What’s the next big thing in business automation? If you ask the tech giants, they are agents, powered by generative AI.
There is no universally accepted definition of agentbut today the term is used to describe AI-powered generative tools that can perform complex tasks through human-like interactions between software and web platforms.
For example, an agent could create an itinerary by filling out a customer’s information on airline and hotel chain websites. Or an agent could request the cheapest private transportation service to a location by automatically comparing prices between apps.
Salespeople sense opportunity. OpenAI, the creator of ChatGPT, is reportedly deeply into the development of AI agent systems. And Google demonstrated a series of agent-like products at its annual Cloud Next conference in early April.
“Enterprises should start preparing for large-scale adoption of autonomous agents today,” Boston Consulting Group analysts recently wrote in a report — citing experts who estimate that autonomous agents will become widespread in three to five years.
Old school automation
So where does that leave RPA?
Robotic process automation (RPA) came into vogue more than a decade ago as companies turned to technology to bolster their digital transformation efforts while reducing costs. Like an agent, RPA drives workflow automation. But it is a much more rigid form, based on pre-established “if-then” rules for processes that can be divided into strictly defined, discretized steps.
“RPA can mimic human actions, such as clicking, typing, or copy-pasting, to perform tasks faster and more accurately than humans,” Saikat Ray, vice president analyst at Gartner, told TechCrunch in an interview. “However, RPA robots have limitations when it comes to handling complex, creative or dynamic tasks that require natural language processing or reasoning skills.”
This rigidity makes RPA expensive to construct and greatly limits its applicability.
A 2022 survey from Robocorp, an RPA provider, finds that of organizations that say they have adopted RPA, 69% experience broken automation workflows at least once a week, many of which take hours to fix. Entire businesses have been built helping companies manage their RPA installations and prevent them from breaking down.
RPA vendors are not naïve. They are well aware of the challenges and believe that generative AI could solve many of them without hastening the demise of their platforms. In the minds of RPA vendors, RPA and AI-powered generative agents can coexist peacefully and perhaps one day even come to complement each other.
Generative AI Automation
UiPath, one of the largest players in the RPA market with approximately 10,000+ customers including Uber, Xerox and CrowdStrike, recently announced new generative AI features focused on document and message processing, as well as taking automated actions to deliver the than Bob, CEO of UiPath. Enslin calls it “one-click digital transformation.”
“These features provide customers with generative AI models that are trained for their specific tasks,” Enslin told TechCrunch. “Our generative AI powers workloads such as text completion for emails, categorization, image detection, language translation, and the ability to filter personally identifiable information. [and] Quickly answer any questions related to people issues based on internal data insights.”
One of UiPath’s most recent explorations into the generative AI domain is Clipboard AI, which combines UiPath’s platform with third-party models from OpenAI, Google, and others to, as Enslin says, “bring the power of automation to anyone.” that you have to copy/paste.” Clipboard AI allows users to highlight data from one form and, leveraging generative AI to determine the correct places for the copied data, point it to another form, application, spreadsheet or database.
Image credits: Path to UI
“UiPath sees the need to combine action and artificial intelligence; this is where value is created,” Enslin said. “We believe the best performance will come from those that combine generative AI and human judgment (what we call human-in-the-loop) in end-to-end processes.”
Automation Anywhere, UiPath’s main rival, is also trying to incorporate generative AI into its RPA technologies.
Last year, Automation Anywhere launched AI-powered generative tools to create workflows from natural language, summarize content, extract data from documents and, perhaps most importantly, adapt to application changes that would normally cause an automation to fail. RPA.
“[Our generative AI models are] developed on top of [open] large, trained language models with anonymized metadata from more than 150 million automation processes across thousands of enterprise applications,” Peter White, senior vice president of enterprise AI and automation at Automation Anywhere, told TechCrunch. “We continue to build custom machine learning models for specific tasks within our platform and are now also building custom models on foundational generative AI models using our automation data sets.”
Next generation RPA
Ray notes that it is important to be aware of the limitations of generative AI (i.e. biases and hallucinations) as it drives a growing number of RPA capabilities. But risks aside, he believes generative AI can add value to RPA by transforming the way these platforms work and “creating new possibilities for automation.”
“Generative AI is a powerful technology that can enhance the capabilities of RPA platforms, allowing them to understand and generate natural language, automate content creation, improve decision-making, and even generate code,” Ray said. “By integrating generative AI models, RPA platforms can deliver more value to their customers, increase their productivity and efficiency, and expand their use cases and applications.”
Craig Le Clair, principal analyst at Forrester, believes that RPA platforms are ripe to expand to support autonomous agents and generative AI as their use cases grow. In fact, he anticipates that RPA platforms will transform into comprehensive toolkits for automation: toolkits that help implement RPA on top of related generative AI technologies.
“RPA platforms have the architecture to handle thousands of task automations and this bodes well for core management of AI agents,” he said. “Thousands of companies are well established with RPA platforms and will be open to using them for AI-infused generative agents. “RPA has grown in part thanks to its ability to easily integrate with existing work patterns, through user interface integration, and this will continue to be valuable for more intelligent agents in the future.”
UiPath is already starting to take steps in this direction with a new capability, Context Grounding, which entered preview earlier this month. As Enslin explained to me, Context Grounding is designed to improve the accuracy of generative AI models (both first-party and third-party) by converting the business data those models might use into an “optimized” format that’s easier to index and search. .
“Context Grounding extracts information from company-specific data sets, such as a knowledge base or internal policies and procedures, to create more accurate and insightful responses,” Enslin said.
If there’s one thing holding RPA vendors back, it’s the ever-present temptation to lock customers in, Le Clair said. He emphasized the need for platforms to “remain agnostic” and offer tools that can be configured to work with a variety of current and future enterprise systems and workflows.
Given that, Enslin promised that UiPath will remain “open, flexible and responsible.”
“The future of AI will require a combination of specialized AI with generative AI,” he continued. “We want customers to be able to use all types of AI with confidence.”
White didn’t exactly commit to neutrality. But he emphasized that the Automation Anywhere roadmap is being largely influenced by customer feedback.
“What we’re hearing from all customers, across all industries, is that their ability to incorporate automation into many more use cases has increased exponentially with generative AI,” he said. “With generative AI embedded in intelligent automation technologies like RPA, we see the potential for organizations to reduce operational costs and increase productivity. Companies that do not adopt these technologies will have a difficult time competing against others that adopt generative AI and automation.”
