Jonathan Green

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 post cites a CEO of a 200-person manufacturer who wanted to stop hand-building the same sales report every Friday, a six-hour job. Green says they automated it in two weeks, freeing him to get home for family dinner rather than adopt an AI strategy.

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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.

At a manufacturing company, staff re-keyed the same customer name, address, PO number and product specs into a CRM, an ERP and a QuickBooks invoice. Green describes building a layer to copy the data between the three, saving an estimated 30 staff hours a month and reducing errors, all without anyone on the team noticing it running.

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Their recurring claim: good systems stop wasting staff and salespeople's time; the people themselves did not get smarter.

An industrial equipment seller had 45% of 500 CRM leads untouched for more than seven days.

• A daily well-permit feed with contact enrichment, territory routing and CRM flags reportedly cut first contact to the same day and quote turnaround from days to minutes.
• Separately, a real estate brokerage with more than 50 agents reportedly moved 14 processes off staff time over eight weeks.

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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.

In one post, a model released on OpenRouter under the codename 'Owl Alpha' scored 79.8 on quality and 92.5 on adherence in Green's own test suite. But it produced jargon-heavy prose, left an HTML sales page unfinished, ran out of context on longer output and gave answers without justification, ranking 20th of the models tested that month.

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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.

With Jens Mobius of Infinity Partners, the discussion covers humanoid robots in manufacturing, robot labour cost moving from about $10/hour today toward $1/hour by 2035 against $18/hour manual labour, and named factory deployments including Figure 02 at BMW, Apptronik's Apollo at Mercedes and Tesla's Optimus.

• With supply-chain expert Mohannad Gomaa, the focus is AI's dependence on physical chips, factories and supply chains.
• With operations advisor Omnya Elshafey, topics include fashion manufacturing still being unautomated, keeping warehouse inventory lean, and small companies running whole supply chains through shocks they do not control.

The hosted conversations take a more macro view than Green's hands-on posts, covering capital costs, robot economics and industry-wide readiness rather than the specific recurring task a single owner wants to stop doing.

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This example shows the approach clearly.

At a manufacturing company, staff entered the same order details into a CRM, ERP and QuickBooks.

• Green describes adding a thin layer to pass data between systems, avoiding a new dashboard, training or rollout.
• The post estimates 30 staff hours saved each month and fewer errors.

For brands planning a campaign

A strong starting point for a campaign about reducing repetitive work while keeping existing systems. Ask which systems and handoffs the client wants to connect, and verify the time and error baseline.

This case follows leads from discovery through follow-up.

The post says 45% of 500 CRM leads had gone untouched for more than seven days.

• Green describes tracking new well permits, enriching them with contacts, routing them by territory and flagging stalled leads.
• The post also describes a tablet app for sizing equipment and producing quotes.
• The reported result is same-day rather than week-plus first contact, with quotes produced in minutes rather than days.

For brands planning a campaign

Useful for a campaign about lead response or sales operations. Ask the client to confirm the figures, attribution and whether its CRM and territory data support a similar approach.

This post shows several automations applied across one business rather than a single task.

For a multi-location real estate brokerage, Green describes connecting lead sources to a CRM, automating vendor reports, screening contracts for common errors and linking payment systems. The post says the work took eight weeks and that 14 processes no longer consumed staff time.

For brands planning a campaign

Could support a campaign about operations automation across departments. Use it to prompt a process inventory, and ask for evidence of the stated savings and details of the implementation scope before making comparable claims.

This post explains their discovery approach through a specific business-owner problem.

Green asks what keeps an owner working late on Friday.

• One manufacturing CEO said a weekly sales report took six hours to build by hand.
• Green says the report was automated in two weeks, framing the value as giving the owner time back rather than selling an abstract AI strategy.

For brands planning a campaign

A fit for a campaign centered on practical discovery and owner-led priorities. Ask operators which recurring task keeps them late, then show how a solution would be scoped and measured.

The post gives a simple evaluation lens.

Green proposes checking whether a model is easy to install, solves a current problem and can be tested safely.

• They distinguish an open-source model from open data and recommend a sandbox.
• The post argues that businesses need a testing process that can keep pace with releases.

For brands planning a campaign

Useful for a campaign about responsible AI evaluation or adoption readiness. Build the story around a bounded test and its business use, not a claim that a newly released model is production-ready.

A 50+ agent real estate brokerage had 14 processes consuming staff time: a $3,000/month answering service; leads from MLS, Zillow, Facebook and walk-ins with nothing tying them together; and hand-built vendor reports taking six hours each.

• Over eight weeks, the fix replaced them with an AI call centre, unified lead capture, a report generator, contract pre-screening and connected payment systems.
• An industrial equipment seller had 45% of 500 CRM leads untouched for 7+ days.
• A daily well-permit tracking feed with contact enrichment, territory routing to the right salesperson and CRM 'shadow analytics' flags cut speed-to-call to same-day and quote turnaround from days to minutes.

For brands planning a campaign

A credible presenter for CRM, workflow and operations-automation products aimed at mid-market service and industrial companies; Jonathan Green describes builds in the operational vocabulary buyers use.

Examples: Source 1 · Source 2

A thin layer copying customer name, address, PO number and product specs between CRM, ERP and invoicing took roughly 30 hours a month off a manufacturer's team's workload without replacing any of the three systems. Green frames the goal as 'the boring part is done' and argues that pitches built around 'change management' or 'company-wide rollout' point to the wrong purchase.

States a blunt position, describes the hidden layer, turns it into a buying rule, then asks readers which task involves retyping data into more than one system.

For brands planning a campaign

Fits integration, middleware and process-automation vendors; also offers a contrast to enterprise-suite replacement pitches a brand may be competing with.

Examples: Source 1

The rule: a specific owner's pain, not industry trends or what large companies are rolling out, should determine the project. The post notes that most AI advice online is written for companies with a chief innovation officer.

A one-line reframe, a named-role anecdote with a personal detail, a general rule for the reader, then a comment prompt.

For brands planning a campaign

A good match for vendors whose buyers are owner-operators of mid-market firms and who want a creator willing to argue against strategy-led AI messaging.

Examples: Source 1

One model, released on OpenRouter under the codename 'Owl Alpha', scored 79.8 on quality and 92.5 on adherence in a personal suite but failed on LinkedIn-article prose, left an HTML sales page unfinished, ran out of context on longer output and gave answers without justification.

• It ranked 20th of the models tested that month.
• A separate post argues that midnight open-source drops are now normal, that waiting for the analyst report widens the gap while competitors test on laptops, and that teams should check whether a model installs in under an hour, solves a problem that cost money yesterday, and can be sandboxed without compliance risk.

A short verdict: ranked score, numbered list of where the model broke, and a direct question to readers who have tested it.

For brands planning a campaign

Relevant to developer-tool, model-hosting and evaluation brands; the posts show a public testing habit and a procurement-speed argument these vendors can build on.

Examples: Source 1 · Source 2

One covers humanoid robots in manufacturing with Jens Mobius, citing robot labour economics running from about $10/hour today toward $1/hour by 2035, against $18/hour manual labour, and named factory-floor deployments at BMW, Mercedes and Tesla.

• Others cover AI's physical dependencies and supply-chain resilience with Mohannad Gomaa, and fashion manufacturing twice with operations advisor Omnya Elshafey.
• They argue that the industry is not fully automated, warehouses should hold as little inventory as possible, global shocks hit supply chains companies do not control, and even a small company can run its whole supply chain with the right systems.

Only the announcement and summary text appears here; the episode conversations themselves do not, so the depth of the interviews cannot be judged.

For brands planning a campaign

Opens interview- or episode-based collaborations for industrial, logistics and apparel supply-chain brands, with a ready-made editorial frame for these sectors.

Examples: Source 1 · Source 2 · Source 3 · Source 4

Bruscia argues that hiring more salespeople will not fix a broken sales foundation and that sales leaders should redesign the role around what AI makes possible. That matches Green’s habit of diagnosing the recurring process problem before proposing automation.

Bruscia stays on the sales-organization side: sales process, ICP, qualification, CRM discipline, forecasting and sales leadership. Green spends more time on hands-on build teardowns, specific automation case studies and model test results.

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Diller argues that organic growth systems reduce investor risk and support valuation in commercial landscaping, while Green shows operational systems removing manual work and speeding up response.

Diller’s material focuses on marketing for commercial landscaping, lawn care, tree care and pest control, including video ROI and website distribution. Green ranges across industrial equipment, real estate, manufacturing and AI model testing.

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Sterling writes about industrial automation islands, PLCs, interoperability standards and pragmatic hybrid engineering, overlapping with Green’s manufacturing case studies and interviews with industrial guests.

Sterling focuses on standards, interoperability and field-level engineering trade-offs such as Edison Motors’ hybrid trucks. Green focuses more on owner-level automation decisions and testing new AI models for practical use.

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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.

Bruscia's posts stay at the level of the argument and the questions a sales leader should ask.

• They present the diagnosis as something commonly seen, rather than showing it on a named account.
• A campaign built on Bruscia promises a way of thinking about the sales role; the buyer supplies the implementation.
• Green's posts take the opposite approach: they name handoffs between CRM, ERP and invoicing, cite a monthly saving of about 30 staff hours, and report first contact on new leads moving from seven-plus days to same day.
• Choose Bruscia to diagnose a broken sales foundation and decide what the role should become; choose Green when the campaign needs evidence that an automation was built and the numbers moved.

Examples: Source 1 · Source 2

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.

Diller's posts cover marketing and content infrastructure, not operations automation.

• The tradeoff against Green is a change of category: buyers get a credible voice on transferable demand systems in a specific trade, but not build proof or an operations-leadership audience.
• The video post is a reallocation guide, and its ROI claim is asserted rather than measured in the two posts.
• The landscaping and field-service context is narrow: manufacturers that do not sell into that trade should treat the relevance as an analogy about owner-dependence, not an audience match.
• The posts do not establish reach among industrial buyers.

Examples: Source 1 · Source 2

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.

Sterling's posts offer industry commentary rather than documented client work.

• The standards post points to a third-party industry article and a newly formed foundation backed by unnamed major technology players; the Edison Motors post discusses a startup's trucks and links to that company's site.
• A campaign would buy analysis and framing rather than documented delivery.
• Green's material is narrower in theme but closer to the buyer decision: it names the systems, elapsed time and change.
• Choose Sterling when the campaign is about standards, interoperability and engineering realism in industrial settings; choose Green when buyers expect a documented automation project with numbers attached.
• If manufacturer experience is essential, ask Sterling for examples of work with manufacturers; the fit for a mid-market manufacturer rests on the second post's argument about pragmatic adoption.

Examples: Source 1 · Source 2