Wiley Strahan writes about supply chain and logistics, including how companies can apply AI, improve warehouse operations, and manage delivery economics. Strahan also created the AI Supply Chain Hub and published Supply Chain Careers: The Field Guide.
Wiley Strahan is useful for practical supply chain arguments linking AI claims to warehouse systems and delivery economics.
Best for
Supply chain data and integration vendors targeting retailers, 3PLs, or manufacturers.
Not for
A direct-to-consumer home furnishings paid social sofa campaign needing consumer reach and short-form video.
Key takeaways
Warehouse AI stalls at data access, not model quality, Strahan argues.
The operations problem AI should solve is the shift handoff.
Operator judgment is a data asset, not a job description.
Check first: Confirm audience attributes and terms before contacting; no media kit or sponsor policy is published.
Main ideas
AI in warehousing stalls at data access, not model quality · Operator judgment is a data asset, not a job description · The operations problem AI should solve is the shift handoff
Wiley Strahan writes about supply chain and logistics, focusing on what changes when AI meets the realities of warehouse systems, frontline work, and delivery economics.
They created AISCHub, described in a post as a free resource hub for evaluating AI tools and use cases, and published Supply Chain Careers: The Field Guide.
They say the book covers 13 supply chain functions, career entry paths, interview preparation, and using AI on the job.
Their posts also cover how a New York City delivery mandate could affect logistics, supply-chain risks through the Strait of Hormuz, and how warehouse-floor jobs differ from analytical roles.
Follow them for practical arguments that connect industry claims to operational details, and for an emphasis on the unglamorous conditions that can determine whether a plan works.
Strahan’s distinctive strength is turning broad claims into concrete operational questions.
On warehouse AI, they ask whether a company can get data out of its legacy systems and put it back; without that access, they argue, the work is a systems project, not an AI project.
On delivery, they trace how a same-day promise for a sofa can pit eCommerce conversion against distribution capacity and margin.
On frontline work, Shift Sync is presented as a concept for voice-note handoffs that preserve what happened across shifts.
Posts about careers and tacit knowledge show what can be missed when decisions are reduced to job titles or written procedures.
Their range extends beyond AI to careers, labor policy, and supply-chain exposure to geopolitical disruption.
The common thread is how people, systems, and economics interact in physical operations.
For Shift Sync, ask whether it is commercially available: it is described as a concept that may be deployed or kept free, not a confirmed commercial product.
Wiley Strahan approaches supply chain from the systems layer up to the buyer's decision.
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Their recurring position: AI in warehousing stalls on data access rather than model quality; an experienced operator's unwritten judgment is worth more as training data than as a job description; and delivery economics break when eCommerce and Distribution are measured on separate scoreboards.
Alongside the commentary, they build things: the AI Supply Chain Hub resource site, the voice-first Shift Sync handoff tool, and Supply Chain Careers: The Field Guide.
A post on the Strait of Hormuz applies the same method—pricing a system by the physical flows through it—well beyond day-to-day logistics.
AI in warehousing stalls at data access, not model quality
Strahan says the biggest barrier to AI in most warehouses is not model capability but access to building data.
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Many buildings that move freight run on custom systems written 30 or 40 years ago, with no source repository, no documented test showing the backups restore, and no one left who wrote the systems.
Put an agent on top and there is no API to call or schema to produce.
Before scoping an AI project, they ask: can we get the data out, and can we put it back?
If not, it is a systems project, not an AI project.
The AI Supply Chain Hub they built follows the same premise, pairing tool comparisons with an AI readiness assessment and an ROI calculator.
The person who understands the data model is a contractor who answers their phone on Tuesdays; meanwhile, AI roadmaps slip a year and vendors take the blame.
Operator judgment is a data asset, not a job description
Strahan reframes the usual question — will AI take this job? — into a sharper one: what does this person know that has never been written down?
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The person is not doing the work; they are teaching a machine how they would do it, then stepping aside.
In logistics, that unwritten knowledge includes how a good dispatcher reads a driver's tone on a call and when to override a routing algorithm that is technically right but practically wrong.
Strahan argues this knowledge is worth more as training data than as a job description.
Operators who capture it before people retire or leave will have an edge over those who pay someone else to extract it later, probably at a worse price.
Why a damaged pallet from Lowe's is handled differently from one from Wayfair: a rule that lives in someone's head after fifteen years, not in a manual.
The operations problem AI should solve is the shift handoff
Strahan says most “AI in supply chain” talk stays at the buzzword level — demand forecasting, route optimization, control towers, digital twins — while operations teams want clearer communication, fewer dropped balls at shift change and a usable record of what happened on the floor.
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They argue email, Slack and Teams were not designed for voice-first handoffs, where tone and nuance matter, or for a view that can be scanned, searched and replayed across shifts, sites and days.
Nor do they make it easy to answer what changed on nights last Tuesday without scrolling through a week of chat.
Their answer, Shift Sync, captures outgoing supervisors’ handoffs as voice notes in an interface built for the job, not a general chat app.
Strahan describes the current workarounds as group chats, long email chains and paper logs — hard to search, standardize and act on.
Strahan says the tool may be deployed commercially or given away free as a side project; its status is unsettled.
One owner from click to doorstep, because functional scoreboards hide the loss
Strahan describes eCommerce and Distribution sabotaging each other as each hits its own metrics. eCommerce promises same-day delivery on a sofa to convert the sale; Distribution cannot meet that promise with current warehouse load and carrier capacity, and eats the margin to make it happen.
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The reverse occurs when Distribution locks down the promise so tightly that eCommerce conversion suffers.
Both teams win; the company bleeds.
Strahan's fix: one person owns the entire path from click to doorstep, with fused P&Ls, shared KPIs, one data model, real-time demand feeding a single plan, orders routed for profit rather than speed, pricing that reflects real cost, and last-mile options configured at checkout.
An overnight delivery to rural Montana priced at $89 because Distribution told eCommerce the margin would disappear; Strahan says Walmart, Amazon and Target already operate this way and predicts companies that do not will not exist by 2028.
Delivery labor rules get argued as fairness and decided by utilization math
On a New York City bill that would move parcel delivery from independent contractors to direct employment within two years, Strahan argues that the route-density math has not been run.
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An independent contractor working across multiple platforms reaches 60-70% vehicle utilization; an employee model breaks below 85%.
That leaves three options: scale the fleet 1.5-2x in a market where real estate runs $50+ per square foot amid a driver shortage, pull out of the market, or watch volume migrate to competitors as prices rise and margins compress.
New York is arguing about fairness, they say, while the market is calculating what different looks like.
Carriers did exactly that after the France and Spain mandates, which Strahan cites as the pattern to expect.
Heavy and bulky delivery is unclaimed because Amazon's reputation outruns its freight reality
Strahan argues consumers transfer Amazon's small-parcel speed to heavy goods, where its actual performance is average at best: a sectional sofa still shows a 10-to-21-day window, standard freight with a scheduling requirement, not Prime.
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In their view, that gap between perception and performance is the category's single largest untapped opportunity. Retailers positioned to win are not those matching Amazon's speed or Walmart's price, but those that make the delivery event a product neither platform can replicate, build a forward stocking network from real estate they already own, solve the returns problem before it solves them, and treat their carrier as a brand asset rather than a cost line.
Walmart's recent upper-income share gains, with 75% coming from households earning over $100K, put it directly in the heavy and bulky buyer market; $849.9 billion in US returns in 2025 is being absorbed by heavy categories as silent write-offs.
Career advice built on what the job is like on a Tuesday
Strahan argues institutions do not show what jobs actually exist in supply chain.
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So they wrote Supply Chain Careers: The Field Guide — the book they wish someone had handed them 20 years ago.
It maps 13 supply chain function areas by pay and what the work actually involves, covers entry routes for graduates, veterans and career-changers, and includes a 44-question interview bank with the answers hiring managers expect, a military-to-civilian translation guide for every branch, a chapter on using AI on the job, and a 90-day plan.
The method is to compare lanes by daily texture rather than title: on-site or remote, reactive or planned, who you manage, what gets measured every hour, and the red flags in a job posting that reveal which lane you are signing up for.
Two people at the same company, sometimes in the same building: one on a warehouse floor at 5am with 200 hourly associates and units-per-hour on a board; the other opening a laptop at nine to investigate why fill rate slipped in the Midwest.
Strahan says the guide starts a small series about functions, businesses and ways of operating in supply chain.
The outlier: reading geopolitics through the physical inputs that stop moving
One post leaves operations, careers and AI behind.
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Strahan breaks down a Strait of Hormuz disruption by commodity rather than oil price, arguing that a 21-mile chokepoint holds the global manufacturing economy hostage and that the useful way to read it is input by input. The method echoes their other posts: quantify what actually moves, then reason from that.
Gulf aluminum output accounts for roughly 20% of US supply, with Bahrain's Alba declaring force majeure and LME prices at near four-year highs; 84% of Gulf polyethylene exports route through the strait; urea jumped from $475 to $680 per ton into spring planting; Qatar supplies a third of the world's helium, which semiconductors and MRI machines have no substitute for.
This reads as range rather than a recurring priority: it is the only post in the reviewed set outside Strahan's usual lanes.
This is the clearest statement of what Strahan spends time on outside client work, and answers a newcomer’s first question: where does their AI-in-supply-chain advice live?
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Strahan announces AISCHub.com, a free hub for supply chain professionals who want to evaluate AI tools without vendor spin or a machine-learning background.
It includes more than 470 curated resources, comparisons across more than 35 AI supply chain platforms, use-case breakdowns for demand planning, procurement, warehousing and last-mile delivery, an AI readiness assessment, and an ROI calculator.
Strahan calls it a passion project they will keep updating and makes the stakes clear: AI already affects forecasting, supplier risk, inventory, transportation and workforce planning, and the professionals who gain most will be those who can use it to build supply chains rather than just run them.
For brands planning a campaign
A logistics technology vendor could make the readiness assessment and ROI calculator natural extensions of a co-marketing campaign: they already serve buyers at the evaluation stage before a demo. The “without the hype, without the vendor spin” framing also sets the tone Strahan would likely want in a sponsored asset.
This is the one piece where Strahan describes building a tool, showing how they turn an operational complaint into a product concept.
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Strahan writes that most “AI in supply chain” talk stays at the buzzword level, while operations teams want clearer communication, fewer dropped balls at shift change, and a record of what happened on the floor.
Shift Sync is their answer: a tool that captures handoffs as voice notes from outgoing supervisors in a centralized interface built for handoffs rather than a general chat app.
Their argument against email, Slack and Teams is specific: those tools were not designed for voice-first notes where tone matters, a handoff view that can be scanned, searched and replayed across shifts, sites and days, or questions like what changed on nights last Tuesday without scrolling through a week of chat.
Strahan describes the current workaround as group chats, long email chains or paper logs, and says they may deploy the tool commercially or keep it free.
For brands planning a campaign
For a handoff, workforce-scheduling or frontline-communication product, this is the closest thing to a ready-made problem statement. Strahan says the tool may or may not be commercialized, so ask for its current status before describing it as a live product or offering.
It shows the careers-first audience Strahan reaches and why people outside logistics tooling circles search for their name.
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Strahan announces Supply Chain Careers: The Field Guide, the book they wished someone had given them 20 years ago, after they arrived in supply chain without planning to.
They say institutions do a poor job of showing what jobs actually exist in the field.
The contents are concrete: the 13 supply chain function areas, what each pays and what the job really is; routes in for graduates, veterans and career-changers; a 44-question interview bank with the answers hiring managers expect; a military-to-civilian translation guide for every branch; a full chapter on using AI on the job; and a 90-day plan.
Strahan says it begins a small series about functions, businesses and ways of operating.
For brands planning a campaign
This opens an employer-branding or recruiting-technology angle rather than a pure software pitch. A campaign aimed at practitioners could use the book’s function-area framing, while keeping the careers material separate from the AI GTM message so both stay credible.
It is a compact example of how Strahan challenges a widely held assumption and prices the gap between what consumers believe and what the operation delivers.
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Strahan points out that a sectional sofa on Amazon shows a 10-to-21-day window, which they identify as standard freight with a scheduling requirement rather than Prime speed.
They argue consumers transfer Amazon’s small-parcel reputation to heavy goods, where its real performance is average.
After several weeks writing a white paper on the category, Strahan shares three findings: Walmart’s recent upper-income gains, with 75% coming from households earning over $100K, land directly in the heavy and bulky buyer; $849.9 billion in US returns in 2025 is being absorbed by heavy categories as silent write-offs; and the winners will not be the retailers matching Amazon’s speed or Walmart’s price, but those that turn the delivery event into a product neither platform can replicate, build a forward stocking network from real estate they already own, solve returns before returns solve them, and treat the carrier as a brand asset rather than a cost line.
For brands planning a campaign
This post is a public sample of white-paper co-marketing. A vendor selling delivery, returns or fulfillment software could propose the same format on an adjacent topic, using Strahan’s practice of citing a specific number for each claim as the campaign’s central argument.
It shows how Strahan links an organizational problem to a systems problem—the bridge between their commercial writing and AI adoption arguments.
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Strahan describes a recurring failure: eCommerce promises same-day delivery on a sofa because it converts the sale, while Distribution cannot meet the promise with current warehouse load and carrier capacity, then eats the margin to fix it—sometimes several hundred dollars per order.
The reverse happens too: Distribution locks down the promise and eCommerce conversion suffers.
Both teams win their own metrics while the company bleeds, Strahan argues, predicting that by 2028 companies operating this way will no longer exist.
Their proposed fix is one person owning the whole path from click to doorstep, with fused P&Ls, shared KPIs and a single data model.
Strahan’s requirements are specific: real-time demand feeding a single plan through tools such as o9, Blue Yonder or FarEye; routing orders for profit rather than speed; pricing that reflects real cost, down to an overnight-to-rural-Montana charge; last-mile options configured in checkout; and immediate shared visibility when something breaks.
They name Walmart, Amazon and Target as already doing this.
For brands planning a campaign
The named tooling and fused-P&L argument give a vendor a ready vocabulary for positioning an integration or orchestration product as the shared data model, rather than another dashboard owned by one function.
It explains the precondition that governs whether Strahan’s AI adoption ideas can be implemented, and offers the clearest statement of their position.
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Strahan argues that the obstacle to AI in warehouses is not model capability but access.
Many buildings run on custom systems written 30 or 40 years ago, with no source repository, no documented test that backups actually restore, and original authors long retired.
The systems work until they abruptly fail, Strahan notes.
Putting an agent on top runs straight into the absence of an API or any producible schema, sometimes leaving one contractor who answers their phone on Tuesdays as the only person who understands the data model.
Strahan says this is why AI roadmaps in the industry slip a year while vendors get blamed.
Their instruction: ask whether you can get the data out and put it back, because if you cannot, you do not have an AI project but a systems project wearing a better name.
For brands planning a campaign
For a data-integration, middleware or warehouse-systems vendor, this is a candidate headline argument. Treat it as Strahan’s industry observation, not an audit of a specific customer; ask for customer-specific evidence.
It shows the level at which Strahan argues about automation—not job titles but unwritten operational judgment—and offers useful context before reading their tool-focused posts.
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Strahan reframes the usual question about AI taking jobs into a sharper one: what does this person know that has never been written down anywhere?
In logistics, their examples are concrete: how a good dispatcher reads a driver's tone on a call, when to override a routing algorithm that is technically right but practically wrong, and why a damaged pallet from one retailer is handled differently from another retailer's.
They say none of that lives in a manual; it exists only in someone's head after fifteen years.
They argue this knowledge is currently worth more as training data than as a job description.
Operators who extract it before people retire or leave will have an edge, they say; everyone else will pay someone else to do it later, probably at a worse price.
For brands planning a campaign
This is a natural message for knowledge-capture, training-data or decision-support products, framing the buyer problem as knowledge risk rather than headcount reduction.
Delivery and last-mile economics, with the arithmetic shown3
AI adoption that stalls below the model layer2
Careers and mapping what supply chain jobs actually look like2
Builds working tools and resource hubs, not just commentary2
What the supply chain job becomes when AI is applied1
Exception: global commodity disruption through a single chokepoint1
AI adoption that stalls below the model layer
Strahan argues that what blocks AI in most warehouses is not model capability but access.
Read more
Many buildings moving freight run on custom systems written 30 or 40 years ago, with no source repository, no documented proof that backups restore, and no one left who wrote the code.
Put an agent on top and there is no API to call and no schema anyone can produce.
Strahan's first question before scoping an AI project: can the data be taken out and put back?
If not, it is a systems project wearing a better name.
The AI Supply Chain Hub addresses the same starting point with a curated resource library, tool comparisons, an AI readiness assessment, and an ROI calculator for people who want to understand adoption without vendor spin.
Turns a technology question into an infrastructure inventory, then gives readers a diagnostic question to ask before spending money.
For brands planning a campaign
Fits a vendor whose buyers keep stalling at pilot stage.
A white paper or webinar on data access, legacy systems, and readiness assessment matches Strahan's framing rather than generic AI enthusiasm.
Their readiness and ROI tools suggest they work comfortably with practical buyer aids.
What the supply chain job becomes when AI is applied
Strahan reframes the AI-and-jobs question: the person is not doing the work; they are teaching a machine how they would do it, then stepping aside.
Read more
The valuable asset is what a good operator knows but has never written down — how a dispatcher reads a driver's tone on a call, when to override a routing algorithm that is technically right and practically wrong, and why a damaged pallet from one retailer is handled differently than one from another. Strahan argues that this knowledge is currently worth more as training data than as a job description, and that operators who extract it before people retire or leave will have an edge over those who pay someone else to do it later.
For brands planning a campaign
Useful for a logistics technology company that wants to talk about automation without triggering the "AI takes jobs" reflex. Concrete examples — dispatcher calls and customer-specific damage handling — give a campaign talking points for a webinar or research piece on capturing operator knowledge before handover.
Careers and mapping what supply chain jobs actually look like
Strahan published Supply Chain Careers: The Field Guide, describing it as the book they wish someone had handed them 20 years ago.
Read more
They wrote it because institutions do not show what jobs actually exist in the field.
It covers 13 supply chain function areas, what each pays and what the job really is, entry routes for graduates, veterans, and career-changers, a 44-question interview bank with answers hiring managers expect, a military-to-civilian translation guide, a chapter on using AI on the job, and a 90-day plan.
A companion post makes the same point about day-to-day reality: two people at the same company, one running a warehouse floor at 5am with units-per-hour on a board, the other working a Midwest fill-rate dataset from a laptop at home.
Strahan maps function areas by whether the work is on-site or remote, reactive or planned, what gets measured hourly, and which red flags in a job posting reveal what the reader is signing up for.
Uses side-by-side job vignettes and checklist-style red flags rather than abstract career advice, helping readers place themselves in one of the lanes.
For brands planning a campaign
Well suited to employers, workforce programmes, and education or recruitment platforms.
The 13-function structure is a ready-made framework a brand can sponsor or license for content.
The "pick the lane that fits how you want to spend a Tuesday" angle also works naturally in recruitment campaigns and career-stage webinars.
Delivery and last-mile economics, with the arithmetic shown
Three posts examine delivery economics at different levels.
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One describes a VP of eCommerce and a VP of Distribution sabotaging each other while both win on their own metrics: same-day promises on sofas convert sales, then distribution eats the margin, or distribution locks the promise down so hard that conversion drops.
Strahan proposes one owner across the whole path from click to doorstep, fused P&Ls, shared KPIs, one data model, and a single demand plan fed by tools such as o9, Blue Yonder, or FarEye, with pricing and last-mile options configured in checkout.
Another post examines a New York City bill requiring delivery worker reclassification and argues that nobody has run the route-density math: independent contractors reach 60-70% vehicle utilization across multiple platforms, while an employee model breaks below 85% utilization.
The options are to scale the fleet 1.5-2x in expensive real estate, leave the market, or see volume migrate.
A third argues that the untapped opportunity in heavy and bulky goods lies in the gap between Amazon's small-parcel reputation and the 10-to-21-day freight windows on a sectional sofa, pointing to Walmart's upper-income share gains and $849.9 billion in U.S. returns in 2025.
Arrives at recommendations through arithmetic others skip — utilization floors, the cost of an impossible promise, and returns eating into margin — then grounds them in named companies and a proposed operating model.
For brands planning a campaign
Strong material for vendors selling order management, routing, delivery experience, or returns technology.
Strahan provides quantified arguments — utilization floors, margin math, and promise-versus-capability gaps — and names incumbent vendors.
Brands can use these as the spine of a white paper, panel, or sales-enablement piece for retail operations leaders.
Builds working tools and resource hubs, not just commentary
Strahan ships prototypes.
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The AI Supply Chain Hub (aischub.com) is a free resource for supply chain professionals, with more than 470 curated resources, platform comparisons across more than 35 AI supply chain tools, use-case breakdowns spanning demand planning, procurement, warehousing, and last-mile delivery, an AI Readiness Assessment, and an ROI calculator to help build a business case.
Shift Sync is a voice-first shift-handoff tool for 24/7 operations: outgoing supervisors record what happened, what is hot, what is broken, and what to watch.
The context lands in a dedicated handoff interface that can be scanned, searched, and replayed across shifts, sites, and days, rather than buried in group chats, long email chains, or paper logs.
Strahan says they may deploy Shift Sync or give it away free as a side project.
For brands planning a campaign
A partner can engage Strahan as a builder rather than a commentator.
Co-developing or sponsoring a readiness assessment, ROI calculator, or shift-handoff workflow gives the brand a tangible asset for operations teams.
Strahan's willingness to iterate in public also makes feedback-driven launch campaigns plausible.
Exception: global commodity disruption through a single chokepoint
One post leaves the operations and careers lane.
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Strahan breaks down what a Strait of Hormuz disruption actually blocks: Gulf aluminum output and its share of U.S. supply; polyethylene, methanol, and MEG volumes and the share of Gulf exports routed through the strait; fertilizer trade and a urea price move from $475 to $680 per ton into planting season; Qatar's share of world helium, for which semiconductors and MRI machines have no substitute; and the sulfur share feeding semiconductor fabrication. The closing line — 21 miles holding the global manufacturing economy hostage — is the argument.
This is the one post in the reviewed set about geopolitics and commodity flows rather than day-to-day operations, careers, or AI adoption.
For brands planning a campaign
Shows Strahan can cover macro supply risk when a news moment demands it, making this a fit for sourcing, trade-compliance, or supply-risk products. It is a secondary lane, so brands should treat it as a news-reactive option rather than the main campaign premise.
The posts describe three businesses and projects Wiley Strahan built or wrote: the AI Supply Chain Hub, the Shift Sync handoff tool, and the book Supply Chain Careers: The Field Guide.
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They show no sponsor, affiliate, client, or employment relationship with an outside brand. Retailers and logistics software vendors named in the posts appear as examples or comparisons, not partners.
AI Supply Chain Hub (AISCHub.com)
Owned business
Wiley Strahan describes AISCHub.com as a free resource hub for supply chain professionals, built as a passion project.
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It features 470+ curated resources, comparisons of 35+ AI supply chain tools, use-case breakdowns for demand planning, procurement, warehousing and last-mile delivery, an AI readiness assessment, and an ROI calculator. Continuous updates are planned.
Wiley Strahan describes Shift Sync as a concept they have been developing: a lightweight, voice-first tool for supervisors and leads to pass shift handoff notes, with a centralized interface for capturing and replaying what happened on the floor.
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The post says the tool may eventually be deployed or remain a free side project. Ask about its commercial status and any ongoing product contract before proceeding.
A book Wiley Strahan wrote and published, presented as the first in a planned series about supply chain functions and ways of operating.
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Its contents include the 13 supply chain function areas and what each pays, routes into the field for graduates, veterans and career-changers, a 44-question interview bank, a military-to-civilian translation guide, a chapter on using AI on the job, and a 90-day starting plan.
Wiley Strahan writes about supply chain from the systems layer up to the buyer's decision, and the fastest way to explore the work is to follow that order.
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Start with the argument that AI in warehousing stalls on data access rather than model quality — if nobody can document the schema of a thirty-year-old warehouse system, there is no AI project, only a systems project wearing a better name.
The delivery-economics posts show the same habit: they price a system by what physically moves through it, whether that is vehicle utilization under a New York delivery mandate, the freight reality behind a sofa listing, or the Gulf commodities routed through a 21-mile strait.
The careers material — Supply Chain Careers: The Field Guide and the post mapping the 13 function areas by what a Tuesday actually looks like — is the practical end of the same method: describe the job honestly, then let the reader choose.
They also build rather than only comment, publishing both a resource hub for AI adoption and a voice-first shift-handoff tool.
AI in supply chainWarehouse data infrastructureShift handoffsDelivery economicsHeavy and bulky deliverySupply chain careersGlobal supply disruptionBooksTools and resource hubsSupply chain professionalsOperations and logistics leadersCareer changers and job seekers
Anthony Robinson
Robinson writes for the same reader Strahan does on carrier and parcel economics, and shares the habit of refusing the headline number.
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A FedEx general rate increase of 5.9% is, in Robinson's telling, a communications strategy rather than a price, because five of seven domestic services rose faster and roughly 2,500 zip codes were reclassified into a more expensive tier — so shippers should reprice their own volume against the new tables before talking to a rep. Like Strahan, Robinson turns operations detail into a decision the reader can act on, down to checking DAS and Remote discounts on affected lanes.
Robinson's recurring format is a weekly parcel roundup tied to a shipping-software business, with product proof attached: a national retailer recovering more than $470,000 in carrier claim payments over sixteen weeks, and the argument that an unfiled claims line looks like a zero.
That is rate, surcharge and recovery work at the carrier-negotiation level.
Strahan ranges wider — AI adoption, operator knowledge, careers, delivery operating models — and writes as a builder of free resource tools rather than as an incumbent vendor's channel.
Bazargan maps the last-mile market the way Strahan maps delivery economics: by what the structure actually is, not by reputation.
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He lays out the four largest providers, the venture-backed regional carriers such as OnTrac, Veho and UniUni that cherry-pick metro zips and parcel sizes, and the consolidators and 3PLs in between, then draws the same conclusion about buyer behaviour — without a dedicated RFP process a brand is likely to overpay or work with the wrong partner. Both also read carrier deals in terms of the trade being made.
Bazargan stays at market structure and deal analysis.
His DHL-USPS piece frames a $10 billion exclusive as certainty bought with optionality, which is a shipper-facing read on a corporate commitment.
Strahan goes inside the company instead, arguing about fused P&Ls, utilization floors and who owns the path from click to doorstep, while also writing about hiring, careers and AI.
Bazargan's two posts are market explainers; Strahan's mix is commentary plus tools plus a book.
Wyatt and Strahan both connect what happens after checkout to whether the customer comes back.
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Wyatt's read of Adobe's holiday forecast — a projected $275.1 billion in US online spend, with personal hygiene products up 150% during the peak weeks — turns into a post-purchase question: a gift buyer cares about one delivery date, while an essentials buyer is deciding about a subscription, so three days past the estimated delivery with no update probably loses the replenishment order. Strahan makes the adjacent argument that the delivery event itself is the product, and both write for ecommerce and operations readers rather than for consumers.
Wyatt writes from inside a post-purchase platform, with seven years at the company and an AI agent handling RMA reviews and delivery exceptions as the forward story, so the lens is retention, subscription and exception flow.
Strahan's delivery posts are about carrier and operations economics — margin eaten to fund an impossible promise, capacity feeding a single demand plan, named routing vendors — and they are equally likely to cover a warehouse floor, a career choice or a shipping chokepoint.
Wyatt is more consistently one-topic and vendor-positioned.
The only published audience figure is a LinkedIn following of 5,662, recorded on 2026-10-09.
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Wiley Strahan’s content addresses supply chain practitioners and career-movers, including a free AI adoption resource hub for supply chain professionals, a field guide to the 13 supply chain function areas and how to enter them, and breakdowns of delivery economics, warehouse systems, and AI readiness.
The posts do not provide followers’ job titles, seniority, employers, geography, age, gender, or engagement depth.
For a logistics technology company sharpening AI-in-supply-chain messaging, ask them for audience survey results or platform demographic data covering employer type, job function, seniority, and geography.
LinkedIn followers
5,662
Wiley Strahan's LinkedIn profile follower count, recorded at collection · 2026-10-09
Posts reviewed
10 complete posts
LinkedIn posts, 2026-03-02 to 2026-09-30 · 2026-09-30
Wiley Strahan's posts do not mention sponsorships, advertising, or paid partnerships.
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They publish no rate card, media kit, sponsor policy, brand-deal example, or partnership intake page.
Their posts focus on supply chain operations rather than commercial offers.
Their work does show the surface a partner would be negotiating around.
Strahan operates AISCHub, an AI supply chain resource hub described as free, with 470+ curated resources, comparisons across 35+ AI supply chain tools, use-case breakdowns, an AI readiness assessment, and an ROI calculator.
They also previewed Shift Sync, a voice-first shift-handover tool for 24/7 operations, and invited operations teams to connect and give feedback while it is still being shaped, describing it explicitly as possibly a free side project rather than a product.
They published Supply Chain Careers: The Field Guide and describe it as the first in a series.
These assets offer natural ground for collaboration with a logistics technology partner, but any webinar, white paper, or co-marketing arrangement would be a partner proposal, not a format with published terms.
There is no public process to buy into or published requirements to meet.
The only contact surface visible on their LinkedIn profile is a general professional channel, not a partnership route, so this section leaves the inquiry link empty rather than presenting it as a booking path.
Before planning a partnership, ask for primary-source confirmation of any sponsor route, media kit, or rate card.
Their posts do not publish sponsorship terms; this does not establish whether they accept sponsorships.
A supply chain data and integration vendor sells to retailers, 3PLs and manufacturers. It wants a co-branded white paper and a 45-minute webinar for operations and IT leaders on why AI projects in warehousing and distribution stall before the model layer, and how to scope shift handoffs and an AI readiness check before spending money. The vendor wants the creator to supply the argument, practitioner language and named voice on the asset.
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Reasons
A campaign about warehouse AI would build on Wiley Strahan's argument. In a post on warehouse AI, they write that the blocker is access, not model capability: buildings run on custom systems written 30 or 40 years ago with no source repository, no documented proof the backups restore, and original authors long retired. The diagnostic line the vendor would be buying is their own: can you get the data out, and can you put it back? If not, it is a systems project wearing a better name.
Strahan has built the assets the campaign needs, not just written about the topic. The AI Supply Chain Hub is described as free and carries 470+ curated resources, comparisons across more than 35 AI supply chain tools, use-case breakdowns, an AI readiness assessment and an ROI calculator. Those last two assets sit squarely in a vendor's evaluation funnel, making this brief a closer fit than a generic thought-leadership ask.
The second half of the brief, shift handoffs, gets a specific treatment rather than a slogan. In the Shift Sync post, Strahan frames the need as clearer communication, fewer dropped balls at shift change and a usable record of what happened on the floor. Group chats, long email chains and paper logs are hard to search, hard to standardise and easy to ignore. Generic tools, they argue, were not designed for voice-first notes or for answering what changed on nights last Tuesday without scrolling a week of chat.
The audience matches the buyer's. The content serves supply chain practitioners and career-movers, from an AI adoption hub for supply chain professionals to a field guide on the 13 function areas and how to enter them, to a side-by-side comparison of a warehouse floor role and a fill-rate analyst role. The vendor sells to businesses, not consumers.
Strahan's method suits white-paper co-marketing. They write numbers-led long-form about the industry, including a white paper on heavy and bulky delivery where each claim carries a figure, and argue in arithmetic elsewhere, such as the route-density math behind a delivery reclassification mandate and the utilisation floor that breaks an employee model. They name incumbent tools, giving a vendor natural places to position an integration or orchestration product as the shared data model rather than another dashboard.
Strahan can talk about automation without triggering the job-loss reflex. In one post, they argue that a good operator's unwritten knowledge is valuable as training data, and that operators who extract it before people retire will have the edge. That is a workable register for a technology vendor selling capability rather than headcount reduction.
Conditions
Treat this as a thought-leadership partnership, not a reach buy.
Wiley Strahan had 5,662 LinkedIn followers on 9 October 2026.
The posts do not describe follower job titles, seniority, employers, geography or engagement, or disclose sponsorship rates, a media kit or sponsor policy.
Before planning the campaign, agree terms and deliverables in writing and ask for audience attributes.
Verdict
Strong fit on the posts here.
When to choose a specialist
A direct-to-consumer home furnishings brand wants a paid social campaign for a sofa range: short-form video shot by the creator, a creator-owned discount code, a target of one million impressions, and success measured by follower-driven reach on the creator's own channels.
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Reasons
Wiley Strahan had 5,662 LinkedIn followers on 9 October 2026. Their posts show no consumer-facing channels, short-form video or second platform for a paid consumer activation.
The audience attributes needed for this campaign are not described. The posts provide no information about job titles, seniority, employers, geography, age or engagement, so they do not show whether the following includes sofa buyers or consists of supply chain practitioners and peers. Ask for audience data showing consumer reach before basing a campaign on consumer impressions.
The relevant writing addresses operators, not shoppers. The heavy and bulky delivery argument targets retailers, carriers and merchandisers, with claims about margin, returns absorbing cost as silent write-offs, forward stocking networks and treating the carrier as a brand asset. That is a message for people who sell and move furniture, not those choosing it in a checkout flow. A consumer sofa campaign would ask the creator to speak to an audience the reviewed work does not address.
Before proceeding, ask for the rate card or media kit, sponsor policy, deliverables, and terms for disclosure and usage rights. None appears in the reviewed material; that does not establish what Wiley Strahan will or will not accept.
The stated KPI does not match the available data. The campaign asks for impression volume and follower-driven scale; the only measured figure is 5,662 followers on one platform, which cannot carry that target.
Verdict
Weak fit based on the posts here. The mismatch is channel, audience and format, not the creator's ability.
What would change this
Ask for audience data showing consumer reach, a rate card or media kit, examples of short-form video performance, or examples of completed consumer campaigns. Any of these would let the campaign be assessed on its actual terms.
A worked campaign example
Angle
Systems before models. Working title: Before the Agent, Getting Warehouse Data Out and Back. The session opens with the diagnostic question the vendor wants to own, shows the smallest useful sequence that proves it, then gives the audience a way to measure where they stand.
Based on
The first brief: a supply chain data and integration vendor wants a co-branded white paper and a 45-minute webinar for operations and IT leaders at retailers, 3PLs and manufacturers.
Confirm first
Agree scope, deliverables, disclosure and usage rights in writing before proceeding.
Confirm whether Shift Sync is deployed, in development or a free side project; Strahan describes it as something they may deploy or give away. Naming it as a live product in a customer-facing asset without checking would be wrong.
Confirm whether the AI Supply Chain Hub's readiness assessment and ROI calculator can be referenced or co-branded, and on what terms.
Ask for the audience attributes the vendor needs. Strahan's posts give one channel and a follower count, not job titles, seniority or geography.
How you would measure it
Completed AI readiness assessments, the nearest step to a sales conversation and the most honest lead measure here.
White paper downloads and webinar attendance among the named target roles.
Meetings booked with the vendor after the assessment, tracked separately from raw attendance.
Note
Both briefs and this worked example are constructed for this page, not real campaigns; neither has run.
Structure
Opening, ten minutes: the blocker is not the model. Most warehouses run on custom systems nobody can read, with no repository or tested restore, and the people who wrote them have retired. State the diagnostic plainly: can you get the data out, and can you put it back? If not, you have a systems project, not an AI project.
Second act, fifteen minutes: shift handoffs as the smallest workflow worth fixing. Show voice-first capture from the outgoing supervisor and one handoff view, searchable and replayable across shifts, sites and days. Contrast it with group chat, long email chains and paper logs. Ask the concrete test question: what changed on nights last Tuesday?
Third act, fifteen minutes: readiness, using the two tools already in the audience’s hands. Walk one company through the AI readiness assessment and ROI calculator, showing what a defensible business case looks like when every claim carries a number.
Close, five minutes: what to ask before funding an AI roadmap, and where the vendor’s integration layer fits once the data question has an answer.
White paper: the webinar argument in writing, combining the vendor’s customer data with Strahan’s published framing. Use the recording as a draft, with the assessment and calculator as linked resources.
What strahan brings
The diagnostic argument and phrasing from the reviewed warehouse AI post.
The shift handoff problem and voice-first answer from the reviewed Shift Sync post, including why generic chat tools fall short.
Concrete operator examples that travel, such as the routing override that is technically right but practically wrong, and the contractor who answers his phone on Tuesdays as the only person holding the data model.
The automation register that avoids job-loss framing, from the post on treating tacit operator knowledge as training data.
Practitioner audience access: the content speaks directly to supply chain professionals and career-movers.
What the vendor brings
Named customer examples and published performance data for the white paper.
Legal and brand review, design, and the media spend and distribution behind the webinar.
Product context to describe the integration layer accurately and avoid presenting capabilities it does not have.
Choose Robinson when the decision turns on parcel cost, carrier rate negotiation or recovering money already spent, rather than warehouse systems.
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The weekly parcel roundup takes apart a rate increase the way a shipper would: the published average of 5.9% is described as a floor rather than a number to budget from, because five of seven domestic services rose faster and accessorials averaged 6.79%.
It then gets to the part most buyers miss: roughly 2,500 zips were reclassified and the residential surcharge moved from $8.80 to $17.75, with no zip getting cheaper.
That is the same buyer task as deciding whether to spend on integration, orchestration or automation, answered from the invoice instead of the roadmap.
The claims post adds the arithmetic on the other side of the ledger: over 13,000 claims filed for one retail account in a quarter across three carriers, more than $470,000 recovered in sixteen weeks at about $29,000 a week, with the argument that the money was always there and collecting it took automation and follow-through.
If the vendor sells integration, orchestration or automation that touches parcel spend, claims or carrier mix, Robinson speaks to a cost line a finance sponsor already owns.
Robinson's ground is narrower than a campaign aimed at CFOs.
Their two posts cover carrier economics and claims recovery, not AI adoption, AI readiness, shift handoffs or why warehouse data is hard to reach.
A campaign built on the premise that AI projects stall before the model layer would need someone else to make that argument.
The roundup is dated the week of 28 September 2026, and the claims result is framed as one account's outcome, so the numbers may age and one reflects a single company's experience.
Ask Robinson for audience, engagement and sponsorship information before assessing reach or willingness to partner.
Choose Bazargan when the buyer's live decision is carrier selection: which final-mile providers to shortlist, what a consolidator actually does, or whether to give up optionality for price.
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The post on last-mile fragmentation sorts the market into large integrated networks, venture-backed regional carriers such as OnTrac, Veho, UniUni, GLS and SpeedX, consolidators and resellers, and 3PLs.
It then names the buyer's real exposure: without a dedicated RFP process, a brand is very likely to overpay or work with the wrong partners for its shipping profile.
The DHL and USPS post handles the sourcing conversation's central tradeoff, weighing a $10 billion-plus exclusive final-mile commitment—and the certainty it offers for better rates—against losing the ability to switch carriers when a network stumbles or a regional option gets more competitive.
For a vendor whose product touches carrier management or network design, that is the tradeoff an operations or procurement lead is already arguing about internally.
Bazargan writes at market level, not from the floor.
Neither post offers first-person operator detail or names a business the writer runs; neither covers shift handoffs, warehouse systems or AI readiness.
The need to scope handoffs and run an AI readiness check before spending has no counterpart here.
The posts are dated 18 February 2026 and 29 May 2026, so the framing predates recent AI adoption discussion and would need refreshing for a current campaign.
Ask Bazargan for audience data and partnership terms before assessing reach or availability.
Choose Wyatt when the campaign lives after checkout rather than before the model layer.
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The seven-year account of joining AfterShip as its first North American employee, coming from a logistics operator role at Dell, reporting shipment performance at a fashion design agency, then running sales, support and success alone from an apartment, gives buyers a creator who has both operated shipping and built the software that runs it.
The holiday post is more useful for a campaign: Adobe's $275.1 billion forecast and a 150% surge in personal hygiene products during Cyber Week set up a sharp distinction between a gift buyer, who cares about one delivery date, and an essentials buyer, who is deciding three months later whether to buy again or subscribe.
From there, the post asks whether brands treat first-time peak buyers differently in the post-purchase flow.
If a vendor sells returns, exchanges, warranties, delivery estimates or exception handling, Wyatt describes the same customer problem the vendor sells against and can speak to what changed once an agent started handling RMA reviews and delivery exceptions while brands kept the playbooks.
The topic shift is real.
Post-purchase and retention sit downstream of the handoff and warehouse-data argument, so swapping Wyatt in changes what the white paper is about, not just who writes it.
The posts also tie Wyatt to AfterShip as employer and product advocate.
That suits an AfterShip co-marketing asset but is awkward for a competing post-purchase or tracking vendor co-branding with a named rival's representative; resolve that conflict before discussing terms.
The career post is largely narrative, useful for credibility but light on transferable frameworks.
The material is current, with posts dated 29 September 2026 and 6 October 2026.
Ask Wyatt for audience, engagement and sponsorship terms before assessing a partnership.
In ten LinkedIn posts, Wiley Strahan writes about AI adoption in warehousing, undocumented operator knowledge, voice-first shift handoffs, eCommerce and distribution misalignment, a New York City delivery reclassification bill, heavy and bulky delivery, supply chain careers, and one Strait of Hormuz commodity breakdown.
No post dates are listed here, so the order and time span of the writing are unknown.
Three similar creators were compared using two posts each.
The AI Supply Chain Hub, the Shift Sync handoff tool, the white paper on heavy and bulky delivery and Supply Chain Careers: The Field Guide are described only as Strahan presents them in those posts; the longer works themselves are not included here.
The only measured audience figure is a LinkedIn follower count from 9 October 2026, and no audience attributes, engagement data, video or sponsorship terms are available.