AI-Driven Workforce Optimization and Your Website or App: A Guide for Logistics Companies in USA

Direct answer: Retailers are no longer defaulting to "hire more people" when order volume climbs — they are using AI-driven workforce optimization to squeeze more output from the staff they already have, because labor costs keep rising. For logistics companies in the USA, the same pressure applies to drivers, warehouse staff, and dispatch teams, and the fix is not a new hiring plan — it is software that plans routes, schedules labor, and surfaces exceptions automatically, so the people you already employ do more without burning out.

The trend is documented in Shopify and Signifyd ecommerce trends reporting from 2026, which describes retailers shifting away from headcount growth and toward AI-driven workforce optimization as their primary lever against rising labor costs. That reporting is specific to retail hiring patterns, but the underlying economics — labor costs climbing faster than order volume, and AI tools becoming capable enough to substitute for incremental staffing — apply just as directly to logistics operations. A regional carrier, a last-mile delivery fleet, or a warehousing and fulfillment operator in the USA faces the identical math: each new dispatcher, warehouse lead, or route planner costs more every year, while the software available to automate parts of their job has gotten dramatically cheaper and more capable. This post is about what that shift means specifically for the software and systems a logistics company runs — not the abstract HR trend, but the concrete changes to dispatch tools, driver apps, warehouse systems, and vendor-facing portals that follow from it.

What AI-Driven Workforce Optimization Actually Is

The phrase covers a fairly narrow, practical set of capabilities, not a vague "AI will replace workers" story. In the retail context described by Shopify and Signifyd, it means demand forecasting that adjusts staffing schedules automatically, task assignment that routes work to whoever is available and qualified, and exception handling that flags problems before a manager has to notice them manually. None of this requires replacing a workforce with robots. It requires software that removes the manual coordination work a human used to do by hand — the dispatcher building tomorrow's route sheet in a spreadsheet, the warehouse supervisor walking the floor to see who is idle, the ops manager cross-referencing three systems to figure out why a shipment is late.

For logistics specifically, the equivalent capabilities are:

• Dynamic route and load optimization that reassigns stops in real time as traffic, cancellations, or new orders come in, instead of a route plan that is fixed at 6 a.m. and stays fixed regardless of what happens during the day.
• Predictive labor scheduling for warehouses and yards, where staffing levels are set based on forecasted inbound and outbound volume rather than a fixed shift pattern that over-staffs slow days and under-staffs peak days.
• Automated exception surfacing — a delayed shipment, a driver running behind, a dock appointment that's about to be missed — pushed to the right person immediately instead of discovered during an end-of-day review.
• Self-service systems for drivers, warehouse staff, and vendors that answer routine questions (where do I go next, is my appointment confirmed, what's the status of this load) without a human dispatcher fielding the call.

The reason this is happening now, and not five years ago, is a combination of two things converging: labor costs have climbed steadily across the transportation and warehousing sector, and the software capable of doing real-time optimization has gone from expensive, custom, enterprise-only tooling to something a mid-sized logistics company can commission as custom software without a seven-figure budget. That second part — the falling cost of building this — is the part that matters most for a decision-maker at a logistics company reading this in 2026.

Why This Matters More for Logistics Than for Most Industries

Retail is the sector cited in the Shopify and Signifyd data, but logistics companies in the USA are arguably more exposed to this shift than retailers are, for a structural reason: labor is a larger share of total operating cost in transportation and warehousing than it is in most retail formats. Retailers can offset labor costs with margin on merchandise. A logistics company's product is labor plus equipment plus fuel — there's no markup layer to absorb rising wages the way a retailer can price it into a product. That makes the incentive to optimize workforce output through software sharper, not softer, for this audience.

There's a second reason it matters specifically in the USA market right now: driver and warehouse labor availability has been tight for years, and rising wages haven't necessarily solved the availability problem — they've just made each hour of labor more expensive without guaranteeing it's easier to find. That combination — high cost, constrained supply — is exactly the condition under which "optimize the workforce you have" beats "hire more people" as a strategy. A logistics operator who can extract 15-20% more effective capacity from an existing driver and warehouse team, through better routing, scheduling, and exception handling, avoids the recruiting cost, the training ramp, and the turnover risk that comes with growing headcount in a tight labor market.

It's worth being precise here about what is and isn't known. The Shopify and Signifyd trend data describes retailer behavior, not logistics-specific adoption figures — a precise statistic on how many logistics companies have already shifted to AI-driven workforce optimization is not publicly available in that reporting, and we won't invent one. What can be reasoned honestly from the pattern is that the same cost pressure retailers are responding to exists in logistics, often more acutely, and the software capability to respond to it exists today and is accessible at a mid-market price point.

There's also a timing argument specific to the USA logistics market. Fuel costs, insurance premiums, and driver wages have all moved in the same direction over the past several years, which compresses margins from multiple sides at once for carriers and fulfillment operators. When several cost lines rise together, the operations lever that doesn't require negotiating with a union, a fuel supplier, or an insurer is the one that gets pulled first — and that's usually the software and process layer, because it's the one a logistics company controls entirely on its own timeline. That's a structural reason to expect the shift described in the retail trend data to show up in logistics operations even without a logistics-specific statistic to point to yet.

Why "just hire a scheduler" doesn't scale the way it used to

A reasonable objection is that logistics companies have always had dispatchers and schedulers doing exactly this coordination work, so what's actually new. The honest answer is that a human dispatcher's capacity to re-optimize scales roughly linearly with headcount — one more dispatcher lets you handle a proportionally larger fleet, at a proportionally higher cost. Software-driven optimization doesn't scale linearly the same way; a routing system that re-sequences 40 drivers' stops in real time does the same work for 400 drivers with only marginal additional cost. That difference in the cost curve is the entire reason this trend is worth paying attention to now rather than treating it as business as usual with a new label.

What Changes in Practice for Your Website, App, or Internal Systems

This is the part that turns a labor-market trend into an actual project. "AI-driven workforce optimization" is not a single product you buy off a shelf and install — for most logistics companies it means specific, targeted changes to the systems your dispatchers, drivers, warehouse staff, and partners already touch every day.

Dispatch and routing software

If your route planning still happens in a spreadsheet, a legacy TMS that doesn't re-optimize mid-day, or a dispatcher's head, the first practical change is a routing layer that adjusts automatically as conditions change — new orders, cancellations, a driver calling in sick, a dock closing early. This doesn't have to replace your existing transportation management system; it can sit alongside it, pulling live data and pushing updated assignments, which is typically a faster and cheaper path than a full TMS replacement.

Driver and warehouse-facing apps

A driver app or warehouse handheld tool that just displays a static list of stops or tasks is leaving efficiency on the table. The optimization happens when the app can re-sequence a driver's remaining stops based on live traffic, push a warehouse worker to the next highest-priority task automatically, and let both surface problems (a blocked dock, a damaged pallet, a locked gate) without a phone call to dispatch. Every one of those phone calls is a few minutes of a dispatcher's day; multiplied across a fleet, it's the difference between one dispatcher managing 40 drivers or 15.

Customer- and partner-facing visibility

Workforce optimization also shows up on the outward-facing side. If customers and shipping partners are calling your team to ask "where is my shipment," that's manual labor spent answering a question the software should answer on its own through a tracking portal or status API. Reducing that inbound volume is a direct, measurable way to free up staff time for higher-value exception handling instead of routine status checks.

Vendor, carrier, and partner coordination

Logistics operations rarely run on one company's labor alone — they run on a network of carriers, brokers, and vendors, and a lot of the coordination overhead sits in that handoff. A dedicated Vendor Portal Development effort — giving carriers and vendors a self-service place to confirm capacity, submit documents, check payment status, and see load assignments — removes a meaningful chunk of the back-and-forth that currently consumes a coordinator's day by phone and email. This is one of the more overlooked places workforce optimization pays off, because it's not customer-facing and rarely gets prioritized, even though the labor savings are often larger than on the customer side.

Scheduling logic that already exists in other industries

It's worth noting that the underlying scheduling problem — matching limited capacity to fluctuating demand while accounting for constraints like time windows, resource availability, and priority — is not unique to logistics. It's the same problem solved by systems like Custom Medical Appointment Booking Software, where a practice needs to fill provider time slots efficiently while respecting appointment types, durations, and no-show risk. A warehouse dock appointment scheduling system, or a driver shift-scheduling tool, is solving a structurally similar problem: fixed capacity, variable demand, and a cost to getting the assignment wrong. Custom software built for one domain's scheduling constraints often ports cleanly to another once you strip it down to the underlying logic, which is one reason this kind of build tends to be faster than logistics operators expect going in.

The Infrastructure Reality Nobody Mentions

There's a pattern worth borrowing from a completely different part of the AI conversation. In the discussion around The Real AI Power Bottleneck Isn't Generation — It's the Grid Connection Queue, the point is that the constraint on AI infrastructure isn't the model — it's the unglamorous connection layer that has to exist before the model can do anything useful. The same is true for workforce optimization in logistics. The constraint isn't finding an AI routing algorithm or a scheduling model — those exist, and vendors will happily sell you one. The constraint is whether your dispatch system, your driver app, your warehouse management software, and your vendor portal can actually feed that model live, accurate data and act on its output in real time. A logistics company that buys an "AI optimization" add-on but bolts it onto systems that don't talk to each other gets very little of the promised benefit, for the same reason a data center with no grid connection gets very little use out of its GPUs. The unglamorous integration and data-plumbing work is where the actual value gets unlocked or lost.

What to Do About It

The practical path for most logistics companies in the USA isn't "adopt AI" as a single initiative — it's identifying the one or two places where dispatcher, warehouse, or coordinator time is currently spent on work a system could do automatically, and building or upgrading the specific software that removes it. That usually starts with an honest audit of where staff time goes: how much of a dispatcher's day is manual re-routing versus genuine judgment calls, how many warehouse-floor decisions are repetitive assignment problems versus real exceptions, how many vendor calls are status checks that a portal could eliminate. From that audit, the highest-leverage build is usually whichever system currently forces the most manual coordination — often dispatch and routing first, vendor coordination second, and customer-facing visibility third.

Because most of this work touches existing systems rather than replacing them wholesale — a routing layer added to an existing TMS, a portal built alongside an existing ERP, live tracking layered onto an existing customer app — it's best approached through Custom Software Development rather than a generic off-the-shelf platform. Off-the-shelf logistics software is built for the median use case across thousands of customers; the specific bottleneck in your dispatch process or your vendor coordination workflow rarely matches that median closely enough to be solved by toggling a settings page.

There's also a sequencing question worth deciding upfront: whether to tackle dispatch, vendor coordination, and customer visibility as one combined project or as separate phases. For most logistics operators, phasing the work is the more defensible choice, for a reason that has nothing to do with budget. Each phase generates real operational data — how dispatchers actually respond to automated re-routing suggestions, how many vendors actually adopt a self-service portal in the first month, how much customer-service call volume actually drops once tracking goes live — and that data should inform the scope of the next phase rather than being guessed at upfront. A single combined build locks in assumptions about all three systems before any of them has been tested against real usage, which is the most common reason large logistics software projects run over budget without a corresponding jump in measured efficiency.

Common mistakes to avoid

A few patterns show up repeatedly in logistics companies that start this kind of project and get less value than expected:

• Automating the plan but not the exception. Building a system that generates an optimized route or schedule but still requires a human to manually notice and fix every deviation from it defeats much of the purpose — exception handling should be as automated as the initial plan.
• Treating the vendor portal as a document dump. A portal that only stores files, without surfacing live load status or payment status, gets used rarely and doesn't reduce the phone and email volume it was meant to replace.
• Skipping the data audit. Committing to a full routing or scheduling system before confirming the underlying data (volume history, telematics feeds, dock schedules) is actually accessible and clean leads to expensive rework mid-project.

Pricing Context: What This Kind of Work Typically Falls Under

Costs vary with scope, but most logistics workforce-optimization projects map cleanly onto Scult's standard service tiers:

Tier	Typical scope for logistics workforce optimization
Essential — $1,000	A focused build: a single-purpose driver status page, a basic vendor status portal, or a lightweight dashboard surfacing one exception type
Growth — $2,000	A more complete system: a driver or warehouse app with real-time task reassignment, or a fuller vendor portal with document handling and load visibility
Enterprise — $4,000+	Deep integration work: live routing logic connected to an existing TMS, predictive labor scheduling across multiple warehouses, or a full vendor/carrier coordination platform

These figures describe what the work typically falls under, not a fixed quote — the right tier depends on how many systems need to connect and how much of the existing stack is being extended versus replaced.

Key Takeaways

• Retailers shifting from hiring to AI-driven workforce optimization, per Shopify and Signifyd's 2026 trend reporting, reflects a labor-cost pressure that applies at least as strongly to logistics, where labor is a larger share of operating cost.
• The shift is not about replacing drivers or warehouse staff with AI — it's about removing the manual coordination work (routing, scheduling, status-checking) that currently consumes their time and dispatchers' time.
• The highest-leverage starting points are usually dispatch/routing software, driver and warehouse apps, vendor portals, and customer-facing tracking — in roughly that order of impact for most fleets.
• Vendor and carrier coordination is one of the most overlooked places to capture labor savings; a dedicated vendor portal often pays back faster than customer-facing features because the labor it displaces is entirely internal.
• The real constraint is integration, not algorithms — a workforce-optimization feature bolted onto disconnected systems produces little value, similar to the grid-connection bottleneck limiting AI infrastructure elsewhere.
• Most of this work is best scoped as targeted custom software additions to existing systems rather than a wholesale platform replacement.

If your dispatch, warehouse, or vendor coordination process still runs on manual work that a system could handle, book a meeting with our team to talk through where the highest-leverage starting point is for your operation.

Frequently Asked Questions

What does "AI-driven workforce optimization" mean for a logistics company specifically?

It means using software to automatically handle scheduling, routing, and task assignment decisions that a dispatcher or warehouse supervisor currently makes manually. For logistics, that typically covers dynamic route re-optimization, predictive labor scheduling for warehouses, and automated exception flagging.

Is this the same trend as retailers using AI chatbots for customer service?

No. The Shopify and Signifyd 2026 trend data is specifically about retailers using AI to optimize existing staff output instead of hiring more people, driven by rising labor costs. It's a staffing and operations trend, not a customer-service-automation trend, though the two can overlap in customer-facing logistics tools.

Why would a logistics company be more affected by this than a retailer?

Labor is typically a larger share of total operating cost in transportation and warehousing than in retail, where margin on merchandise can absorb some of the cost pressure. That makes the incentive to optimize workforce output through software proportionally stronger for logistics operators.

Do we need to replace our existing TMS to do this?

Usually not. Most workforce-optimization gains come from adding a routing or scheduling layer that connects to your existing transportation management system rather than replacing it outright, which is typically faster and less disruptive.

What's the first system we should look at upgrading?

For most fleets, dispatch and routing software offers the fastest payback because it directly reduces the manual re-planning work a dispatcher does every day. Vendor coordination and customer-facing tracking are usually the next priorities.

How is this different from just buying an off-the-shelf logistics platform?

Off-the-shelf platforms are built for a median use case across many customers, so they rarely match the specific bottleneck in your dispatch or vendor process closely. Custom software targets the exact workflow causing the most manual labor, which usually delivers a better return per dollar spent.

How much does a project like this typically cost?

Scope-dependent, but a focused single-purpose tool (like a basic vendor status page) typically falls under the Essential tier around $1,000, a fuller app or portal under Growth around $2,000, and deep multi-system integration work under Enterprise at $4,000 and up.

How long does a typical build take?

Timelines depend heavily on how many existing systems need to be integrated. A narrowly scoped Essential-tier tool can often be delivered in a few weeks, while Enterprise-tier integration work spanning multiple warehouses or a full TMS connection takes longer.

What is a vendor portal, and why does it matter here?

A vendor portal is a self-service system where carriers, brokers, and other partners can check load assignments, submit documents, and confirm payment status without calling a coordinator. It matters because a large share of internal coordination labor in logistics goes toward exactly these manual status checks.

Can a vendor portal really save meaningful staff time?

Yes, because vendor coordination calls and emails are often high-frequency, low-complexity interactions — exactly the kind of routine work that a self-service system removes most efficiently, freeing coordinators for higher-value exception handling.

How does dock appointment scheduling relate to medical appointment booking software?

Both solve the same underlying problem: matching limited, fixed capacity (a dock slot, a provider's time) to fluctuating demand while respecting constraints like appointment duration and priority. Custom scheduling logic built for one domain often adapts efficiently to the other once the core assignment logic is separated from the domain-specific details.

Will this replace our dispatchers or warehouse supervisors?

The goal described in the retail trend data, and the pattern that applies to logistics, is optimizing existing staff output rather than eliminating roles outright. In practice this usually means each dispatcher or supervisor can effectively manage a larger scope without additional headcount.

What's the biggest reason AI workforce tools fail to deliver value?

Poor integration. A routing or scheduling algorithm added on top of systems that don't share live data, or a tool whose output nobody acts on automatically, produces little benefit — the constraint is almost always the plumbing, not the algorithm itself.

Is this only relevant to large fleets, or does it apply to smaller logistics operators too?

It applies to operators of most sizes, since the underlying labor-cost pressure isn't scale-dependent. Smaller operators often see proportionally larger benefit from even a modest Essential-tier tool, because a small dispatch or coordination team feels the labor cost pressure more acutely per person.

Does this require hiring an in-house AI or data science team?

No. Most of the value described here comes from custom software development connecting existing data sources to routing, scheduling, and notification logic — not from building or training new AI models in-house.

How does predictive labor scheduling work for a warehouse?

It uses historical and forecasted inbound/outbound volume to set staffing levels ahead of time, rather than relying on a fixed shift pattern. This reduces both overstaffing on slow days and understaffing during peak periods.

What data do we need to have in place before starting a project like this?

At minimum, reliable historical volume and timing data (orders, shipments, dock activity) and a way to access it from existing systems. Projects can often start with partial data and improve as more historical data accumulates.

Can this integrate with the ELD and telematics systems we already use?

In most cases, yes — telematics and ELD data feeds are a common input for dynamic routing and driver app updates, and integration is typically part of the custom software scope rather than a separate project.

What happens to customer service call volume if we add live tracking?

Live, accurate tracking visible to customers or partners typically reduces "where is my shipment" inquiries, since the answer is available without a phone call. The scale of reduction depends on current call volume and how visible the current tracking gap is.

Is there a compliance risk in automating dispatch or scheduling decisions?

The main risk area is ensuring driver hours-of-service rules and safety regulations remain enforced correctly when routing or scheduling logic changes dynamically. This should be a specific requirement built into the system rather than an afterthought.

How do we know if our current systems are the bottleneck?

A practical audit looks at how much dispatcher, supervisor, or coordinator time goes to manual re-planning, status-checking, or repetitive assignment decisions versus genuine judgment calls. Whichever category consumes the most hours is usually the highest-leverage place to start.

Should we build this ourselves or bring in outside custom software help?

It depends on whether you have in-house engineering capacity focused on logistics-specific integration work. Many logistics companies don't carry that capacity internally, which is why targeted custom software development is a common path for this kind of project.

What's the difference between Essential, Growth, and Enterprise tiers for this kind of work?

Essential typically covers a single-purpose tool, Growth covers a fuller app or portal with more functionality, and Enterprise covers deep multi-system integration work like connecting live routing logic to an existing TMS across multiple facilities.

Can we start with a small pilot before committing to a larger system?

Yes — starting with an Essential-tier tool targeting one clear bottleneck (like a single vendor status page or one dispatch exception alert) is a reasonable way to validate the approach before expanding scope.

How does this trend affect hiring plans for logistics companies in the USA?

It suggests that adding capacity through software-driven efficiency gains, rather than defaulting to new hires, is becoming a more common strategy as labor costs continue climbing — mirroring what the Shopify and Signifyd data shows happening in retail.

Does AI-driven workforce optimization apply to owner-operators and small fleets too?

The underlying labor-cost pressure applies broadly, though the return on a given software investment scales with fleet size and coordination complexity. Smaller fleets may see the most value in narrowly scoped tools rather than large integrated systems.

What's an example of an "automated exception" in a logistics context?

A shipment running behind schedule, a driver approaching an hours-of-service limit, or a dock appointment about to be missed are all examples — the system flags these automatically instead of a manager discovering them during a manual review.

Will our drivers need retraining to use an upgraded app?

Most upgrades to a driver app are additive — real-time re-sequencing or automated exception reporting layered onto an interface drivers already use — so retraining is usually minimal compared to switching to an entirely new platform.

How does this connect to the AI infrastructure and power grid story mentioned in the post?

It's an analogy: just as AI data centers are bottlenecked by grid connection capacity rather than model capability, logistics workforce optimization is bottlenecked by system integration rather than by the availability of routing or scheduling algorithms.

What's the risk of doing nothing about this trend?

The likely outcome is continued reliance on adding headcount to handle growth, at a time when labor costs are climbing and available software could handle a meaningful share of that additional coordination work instead.

Does this only apply to last-mile delivery, or also to freight and warehousing?

It applies across the logistics spectrum — dispatch and routing optimization is most associated with last-mile and fleet operations, while predictive labor scheduling and dock appointment systems apply directly to warehousing and freight handling.

How do we measure whether a workforce optimization project actually worked?

Practical measures include reduction in manual re-routing time, fewer inbound status-check calls, faster vendor onboarding or confirmation cycles, and whether existing staff can handle higher volume without proportional headcount growth.

Can this software work alongside a legacy warehouse management system?

Yes, in most cases a scheduling or optimization layer can be built to read from and write to an existing WMS rather than replacing it, which reduces both cost and disruption.

What if our vendors and carriers aren't tech-savvy — will they actually use a portal?

Portals built for this use case are typically designed for simple, routine tasks (confirming a load, uploading a document, checking payment status), which keeps the learning curve low even for partners who aren't especially technical.

Is there a security concern with giving external vendors portal access?

Yes — vendor and carrier access should be scoped narrowly to only the data and actions relevant to that partner, with proper authentication, which is a standard requirement built into portal development rather than an optional add-on.

How does rising fuel and labor cost together change the calculus here?

When both fuel and labor costs rise simultaneously, the return on software that reduces wasted miles and wasted coordination time increases correspondingly, since both cost categories are directly addressed by better routing and scheduling.

What's a realistic first project for a mid-sized logistics company testing this approach?

A commonly practical starting point is a vendor or carrier status portal, since it's self-contained, doesn't require deep integration with core dispatch systems, and delivers a measurable reduction in coordinator phone and email volume relatively quickly.

Do we need real-time GPS tracking to benefit from this trend?

Real-time location data improves the accuracy of dynamic routing and customer-facing tracking, but scheduling and vendor-coordination improvements can deliver value even without full real-time GPS integration.

How does this affect our customer-facing website, not just internal tools?

A customer-facing site or app benefits most directly through live shipment tracking and self-service status checks, which reduce the support labor spent answering routine "where is it" questions.

What if we've already tried an AI tool and it didn't deliver results?

That outcome is common when the tool isn't integrated with live operational data or its output isn't acted on automatically — the fix is usually in the integration layer, not necessarily the underlying algorithm.

Should this be one big project or several smaller ones?

Several smaller, targeted projects — starting with the highest-leverage bottleneck — is generally more manageable and lets you validate results before expanding scope, compared to one large undertaking covering every system at once.

How does Custom Software Development differ from buying logistics SaaS add-ons?

SaaS add-ons are built for a broad customer base and rarely match your specific dispatch, scheduling, or vendor workflow exactly. Custom software is built around your actual bottleneck and existing systems, which typically produces a tighter fit and better measurable return.

Are there industry-specific regulations we need to account for in the USA?

Yes — driver hours-of-service rules, DOT requirements, and safety regulations need to be respected in any automated scheduling or routing logic, and this should be specified explicitly as a requirement during the build.

What ongoing maintenance does a system like this require?

Routing and scheduling logic generally needs periodic tuning as volume patterns, routes, or partner relationships change, along with standard software maintenance for integrations as connected systems are updated.

How quickly can we expect to see labor savings after deployment?

This varies by scope, but targeted tools addressing a clear, high-frequency bottleneck (like vendor status checks) tend to show measurable time savings within the first few weeks of adoption, while larger integrated systems take longer to fully ramp.

Can this help with driver retention, not just efficiency?

Reducing repetitive manual coordination and confusing dispatch changes can make a driver's day less frustrating, which indirectly supports retention, though retention depends on many factors beyond software alone.

What's the difference between workforce optimization and full automation?

Workforce optimization augments the decisions your existing staff make, handling the repetitive coordination work automatically while leaving judgment calls and exception handling to people. Full automation would remove human decision-making entirely, which is not what this trend describes.

How does this apply if we operate across multiple states in the USA?

Multi-state operations often mean more complex scheduling and routing constraints (varying regulations, time zones, facility hours), which typically pushes projects toward the Growth or Enterprise tier due to the added integration complexity.

What should we ask a custom software partner before starting this kind of project?

Ask how they plan to integrate with your existing TMS, WMS, or driver app rather than replace it, what data they need access to, and how they'll measure whether the project actually reduced manual coordination time.

Where should we start if we're not sure this trend even applies to us?

An honest internal audit of where dispatcher, warehouse, or coordinator time currently goes is the right starting point — if a meaningful share of it is manual coordination rather than genuine judgment calls, the trend applies directly to your operation.