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In this opinion piece, Charlie Bellingham, General Manager at Intelligent Document Processing (IDP) vendor Affinda, explains why AI-powered document processing is here, yet organizations in regulated industries are still hesitant.
AI-powered document processing has arrived, and most organisations know it. The efficiency gains are real, the accuracy improvements are real, and the business case for replacing manual document workflows with automation is hard to argue with.
And yet, a significant number of organisations – particularly those operating in regulated industries like financial services and insurance – are still hesitating. Not because they doubt the technology exists, but because they’re asking a more important question: Can I actually trust it?
That’s the right question. And the fact that so many vendors haven’t answered it satisfactorily is precisely why the hesitation persists.
Extraction is the easy part
Here’s something the industry doesn’t say loudly enough: pulling data out of a document is no longer the hard problem. Modern AI models are genuinely good at reading documents and returning fields. The demos look impressive and the benchmarks are encouraging.
But extraction is just one step in a document workflow, and in a high-stakes environment, getting any single step wrong can undermine everything that follows.
What matters in production is what happens after the data is extracted. Does it match what your system of record expects? Does it comply with your business rules? Is it complete enough to act on, or does it need a human to review it before it moves forward? And critically, if something goes wrong, can you trace exactly where the error occurred and why?
These are operational and compliance questions, not AI questions. And most extraction tools aren’t built to answer them.
The production reality
Real documents are messy. They come in inconsistent formats, they’re poorly scanned, fields appear in unexpected places, and edge cases multiply as volume grows. In a low-stakes workflow, an occasional extraction error is a minor inconvenience. In a regulated environment – think mortgage applications, insurance claims, trade finance, or clinical documentation – an error can mean a compliance breach, a financial loss, or a regulatory penalty.
The gap between “works in a demo” and “works reliably in production” is where many automation projects stall or fail. Not because the underlying technology wasn’t capable, but because the surrounding infrastructure wasn’t built for the reality of production workflows.
What trustworthy automation actually requires
Grounding every answer to its source. Trustworthy extraction isn’t just about returning the right value – it’s about being able to show exactly where on the document that value came from. When extracted data is positioned back against the source document, reviewers can verify it quickly, auditors can trace it, and the system can flag when something looks off. This is what separates a defensible workflow from a black box.
Validation against your business logic, not just the document. Data extracted from a document needs to make sense in context. That means checking it against your own systems, your own rules, and your own data. Does the supplier name match your vendor database? Does the invoice amount fall within expected parameters? Extraction without validation hands your team raw data and asks them to figure out whether it’s usable. That’s not automation, it’s just a faster form of manual processing.
Human-in-the-loop review, built into the workflow. The goal of automation isn’t to remove humans from every decision – it’s to make sure humans are involved in the right decisions. Exceptions need to be surfaced clearly, routed to the right reviewers, and resolved within the same platform where the automation is happening. When that process is seamless, straight-through processing rates go up and trust in the system increases, because people know there’s a safety net.
Full auditability. In regulated environments, the ability to demonstrate what happened, when, and why is not optional. Every extraction, every validation check, every human review decision should be traceable. That audit trail is what makes automation defensible, both internally and to external regulators.
Starting small, scaling with confidence
One reason organisations resist automation is the perception that it requires a lengthy, expensive implementation before you see any value. That doesn’t have to be the case. The right approach is to start with a defined, manageable use case, prove the workflow in production, validate the trust controls, and then scale.
That sequence matters. Scaling automation before you’ve established trust in the output is where things go wrong. Scaling after you’ve proven the system handles edge cases, validates correctly and routes exceptions appropriately is how you grow with confidence.
The organisations that will get the most out of document automation are the ones who move fast AND ask the right questions below they scale.
In a regulated environment, the cost of automation failing isn’t a delayed project or a budget overrun. It’s a compliance breach, a financial loss, or a decision made on data no one should have trusted in the first place.
Automation that simply extracts data is increasingly a commodity. Automation you can stake your entire workflow on is something else entirely, and that’s the standard worth insisting on.
About the Author
Charlie Bellingham is General Manager for Affinda’s intelligent document processing and agentic AI platform. He helps organisations make sense of document-heavy workflows by turning PDFs, emails and forms into stable, accurate structured data teams can rely on. Charlie balances deep technical understanding with a focus on business outcomes, risk and customer value.
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