We’re rapidly moving towards a world where the back office is almost fully automated. Consider: Gartner predicts that within a year, half of B2B invoices will be paid without manual intervention, further increasing to 80% by 2030.
We’re not there yet—recent research suggests that, today, 57% of invoice data still needs manual entry—but it’s clearly coming soon. That’s because the speed, capacity, cost savings, and accuracy delivered by modern intelligent document processing (IDP) are nearly impossible to resist. And adoption will accelerate even more as generative AI (GenAI) powers big leaps forward in IDP’s capabilities, robustness, and ease of implementation.
UiPath is one of the major innovators in the IDP space. Our IDP innovation and product strategy can be summed up like this: leverage automation, specialized AI, and GenAI to blast through IDP roadblocks that historically have inhibited its adoption. These roadblocks include:
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Challenges in handling high variability in document formats
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Difficulty in handling unstructured data
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Laborious model training
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Scalability limitations
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Security concerns
Over the past year or so, we have made notable strides forward in addressing these issues. In fact, Everest Group recently named us, for the second year running, as a Leader in the IDP category PEAK Matrix® Assessment. This year, we had the highest ratings for both ‘vision and capability’ and ‘impact in the market.’
So, with that as the backdrop, let’s take a look at the ways we are combining automation, specialized AI, and GenAI to take IDP to the next level and make it an even more irresistible value proposition.
Using foundational LLMs to tackle unstructured data challenges
One of the ways we have pushed past existing boundaries in IDP is through our use of foundational LLMs like GPT-4, which are trained on diverse datasets.
We have incorporated new GenAI capabilities throughout our existing products. For example, we now use LLMs within UiPath Document Understanding, which has greatly improved our ability to accurately process freeform unstructured documents like legal agreements, contracts, and emails, at high scale. GenAI has also enabled us to dramatically reduce the effort it takes to train models to understand specific documents and forms—cutting the time up to 80%, from weeks to hours or a day or two.
