Traditional Finance leaders are skeptical of AI-Native ERPs

TL;DR:

• In physical-goods industries, most of the cost isn’t in running the ERP. It’s in getting reality into the ERP and reconciling exceptions.

• AI-native ERPs can work well for digital-first companies, but they rarely touch the real complexity that humans work through in these industries.

• The opportunity is not replacing SAP, but surrounding it with AI that handles documents, reconciliations, and exceptions.

AI accounting tools are red hot and raising big money ($400m+ in 2025), but when I ask finance leaders in traditional businesses like manufacturing and consumer goods about the new wave of “AI-native” ledgers, the response is skeptical.

The technology isn’t bad; the issue is who the tools are built for and what workflows they assume.

When you look at early customer lists, most of the adoption is in high growth software companies. The founders come from that world, so they design for that world.

For a SaaS company with clean data, one revenue stream, and API friendly tools, that’s fine. For a food producer, a distributor, or a manufacturer shipping containers through ports and customs, it is not.

Features don’t match the job

Because the tools are built for tech finance teams, they reflect that reality. Instead of logging into an interface to send an invoice reminder, an agent takes care of it and alerts you of the progress in an internal messaging app. It’s a smooth experience for lightweight tasks.

In document-heavy traditional finance environments, that’s not how the work happens.

Entire teams spend their days on reconciliation and exceptions. They live across SAP, Excel, email, carrier portals, and folders full of PDFs. Their job is not simple approvals in Slack or Teams. They figure out what actually happened across 15 different systems and documents.

The contract vs the manifest

In physical-goods businesses, transactions are not as simple as contract and invoice. They represent a sequence of events in the real world.

A single international shipment for a CPG company might involve:

• A purchase order & confirmation

• A packing list & bill of lading

• Customs documents & export declarations

• Insurance & inspection certificates

• Proofs of delivery

• Supplier invoices, freight invoices, & credit notes
  

Each document is a snapshot in time from a different party. Quantities change, delivery dates slip, and claims appear after the fact. Every company has its own way of stitching this together, so you cannot just ship one “intake template” and assume it fits. The variability and custom logic is why Excel is still the dominant tool.

Trying to hide this complexity inside a single AI-native ERP quickly hits a wall. Finance teams need to control the logic and can’t trust a black box.

The Swivel-Chair API

Getting this data into the ERP is still very manual. You can’t force every customer, customs broker, and vendor to integrate with your system. They send PDFs, CSVs, and emails when they are ready.

So teams fall back on a swivel chair API, a real human to do the brutal work.

You have operators with the ERP on one screen and a PDF on the other. They are comparing lines, spotting mismatches in quantity or price, investigating why the mismatch exists, and calling partners to clarify.

Exceptions transition the job from data entry into investigation.

In most finance teams, a majority of time is spent on this reconciliation work. The big cost is in stitching together a coherent story from messy, conflicting evidence and finding a resolution.

Replacing the core feels wrong

The pain is around the system, not inside it. Swapping one set of tables (SAP) for another (Modern ERP) doesn’t address where the money is actually lost.

The real costs sit upstream: in document handling, in errors that slip through, and in overpayments that only show up in recovery audits. ERP replacement is a multi-year undertaking, and for these teams, the risk is simply not worth the benefit.

What a better approach looks like

If the ERP is entrenched and the pain is upstream, the goal should be different: Do not replace the core. Build smart systems that surround it and operate with the same context a human uses.

Keep the legacy ERP as the system of record, but replace the toil around operating it.

Concretely, you want AI that can ingest messy files (PDFs, Emails, EDI), extract and normalize the data, and match it against your POs and receipts while handling the edge cases.

AI becomes the reconciliation and compliance layer around the ERP, with infinite time to investigate issues and solve problems humans miss.