Most finance teams still do remittance reconciliation manually because until recently, the alternatives were worse.
The pattern is straightforward: payment data comes in one place, remittance details in another. Reference fields don’t match. Customer codes map poorly. The actual business logic lives in spreadsheets, one-off mapping files, and the heads of the two people who know how to make it work. So you download files, run comparisons, apply rules, fix exceptions, repeat.
This persisted not because companies enjoyed manual work, but because traditional automation didn’t solve the problem. Either the software was too generic, forcing your business to adapt to someone else’s workflow, or it required a six-month implementation for what should be a straightforward operational task. And nobody really wanted to take that risk or pay that price. So the operational pain stayed, and grew as the company grew.
But all of that has now changed due to the maturity of AI agents, and agentic workflows / development capabilities. All those human repetitions have created a treasure trove of training data for agents to learn the internal business logic simply from historic inputs and outputs. Enabling automation at scale.
Here is how it works: Agentic development lets you work from actual ground truth. Your historical input files, known-good outputs, existing mapping references becomes the training data. The agentic framework ingest these, and start to map out the business logic embedded within. It iterates over schemas, tests out different strategies for automation, and continuously improve its “reasoning” until it is able to re-create your manual efforts.
Normally this would be an intractable scoping exercise, followed by development, test cases, validation loops etc. Now, AI can “just figure it out”.
Not every workflow fits this pattern. But from a conceptual point of view your are most often looking at a narrow translation problem between formats and that sort of mapping is ideally suited for agentic workflows: Take three input files, apply specific business rules, produce two outputs. The work is repetitive and rules-driven, and rules can be learned if there are enough examples.
Companies did it manually because building custom software for each workflow was too slow and expensive. That constraint changed, and today these companies are sitting on the very data that will allow them to automate quickly and inexpensively.
Here is how we do it at Alida Labs
We take historical files, known-good outputs, and existing mapping references. Using an agentic development workflow, we reconstruct the business logic into a custom pipeline. This pipeline is standard code, and fully transparent and deterministic. Then we wrap it in standard production infrastructure: monitoring, health checks, schema drift detection, issue handling. The platform layer is reusable. The business logic is bespoke.
Outside of the bespoke logic, we are adding the essentials of detecting changes in input files, or lower matching scores than historic. All of the standard detections of drift, that tells us something has changed upstream, a vendor changed their file format but forgot to tell you, or a new SKU was added without new rules for mapping. Allowing you to react immediately, and fix before minor inconsistencies balloon into end of month detective nightmares.
We are essentially asking a simple question: is this workflow structurally learnable from the artifacts you already have? If yes, we can prove it quickly. If no, we can tell you that quickly too. That allows for both us and our clients to approach this risk free.
Sure there are exceptions, not every workflow can be automated this way. Some are too inconsistent, too poorly documented, or depend too heavily on judgment calls. But many are good candidates, and now they can be identified solely by looking at the historical data.
Recently we took a high-volume remittance workflow that consumed multiple people’s time and turned it into a 10 second automated process. Same files in, same business logic applied, required output produced reliably. No manual effort, no scoping meeting, no discovery phase. This is not AI hype, this is concrete operational efficiency.
Your first AI use-case where you already have the data
Remember, a workflow doesn’t need to become a platform to be valuable. If a recurring manual process becomes a bounded, reliable automation that saves time and produces the required business deliverable, that’s a meaningful outcome. Once one workflow is running, the same pattern often applies to other back-office processes. That’s where the leverage compounds. And this is were a lot of companies could and should start some of their AI pilots. Turning drudgery into quick wins, and build momentum. You likely already have all the building blocks.
Manual remittance work persists because, until recently, the economics of bespoke automation didn’t work. Now they do. If your team is still manually stitching together files and rebuilding accounting outputs by hand, it’s worth questioning whether this work actually has to stay manual.