# Steve Sterling

Steve Sterling shares commentary on industrial automation and manufacturing, connecting topics such as industrial AI, robotics, interoperability, workforce training, and practical engineering decisions.

- Profile: https://b2binfluencer.marketing/creators/steve-sterling/
- LinkedIn: https://www.linkedin.com/in/stevesterling
- Topics: artificial-intelligence, industrial-automation, manufacturing-workforce
- Based on 12 dated posts, latest 2026-10-04

## Summary

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.
- Check first: Confirm audience analytics for target roles and industries before contacting them.

### 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.

## Main ideas

- **Interoperability is what lets automation scale, not what slows it down**: Steve Sterling argues that industrial AI is repeating an earlier automation problem. 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.
- **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. 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.
- **Progress comes from practical bridges, not from insisting on the perfect end state**: Steve Sterling argues that innovation has to meet reality halfway. 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.
- **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. The pattern is early training, consistency, and an employer-facing design. Community colleges, often underestimated, carry more weight in the next manufacturing workforce.
- **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. 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.

## Work to read

- [When Smart Systems Didn’t Talk: A Lesson AI Is Relearning](https://www.linkedin.com/posts/stevesterling_big-tech-takes-steps-to-build-open-standards-activity-7405325619577880576-A0Fn)
- [Humanoid robots complete a real factory pilot inside BMW](https://www.linkedin.com/posts/stevesterling_figure-humanoid-robots-retire-bruised-after-activity-7397689324718366720-Krkq)
- [Edison Motors and the case for practical hybrid heavy-duty trucks](https://www.linkedin.com/posts/stevesterling_edison-motors-diesel-electric-trucks-redefined-activity-7407829903489896448-L7xX)
- [Monks in Wyoming are building a Gothic monastery using CNC machines](https://www.linkedin.com/posts/stevesterling_divine-interventionthe-carmelite-brothers-activity-7402762097820098560-xtWA)
- [Ivy Tech and a working skills-gap pipeline](https://www.linkedin.com/posts/stevesterling_the-world-economic-forum-activity-7400251353341566976-kgTq)

## Brand relationships

- **Figure AI** (mention)
- **Edison Motors** (mention)
- **Ivy Tech Community College** (mention)
- **Agentic AI Foundation** (mention)

## Partnerships

Steve Sterling's posts do not describe a partnership offer. 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.

## Alternatives

- Anthony Cacciatore: 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. 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.
- Ozgur Tohumcu: 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. 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.
- Nilesh Samel: 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. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/