A subset of artificial intelligence known as “generative artificial intelligence” (sometimes referred to as “generative AI,” “GAI,” or “GenAI”) uses generative models to produce text, images, and other types of media. After assimilating the patterns and structure of the training data, generative AI models produce new data with comparable properties.
The advancement of deep neural networks has led to the development of various generative AI systems that can accept natural language as input. Large language models (LLMs) and chatbots like ChatGPT are examples of such systems. Industries like healthcare, banking, scriptwriting, software development, product design, marketing, gaming, and many more can benefit from the applications of generative AI.
While much of the research in generative AI is focused on healthcare, not much has been explored in the field of accounting and finance. In this blog post, we narrow our focus to a specific aspect of accounting — the processing of vendor invoices in accounts payable.
The Challenge in Invoice Processing
The days of processing vendor invoices after physically receiving them are long gone. As everyone knows, processing softcopy invoice images using accounting software is a great method to process invoices quickly and ensure timely payments. However, there are instances when we receive softcopies of low-quality invoice images, resulting in loss of goodwill, delayed vendor payment, and loss of early payment discounts. In such cases, we are compelled to contact the seller again for a proper, legible invoice and process it once received.
The Role of Open Text and Machine Learning
“Open Text” is a tool that extracts text from an image and automatically populates fields such as invoice number, date, vendor name, and amount. However, it may not initially fill in all fields. It is therefore inevitable for Open Text to learn the missing fields and improve using its built-in machine learning capabilities. By processing or reading the invoice image multiple times, it can remember the fields and automatically populate them in future instances.
However, all of this is contingent on the quality of the invoice image. So, what happens when the image is of poor quality?
Where Generative AI Steps In
This is where the power of generative AI comes into play. When there is insufficient data available for an application to train itself, generative AI can fill the gap. Based on the limited amount of accessible or readable data, it can generate synthetic data and continue to train either a third-party application or itself.
For example, in the context of Open Text, a clear, readable invoice image can be transformed into hundreds of low-quality invoice images. This synthetic dataset can help train Open Text to identify and interpret fields that may be unreadable in real-world low-resolution or distorted invoices.
As a result, Open Text becomes capable of auto-populating the necessary fields based on previously learned patterns, even when it receives an invoice with illegible text. Generative AI thus addresses the core problem by transforming data limitations into training opportunities.
Generative AI is not just about creating — it's about enhancing existing systems to become smarter, more resilient, and more adaptive. Vendor invoice processing is just one example where this collaboration is pushing the boundaries of what's possible in enterprise automation.