A Dynamics 365 Finance implementation team building an automated three-way match workflow ran into a strange gap last month. Their AI Builder model pulled line items off vendor invoices cleanly enough, quantities, unit prices, and extended amounts all landing in the right columns, but the confidence score field meant to flag shaky extractions for human review came back empty for every table row. The fields around the table had confidence values. The table itself did not. Nobody on the team had touched the model’s configuration since it went into production eight months earlier, so the assumption was a bug, or maybe a licensing issue. It was neither. It was a decision made on day one, buried in a dropdown most builders click past without a second thought: whether the model was trained as a Fixed Template document or a General document.
That distinction matters more than it used to, because AI Builder table confidence scores quietly became a real capability when Microsoft folded Azure AI Document Intelligence 4.0 into the document processing model, and that upgrade behaves differently depending on which training path a model took.
What Document Intelligence 4.0 Actually Changed
Microsoft moved AI Builder’s document processing engine onto Azure AI Document Intelligence 4.0 in stages, with public preview starting December 12, 2024 and general availability landing April 30, 2025. For a lot of Dynamics 365 and Power Platform teams, that rollout came and went without much fanfare, since existing models kept working exactly as before. The upgrade wasn’t a forced migration. It was closer to a set of new tools sitting behind the same interface, available to anyone building or rebuilding a model from that point forward.
The practical additions are worth knowing because they change what’s realistic to automate. OCR accuracy improved, particularly on dense or low-quality scans, the kind that come off a fax gateway or a photographed receipt rather than a clean PDF. Layout detection got better at handling documents where content shifts position from one vendor to the next. Signatures became a taggable field type in their own right, not just background noise the model had to work around, which opens the door to processing signed contracts, lease agreements, and approval forms in ways the older engine handled poorly. And confidence scoring extended down to the table level: individual cells, not just whole fields, can now carry a numeric value a flow or canvas app can act on.
That last point is where the invoice team’s gap came from.
The Model Type Decision Behind AI Builder Table Confidence Scores
AI Builder’s document processing wizard offers three starting points: Fixed Template documents, General documents, and an option to extend the prebuilt Invoices model with custom fields. Fixed Template is built for layouts that don’t move, the same vendor sending the same invoice format every time, and it trains fast because the model only has to learn a small set of positions rather than reason about structure from scratch. General documents is built for the opposite case, varied formats, inconsistent layouts, documents that might come from dozens of different sources, and it trains more slowly because it has to learn structural patterns rather than fixed coordinates.
Here’s the part that doesn’t show up until you go looking for it: table and cell-level confidence scores are only available on models trained as General documents. A Fixed Template model can absolutely extract a table, and it can extract it accurately, but it will not return a confidence score for that table’s cells. Field-level confidence outside of tables works normally on both training types. It’s specifically the table data, line items, schedules, itemized charges, where the gap shows up.
For an accounts payable automation scenario built around Dynamics 365 Finance or Business Central, this is not a minor footnote. Line-item extraction is usually the whole point. A header field like vendor name or invoice total is easy to validate against a purchase order regardless of confidence scoring, but line-item quantities and unit prices are where a bad OCR read causes a real financial error, and they’re exactly the data a human-in-the-loop review queue needs a confidence threshold to triage. A team that chose Fixed Template because their vendor’s invoice format never changes gets faster training and solid extraction, but loses the one signal that would tell them which rows need a second look before they hit the general ledger.
Signatures Change What’s Worth Automating
The signature field type deserves its own mention because it shifts the category of documents worth running through AI Builder at all. Before Document Intelligence 4.0, a signed document was something the model either ignored or occasionally misread as stray marks corrupting a nearby field. Now a signature can be tagged, detected, and reported on as a discrete extraction result, true or false, with its own bounding box.
That matters for scenarios well outside invoice processing. A Project Operations team processing subcontractor agreements can now confirm programmatically whether a returned PDF actually carries a signature before routing it into an approval flow, rather than relying on someone opening every file. A procurement team validating vendor onboarding paperwork gets the same benefit. It’s a narrow capability, but it removes a manual verification step that previously had no automated equivalent in AI Builder at all.
Migrating an Existing Model Without Starting Over
Teams running an older model built before the Document Intelligence 4.0 rollout are not required to rebuild from zero to get these capabilities, and the upgrade path is more forgiving than the version number might suggest. According to Microsoft’s own documentation, re-tagging every training document is only necessary if you’re adding new fields, checkboxes, tables, or signature detection that the original model didn’t have, or adding new documents to a collection. If the goal is simply to move an existing, working set of tagged fields onto the newer engine, editing and republishing the model carries the upgrade forward without forcing a full retagging pass.
That’s a lower bar than most people assume when they hear “new model version,” so the practical blocker for most teams isn’t technical effort. It’s the training-type decision made at the start, which can’t be changed after the fact. There’s no toggle that converts a Fixed Template model into a General documents model in place. If table confidence scores turn out to matter after production models are already live, the fix is a genuine rebuild, tagging documents again under the General documents path, not a settings change.
Getting Confidence Scores Into an Actual Review Workflow
Once a model is trained the right way, pulling confidence values into something a human reviewer or a flow can act on is straightforward. In a canvas app, a formula like First(FormProcessor1.Results.'Table 1').Amount.Confidence returns the score for a specific cell, and that value can drive conditional formatting, flag rows below a chosen threshold in red, or filter a gallery down to only the extractions that need eyes on them. In a cloud flow, the Extract Information from Documents action exposes the same values through dynamic content, which makes it possible to branch a flow so that high-confidence invoices post automatically while anything below the threshold routes to an approval step in Teams or a shared queue.
Setting that threshold is more art than science. Microsoft doesn’t publish a universal number, because the right cutoff depends on the cost of an error against the cost of unnecessary review. A finance team automating high-dollar vendor invoices will reasonably set a stricter threshold than a team processing low-value expense reports, and it’s worth running a batch of real historical documents through the Quick Test tool before committing to a number, since that’s where the actual confidence distribution for your document set becomes visible rather than theoretical.
Before You Build the Next One
The rebalancing lesson here isn’t that General documents is always the right choice. Fixed Template models train faster, tend to hit high accuracy sooner with fewer sample documents, and remain the right call for genuinely stable, single-format use cases where table validation isn’t part of the workflow. The mistake is making the training-type decision without knowing it also decides whether confidence scoring will be available on table data later, and finding that out only after a review process built around those scores comes up empty.
Anyone scoping a new document processing model, or auditing one already in production, should treat this as a five-minute check worth doing now rather than a surprise found in eight months. If line-item or table-level confidence scoring is part of the plan, even a future plan, General documents is the only path that supports it, and that has to be decided before the first sample document gets tagged.
At Routeget Technologies, this is the kind of gap we look for early in an automation build, not because the underlying capability is complicated, but because a wrong assumption made in a five-minute setup screen tends to surface eight months later, in production, exactly when it’s most expensive to fix.
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