How To Cut The Cost Of Running Your Freight Operations — Without Replacing Your TMS

The invoice arrived. It wasn't the only bill

A forwarder opens a software invoice. The number is higher than expected — materially higher. The instinct is to evaluate alternatives, start a migration, find a better deal.

Worth pausing on that instinct. Because the platform invoice and the cost of running the operation are two separate numbers — and most forwarders have only ever calculated the first one.

The platform invoice is visible. The cost of the manual work that feeds it — the document processing, the classification, the inbox triage, the quoting — never appears on an invoice. It shows up in headcount, in rework cycles, in experienced operators spending their day on work that should never have reached a human.

That gap is where the real problem sits. A platform migration, on its own, doesn't touch it.

The platform records the work. It doesn't do it

Industry estimates put the global TMS market at between $9.7 billion and $18.5 billion, depending on methodology — with forecasts pointing to sustained double-digit growth through the end of the decade. Every major freight forwarder runs one.

TMS platforms are systems of record. Built to capture, track, and report on what has happened. The work that produces that data — reading the invoice, running the classification, building the quote or proposal, chasing the supplier document — works alongside the platform, outside the platform’s scope, and in most operations, still lands on a person.

Across the specific workflows that feed those platforms — document processing, customs classification, inbox-driven quoting, supplier compliance — a significant share of the work is still handled manually, particularly in the exception-heavy, high-variability tasks where rules-based automation has always struggled. That cost accumulates invisibly, every day.

It shows up infour places:

1. The work that happens beforethe platform sees anything

Before a shipment reaches the TMS, a significant amount of work has already happened. Commercial invoices read and interpreted by hand. Commodity codes researched and entered individually. RFQs received, triaged, priced, and responded to by a coordinator. Supplier documents chased, validated, and filed manually. None of this appearson the platform invoice. All of it costs money.

2. The rework loop

Industry research suggests classification and documentation errors contribute to between 20–40% of customs delays. Rework doesn't appear as a line item. It shows up as overtime, missed SLAs, and senior operators spending their afternoon correcting entries that should have been right the first time. The platform records the corrected version. The cost of getting there disappears into operational noise.

3. The expertise problem

According to Forto's Logistics Trend Compass 2025, only 2% of logistics operators have achieved wide-scale AI adoption. The document-heavy, judgment-intensive workflows — customs classification, invoice processing, quoting — remain predominantly manual across the sector. Skilled operators are spending their time on work that doesn't require their skill. That's the real cost.

4. The variability problem

Volume can be managed with headcount. Variability is a different problem. Inconsistent invoice formats, missing data, ambiguous product descriptions, customer-specific pricing rules — these create non-linear complexity that grows faster than any team can absorb. Hiring more people defers the problem. It doesn't solve it.

BCG's January 2026 survey of more than 180 LSPs and shippers across Europe, North America, Asia, and the Middle East found that some of the largest productivity opportunities in logistics lie precisely here — in reducing back-office workloads: document handling, booking processing, internal coordination. These use cases are, in BCG's words, 'often less visible but can deliver substantial impact at scale.'

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Switching platforms doesn't fix the underlying cost still there

When a platform'spricing becomes painful, finding a cheaper alternative feels like the logical move. In most cases it's the wrong first move — and an expensive one.

A TMS migrationis a 4–12 month project. Implementation costs range from €30,000 to €900,000 depending on the complexity of integrations, data migration, and change management. During the transition, the operation carries dual risk — the cost of the new platform and the disruption of rebuilding workflows around adifferent system of record.

At the end of it, the manual work is still there.

A new TMS doesn't change who processes the documents. It doesn't reclassify the commercial invoices. It doesn't respond to the RFQs or RFPs. It doesn't chase the supplier certificates. It changes where outcomes are recorded — not who produces them.

The operators solving the invoice problem are evaluating platforms. The operators solving the operational cost problem are asking a harder question first: what does it cos tto run the work that feeds the platform, and how do we reduce that? Those are different questions. They lead to different places.

BCG's research identifies unclear ROI and internal capability gaps as the primary barriers to AI adoption — not cost and not technology. The question most operations are wrestling with is execution, not access. That's a different problem, and it has a different answer.

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Platform migration

•       4–12 month implementation timeline

•       €30,000–€900,000 upfront cost

•       Same manual workflows, new system of record

•       Operational risk during transition

•       Solves the invoice problem

Ripple— AI Operations Team

•       Live in days, not months

•       No migration project required

•       Manual work taken off the team entirely

•       Works alongside existing TMS — no disruption

•       Measurable margin improvement within 30 days of go-live

•       Solves the operational cost problem

What high-performing freight operations are doing differently

The freight operators reducing operational cost most effectively are restructuring where work happens and who — or what — owns it. They're not automating individual tasks. They're changing the model.

Four shifts define how they operate:

Move document processing upstream

The work that feeds the TMS should happen before the shipment moves — not after it arrives at the border, sits in an inbox queue, or lands on a coordinator's desk at 4pm. Classification, quoting, and document validation done upstream means fewer delays, fewer exceptions, and fewer corrections after the fact.

Catch data gaps at intake, not mid-workflow

Missing or inconsistent data caught at the point of document receipt costs a fraction ofwhat it costs to fix mid-workflow. A commercial invoice with an ambiguous product description needs clarification before classification proceeds — not after an incorrect entry has been submitted and the shipment is waiting.

Keep skilled people on the work that needs them

The 80% of work that is repeatable and predictable should never reach a licensed broker orsenior operator. Reserve those people for the 20% that genuinely requires their judgment — the edge cases, the complex classifications, the disputes that need expertise to resolve. In high-performing operations, that separation is built into the structure, not left to individuals to manage.

Run the execution layer independently of the platform

The TMS records what happened. The execution layer — the agent that reads the document, extracts the data, runs the classification or the quote, and pushes the output into the system — operates independently of the system of record. It works regardless of which TMS the operation runs. When the platform changes, it moves with the operation.

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How Ripple works alongside your TMS

Ripple operates as an AI Operations Team embedded within existing freight workflows. Not a copilot. Not an assistant. Ripple takes ownership of the work — from document intake through to system-ready output — and connects directly into whatever TMS the operation runs.

Document intake

Commercial invoices, RFQs, RFPs, supplier documents, emails, and unstructured inputs are processed as they arrive. No template configuration required. Ripple reads documents the way an experienced operator would — interpreting what's there, flagging what's missing, and moving forward without stalling the workflow.

Workflowexecution

Classification, quoting, document preparation, and customs submission are owned end-to-end. When data is present and unambiguous, the workflow completes automatically. When something is missing or genuinely unclear, Ripple flags the gap and requests clarification before proceeding — not after a mistake has been made downstream.

TMS integration

Structured outputs — validated classifications, completed quotes, processed documents —are pushed directly into CargoWise or equivalent platforms. The TMS receives clean, structured data. No manual data entry at the point of record. The operation keeps its system of record. Ripple handles the work that feeds it.

Exception handling

Only cases with insufficient data or genuine operational complexity reach a human operator. Everything else is completed automatically. The model is simple: the team handles what requires human judgment. Ripple handles everything else.

Consistencyat scale

Validated decisions are stored at client level. Repeat work is matched instantly — no rework, no inconsistent outcomes across operators or locations. As volume grows, the operation gets more consistent, not less.

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Operational results — live production

These results come from live production deployments.

‍Pentagon Freight Services — UK customs clearance preparation

Pentagon Freight Services manages hundreds of complex clearances daily, including multi-hundred-line CIPLs. Ripple works alongside their declaration system — reading commercial invoices, structuring data to downstream requirements, and applying consistent UK customs logic before submission.

60%  reductionin clearance  costs

90%  reduction in rework and variation

Hundreds of complex clearances processed daily — including large multi-line CIPLs

"Large complex entries were coming out perfect. Andhonestly — wow, it's really easy."

— UK Customs Operator, Pentagon Freight Services

"We had tried other solutions to automate this problem, but nothing was as flexible, cost effective or as easy to manage as Ripple."

— UK Customs Operator, Pentagon Freight Services

‍W.W. Rowland Trucking — Inbox-driven operations

W.W. RowlandTrucking (Rowland) is one of the largest intermodal drayage operators in the Houston area, running ten terminals across Texas, Tennessee, Detroit, Michigan, and Georgia. Every inbound RFQ and RFP was handled manually. Response times ranto hours. In spot drayage, hours mean lost loads.

The challenge came in three forms.

Speed: by the time a quote was built and sent, a competitor had already responded.

Accuracy: customers required their own FSC rates on every quote and were checking.

Scale: volume across multiple terminals was outpacing what the team could handle without growing headcount.

Ripple's agents were deployed directly into Rowland's inboxes. No system replacement. The agents sat in the inbox and ran the work — deciphering thousands ofunstructured emails and processing inbound RFQs and RFPs end-to-end without a dispatcher opening the email, calculating customer-specific FSC rates dynamically on every quote, escalating only the cases that genuinely required human judgment.

90%+ of inbound quotes processed correctly and accurately from go-live

Hours to Seconds quote response time

90%  reductionin manual effort

Higher win rate on first response — customers noticed and said so

Multi-terminal  Ripple deployed across multiple terminals with the same agent team

Volume grew so fast Rowland added a disclaimer: quote receipt is not acceptance of the workorder.

"It's quoting over 90% of everything coming in correctlyand accurately."

— Michael Millar, W.W. Rowland Trucking

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Why your finance director should care

Most freight operations treat the cost of manual processing as a fixed overhead — something that scales with volume and gets managed by hiring. That's an expensive way to run a business.

When document-heavy workflows are owned by an AI Operations Team rather than a headcount, the cost structure changes fundamentally. Processing cost stops scaling linearly with volume. Rework cycles are eliminated rather than managed. Classification accuracy becomes a financial asset — not a recurring source ofliability and correction.

OECD and WCO-derived research suggests customs digitisation can reduce trade costs by 5–12%. For operations running high document volumes across multiple lanes and commodity types, that's a material balance sheet improvement.

BCG's survey found that only 13% of LSPs report measurable financial impact from AI. In mostcases, the value that does exist comes from targeted use cases rather than enterprise-wide transformation. The operations capturing real margin improvement aren't the ones with the most ambitious programmes — they're the ones that identified one specific workflow where manual cost was highest, automated it end-to-end, and measured the result.

The immediate financial case is straight forward. When the work that runs through the TMS is completed accurately, at scale, before the shipment moves, costs that currently sit invisibly in operational overhead become measurable and manageable. The platform invoice is one number. The cost of the work the platform doesn't do is another. The operations making the best decisions about their stack right noware the ones that have calculated both.

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The right question to ask before you make a platform decision

The TMS pricing conversation has pushed a lot of freight operators into platform evaluation mode. That scrutiny is useful. The migration conversation and the operational cost conversation are separate problems — and treating them as the same one is expensive.

A platform migration takes 4–12 months and costs up to €900,000. It changes the system ofrecord. The cost of the work that runs through it stays exactly where it was.

Ripple's AI Operations Team sits alongside the existing TMS and can be live in days. It takes ownership of the document-heavy, data-intensive work that has always satoutside the platform's scope. When the platform decision eventually gets made —staying, migrating, or consolidating — Ripple moves with the operation.

The forwarders moving first on this are reducing the operational cost that exists regardless of which platform they choose. They arrive at the platform decision with a cleaner, cheaper, more scalable operation already in place.

That's the right order.

Frequently asked questions

What is the difference between a TMS and an AI Operations Team?

A TMS is a system of record — it captures, tracks, and reports on operational outcomes. An AI Operations Team works alongside the TMS, taking ownership of the work that produces those outcomes: document processing, classification, quoting, customs preparation. The two work together — Ripple feeds structured data into the TMS automatically, so the system of record receives clean outputs without manual data entry.

‍Does Ripple replace CargoWise or other TMS platforms?

No. Ripple can work alongside existing TMS platforms — CargoWise, Descartes, and others. It connects via API and pushes structured outputs into the system of record automatically. No system replacement. No migration project. The TMS continues as the operational backbone. Ripple handles the work that feeds it.

How quickly can Ripple be deployed alongside an existing TMS?

Ripple is logic-driven rather than template-based, so deployment doesn't require months of configuration. Operations have gone live in days — including optimising legacy workflow logic within a single weekend. There's no lengthy implementation cycle and no requirement to change the underlying platform.

What workflows does Ripple take ownership of?

Ripple currently runs seven workflow categories in live production: UK customs clearance; US HTS classification and duty calculation; Inbox-driven operations; WMS and TMS process optimization; Supplier onboarding and compliance; ESG data management and reporting; and Incident management and reporting.

Each workflow operates on the same model— end-to-end execution, with human involvement limited to genuine exceptions.

How does Ripple handle documents that arrive in different formats?

Ripple reads any document format without template configuration — PDFs, spreadsheets, emails, hand written notes, unstructured inputs. It interprets documents the way an experienced operator would: extracting what's there, flagging what's missing, and requesting clarification before proceeding rather than defaulting to an incorrect output.

What happens when data is missing or ambiguous?

Ripple identifies the gap at the point of intake and routes a clarification request before classification or processing proceeds. The workflow doesn't stall and it doesn't default to an inaccurate output. The issue is caught early — when it's cheapest to fix — not discovered downstream when the cost is higher.

Does Ripple work with non-CargoWise TMS platforms?

Yes. Ripple integrates with CargoWise, Descartes, and other equivalent platforms. The AI Operations Team operates independently of the system of record — which means it operates independently of the platform switch decision too. When a migration happens, Ripple moves with the operation.

What results can freight operators expect?

Live production results include: 60% reduction in customs clearance costs (Pentagon FreightServices, UK); 90%+ of inbound drayage RFQs/RFPs processed correctly from go-live (W.W. Rowland Trucking, US); 90–95% reduction in manual effort across production workflows; and throughput increases of 3–10x with the same team. (Novex 90% manual effort reduction).  

See how Ripple integrates with your existing TMS — no migrationrequired

Book a 15-minute discussion and we'll show you how Ripple works on your documents, in your workflow, alongside your current TMS. No system changes. No commitment required.