Original Post (BackOfficeManager_Tom):
Our RPA bots handle invoice processing but they're extremely brittle. Any formatting change in vendor invoices breaks the automation and we're back to manual processing until dev team fixes it. Meanwhile I'm seeing IDP solutions using AI and OCR that claim they can handle unstructured documents and format variations without breaking. The demos look impressive but pricing is steep and I'm worried it's another technology that promises more than it delivers. Anyone actually deployed IDP at scale? Does it live up to the hype or are we going to end up with the same maintenance nightmare just with fancier technology? Our current RPA handles about 5000 invoices monthly from 200 vendors and breaks roughly 15% of the time on format variations. That 15% is killing our efficiency gains. Worth jumping to IDP or should we just improve our RPA error handling and build better format parsers?
(DocumentAI_Specialist):
IDP is legit but you need to understand what you're actually getting. Good IDP platforms use machine learning to extract data from unstructured documents regardless of format, which solves your variation problem. Where companies get burned is thinking IDP is plug-and-play. You still need significant training data, ongoing model tuning, and exception handling workflows. The advantage is that once trained, the system learns and adapts instead of breaking on every small change like rule-based RPA. We deployed IDP for AP processing with 12k invoices monthly from 500+ vendors and got our straight-through processing rate to 78% within six months. That's massive improvement from the 60% we had with pure RPA. Setup took longer and cost more upfront but maintenance is way lower because the system handles format variations. The catch is you need clean master data and good validation rules. If your vendor records are messy or approval workflows unclear, IDP won't magically fix that. It'll just automate the chaos faster.
(Finance_Director_Alex):
Cost comparison is critical here. IDP licensing is typically more expensive than standard RPA, sometimes 2-3x depending on volume. If your break rate is 15%, do the math on whether the IDP cost premium is less than what you're spending on manual intervention for those failures. In our case, we calculated that maintaining RPA with manual exception handling for 20% break rate was still cheaper than IDP for two years, but IDP became more economical after that because maintenance costs on RPA kept climbing. The other factor is growth - if you're adding vendors and invoice volume is increasing, IDP scales better. RPA requires adding more rules and conditions for each new format which makes the code increasingly complex and fragile. IDP should theoretically handle new formats with minimal retraining. Reality is somewhere in between but the scaling economics favor IDP for high-variation document processing. Low-variation high-volume might still be better with traditional RPA.
(MLEngineer_Steve):
Technical reality check on IDP - it's not magic and the "learns on its own" marketing is oversimplified. You need data scientists or ML engineers to tune models, manage training data, monitor performance drift, and handle edge cases. Many companies buy IDP expecting it to work like traditional software and get disappointed when accuracy degrades over time without proper model maintenance. If you don't have ML expertise in-house, factor in those ongoing costs. Vendor managed services help but they're expensive and you lose control over model behavior. The hybrid approach we use is RPA for structured predictable documents and IDP only for the truly unstructured variable stuff. That way you're not paying premium pricing to process simple fixed-format invoices that RPA handles fine. Target IDP at the 15% where you're breaking and keep RPA for the 85% that works. Best of both worlds and more cost-effective than ripping and replacing your whole automation stack.
