In Freight Audit, AI is a Tool. Expertise is the Advantage.

As companies evaluate freight and parcel audit partners, in-house solutions, and newer platforms that position themselves as AI-centric, one distinction is becoming increasingly important.

Agentic AI alone cannot solve the complexity of transportation and logistics auditing.

That does not mean AI is not valuable. It is. AI can automate detection, streamline workflows, support anomaly detection, and help shippers work with their data in smarter ways. 

Hannah Testani, CEO of Intelligent Audit, makes this point clearly: 

“AI is now here in a way that it can really be driving impact to every organization.” 

But the real opportunity is understanding where AI can do what humans could not do before.

In freight and parcel audit, that distinction matters.

AI can process enormous volumes of transportation data, identify anomalous patterns, and surface issues that would be impossible for human teams to find manually at scale. 

But freight audit is not a purely technical problem. It is a business-critical, highly nuanced process where context matters.

As David Wedekind, VP of Strategic Accounts at Intelligent Audit, put it: 

“It’s still a service-based business. It is not a product that can simply be licensed, turned on, and left to operate in the dark.”

Bart De Muynck makes a similar point in the Better Supply Chains Freight Audit & Payment Market Radar, saying the future of FAP is not defined by automation alone, but by how effectively providers combine data, technology, expertise, and execution. 

The guide puts it clearly: 

“For shippers, AI should be evaluated based on outcomes. Not claims.”

The companies that get the most value from AI will be the ones that pair it with people who understand the process, the data, the carriers, the contracts, and the real-world operational decisions behind every exception.

Freight Audit is No Longer Just Cost Recovery

From the outside, freight and parcel audit can look transactional. A carrier sends an invoice. A system checks it. An error is found. A credit is recovered.

But that view oversimplifies the function.

Transportation auditing sits at the intersection of carrier behavior, customer systems, contract terms, operational workflows, and financial accountability. It is not enough to identify that something looks different. You have to understand why it happened, whether it is valid, what caused it, and how to resolve it.

David described the work as far more than a simple audit process: 

“Good FAP, if you’re doing exception management well, would be working proactively with the carrier and the customer to correct the issue so that it’s not an ongoing thing.”

That is the shift many organizations miss. The value is not just in finding errors after the fact. The value is in correcting the underlying issue, improving the data stream, and preventing the same problem from continuing.

Bart’s guide reinforces this market-wide shift. It describes FAP as evolving into a strategic control point that combines technology and expertise to continuously refine network performance and maintain operations during disruption. 

His conclusion is direct, “FAP is no longer a cost center. It is an agility engine.”

That distinction matters. Freight audit is no longer just about recovering dollars. It is about helping organizations maintain control in an environment where cost, capacity, routing, surcharges, and service conditions are constantly changing.

Why AI Has Become So Powerful in Freight Audit

The reason AI is so compelling in freight audit is simple: the data is too large, too fragmented, and too fast-moving for manual review alone.

Hannah describes the transportation data environment as “hundreds of carriers across the world, all modes,” including courier, parcel, LTL, air, ocean, and rail. All of that data comes from many disparate sources, and KPIs vary depending on the mode. 

The goal is to centralize that data and answer two core questions: “What did the shipper do? And then what should they have done?”

That is exactly where AI can help.

As Hannah explains, AI is especially strong at managing a lot of data and making many decisions from it. It can take millions of data points, identify anomalous patterns, and flag what it could not predict. 

In her words, AI can say, “I’m gonna consume all this data, and when something happens that I couldn’t predict, I’m making you aware of it.”

That capability is meaningful because the highest-value freight audit issues are not always obvious. They may be buried in accessorial patterns, carrier behavior, lane changes, rate shifts, shipment exceptions, cost allocation issues, or recurring invoice discrepancies.

AI can see patterns humans would miss. But seeing the pattern is only the beginning.

Why AI Alone Struggles With Freight Audit Complexity

A common AI-first message is that automation can normalize data, process exceptions, and run the audit with minimal human involvement.

In freight and parcel auditing, that promise quickly gets a reality check.

Transportation data is messy. It comes from many different carriers, systems, formats, and sources. Some of it is structured. Some of it is unstructured. Some arrives through different mediums, including scanned documents or formats that are difficult to read accurately.

David’s point is direct:

“Bad data doesn’t result in good answers if you’re just using AI.”

Hannah makes a similar point through the lens of implementation and cost allocation. In theory, rules often look clean on paper. 

“But once the data arrives, ‘it’s garbage,’ or the rules in practice reveal exceptions and logic gaps that do not make sense without expert review.

AI does not eliminate the need to understand the input. It does not automatically resolve inconsistencies across carriers. It does not know every client’s validation requirements. And it does not always understand the business rules behind the invoice, shipment, contract, or exception.

There is also no universal standard. As David puts it:

“No two carriers are the same, and even for a carrier that is the same, each client might have somewhat of a unique requirement for how they validate those bills.”

If every carrier file looked the same, every customer validated bills the same way, and every system connection solved the same problem, the process would be much easier to automate. 

But that is not the environment shippers operate in.

Where AI-Only Auditing Platforms Fall Short

AI-only platforms often present a compelling story: faster automation, fewer manual steps, and a more modern user experience.

But outsourced freight and parcel audit isn’t valuable simply because it’s automated. It is valuable when it produces accurate, actionable, contract-grounded outcomes.

That is where AI-only models tend to break down. They may detect exceptions, but detection is not the same as resolution. False positives create unnecessary work and can strain supplier or carrier relationships. False negatives leave recoverable dollars on the table. Complex contract language still requires interpretation. Ambiguous or high-impact exceptions still need escalation. 

And when something breaks, someone still has to understand the process well enough to fix it.

David frames the limitation clearly:

“AI can give you some low-hanging fruit kind of things, but it’s really not consultative and doesn’t understand a lot of the real-world application of the things we’re trying to do.”

Bart’s guide provides a broader AI reality check, “In FAP, success depends less on the technology itself” and more on “data quality, operational integration, and execution.” 

It also identifies three common failure patterns in logistics AI: pilot purgatory, data unreadiness, and disconnected execution.

The guide is even more explicit about overpromising automation:

“The promise of fully automated, touchless FAP remains largely theoretical and, in many cases, misleading.”

Hannah adds another important caution: AI “aims to please.” 

Without the right guardrails, AI may try to “fix” information that should not be changed, including historical data. That is why governance, read-only access, protections, and expert oversight matter.

That is the difference between detection and value.

AI can find an exception. Experts determine whether it matters, why it happened, how it should be resolved, and what needs to change so it does not happen again.

And there’s still a significant gap between what looks impressive in a demo and what actually works reliably in a real operating environment.

Building Freight Audit Internally Does Not Remove the Complexity

For some shippers, the question is not whether to choose one provider over another. It is whether to build the capability internally.

That instinct is understandable, especially for companies with strong technology teams. If an organization is already investing in AI across finance, procurement, transportation, and operations, building an internal freight audit capability can seem like a natural next step.

But the build question is not only, “Can we create the technology?” The better question is, “Do we want to own the operational complexity that comes with it?”

A company may be able to build automation, dashboards, reporting, or even AI-driven exception workflows. But a team still has to manage carrier changes, maintain business rules, account for new formats, resolve exceptions, and work with the carrier when something is wrong.

David made this point in the context of technology-forward companies that may assume they can simply build it themselves: 

“True, you probably could, but you’re going to have the same issues.” 

When something is not done right, someone still has to work with the carrier to address it. When the data changes, someone has to manage that process.

Hannah’s guidance is especially useful here. She acknowledges that AI has made many things easier to build internally, but she also emphasizes that most shippers are not technology companies. 

Her advice is to go to existing partners and ask: 

“Here’s where I’m trying to get to. How can you help me?”

Her point is not that companies should avoid AI. It is that they should be thoughtful about where they build, where they partner, and where domain expertise matters most.

As Hannah put it, “The winners in the near future are experts in their industries, not just pure tech companies.”

Bart’s guide reaches the same conclusion from the market side. It notes that many organizations attempting internal AI-driven FAP capabilities run into the same enterprise AI challenges: poor data readiness, limited integration, lack of operational alignment, and internal bandwidth constraints.

The technology may be possible. The operating model is the harder part.

Implementation Requires More Than Connecting Systems

Another place where freight audit is often oversimplified is implementation.

It is easy to talk about connecting to an ERP, TMS, WMS, carrier feed, or financial system. But the connection itself is not the strategy.

Each client has specific data they need to share, capture, validate, and analyze. Two companies may use the same enterprise system but require completely different workflows because they are solving different problems.

As David explained, “While it might be in a similar format, it might be to the same system we’ve integrated with for another customer, we might be solving a completely different problem for them.”

Hannah adds a practical layer here. She recommends starting small, connecting two systems together, and solving a unique problem before expanding. Her guidance is to:

“Dream big, but start incredibly small.” 

Because teams need quick wins, iterative testing, and experts guiding the process.

That is the right mindset for freight audit implementation.

It is not enough to connect systems and assume value will follow. Implementation has to start with the problem: What data matters? What rules apply? What exceptions need review? What outcomes does the shipper need?

Bart’s guide makes this a key part of vendor evaluation. It says shippers should look at data quality and accessibility, integration into operational workflows, the balance between automation and human expertise, and evidence of measurable impact.

AI can accelerate pieces of the work. But it cannot replace the discovery and judgment required to design the process correctly in the first place.

The Real Opportunity is Smarter Use of Data

Many companies are interested in AI because they want better access to answers. They want to ask questions about their data. They want to understand anomalies. They want to see patterns earlier. They want to identify where costs are rising, where process breakdowns are happening, and where operations can improve.

That is where AI, machine learning, and anomaly detection can create real value.

David noted that shippers are often less focused on the behind-the-scenes automation of the audit workflow and more interested in how AI can help them “work with the data smarter.”

Hannah expands that idea with a clear innovation lens. AI is at its best when it helps teams ask what was previously impossible. Her recommended question is: when the answer used to be no, ask:

“What would have to be true for that to be a yes?”

Could a shipper understand anomalous spend patterns across hundreds of millions of records? Could they connect cost allocation logic to shipment behavior, carrier performance, and financial outcomes? Could they identify emerging exceptions before they become recurring cost issues?

AI makes the answers to more of those questions possible. But the answer still depends on data quality, domain expertise, and the ability to turn insight into action.

Bart’s guide describes this shift as the move from processing to “decision intelligence and financial control.” In that model, modern FAP platforms become a decision support layer, giving teams a consistent source of financial and operational insight.

That is the right goal. AI should not just make freight audit faster. It should make the data more useful.

AI Is the Tool. Expertise Unlocks the Value.

The strongest freight audit model is not AI instead of people. It is not people instead of AI.

It is AI guided by people who know how to get the most out of it.

David summarized it well: 

“We’re focused on developing technology. That’s super important, but we also have the people who understand how to get the most out of that technology.”

Bart’s guide calls this the “Human-AI Partnership.” It states that the goal is “not full automation but augmented decision-making.” AI handles high-volume, data-intensive tasks, while humans provide validation, judgment, and operational context.

Hannah brings the same idea into the future of work. In her view, repetitive manual work is what gets replaced, but “when you need to use your brain, when you need to think, when you need to collaborate, those are the jobs that you’re just gonna be supercharged with.”

That is the real differentiator.

Technology can surface the issue. Expertise determines whether it matters.

Technology can create the alert. Expertise turns it into action.

Technology can process more data. Expertise helps the business understand what the data means.

For companies evaluating AI-centric platforms or considering whether to build internally, the question should not be whether AI has a role. It clearly does.

The question is whether AI is surrounded by the right expertise to make it useful.

Bart’s guide puts it plainly: 

“The question is not whether a provider uses AI, but how effectively it combines automation with human expertise to deliver reliable, real-world outcomes.”

Hannah’s perspective brings that point home from the CEO seat: companies should not let AI change who they are or why customers come to them. 

“We still have our principles. We still have our values. We still know why our customers come to us. So we’ll just give them more and faster.”

That is the future of freight audit.

Not AI for the sake of AI. Not automation for the sake of automation. Not a black box running in the background.

It is AI applied to real transportation data, guided by people who understand the work, and focused on outcomes that hold up in the real world.

Or, as David put it,

“It’s not just the product. It’ll help. It’s a tool. Yes. But you need to have the experience to really go forward.”

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