Generative Process Automation (GPA) is poised to surpass Robotic Process Automation (RPA) by leveraging Vision Large Language Models (LLM). Unlike RPA, which relies on predefined rules, GPA adapts to dynamic task changes using advanced AI algorithms. Its ability to interpret unstructured data enables it to handle complex tasks with variability and complexity, such as image recognition and natural language understanding. GPA’s integration of Vision LLM empowers it to validate and adjust workflows in real-time, driving operational efficiency and agility. This shift heralds a new era of automation, where organizations can streamline processes, innovate, and adapt to evolving business needs. GPA’s flexibility and intelligence position it as the future of automation, offering unprecedented opportunities for efficiency gains and innovation across industries.

Big companies have invested significant amounts of money in recent years into software designed to automate routine back-office tasks, many of which involve simply cutting and pasting data from one software application to another or using drop-down menus to populate database fields.

Known as “robotic process automation” or RPA, these kinds of software “robots” aren’t AI. Some are little more than souped-up versions of Excel macros, recording mouse movements and keyboard strokes. Others use “if-then” rules to help software complete a workflow.

And yet, businesses are now estimated to spend more than $6 billion per year on RPA software, according to technology analytics firm Forrester Research, a figure that is growing at a double-digit percentage clip. UiPath, one of the leading players in the RPA field, is valued at $13.5 billion. Appian, Blue Prism, and IBM also offer RPA solutions.

GPA (Generative Process Automation) can disrupt this entire market by rebuilding process automation on top of the new wave of vision large language models and generative AI assistants. It specializes in extracting data from unstructured documents. Similar AI methods can be used in the future to extract information from video recordings, call logs, notes, and more, enabling AI software to learn how professionals actually work. Then AI agents, based on the same underlying AI methods that power today’s large language models, can be used to automate many parts of these tasks and evaluate.

This could allow for much higher-value tasks to be automated than can be addressed with today’s RPA, which only works well for tasks with highly routinized and repeated workflows. Current RPA tech is capable of automating about a third of business tasks — a limitation that helps explain why reports by consulting firms EY and Deloitte have found that a majority of RPA projects either fail completely or never live up to their potential.

Rather than starting from idealized workflows, AI software can be trained on what a company actually does in real-world situations. The AI can intuit what the right workflow is for that particular situation, instead of adhering to an overly standardized and rigid template.

The tasks most suitable for this kind of automation would include procurement and risk assessments, customer onboarding, mortgage and insurance claims processing, and managing import and export logistics documentation.

While humans will still be needed to act as quality control, we believe that for many processes, AI robots such as the ones they are building will be able to increase the output an employee can produce in a given amount of time by 30 times.

Every sector of the economy is eventually going to be rebuilt AI-first.