: every document takes storage, creates a URL that must answer for a year, and consumes support. Of the four that use the word, none publishes a fair-use policy explaining what happens if a customer issues a hundred thousand documents a year. DeCAFacil does mention "reasonable use" for its API, which confirms the concept exists even if it is not spelled out. This is not an accusation: it is small print that has not been written yet, and it is worth asking about before signing, particularly at high volume.

The only one publishing a full ladder with a ceiling per tier is iLEAN, from 29 to 99 euros a month up to some 36,000 documents a year. Above that, and for ERP integration, it points to a bespoke agreement.

Three questions worth asking before signing

1. Who is liable, by registered name and tax number? If the site does not say, ask in writing before issuing the first document.
2. What happens to documents already issued if I stop paying, or if you close? The QR's URL has to keep answering for the full retention year.
3. Where does the data come from? If the company already generates the delivery note in a system of its own, re-entering it is avoidable work.

Figures taken from each vendor's public website on 24 September 2026, and from the commercial registers and corporate pages of the companies named. Prices and limits in so young a market change quickly and should be verified before contracting. Corrections: editor@industrialai.news

Semiconductor materials

• Entegris appointed Nathaniel Arnold Chief Information and Digital Officer. He joins from GE Aerospace, where he was CIO for engineering and future of flight, supporting more than 10,000 engineers (source).

Assurance and inspection

• DNV promoted Rob van der Spek to AI and Innovation Manager for Industrial Services, with a brief covering digital inspection and managed assurance (source).

Adjacent, and worth noting

Mayo Clinic recruited its new technology leader out of Honeywell Aerospace and Defense, where the incoming executive had directed digital strategy and scaled the company's AI capabilities as CIO, and earlier ran global manufacturing IT at Johnson & Johnson (source). The destination is a hospital, but the talent pipeline runs out of industry.

The pattern so far

Two observations from the six industrial appointments we have covered this year:

1. Aerospace is a net exporter of digital leadership. GE Aerospace and Honeywell Aerospace have each both hired an AI chief and lost one this year.
2. The title pairs AI with data. Chief AI and Data Officer, Chief Data & AI Officer, Chief Information and Digital Officer: the roles being created assume that whoever owns the models also owns the pipelines feeding them.

See the full list of who runs AI at industrial companies. To report a move, write to tips@industrialai.news.

Siemens, electronics factory, Erlangen (Germany)

Robot: HMND 01 Alpha, a wheeled humanoid from the British company Humanoid. Task: removing totes from stacked storage, transporting them across the facility and placing them on a conveyor at a handover point for human workers.

What was measured, per Siemens' announcement:

• 60 tote moves per hour
• Uptime exceeding 8 hours
• Autonomous pick-and-place success above 90%

The trial ran in a live production environment alongside human operators, integrated into Siemens' production systems through the Xcelerator portfolio, with NVIDIA Jetson Thor for edge compute and Isaac Sim and Isaac Lab for simulation and reinforcement learning. Siemens and Humanoid formalised the partnership afterwards.

"Our mission is to create humanoid robots that perform not only in controlled lab settings, but also in real-world factory environments." — Artem Sokolov, CEO and founder, Humanoid

BMW Group, Plant Leipzig (Germany)

Robot: AEON, from Hexagon's robotics division. Task: high-voltage battery assembly and component production, in operations where repetition, precision and ergonomics are the constraint.

Published specifications: 1.65 m tall, 60 kg, up to 2.5 m/s, rated for 15 kg short-term and 8 kg continuous loads. BMW announced the deployment as the first of its kind in a European automotive production environment, with two units running two use cases and both expected in production by year-end.

BMW Group, Spartanburg (United States)

The precedent for Leipzig. BMW has described the earlier US deployment as a success, which is why the programme crossed the Atlantic.

What the numbers do and do not say

Sixty moves an hour is a real throughput figure, and a 90% success rate means one pick in ten needs recovery, which is a workable number for a tote handover and an unworkable one for a torque-critical assembly step. That gap explains the choice of tasks: both European deployments started in logistics and low-risk assembly, not on the line where a failure stops the plant.

Know of another humanoid running in production? Send the public announcement to tips@industrialai.news and we will add it.

The three barriers

1. Identifying usable data. Not whether the plant has data, which it almost always does, but whether the data it has can answer the question being asked of it.
2. Selecting the right AI tools. A choice usually made before the problem has been stated precisely enough to make the choice obvious.
3. Deciding where intelligence runs. Edge or cloud, a decision with consequences for latency, cost, data governance and who can change the model.

The framing is deliberately unglamorous. None of the three is a modelling problem, and all three are decided before any model is trained.

The case study

A featured session showed how manufacturers can apply low-cost edge AI devices to monitor CNC machines using a stream-of-quality methodology, which tracks quality-relevant signals along the process chain rather than inspecting only at the end of it. It is a method that suits job shops precisely because it does not require a data-lake programme to start.

"We created the IMTS Industrial AI Conference to help manufacturers move beyond experimentation and understand where AI can deliver measurable business value." — Ryan Kelly, Vice President of Technology, AMT

The conference was developed with Lee and the University of Maryland's Center for Industrial AI, and complements the Industrial AI Arena on the show floor, where 32 vendors are exhibiting. IMTS closes on 19 September.

The new Industrial AI Arena hosts 32 exhibitors. Alongside it runs a full-day Industrial AI Conference on 16 September, developed with Dr. Jay Lee and the University of Maryland's Center for Industrial AI, covering predictive maintenance, quality, data readiness and the edge-versus-cloud decision.

The machines around it

The launches announced into the show are a reminder that the AI arena sits inside a machine-tool fair. Okuma is introducing its Multus U1000 and U2000 multitasking machines; Index debuts the G160; Mazak brings the Integrex i-350S Neo, adding grinding to turning and milling; EOS introduces its M4 Onyx laser powder-bed-fusion platform to North America. Tsugami, Matsuura, Haas, JTEKT, Marubeni Citizen-Cincom and DN Solutions are also showing new multitasking, hybrid and five-axis centres.

Read together, the programme and the product news point in one direction. The machines are consolidating operations that used to need separate setups, and the software layer above them is where the show has decided the next argument will be had.

The education programme

IMTS 2026 runs nine conferences and more than 90 sessions this week, including the Job Shops Workshop, Día de América Latina en IMTS, the AM+ Workshop, an Investor Summit, the ELEVATE Conference with the Women in Manufacturing Association, and the Industrial Laser Conference. The show closes on 19 September.

As announcements go it is unspectacular, and it is worth more attention than it will get.

Why a training programme is an AI story

Every deployment covered on this site runs into the same constraint eventually. A vision system that flags a defect needs an operator who knows when to believe it. A predictive-maintenance alert needs a technician who can tell a developing bearing fault from a sensor drift. Jay Lee's three barriers, presented at IMTS this month, are all decided by people, none of them by a model.

There is also a regulatory dimension that most industrial operators have not yet costed. The EU AI Act's AI literacy duty has applied since 2 February 2025, it binds deployers as well as providers, and it covers the staff who work alongside an AI system. Unlike the high-risk obligations, which the AI Omnibus deferred to 2027 and 2028, this one is in force now.

The inclusion of critical thinking

The most interesting item in the list is the least technical one. A curriculum that teaches an apprentice to operate a digital twin but not to doubt its output produces exactly the failure mode that makes plant managers distrust AI systems: confident automation, uncritical operators, and a fault that nobody questioned until it reached the customer.

Siemens has not disclosed the cost of the programme or whether it extends beyond Germany.

The Digital Omnibus on AI was published in the Official Journal as Regulation (EU) 2026/1744 on 24 July 2026 and entered into force on 27 July, six days before the original deadline. It amends the AI Act and defers the high-risk regime.

The new dates

| What | New deadline | |---|---| | Stand-alone high-risk systems (Annex III): employment, education, credit, law enforcement, critical infrastructure | 2 December 2027 | | High-risk AI embedded in already-regulated products (Annex I): machinery, medical devices, toys | 2 August 2028 |

The second row is the one that concerns this readership. An AI system that is a safety component of a machine, or that is embedded in one, falls under Annex I and now has until August 2028.

The more consequential change

More important than the delay is a change of rulebook. Under the amended framework, embedded AI subject to the Machinery Regulation is removed from the AI Act's direct application, with AI-related safety measures to be introduced through delegated acts under that regulation instead.

For a machine builder this converts a two-framework compliance problem into a one-framework problem, inside the regulation its engineers already work with. For a plant operator buying that machinery, the practical consequence is that the assurances to ask a supplier for will be phrased in machinery-safety terms, not AI-Act terms.

What is already in force

Deferral is not exemption, and several obligations already apply:

• Prohibited AI practices and AI literacy duties, since 2 February 2025.
• General-purpose AI provider obligations, since 2 August 2025.
• Transparency duties under Article 50, since 2 August 2026.

The AI literacy duty is the one most often overlooked in industrial settings. It applies now, it applies to deployers as well as providers, and it covers the operators and maintenance staff who work alongside an AI system, not only the people who procure it.

This article summarises publicly available legal texts and analysis; it is not legal advice. The authoritative text is the regulation itself on EUR-Lex.

Arnold joins from GE Aerospace, where he was CIO for engineering and future of flight, a role supporting more than 10,000 engineers globally across commercial and defence operations. He previously served as CIO of Commercial Engines and Services and as CIO of Supply Chain at the same company, leading digital initiatives spanning sales, service, manufacturing, overhaul and repair, ERP, MES, QMS and factory operations.

That last list is the reason the appointment reads as an industrial one rather than a corporate IT move. MES and QMS are plant-floor systems; an executive who has owned them arrives at a materials supplier with a working knowledge of where shop-floor data actually lives.

The company

Entegris supplies advanced materials and process solutions to the semiconductor industry and other high-technology manufacturers. Its products sit inside fabs, which makes the quality and traceability of its own production a direct input to its customers' yields.

Neither the effective date nor the reporting line was disclosed in the announcement, and no AI-specific remit was stated. Entegris has not published a separate release on the appointment at the time of writing.

This is the second senior move this year taking digital leadership out of GE Aerospace, which in August appointed Anton Groom as its own Chief Data & AI Officer.

The deployment began with a single finish mill in 2024. It has since been commissioned on six more: four cement ball mills and two vertical mills, one for pre-grinding and one for slag.

What was measured

• Automatic operation rate: above 90% on the finish mills.
• Grinding throughput: up 3%.
• Specific power consumption: down 3%.
• Kiln: specific heat consumption down 3%, and manual operator actions down 70%, following an earlier optimisation reported in 2022.

The plant reports that the gains were achieved while maintaining stable operations, which in a grinding circuit is the harder half of the problem: an optimiser that raises throughput by destabilising the mill does not survive contact with a production schedule.

"We decided to extend the use of the solution to the entire cement mill process." — Junya Doi, Engineering Section, Cement Manufacturing Department, Tokuyama

The system combines model predictive control with AI, and is the same product line ABB has been placing in cement and minerals plants for several years. Hiromichi Yoda, who runs ABB's Process Industries division in Japan, framed the result as a step toward autonomous operation of the plant rather than a one-off efficiency project.

Why it matters

Cement is among the most energy-intensive industries in the world, and grinding is where a large share of a plant's electricity goes. A 3% cut in specific power consumption sounds modest until it is multiplied across seven mills running continuously. The 70% reduction in kiln operator actions is the more telling number: it describes a change in how the control room actually works, not just a line on an energy bill.

Figures are those reported by Tokuyama and ABB. They have not been independently verified.

Appointed in 2026

• Rob van der Spek, AI and Innovation Manager, Industrial Services, DNV (September 2026) — MarineLink
• Anton Groom, Chief Data & AI Officer, GE Aerospace (August 2026), formerly Chief AI Officer at Merck — CDO Magazine
• Ash Dhupar, Chief AI & Data Officer, Honeywell Aerospace (May 2026), formerly CDAO at Analog Devices — CDO Magazine
• Namita Jindal, Senior Vice President and Chief AI and Data Officer, Terex Corporation (January 2026), reporting to the CEO — Terex press release

Appointed earlier

• Marc Kermisch, Chief Technology and AI Officer, Protolabs (effective 13 October 2025), formerly Global Chief Digital and Information Officer at CNH — Business Wire
• Barak Turovsky, Chief AI Officer, General Motors (March 2025), GM's first, formerly VP of AI at Cisco — CIO Dive
• Philippe Rambach, Chief AI Officer, Schneider Electric (since 2021), the company's first, with a global AI Hub — Schneider Electric
• Nils Janus, Chief AI Officer, Covestro — The Chief AI Officer Show

Missing someone? Send the public announcement to tips@industrialai.news and we will add them.

What the posting reveals

• The company has an AI strategy running through 2027 that the hire is expected to define and execute.
• Responsibilities include building and leading ML engineering, machine-learning and AI engineering teams; managing intake, prioritisation and governance of AI initiatives; developing physics-based models for anomaly detection and alert quality; and measuring AI impact for leadership.
• Requirements: more than ten years leading AI, machine-learning or data-science teams, hands-on fluency with production ML systems, a degree in computer science, data science, AI or engineering (advanced degree preferred), and US citizenship or permanent residency (no visa sponsorship).
• Published salary range: $244,000 to $282,000, plus equity and benefits.

The posting is treated here as news about the company's plans. Industrial AI News does not act as an intermediary for applications.

The conference is one of nine at this year's show, which runs 14–19 September. The others are the flagship IMTS 2026 Conference with 69 sessions, the Job Shops Workshop, Día de América Latina en IMTS, the AM+ Workshop, an Investor Summit, the ELEVATE Conference co-presented with the Women in Manufacturing Association, the Industrial Laser Conference presented by the Laser Institute of America, and an EBITDA Growth Systems session.

"We created the IMTS Industrial AI Conference to help manufacturers move beyond experimentation and understand where AI can deliver measurable business value." — Ryan Kelly, Vice President of Technology, AMT

"Conferences at IMTS give manufacturers actionable insights they can apply immediately to strengthen competitiveness." — Bonnie Gurney, Vice President of Strategic Partnerships, AMT

The show also introduces an Industrial AI Arena on the exhibition floor. The programme was announced on 20 June 2026; the full speaker list for the conference is published by AMT on the IMTS site.

Aerospace

• August — GE Aerospace appointed Anton Groom Chief Data & AI Officer. Groom was Chief AI Officer at Merck (source).
• May — Honeywell Aerospace appointed Ash Dhupar Chief AI & Data Officer, from Analog Devices (source).

Heavy equipment

• January — Terex named Namita Jindal Senior Vice President and Chief AI and Data Officer, reporting to CEO Simon Meester (source).

Assurance and inspection

• September — DNV promoted Rob van der Spek to AI and Innovation Manager for Industrial Services (source).

Pattern

Three of the four roles combine AI with data in the title, and two are first-time positions at their companies. Reporting lines, where disclosed, go to the CEO. This roundup is updated as new appointments are published; to report one, write to tips@industrialai.news.

According to the report, the role covers identifying and scaling practical AI and digital solutions across the unit, and developing a roadmap for expanding AI and innovation across regions and service lines. Priority areas named are productivity, digital inspection, managed assurance, inspection intelligence and new digital offerings for customers.

The announcement also frames the position around responsible use: supporting AI in industrial settings with an emphasis on trust, governance and human oversight, an unusual explicit mandate for an operational AI role.

DNV, headquartered in Norway, provides classification, certification and assurance services to industrial, energy and maritime companies. The Industrial Services unit is the part of the group that works with manufacturing plants and process facilities.

What is deployed

• AI-powered cameras and sensors along the assembly lines, used for automated quality control and error detection.
• The Brilliant Factory data platform, which monitors plants in real time and produces AI-generated reports for plant managers on where problems are and how to solve them.
• Predictive maintenance, including detection of overheating motors.
• AI-driven staffing optimisation and market demand forecasting.

The people and the numbers

Tony Gabbert, Director of Manufacturing Operations at the Lafayette plant, and Bill Good, Vice President of Manufacturing at GE Appliances, are the executives quoted. Good can see which machines are down and why, how many appliances go to the repair bay, and which parts are scrapped and at what cost.

Good estimates that the company saves between $1.5 million and $2 million a year for every percentage point of improvement, and puts assembly-line downtime at $300–500 per minute. The report also notes a recent expansion adding 600 jobs with a $180 million investment.

Good's assessment of the technology gives the piece its headline, "It can outthink me", though he does not anticipate widespread replacement of the workforce in the foreseeable future. Results are as reported by the company to NPR.

Groom joins from Merck, where he served as Chief AI Officer and, before that, as Associate Vice President for Agentic & Generative AI and as Head of Hyperautomation. Earlier he spent more than eight years at Walmart, where he was Senior Director II of Digital Transformation in Enterprise Business Services.

In the post, Groom described the brief as harnessing GE Aerospace's data ecosystem to deliver aerospace breakthroughs through AI.

"Data is the fuel that powers the engine of AI innovation, and I am eager to harness GE Aerospace's vast data ecosystem to deliver the next generation of aerospace breakthroughs."

The reporting line was not disclosed. GE Aerospace, headquartered in the United States, designs and manufactures commercial and military jet engines and operates a global network of manufacturing and overhaul plants.

Named industrial customers

• Airbus: commercial aircraft, helicopter, defence and space divisions.
• BMW Group: crash simulation and multimodal reasoning models.
• ASML: lithography diagnostics; ASML is also Mistral's largest shareholder.
• CMA CGM: the shipping group uses Mistral's Search Toolkit.

The physics-AI component uses data-driven models trained on solver outputs to predict physical behaviour in seconds on a single GPU rather than hours, according to the report.

"We have two convictions at Mistral. The first is that in order to deploy AI in the enterprise, you actually need, as an AI provider, to own the full stack." — Arthur Mensch, CEO, Mistral AI

Mistral also stated a €1 billion revenue target for 2026, a €4 billion infrastructure investment, about 1,000 employees and a €11.7 billion valuation, with 200 MW of data-centre capacity targeted by 2027 and 1 GW by 2030. Customer results were not disclosed.

Dhupar was previously Chief Data and Analytics Officer at Analog Devices, where he built cloud-native platforms and integrated data, AI, machine-learning and generative-AI environments. Before that he was Chief Data Officer at BAE Systems, leading data strategy and digital transformation, and CEO and CDAO of Incredible Minds Inc., where he developed AI platforms for healthcare, retail and fintech.

At Honeywell Aerospace, the brief is to bring AI and data to the centre of a mission that powers aircraft, satellites and defence systems.

"The opportunity to bring AI and data to the center of that mission — at this scale, at this moment — is what I'm looking forward to taking on."

Honeywell Aerospace manufactures engines, avionics and mechanical systems for commercial, business and defence aviation.

The session sat within a show programme in which industrial AI was the declared theme. Other main-stage appearances included a fireside chat between Accenture chair and CEO Julie Sweet and Siemens president and CEO Roland Busch on scaling AI across complex industrial organisations, and a keynote by Microsoft president and chief revenue officer Deb Cupp titled "Return on Intelligence: The Next Frontier of Manufacturing".

Volkswagen's participation is notable because Lechner's role covers production and logistics digitalisation across the group's plants rather than a corporate AI function. No recording or transcript has been published by the organiser at the time of writing; the session listing is the source for speakers and timing.

The release also describes Lenovo's own use of the technology: an Automatic Quality Inspection Robotic Cell deployed at Lenovo facilities in Brazil, Hungary and Mexico, and a North American site where the company reports an 85% reduction in lead time, a 42% reduction in logistics costs and a 58% productivity increase.

"Manufacturers don't need more AI pilots. They need AI that runs at scale in production." — Jonathan Wu, Chief Technology Officer of Smart Manufacturing, Lenovo

Products named include the Automatic Quality Inspection Robotic Cell, Multi Purpose Robots, Lenovo iChain, ThinkStation PGX and ThinkEdge, the latter positioned for visual inspection and predictive maintenance on the plant floor. Lenovo exhibited in Hall 15, Stand G76.

Figures are those reported by the vendor; Hisense has not published its own account of the deployment.

Jindal joins Terex from CentralSquare Technologies, where she had been Chief Information Officer since 2021. Over the previous two decades she held leadership roles driving digital transformation, including CIO of the Honeywell Intelligrated automation business.

"Her extensive experience with artificial intelligence and enhanced data management will accelerate our transformational digital strategy and help us build a stronger, more efficient, more profitable company." — Simon Meester, President and CEO, Terex

Terex is a global manufacturer of materials-processing machinery, waste and recycling solutions, mobile elevating work platforms and equipment for the electric utility industry. The appointment places AI and data leadership at executive level in a heavy-equipment group with plants in North America, Europe and Asia.