Steve Sterling shares commentary on industrial automation and manufacturing, connecting topics such as industrial AI, robotics, interoperability, workforce training, and practical engineering decisions.
Practical commentary on industrial AI, interoperability, robotics, and manufacturing adoption.
Best for
An industrial automation company seeking a thought leader or content partner on manufacturing and AI.
Not for
A campaign needing verified reach and substantiated product performance.
Key takeaways
Interoperability is what lets automation scale, not what slows it down.
A live factory pilot is the test that matters, not a lab demo.
The skills gap is being solved by early training and industry partnership.
Check first: Confirm audience analytics for target roles and industries before contacting them.
Main ideas
Interoperability is what lets automation scale, not what slows it down · A live factory pilot is the test that matters, not a lab demo · Progress comes from practical bridges, not from insisting on the perfect end state
Steve Sterling shares commentary on industrial automation and manufacturing, linking factory technology to practical questions about how work gets done.
Their posts cover industrial AI and interoperability, humanoid robots in a factory, heavy-duty trucks, workforce training, and CNC machining.
The examples show why readers may follow them: they connect emerging technology to operating conditions, customer needs, and real-world applications rather than treating innovation as an end in itself.
Sterling’s distinctive approach is to use a concrete case to make a broader manufacturing point while acknowledging its limits.
They compare isolated industrial AI systems with earlier machines that could not communicate, making interoperability’s value easier to grasp.
In another post, Edison Motors’ hybrid trucks illustrate the argument that engineering choices must fit demanding jobs such as forestry and snow plowing, not just an ideal end state.
Their examples also reach beyond factory-floor technology: a monastery project involving CNC machining shows an unexpected application, while an Ivy Tech training pipeline makes the case for early credentials and industry partnership.
Steve Sterling writes about industrial automation and manufacturing, judging technology by whether it holds up in real working conditions.
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Two arguments run through the posts: new technology needs shared standards and interoperability to deliver value beyond isolated islands, and progress usually arrives as a practical bridge rather than a perfect end state. The same test applies across industrial AI, humanoid robots, heavy-duty trucks, machine tools, and workforce training: a real pilot, a real duty cycle, or a documented program counts for more than a demonstration or a slogan.
Interoperability is what lets automation scale, not what slows it down
Steve Sterling argues that industrial AI is repeating an earlier automation problem.
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Smart machines had their own controllers but no shared way to talk, so each understood only its own world.
System-level insight came later, once standards and common architectures existed.
Machine-level AI, autonomous mobile robots, and vision systems risk becoming the same kind of islands of intelligence.
Standards are not a brake on innovation but the mechanism that lets it scale, integrate, and produce value across a plant rather than in one cell.
Steve Sterling points to industrial automation's history: fillers and conveyors each knew their own state, and only shared standards made plant-wide visibility possible. The post cites the newly formed Agentic AI Foundation, backed by major technology players, and a CIO Dive report as signs that the industry is moving toward open standards early this time.
A live factory pilot is the test that matters, not a lab demo
Steve Sterling treats the setting of a test as a signal of whether a technology is real.
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A demonstration in controlled conditions shows capability; running under actual production pressure shows whether it survives contact with the job. This is a consistent evaluative habit, not a claim about any specific vendor.
Steve Sterling highlights Figure AI's humanoid robots completing a pilot inside a BMW production plant on actual shop-floor tasks, and calls the difference between that and a lab demonstration the useful piece of information. The post offers no task list, performance figures, or safety data; buyers should ask for those before treating the pilot as a measured result.
Progress comes from practical bridges, not from insisting on the perfect end state
Steve Sterling argues that innovation has to meet reality halfway.
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When a technology cannot yet do every job reliably, the answer is a transitional approach grounded in physics, customer needs, and engineering judgment, rather than a solution that fails in the field. Reliability builds trust, and trust makes change stick.
Edison Motors set out to build fully electric heavy-duty trucks, then ran into jobs — forestry, snow plowing, heavy-duty cycles — that could not be done reliably with 100 percent electric power at the time.
The company engineered a hybrid instead, which still cuts fuel use and emissions while getting the work done.
Steve Sterling reads that as a manufacturing lesson about practical bridges.
The skills gap is being solved quietly, by training people early in partnership with industry
Rather than repeating that manufacturing has a workforce problem, Steve Sterling looks for programs that already work and asks what makes them work.
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The pattern is early training, consistency, and an employer-facing design. Community colleges, often underestimated, carry more weight in the next manufacturing workforce.
Steve Sterling points to a World Economic Forum example at Ivy Tech Community College in Indiana, where high-school students graduate with industry-recognized credentials before entering the workforce.
They describe it as practical, scalable, and aligned with employers' needs.
The post cites no graduation or placement data; buyers should ask for outcome figures before judging the model's results.
Automation's value shows up outside the production line
Steve Sterling widens the frame for what machine tools and automation are for, arguing that advanced automation can reshape how purpose-driven projects get realized, not just how production lines run.
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In the same post, Steve Sterling also names what the story does not answer, which is a useful habit to note: the reach of the example is not overstated, and the gap is left visible rather than filled in.
Monks in Wyoming are building a Gothic monastery using CNC machines and CAD/CAM tools to carve figures and architectural details that would have taken medieval artisans months or years by hand. Steve Sterling calls it an unexpected mix of tradition and modern manufacturing.
Explains why interoperability and open standards matter for industrial AI by comparing today’s machine-level AI with earlier isolated factory systems.
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Start here for Sterling’s argument that standards are a scaling requirement, not a brake on innovation.
Sterling describes a familiar industrial automation problem: PLC-equipped machines could not talk to each other, so system-level insight arrived only after shared architectures and standards.
The post argues industrial AI is at a similar moment, with machine-level AI, AMRs, and vision systems often developed as islands of intelligence.
It points to the newly formed Agentic AI Foundation and a CIO Dive report as signs of momentum toward open standards.
For a technical campaign, ask for a deeper look at the relevant standards and interfaces.
For brands planning a campaign
Useful for a standards, interoperability, or industrial AI campaign that needs a plain-language bridge from shop-floor automation history to agentic AI.
A collaboration could produce a “what needs to interoperate” explainer, panel framing, or buyer’s checklist for evaluating open interfaces.
Ask Sterling to clarify any vendor or standards-body affiliations before positioning them as an independent voice.
Explains what “real pilot” versus “lab demo” means for humanoid robotics in manufacturing.
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A useful starting point for readers looking for one concrete factory test rather than broad speculation.
Sterling reports that Figure AI tested humanoid robots on actual shop-floor tasks inside a live BMW production plant.
The post emphasizes that this was a real pilot under production pressure, not marketing theater.
Before using it to support performance claims, ask for the task list, performance metrics, and safety data.
For brands planning a campaign
For a robotics adoption campaign, this can anchor a “pilot versus demo” evaluation frame.
A collaboration could compare plant tasks suited to humanoids, the information buyers should request from a pilot, and where conventional automation still wins.
Get primary Figure or BMW details before making performance claims; the post summarizes the pilot but does not provide its underlying data.
Explains what works in manufacturing workforce development instead of repeating the skills-gap problem.
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Start here for a practical example of early credentialing and employer alignment.
Sterling highlights a World Economic Forum article about Ivy Tech Community College in Indiana building a pipeline where high-school students graduate with industry-recognized credentials before entering the workforce.
The post calls the approach practical, scalable, and aligned with employer needs.
Sterling argues that the institutions solving the skills gap will be those training people early, consistently, and in partnership with industry, and calls community colleges the backbone of the next manufacturing workforce.
Ask for program outcomes, graduation rates, and employer data before making claims about results or scale.
For brands planning a campaign
For a workforce development, education technology, or community college campaign, this supports an early-credentialing and industry-partnership message.
A collaboration could focus on replicable pipeline models, employer advisory roles, or what manufacturers should ask a local program.
Get program-level outcome data before making scale claims.
Across Steve Sterling's posts, they return to a similar test: whether a technology works in the real conditions it is meant for.
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Industrial AI is judged by interoperability and system-level value, humanoid robots by live factory pilots, electric trucks by field jobs, and automation by what it can actually produce. The through-line gives a brand a clear evaluative frame rather than broad enthusiasm for new technology.
For brands planning a campaign
A brand can use this frame for a webinar, interview series, or article asking what industrial technology needs to prove before adoption, with Steve Sterling applying the same practical test across AI, robotics, and manufacturing transitions.
Steve Sterling argues that industrial AI is repeating an earlier automation problem: smart machines and systems developed in isolation, with system-level insight arriving only after shared standards and architectures make interoperability possible.
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The post points to the newly formed Agentic AI Foundation, backed by major technology players, as momentum toward open standards. Standards are framed not as a brake on innovation but as what allows innovation to scale, integrate, and deliver value.
Uses a historical industrial automation analogy to make the current AI moment legible, then anchors it with a standards-body example.
For brands planning a campaign
A brand can feature Steve Sterling in an explainer, panel, or speaking slot on industrial AI standards and interoperability, especially for an audience weighing isolated pilots against plant-wide integration.
Steve Sterling highlights Figure AI's humanoid robot pilot inside a BMW production plant as a shop-floor test under production pressure, not a lab demonstration.
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The key distinction is between a controlled demo and a pilot in a live automotive environment. They treat that distinction as a useful signal of where humanoid robotics is headed.
Tests a technology claim against the setting where it must work—live production—rather than a lab demonstration.
The post relies on a linked article; ask for independent task-level performance measurements before treating it as a case study.
For brands planning a campaign
A brand can use Steve Sterling to help manufacturing audiences distinguish credible factory-floor robotics progress from marketing theater, for example in a sponsored briefing or conference session on humanoid robots in production.
Steve Sterling uses Edison Motors to argue that innovation has to meet reality halfway.
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Edison Motors began with an electric heavy-duty truck mission, then faced jobs such as forestry, snow plowing, and heavy-duty cycles that could not be done reliably with 100 percent electric trucks at the time.
The company engineered a hybrid approach that still reduces fuel use and emissions while getting the work done.
They draw a broader manufacturing lesson: progress often comes from practical bridges grounded in physics, customer needs, and honest engineering judgment.
Frames a technology debate around field constraints and customer jobs rather than a preferred end state.
For brands planning a campaign
A brand can collaborate on content about realistic decarbonization paths in heavy industry, using Steve Sterling to frame hybrid and transitional technologies as engineering decisions rather than compromises on ambition.
Steve Sterling points to a World Economic Forum example of Ivy Tech Community College in Indiana, where high-school students graduate with industry-recognized credentials before entering the workforce.
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The argument is that the institutions solving the skills gap may not be the loudest; they are the ones training people early, consistently, and in partnership with industry. Community colleges are presented as the backbone of the next manufacturing workforce.
Uses one documented program as a proof point instead of issuing a general complaint about the skills gap.
For brands planning a campaign
A brand can work with Steve Sterling on workforce-development content, employer-education partnerships, or a speaking engagement connecting automation adoption to the pipeline of trained manufacturing workers.
Steve Sterling notes an unexpected application: monks in Wyoming building a Gothic monastery with CNC machines, using CAD/CAM tools and precision machining to carve figures and architectural details that would have taken medieval artisans much longer by hand.
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The post treats this as a reminder that advanced automation can reshape purpose-driven projects, not just production lines.
Presents an unusual application as a way to broaden the discussion of automation beyond familiar industrial use cases.
The post itself says the story leaves questions unanswered, so it should be used as an illustrative example rather than a fully evidenced case.
For brands planning a campaign
A brand can use this angle for awareness or creative content that broadens the discussion of automation beyond factory settings, showing practical applications in preservation, craft, and mission-driven work.
The posts do not disclose any sponsorship, paid partnership, employment, client, investment, or ownership relationship.
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Each brand appears as a subject Sterling writes about — industrial AI standards, factory robotics, hybrid heavy-duty trucks, and manufacturing workforce training. Ask Sterling to confirm whether any such relationship exists.
Figure AI
Mention
Sterling wrote about Figure AI's humanoid robot pilot inside a BMW production plant, framing it as a live shop-floor test under production pressure rather than a lab demo, and pointed readers to the underlying article.
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The post names Figure AI and BMW as subjects of the pilot.
Sterling held up Edison Motors, a startup truck manufacturer, as an example of pragmatic engineering: the company set out to go fully electric, then built a hybrid diesel-electric truck for forestry, snow plowing, and other heavy-duty cycles that batteries could not yet handle reliably.
Sterling pointed to a World Economic Forum article about Ivy Tech Community College in Indiana, where high-school students graduate with industry-recognized credentials before entering the workforce, and argued that community colleges are the backbone of the next manufacturing workforce.
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They cite the college and the Forum as examples of what works on the skills gap.
Sterling referenced the newly formed Agentic AI Foundation, described in the post as backed by major technology players, as evidence that industrial AI is moving toward open standards and interoperability, citing a CIO Dive report.
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They compare it to earlier industrial automation standards work.
Steve Sterling writes short posts about industrial automation, manufacturing technology, and what it takes for a new tool to survive contact with a real plant floor.
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Two habits make the work easy to follow.
First, they favor decided examples over general enthusiasm: an AI standards effort, a humanoid robot pilot inside an automotive plant, a hybrid heavy-duty truck built after an all-electric design failed the field job, and a community college program that graduates high-school students with industry credentials.
Second, they return to the same test: did the technology hold up under the conditions it was meant for, and if not, what practical bridge gets it closer?
Start with the interoperability post if you follow industrial AI, the robotics pilot post if you want to know where humanoids stand in production, and the workforce and electrification posts if you’re interested in manufacturing's slower structural changes.
Tags group the work by subject; the similar creators below cover adjacent ground from cybersecurity, cloud-provider, and shopfloor-solutions angles.
Cacciatore writes for the same industrial-automation and manufacturing audience, covering robot selection, connected plant equipment, and Canadian production capability.
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Both writers ask what must be true before a plant buys or modernizes: Cacciatore focuses on device-level cybersecurity certification and traceable domestic supply chains; Steve Sterling on interoperability and whether a system works under real operating conditions.
Cacciatore argues from standards and purchasing risk — IEC 62443-4-2 device certification and defence procurement as an industrial modernization opportunity — and writes long, heavily formatted posts that end with a direct call to action. Steve Sterling focuses on adoption sequencing, uses third-party examples rather than certification requirements, and writes plain commentary.
Tohumcu covers the same convergence of AI, manufacturing, and physical robotics: software-defined vehicle architecture, compute constraints, humanoid robots moving from pilot to factory floor, and AI-driven supply chains.
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Both writers treat the shift from isolated pilots to scaled operations as the signal worth tracking.
Tohumcu writes from inside the cloud provider being described, reporting on customer visits and industry events with named partners and the company's own figures and forecasts. Steve Sterling stays outside the vendor position, commenting on published articles and other companies' projects without verifying those sources' claims.
Samel writes about shopfloor control — PLC logic, SCADA alarms, interlocks, fault tagging — and draws the same distinction as Steve Sterling between equipment that is nominally automated and a system that produces useful decisions.
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Both address operations audiences trying to get more out of machinery already installed.
Samel writes from a solutions-provider position, using first-person claims from the company's client work, including audit counts and downtime, energy, and fault-resolution percentages, and ending with a promotion. Steve Sterling writes as an observer, citing other companies' projects and training programs and offering interpretation rather than results from implementations they ran.
Steve Sterling’s posts address industrial AI and interoperability, humanoid robots in a factory pilot, practical electrification tradeoffs, and manufacturing workforce training—topics relevant to campaigns aimed at industrial automation audiences.
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The posts do not establish whether their followers work in industrial automation or relevant buying roles. Before booking, request recent LinkedIn audience analytics for the campaign’s target roles and industries or, if those are unavailable, documented audience survey results.
Steve Sterling's posts do not describe a partnership offer.
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Across five posts, none is labeled as sponsored, names an advertiser, or links to a media kit, rate card, or booking page.
Every outbound link points to third-party reporting or a company mentioned in the post.
The account's only official link is a LinkedIn profile, a contact surface rather than a partnership route.
There is no published format or stated guidance on lead times, approval steps, disclosure practices, or deliverables.
A buyer would need to ask directly about those terms and the available package.
The posts are short commentary pieces built around linked articles, with a point of view on industrial automation and manufacturing.
The work's shape is a written take connecting an outside story to a practical manufacturing lesson.
Recurring editorial habits include drawing an industry parallel (component-level AI repeating the earlier problem of machines that could not interoperate), favoring a pragmatic conclusion over an ideal end state (Edison Motors’ hybrid trucks), and highlighting an under-credited example (community college training pipelines).
A brand considering sponsorship would be buying that editorial voice, not a documented package.
The five posts show no commercial integrations, so there is no track record of how they handle sponsored posts, including disclosure, edits to brand copy, or adapting the linked-article format for a client.
The account also shows a modest audience, which affects reach assumptions for any paid placement.
An industrial automation company evaluating Steve Sterling as a thought leader or content partner on manufacturing and AI is the campaign need these posts support.
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Across five full LinkedIn posts, they write about industrial AI and interoperability standards, a humanoid robot pilot inside a live BMW plant, a hybrid heavy-duty truck decision at Edison Motors, CNC machining in a monastery build, and a community-college manufacturing pipeline.
The recurring frame is a practical-adoption test: does the technology work in the real conditions it was built for?
They apply that test to AI, robotics, electrification, and automation, giving a brand a clear point of view to commission rather than generic enthusiasm for new technology.
Two reasons make this a strong fit.
First, the subject matter lines up directly with the reader's stated interest in industrial AI, interoperability, and practical automation.
They already use the kind of explainer a brand would want: taking the history of shop-floor automation, where PLC-equipped machines could not talk to each other, and using it to make the current moment in agentic AI legible for a manufacturing audience.
That bridge can help an automation vendor address buyers weighing isolated pilots against plant-wide integration.
Second, the format works for collaboration.
Each post is short commentary built around a linked article with a stated takeaway, giving a sponsored or co-authored version a starting shape: a framing paragraph in their voice, a concrete outside example, and a stated lesson for manufacturers.
The fit is as a subject-matter voice and contributor, not as a reach purchase.
The account has 243 followers, and none of the five posts is labeled as sponsored, discloses an advertiser, or links to a media kit, rate card, or booking page.
There is no example of how they handle a paid placement, so disclosure practice, copy approval, and lead times would need to be agreed directly.
Before booking, request recent LinkedIn audience analytics for the target roles and industries; if those are unavailable, ask for documented survey results.
Without them, plan on buying editorial judgment and a credible manufacturing voice, not distribution.
The posts link to other outlets' reporting and do not carry original data, so any brand claim based on performance numbers, safety results, or pilot metrics needs primary substantiation from the sponsor.
That division of labor is workable: the brand provides the technical substance, and they provide plain-language framing and the practical test.
Settle it before writing the campaign plan.
When to choose a specialist
A campaign that needs verified reach and substantiated product performance is a poor fit based on these posts.
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Consider a launch campaign requiring a large, measured audience, third-party reach reporting, and a sponsored format with tracked deliverables — for example, a robotics vendor buying guaranteed impressions plus a case study asserting specific throughput or safety gains from a humanoid deployment.
The posts offer no basis for that plan.
The account has 243 followers.
The five posts are short commentary pieces linking to outside articles, and none is sponsored or carries a disclosed advertiser.
There is no media kit, rate card, approval process, or disclosure precedent, so a buyer cannot plan slots, lead times, or a brand-safety standard from the available evidence.
Reach guarantees and structured sponsorship packages would need to be established directly.
The claims problem matters as much as the audience size.
The post reports that Figure AI tested humanoid robots on real shop-floor tasks inside a BMW plant, but it includes no task list, performance metrics, or safety data, and does not describe the linked article's contents.
The post itself says the monastery story leaves questions unanswered.
A brand seeking a partner to publish product outcomes, benchmark comparisons, or procurement-grade support would need to provide that evidence independently.
Format is another mismatch.
A campaign built on video, podcasts, or webinars assumes transcripts or recorded appearances; the posts are written text only, so audio or filmed deliverables would rely on an unproven capability.
The same applies to a campaign requiring a strong public position on a specific vendor or product category.
The posts show no comparative or evaluative commentary, and a sponsored review would sit awkwardly with a track record of explaining outside examples rather than ranking suppliers.
The posts show no sponsorships.
Ask directly whether they accept sponsorships or paid work, and request an example of a previous sponsored post if that matters to the plan.
A worked campaign example
Campaign need: an industrial automation company wants to reach manufacturing engineering and operations leaders weighing whether to move from isolated pilot projects to plant-wide integration. The company sells integration software and wants a named contributor whose framing helps buyers think clearly about interoperability, with a speaking slot at its annual customer event as the closing asset.
Proposed collaboration — a three-part explainer series, "From islands of intelligence to system-level value," written in Steve Sterling's voice and published on their LinkedIn account, with the brand's resource linked once per piece:
Piece 1 sets the historical frame: machines with PLCs that could not talk to each other, and system-level insight arriving only after shared architectures and standards.
The automation company provides one concrete internal example of an integration that failed for interface reasons and one that worked, taking the piece beyond analogy into specifics.
It ends with their stated conclusion that standards let innovation scale rather than slow it.
Piece 2 applies a pilot-versus-demo test to the reader's decision: what a buyer should ask before scaling a single-cell success, which tasks suit the technology and which do not, and what support to demand from a vendor pilot. The company provides task-level detail and any measurement it is willing to publish, since the posts carry none.
Piece 3 is a buyer's checklist: which interfaces need to be open, what to document before a second site goes live, and the questions that separate a demonstration from a deployment. Deliver it as a downloadable checklist on the company's site, requiring only an email address, so the asset works outside the article.
Campaign angle and event tie-in: the series closes with an invitation to a 30-minute session at the customer event, where Steve Sterling interviews the company's head of integration about the practical bridge idea: progress comes from what works in field conditions rather than from an ideal end state. The interview format suits the evaluative frame across the posts and keeps the company's technical staff as the source of product claims.
Measure qualified engagement from manufacturing engineering and operations titles on the three posts, checklist downloads, and session registrations — not total impressions. With 243 followers on the account, the series is unlikely to produce a meaningful reach number; event registration and checklist downloads can carry measurable outcomes.
Before writing, settle audience analytics for manufacturing engineering and operations titles; whether they will commit to a sponsored or co-authored format and what disclosure line will appear; editorial sign-off and lead time; and confirmation that the company, not the writer, owns the performance claims.
If analytics do not show a relevant professional audience, reconsider the reach component and keep the collaboration as a small editorial contribution rather than a paid distribution buy.
If they decline sponsored work, pitch the same series as an unpaid guest contribution with the speaking slot as the only commercial element.
The deliverable is three short commentary articles and one checklist. Anything longer would exceed the writing format demonstrated in the posts.
For an industrial automation company choosing a contributor or speaker on manufacturing and AI, Anthony Cacciatore is the stronger choice when the campaign has to explain a specific technical standard or help buyers purchase safely.
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His posts do technical work at a level Steve Sterling's do not.
One walks through IEC 62443-4-2 and Security Level 2 device requirements in a connected factory: robot controllers now talk to PLCs, HMIs, MES, ERP systems and cloud platforms, and each new connection is another path to secure.
Its buyer-facing conclusion is direct — ask the manufacturer, ask the integrator, ask for the certificate, and understand the difference between a manufacturer saying a product has cybersecurity features and having independent certification against a recognized industrial cybersecurity standard.
It grounds the argument in the 2023 ABB ransomware disclosure while stating plainly that the disclosure did not identify robot controllers as affected.
The other post treats Canada's increased defence spending as a manufacturing modernization opportunity and lists what suppliers will need to compete for it: traceability, quality control, cybersecurity, production consistency, scalability, and an automation roadmap, ending with an invitation to talk about what capabilities must be built domestically.
If the deliverable is a connected-factory security explainer or a procurement-guidance piece for buyers weighing remote access, cloud connectivity or future AI integration, Cacciatore already writes at the specificity a sponsor can build on, and pairs the device-level argument with a policy-and-supply-chain one.
The tradeoff is range and focus.
Cacciatore's two posts sit in industrial cybersecurity and Canadian defence-manufacturing policy; a campaign that needs a broad practical-adoption story spanning AI, robotics and electrification, or a speaker for general manufacturing audiences, would have to stretch his frame further than the posts do.
The technical post is long and set in heavy bold styling, so a co-authored piece in that voice would need real structural editing rather than light polish.
The posts do not discuss the linked reporting behind their standard and incident references, so check any IEC clause or breach detail a brand repeats against the primary source.
The posts contain no rate card, media kit or disclosed sponsorship; ask for audience composition, pricing and disclosure practices before booking.
For an industrial automation company choosing a contributor or speaker on manufacturing and AI, Ozgur Tohumcu is the better choice when the collaboration needs an enterprise-adoption and supply-chain angle rather than a plant-floor one, and when the audience is the buying committee above the line.
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The posts are first-hand field notes.
One reports a week in Tokyo and Nagoya with automotive and manufacturing leaders and lands five takeaways: desirable products come first and technology serves the product; the best teams are getting sharper about which parts of the software-defined vehicle stack to keep in-house and which to partner on; a real GPU compute crunch is squeezing supply and start-up budgets; choosing a long-term AI partner has moved up to CEO and board level rather than being delegated; and physical AI is moving from idea to factory floor, with the humanoid robot market forecast to grow from roughly $3B in 2025 to more than $15B by 2030, and Japan among the places it happens first.
The VivaTech post covers agentic AI for industrial supply chain, argues that fragmented data infrastructure rather than model quality is the blocker, and cites figures for optimized supply chains of 10-15% cost reduction and 20-30% better forecast accuracy on the way from reactive to predictive to autonomous operations.
For a vendor selling integration or data platforms to executives, that maps more directly onto the buying committee's questions than a pilot-versus-demo test does.
The tradeoff is posture and format.
Tohumcu writes from inside a cloud provider: the posts name AWS, its regional team and its supply-chain experience, so the perspective is a vendor's and a sponsor would be adding its message to a voice that already carries another company's positioning.
That is workable for a partnership-style activation and awkward for a piece that needs an independent read on the market.
The posts are also event recaps that move across themes rather than building one argument, so a co-authored item would need a tighter structure.
The supply-chain figures are described as learning shared across the industry from the writer's own panel contribution, not measured outcomes from a named deployment; trace them to primary research before reusing them.
Ask for audience analytics, rates and sponsorship history before booking.
For an industrial automation company choosing a contributor or speaker on manufacturing and AI, Nilesh Samel is the better choice when the deliverable is aimed at plant managers, control engineers and owners who want execution detail and payback rather than framing.
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This is the most concrete material of the three alternatives.
The first post gives seven shop-floor practices with the reasoning attached: use interlocks so a pump cannot start without flow feedback, tag every trip cause in PLC logic to end unknown faults, insert timers between heavy loads to extend equipment life, log OEE with timestamps instead of end-of-day guesses, wire SCADA alarms to actions rather than beeps, install remote access for round-the-clock visibility, and train operators to interpret signals rather than press buttons.
It reports the result across 50+ plant audits — downtime down 20%, energy waste down 15%, fault resolution 30% faster.
The year-end post adds 15% cost savings from interlock-tuned logic and 30% faster decision loops through predictive dashboards, and states a building direction for 2026 that includes AI-driven edge systems and an Execution OS framed for Indian manufacturing.
A campaign that wants field-level tips, a named regional audience and a payback framing such as clarity per hour has a ready-made structure in these posts.
The tradeoff is independence and evidence.
Both posts are promotional: they are written to bring factory owners to GSN Solutions and close with a direct call to action, so a sponsor is commissioning a vendor-voice asset and a brand selling a competing product would be placing itself inside another company's funnel.
The performance numbers are self-reported from the author's own audits, with no methodology, sample size or third-party verification, so present them as what the audits are said to have found, not as independently measured results.
That is the same caution that applies to Steve Sterling's missing pilot metrics, sharper here because the figures read as product claims.
The heavy bold formatting and hashtag blocks would also need cleaning for a brand's own channels.
Ask for audience data, rates and sponsorship terms before booking.
Five LinkedIn posts by Steve Sterling discuss open standards for industrial AI and interoperability, Figure AI's humanoid robot pilot inside a BMW production plant, Edison Motors' hybrid diesel-electric heavy-duty trucks, CNC machining used in a monastery construction project, and Ivy Tech's high-school workforce pipeline in Indiana.
The posts were observed on October 10, 2026, and do not carry individual publication dates, so no per-post dates are given.
Articles, videos, and other profiles linked inside the posts are not covered here, so figures and project details appear as the posts describe them.
None of the five posts discloses a sponsorship, employment, client, or ownership relationship; the posts do not identify a brand relationship, which does not establish that no such relationship exists.
The posts do not establish any signature books, shows, businesses, or earlier career history, leaving an open gap for anyone searching the name for background rather than for campaign purposes.
No other primary information about this person is included here.
