Document automation software solved a painful, specific problem: generate, process, or extract data from documents at scale. It works, but only inside the document. To get there you label fields across templates, maintain those templates as formats drift, and then hand the output to a person to move into the systems where work actually happens.
The 2026 version zooms out. The document is one step in a larger workflow, so the automation covers the whole thing, many data types, many systems, and you set it up by recording the task instead of labeling fields. Caddi is what document automation becomes when it stops being a silo and starts being the workflow.
Document automation 1.0 → 2.0
Four shifts turn a document silo into automation for the whole workflow.
From the document to the whole workflow
Traditional tools own a single step: in and out of the document. But a document is almost always part of something bigger, an intake to complete, a record to update, an approval to route. The 2026 version sees the whole workflow the document lives in and automates all of it, so the value isn't "a parsed document" but "the work done."
From documents to many data types
Documents are just one input. Real processes also run on email, web forms, spreadsheets, and data living in other apps. The new model handles many data types in one automation, so you're not bolting a document tool onto five other point solutions to cover a single process.
From a silo to multi-integration
Classic document software is a silo: document in, data out, then a human moves it onward. The 2026 version is multi-integration by design, it reads from and writes to your DMS, CRM, custodians, and practice systems (70+ tools), so the extracted data lands where work happens automatically.
From field labeling to screen recording
The most painful part of document automation is setup: labeling fields across templates and re-labeling when formats change. The 2026 model removes it. You record the task on a screen-share and AI handles the understanding, no cumbersome per-field tagging, and far less maintenance when documents drift.
The old way vs. the 2026 way, at a glance
| Document automation software | Caddi | |
|---|---|---|
| Scope | The document step only | The whole workflow around it |
| Data types | Documents | Docs, email, forms, app data |
| Setup | Label fields across templates | Record the task |
| Integrations | Document in / data out (silo) | DMS, CRM, custodians (70+ tools) |
| Decisions | Template rules | Validated AI decisions |
| Maintenance | Re-label when formats drift | Maintained for you |
How they score where it counts
Which fits your situation?
Both models have a place. Tap the scenario closest to yours to see which approach wins — and why.
Which fits your situation?
Document automation software
If the job truly begins and ends inside a few fixed templates, a dedicated document tool can do that one step well.
Beyond the document, to the whole workflow
See Caddi build a workflow from a screen recording and run it across 70+ tools. Explore real examples, compare Caddi to the tools you know on the comparison hub, or book a demo.
Do more with less
See Caddi in action
Tell us where to reach you and the calendar opens right here. In 30 minutes we'll show you how Caddi automates the back-office work that grows with your clients—built, run, and maintained for you.
Frequently asked questions
How is document automation software changing in 2026?
It's expanding from the document step to the whole workflow the document lives in. The 2026 model reads documents without per-field labeling (setup by recording), handles many data types beyond documents, integrates deeply with your DMS, CRM, and other systems, and writes extracted data where work happens, automatically.
What's the difference between document automation and workflow automation?
Traditional document automation owns a single step: getting data in or out of a document, usually via templates and labeling. Workflow automation like Caddi treats the document as one step in a larger process, pulling from email and apps, making decisions, and updating systems of record, so it automates the work, not just the parsing.
Why is labeling fields across document templates so painful?
Every new document type becomes a new tagging project, and formats drift, so you re-label to keep extraction accurate. The 2026 model removes this by using AI to read documents from a recorded example instead of per-field templates, which cuts setup time and ongoing maintenance.
Can document automation handle more than PDFs in 2026?
Yes. The modern approach handles many data types, documents, email, forms, spreadsheets, and data in other apps, within a single automation, and connects to the systems of record where the data needs to land, rather than acting as a document-only silo.