# Jonathan Green

Jonathan Green shares practical AI and automation work for businesses, including systems designed to reduce manual operations, and discusses AI’s role in manufacturing and supply chains.

- Profile: https://b2binfluencer.marketing/creators/jonathan-green/
- LinkedIn: https://www.linkedin.com/in/servenomaster
- LinkedIn followers: 11,238
- Topics: artificial-intelligence, sales, supply-chain, ai-adoption, manufacturing
- Based on 19 dated posts, latest 2026-09-17

## Summary

Jonathan Green is most useful for practical AI and automation examples that reduce manual operations and connect existing business systems.

- Best for: Mid-market manufacturing operations campaigns that want measurable cuts to repetitive work and inspectable automation examples.
- Not for: Consumer fashion campaigns aimed at designers, trend forecasters or shoppers, or whole-platform rollouts.
- Check first: Confirm audience job titles, company size, industry, seniority, and any rate card or media kit.

### Takeaways

- Connect CRM, ERP and QuickBooks so staff no longer re-enter order details.
- Slow follow-up traced to leads not enriched or routed to right salesperson.
- Ask owners what keeps them working late, then build a narrow project.

## Main ideas

- **Start with the owner's biggest recurring headache, not a trend**: Jonathan Green argues that choosing an AI project should start by asking a business owner what keeps them at work late; whatever they name becomes the project. They push back against advice built around industry trends or what large companies are rolling out, noting that most online AI advice is written for companies with a chief innovation officer.
- **The best automation is invisible plumbing between systems a company already uses**: Green argues against replacing software with a new platform. The improvement worth paying for is a thin layer that moves the same data between systems staff already use, with no new dashboard, training or rollout. Pitches centred on 'change management' or a 'company-wide rollout' signal the wrong purchase, in their view.
- **Judge an automation by hours removed and response speed, not by the sophistication of the tools**: Across case studies, Green reports operational results, such as staff time freed and slow steps made faster, rather than models or platforms. Their recurring claim: good systems stop wasting staff and salespeople's time; the people themselves did not get smarter.
- **Test new AI models yourself; don't wait for the analyst report**: Green treats fast-moving model releases as a testing problem. Open-source drops arrive without press tours; they argue that waiting for formal evaluations widens the gap while others experiment, so teams need a testing process faster than the release cycle. Their stated filter: whether a model installs in under an hour, solves a problem that already cost money, and can be sandboxed without compliance risk. They also stress that an open-source model is not the same as open data.
- **The physical and supply-chain side of AI, explored with guests**: Posts announce and summarise conversations Green hosts on AI's physical dependencies and manufacturing and fashion supply chains. Guests' views, relayed by Green, cover AI as a defensive necessity for mid-market manufacturers, robot labour economics, brittle supply chains and resilience over pure efficiency, fashion manufacturing not yet being automated, and keeping inventory lean.

## Work to read

- [The best automation I ever built wasn't flashy](https://www.linkedin.com/posts/servenomaster_the-best-automation-i-ever-built-wasnt-flashy-activity-7466262717880758272-04g_)
- [We pulled 500 leads from this client's CRM](https://www.linkedin.com/posts/servenomaster_we-pulled-500-leads-from-this-clients-crm-activity-7468074971991420928-IKQn)
- [14 manual processes. Zero staff time. Here's how.](https://www.linkedin.com/posts/servenomaster_14-manual-processes-zero-staff-time-heres-activity-7463001330991706112-e7z6)
- [I don't pitch AI to business owners. I ask them one question](https://www.linkedin.com/posts/servenomaster_i-dont-pitch-ai-to-business-owners-i-ask-activity-7468799810335035392-WsWb)
- [New open source AI model dropped overnight](https://www.linkedin.com/posts/servenomaster_new-open-source-ai-model-dropped-overnight-activity-7450135879077142528-vjLa)

## Partnerships

The material reviewed lists no sponsorship terms, media kit, rate card or partnership inquiry route for Jonathan Green, so paid placements must be negotiated directly. Their editorial pattern centers on long-form conversations with operators and advisors about AI in physical industry: a manufacturing discussion with Jens Mobius of Infinity Partners covering humanoid robots, gen-AI adoption and change management; a supply-chain conversation with Mohannad Gomaa; and fashion-manufacturing clips with operations advisor Omnya Elshafey. Green also publishes practitioner case studies detailing automation work, including an industrial equipment supplier selling into oil and gas and a multi-location real-estate brokerage. The case-study posts close with comment-keyword prompts (comment LEADS, comment CASE STUDY) that route readers into a follow-up message—a lead-capture device, not a documented sponsor package. Buyers should ask directly about scope, editorial control, disclosure wording and timing, and confirm the terms in writing before committing budget.

## Alternatives

- Cory Bruscia: Choose Bruscia when a manufacturer's overhead sits inside the sales organisation rather than in back-office data entry. Bruscia's two posts make one argument from two directions: hiring more salespeople will not fix a team without a documented process, defined ICP, consistent messaging, qualification method, CRM discipline, forecasting routine or onboarding plan; adding AI tools on top of an unchanged seller role is not transformation. The second post asks the question the first implies: now that AI can research accounts, summarise meetings, draft follow-ups and update CRM records, what work should salespeople stop doing, and which parts of the role should be redesigned around customer conversations, diagnosis and negotiation? Manufacturers whose quotes and follow-ups stall because every rep sells differently, or whose AI pilot is being bolted onto unchanged responsibilities, get a buyer-decision frame from Bruscia that Green's build narratives do not supply.
- Chad Diller: Choose Diller when the growth problem is demand generation and owner-dependence rather than repetitive internal work. The posts address commercial landscaping and adjacent field-service owners. One argues that acquisitive platforms are still expected to prove they can grow organically and predictably without leaning on the owner's relationships or the next acquisition. Repeatable systems — brand authority, inbound demand, credible digital presence and sales enablement assets — make growth transferable, reduce risk and support the valuation conversation. The other lists twelve ways to get more return from expensive video: a website gallery, a service page, blog articles, written case studies, paid landing pages, repeat social posting, email teasers, digital proposals, tradeshow flyers, remixed footage, recruiting content, and looping clips for booths and website pages. Manufacturers selling to grounds, facilities, construction or similar field-service buyers can use that framing when the campaign aims to make pipeline less dependent on the founder.
- Steve Sterling: Choose Sterling when manual overhead lives in the plant or in fragmented machines and data systems rather than in office workflows. One post argues that industrial AI is repeating the smart-machines problem: machines each held their own world, and system-level insight arrived only once shared standards and architectures made interoperability possible. Component-level AI, autonomous mobile robots and vision systems risk forming islands of intelligence unless open standards let innovation scale and integrate. The other draws a manufacturing lesson from Edison Motors, a startup OEM that set out to go fully electric, found that forestry, snow plowing and heavy-duty cycles could not be done reliably that way, and engineered a hybrid instead. Progress, the post argues, often comes from practical bridges grounded in physics, customer needs and honest engineering judgment rather than insisting on a perfect end state. Manufacturers weighing an AI pilot on the shop floor or struggling to combine machine data get an interoperability-and-realism argument from Sterling that Green's office-automation case studies do not cover.

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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/