Driven by the wave of technology, the AI agent market is undergoing profound changes. Kognitos' intelligent RPA vendor is attracting industry attention with its $6.75 million funding round and positioning for generative AI automation. However, Microsoft has long since integrated ChatGPT into the Power Platform to provide a low-code application development experience and lead the market. The start-up Rulan Code Technology is also actively embracing generative AI technology and focusing on human-machine collaborative R&D. At the same time, other players in the RPA field are also accelerating the layout of the hyperautomation track, actively introducing generative AI technology to optimize the automation process. These applications improve the efficiency and quality of hyperautomation, bringing intelligent, personalized service experiences to users.
Overall, most hyperautomation vendors have already introduced, integrated, and deployed LLM-based generative AI technologies.
So, which vendors have introduced generative AI, what are the applications of generative AI in hyperautomation, and what is the impact on hyperautomation?
Explore generative AI agents that put automation at the public's fingertips
Hyperautomation Again: Leading the Technological Revolution
The concept of hyperautomation has attracted much attention in the industry since Gartner was proposed. It integrates RPA, process mining, intelligent BPM and other technologies to comprehensively optimize business processes and emphasize the efficient collaboration between people, technology, applications, and services.
Gartner has defined its position as hyperautomation, and is committed to fully automating automatable processes, reducing enterprise costs and unlocking potential. In the context of the rapid development of digital business, hyperautomation provides enterprises with efficient solutions to improve efficiency, performance, and business agility.
Hyperautomation has become the key to the digital transformation of enterprises, going beyond a single technology to becoming an important part of the automation strategy. It's not just a technology optimization, it's a business-driven approach that makes digitalization more resilient, scalable, and cost-effective.
Achieving hyperautomation requires a variety of technologies, tools, and platforms to work together, including event-driven software architectures, RPA, low-code/no-code tools, machine learning, BPM suites, integration platforms, and artificial intelligence. In particular, the integration of generative AI technology has injected new vitality into hyperautomation.
Hyperautomation involves a comprehensive transformation of IT infrastructure to business processes, enabling cross-domain end-to-end automation and greatly improving organizational efficiency. As the technology matures and market acceptance increases, the potential of the hyperautomation market is becoming increasingly apparent. Gartner predicts that by 2024, more than 65% of large organizations will deploy hyperautomation, and the market size is expected to continue to grow.
In the face of technological innovation and market opportunities, manufacturers are actively investing in the layout. Hyperautomation will lead the way in the future of technology, bringing more efficient, intelligent, and convenient business process experiences to various industries.
Explore the convergence of hyperautomation and generative AI
Hyperautomation is maturing, and its combination with generative AI is becoming the new darling of the industry. The generative AI trend led by ChatGPT has led hyperautomation manufacturers to test the waters and integrate advanced technologies. Not to be outdone, domestic manufacturers have introduced domestic AI models to stand out in the market competition.
Generative AI plays a pivotal role in hyperautomation architectures. It has become mainstream in the industry to implement its functionality by integrating into specific tools or deploying related large models. RPA vendors are actively introducing GPT technology to improve the efficiency and accuracy of automated processes by using natural language processing, machine learning and other functions. Low/no-code platforms are not far behind, integrating generative AI technology to bring developers an intelligent and convenient development experience.
In addition, subsets of hyperautomation technologies such as BPM, iPaaS, BI, and process mining are also actively introducing generative AI to achieve comprehensive optimization and intelligence of business processes. Generative AI has become ubiquitous in the collection of hyperautomation technologies, not only improving automation efficiency, but also driving hyperautomation to a higher level.
As technology continues to advance and the market matures, the integration of hyperautomation and generative AI will lead to a new direction of future technology, bringing more efficient, intelligent, and convenient business process experiences to various industries.
The convergence and innovation of generative AI in hyperautomation
Generative AI shows great potential and value in the field of hyperautomation. Not only can it significantly improve work efficiency and reduce maintenance costs, but it also greatly optimizes the user experience and injects new vitality into the advancement of hyperautomation.
Generative AI plays a crucial role when it comes to automating process implementation. By automatically generating business process documents, test cases, and optimization recommendations, it dramatically reduces the time and errors of manual writing, improving the efficiency and accuracy of automated processes. This not only saves a lot of manpower and time costs for enterprises, but also improves the reliability and stability of business processes.
In addition, generative AI provides strong support for enterprises to automate decision-making. It provides rich data sources and content formats, increasing the reach and use cases of hyperautomation. By generating personalized ad copy, creatives, and evaluation reports and recommendations for improvement, generative AI helps companies make more accurate and faster decisions, improving their competitiveness and market responsiveness.
Generative AI also plays an irreplaceable role in data analysis and prediction. It automates data cleansing, organizing, exploring, and visualizing to help users quickly understand the overview and characteristics of their data. At the same time, it can automatically generate appropriate data modeling and prediction solutions based on the characteristics and goals of the data, so as to achieve a smarter and more efficient data-driven business.
An example of the combination of generative AI and hyperautomation
Use cases for the integration of hyperautomation and generative AI are emerging.
Among them, UiPath's practice is particularly compelling. UiPath uses ChatGPT to analyze customer feedback and provide market insights by identifying sentiment. ChatGPT's response results are automatically processed by robots, accurately categorized and directed to the product development team, improving the efficiency and accuracy of feedback processing. At the AI Summit, UiPath's Clipboard AI showcased cross-application auto-paste and copy capabilities, as well as unstructured data queries and currency conversions, exploring a new path for the convergence of hyperautomation and generative AI.
At the same time, domestic RPA manufacturers are also actively exploring the road of integration, and the Chat-IDP product launched by Real Intelligence embeds the semantic understanding and multi-round dialogue capabilities of large language model LLM into document review, realizing direct questioning and communication of document content. Users can easily complete operations such as searching, rewriting and rewriting key information without jumping between multiple platforms, which greatly improves work efficiency.
With the continuous development of generative AI technology and the improvement of user acceptance, more and more use cases have proven its great potential to integrate with hyperautomation, bringing a smarter, more efficient, and more convenient business process experience to the industry. We have reason to believe that the integration of the two will be closer in the future, pushing the industry to a new stage of development.
The Impact of Generative AI on Hyperautomation
The impact of generative AI on hyperautomation is broad and far-reaching, not only improving efficiency and accuracy, reducing human intervention, but also improving the quality of decision-making and strengthening the overall level of intelligence.
The impact is not limited to a subset of hyperautomation technologies, but permeates the entire architecture to drive efficient operations that automate business processes.
Taking RPA as an example, combined with generative AI technologies such as AutoGPT, robots are able to perform tasks autonomously and even handle complex tasks, reducing the need for human intervention. In the low/no-code field, generative AI can automatically generate code and programs, lowering the threshold for development and facilitating the digital transformation of enterprises. For example, the real agent launched by Real Intelligence has powerful intelligent capabilities, which can understand and analyze complex business scenarios like a human, make flexible decisions based on the context, and automatically perform corresponding operations. This capability enables RPA to no longer be limited to simple process operations, but can go deep into the core of the business and achieve true intelligence.
Real Agent
In practical applications, the combination of AI Agent and RPA provides strong support for enterprises to achieve business intelligence and automation, and the intelligent TARS-RPA-Agent has been successfully applied to multiple industry scenarios, realizing the transformation from simple manual operation to intelligent automation.
Whether it's introducing generative AI to a subset of hyperautomation or reinventing an entire architecture, it will have a profound impact on the hyperautomation process. Therefore, the deep integration of generative AI and hyperautomation will be the focus of future research to bring a more intelligent, efficient, and convenient business process experience to various industries.