In the wave of digital transformation, RPA (Robotic Process Automation) technology has become the darling of enterprise automation due to its efficient and stable characteristics. The development of RPA can be described as rapid, from the initial simple script automation, to today's intelligent process automation, super automation, RPA is gradually penetrating into every business process of the enterprise. However, as enterprises continue to increase their requirements for the depth and breadth of automation, RPA technology is also facing new challenges and opportunities.
In recent years, an emerging technology trend, large model technology, is bringing a revolutionary impact to the RPA industry with its powerful data processing capabilities and natural language understanding. The ability of large models, especially generative AI models represented by ChatGPT, to understand complex texts and generate natural language descriptions provides new ideas and tools for the intelligent upgrade of RPA.
Data Ape observed that many RPA vendors, both foreign and domestic, have introduced large model technology, aiming to break the boundaries of traditional automation through the integration of this technology, realize the deep intelligence of business processes, and then promote the transformation of the entire industry. Next, we will take a closer look at this phenomenon.
Which domestic manufacturers are integrating large models?
The domestic RPA market is on the cusp of digital transformation and has shown a strong momentum of development. With the surge in demand for business process automation by domestic enterprises, RPA technology has quickly become an important tool for enterprises to improve quality and efficiency due to its rapid deployment and efficient operation. In many industries such as financial services, telecommunications, and manufacturing, RPA is helping enterprises realize the automation and intelligence of business processes, especially in high-data-volume, repetitive and labor-intensive business scenarios.
In terms of the integrated application of RPA and large models, many domestic manufacturers are actively exploring.
Real Intelligence - Vertical Large Model TARS and AI Agent
With the advent of the era of large models, as an AI quasi-unicorn and hyper-automation enterprise, in addition to releasing self-developed vertical large model TARS with differentiated advantages such as "effect availability, cost controllability, customized training, and privatized deployment", Real Intelligence has also actively explored the combination of large models and RPA, and released real AI Agent agent products that can "generate digital employees in one sentence". The article "Finally, the AI model has grown its own hands and feet" released by Data Ape, which has a more in-depth introduction to the related technical products of real intelligence.
Through the real agent, users can directly generate digital employees through text or conversation, so that "you say, PC does". Based on the "TARS+ISSUT" dual-mode engine, Real Agent can easily become a hyper-automated agent with "brain" and "eyes, hands and feet", which can autonomously disassemble tasks, perceive the environment, perform feedback, and memorize historical experience, further lowering the threshold for RPA use. Real Agent is not only an AI assistant for individual users, but also an office assistant for government and enterprise employees, and is a new quality productivity delivered by Real Intelligence for all walks of life.
艺赛旗——旗旗助手（AI-agent）
At the beginning of OpenAI's launch of ChatGPT, iSaiqi actively embraced large language model technology, and deeply integrated large models and RPA in the direction of easy-to-learn, easy-to-use and stable RPA product evolution. In terms of ease of learning, the semantic understanding ability of the large model is used to intelligently recommend the operation steps to complete the task, which is convenient for users to quickly master the process development of RPA and improve the learning efficiency and user experience. In terms of ease of use, with the help of large model generative AI capabilities, natural language is converted into code according to user instructions and integrated into RPA designers to improve the process development efficiency of developers.
In addition, the AI-agent launched by i-Sail can convert it into executable instructions based on the user's natural language input through the ability to integrate large models, and automatically judge and execute automated processes, and at the same time, it can also return processing results in a language that humans can understand.
In terms of stability, in the process of process development, i-Saqi uses the knowledge base of the large model to assist developers in problem positioning and error troubleshooting, so as to enhance the stability of the process.
金智维——基于K-Agent平台打造了金智维Kopilot
Based on RPA+LLM, Jin Zhiwei has built the K-Agent platform of Jin Zhiwei's AI Agent products, which has the capabilities of intelligent interaction, thinking, analysis and decision-making. In addition, based on the K-Agent platform, users can quickly develop and deploy various intelligent assistant (Copilot) digital employees to meet the needs of different business scenarios. The article "The Strongest Brain Meets the Most Agile Hands, and the New Quality Productivity Has the Strongest Assistant" published by Data Ape has a more in-depth analysis of it.
Relying on the finely tuned domain model, the intelligent assistant can independently analyze task instructions and plan operation processes, use RPA scripts to call the corresponding platform or application, efficiently and high-quality to complete business needs, give feedback on execution results or answer user questions, and transform complex business decisions into executable business capabilities.
In terms of technology, K-Agent strengthens the learning mechanism of intelligent assistants with the help of combined large model technology and RPA technology, so that the model becomes smarter and smarter. In terms of scenarios, K-Agent is implanted with an industry-specific knowledge base to ensure the coverage, professionalism, compliance and correctness of the knowledge required by the industry, and reduce the cost of personalized services.
影刀——影刀GO(极简应用)、影刀Copilot(魔法指令)、影刀AI Power
Shadow Knife has made significant progress in integrating RPA and large model technology, mainly releasing the product Shadow Knife AI Power, as well as integrating AI capabilities in Shadow Knife RPA, and launching two new functions: Shadow Knife GO and Shadow Knife Copilot.
Specifically, Shadow Knife GO (Minimalist App) improves office productivity by providing an interface for searching and quickly accessing office tools. Copilot allows users to generate RPA commands through the chat interface, simplifying the operation process.
In practice, the main challenges of the integration of large models and RPA include the gap between the general capabilities of large models and specific business requirements, as well as the high-cost data training and model tuning requirements. In order to solve these problems, Shadow Knife has launched Shadow Knife AI Power, which is a new no-code AI development tool, which integrates enterprise knowledge base, global diversified large models and rich AI components, and is committed to creating an efficient enterprise-level AI solution through visual workflow design and diverse integrated call functions. At present, the solution has demonstrated good application results in multiple scenarios such as recruitment, customer service, live broadcasting, and operation.
In addition, companies such as Hongji and Laiye Technology are also actively exploring. Cyclone actively explores the combination of RPA and large models, and encapsulates GPT with original and newly developed components through model fine-tuning technology to form multiple intelligent components, which are driven by natural language or APIs. Laiye Technology has launched a large language model-based product, "Magic Hat", which allows developers to generate automated process fragments through natural language.
Which RPA vendors are integrating large models abroad?
After years of development, the foreign RPA market has formed a mature and highly competitive ecosystem. Leading companies such as UiPath, Automation Anywhere and Blue Prism have dominated the market by virtue of their early market entry advantages and strong technological innovation capabilities. These vendors not only provide basic RPA tools, but also promote the development of RPA in the direction of intelligence and platform by integrating cutting-edge technologies such as AI and data analysis.
The foreign RPA market presents the following characteristics: first, technological innovation is active, and manufacturers are competing to develop more competitive products and solutions; Second, the market concentration is high, and large manufacturers continue to expand their market share through mergers and acquisitions, cooperation, etc.; Third, with the integration of AI technology, the intelligent level of RPA continues to improve, bringing new growth momentum to the market.
In terms of the integration and application of RPA and large models, foreign RPA manufacturers are also at the forefront.
1. UiPath – Integrating generative AI technology
UiPath is a global leader in the RPA industry, providing a complete automation platform that covers the entire process from discovery and automation to operation and optimization, serving thousands of enterprise customers around the world.
UiPath has been active in introducing large model technology, and by integrating generative AI technology, UiPath has enhanced the intelligence level of its RPA products, making automated processes more intelligent and efficient. UiPath provides customers with end-to-end automation solutions by integrating AI technologies such as NLP and OCR.
2. Automation Anywhere——发布了GPT插件
Automation Anywhere is another world-renowned RPA vendor whose products also cover the entire lifecycle of automation, and have a wide customer base and market presence around the world.
Automation Anywhere is actively exploring the integration of AI technology into its RPA platform to make automated processes more intelligent. The company has officially announced or released the GPT plug-in, and has provided relevant tutorials and videos to guide users on how to use large model technology.
3. 微软（Power Automate）——融入大模型
Microsoft, a global tech giant, offers its services in the RPA space through Power Automate, which allows users to create automated workflows and connect various applications and services.
Microsoft leverages its strong R&D capabilities in the field of AI to integrate large model technology into Power Automate and enhance the intelligent capabilities of RPA.
4. NICE——与ChatGPT的集成
As an intelligent automation manufacturer, NICE provides a variety of intelligent automation solutions, including RPA, and its products are widely used in finance, insurance, telecommunications and other industries.
NICE was one of the first companies to announce an integration with ChatGPT technology. Through the integration with ChatGPT, NICE has taken an important step in the combination of RPA and large models, which marks its progress in using large models to improve RPA intelligence.
Why Incorporate Big Models into RPA?
Since so many RPA vendors are looking for ways to integrate large models with RPA, why are they doing this, and what are the benefits?
In the wave of digital transformation, enterprises have an increasing demand for automation and intelligence, which not only promotes the widespread application of RPA technology, but also gives rise to an urgent need to integrate it with large model technology. RPA technology originally had significant advantages in performing repetitive, rule-based tasks, but the limitations of traditional RPA began to emerge in the face of more complex business scenarios, especially when it involves unstructured data processing and complex decision-making. It is the evolution of market demand and the inevitability of technological development that prompts RPA vendors to seek deep integration with large model technology.
Large model technology, with its breakthrough progress in the fields of natural language processing, deep learning and reinforcement learning, has brought new development opportunities for RPA. This convergence not only satisfies the market's urgent demand for technology integration, but also provides a powerful driving force for enterprises to achieve intelligent business processes. By introducing large models, RPA systems can understand natural language instructions more accurately and perform more complex text and data analysis tasks, thereby achieving deeper automation and intelligence in business processes.
In addition, with the growing demand for data-driven decision-making, RPA systems that integrate large models can process and analyze large amounts of unstructured data more effectively, providing more accurate and forward-looking support for enterprise decision-making. This data-driven decision-making model not only improves the operational efficiency of enterprises, but also provides strong support for enterprises to maintain a competitive advantage in the fierce market competition.
At the technical level, the integration of RPA and large models has brought significant improvements in core advantages. The integrated RPA system has enhanced natural language processing capabilities, which can more accurately understand and execute user instructions and process more complex text tasks. At the same time, the improvement of decision support and analysis capabilities enables RPA systems to provide deeper business insights and optimization suggestions based on data. In addition, the application scenarios of automation have been expanded, and RPA is no longer limited to traditional data entry and processing, but can be extended to customer service, risk assessment, market analysis and other fields. Most importantly, the optimization of user experience and interaction methods makes the RPA system easier to use and better adaptable to the needs of different users.
The exploration and practice of domestic RPA manufacturers in this field, such as Real Intelligence, iSaiqi, Jin Zhiwei, Hongji Cyclone, and Laiye Technology, has proved the effectiveness and feasibility of the integration of RPA and large models. Through technological innovation and product upgrades, it not only improves the intelligence level of RPA products, but also provides strong support for the digital transformation of enterprises. With the continuous advancement of technology and the in-depth exploration of application scenarios, it is expected that the integration of RPA and large models will be further deepened, promoting the rapid development of the RPA industry and bringing more intelligent and automated solutions to enterprises.
How exactly should it be integrated?
Next, let's take a step further and see what specific ways to integrate RPA with large models. In the practice of integrating RPA and large model technology, different vendors have adopted diversified technical routes and implementation strategies to achieve the purpose of improving the level of product intelligence and broadening application scenarios. Here are a few of the main ways to implement convergence technologies, along with their benefits, use cases, and potential limitations.
1. Encapsulation of smart components and APIs
Many RPA vendors choose to encapsulate large model technology as independent intelligent components or APIs for developers and business personnel to call. The advantage of this approach is its flexibility and ease of use, allowing users to bring in AI capabilities without impacting existing RPA processes. For example, by calling the NLP API to understand the natural language instructions entered by the user, RPA bots can perform more complex tasks. However, this type of encapsulation can be a problem with a low level of integration, and there may be a barrier to understanding and using these components for non-technical users.
2. Platform-level integration and service innovation
Some vendors have adopted a deeper integration strategy, embedding large model technology directly into the core of the RPA platform. This integration approach makes AI capabilities a native service of the platform, allowing users to create and manage intelligent automation processes directly within the RPA platform, regardless of the complexity of the underlying AI model. Platform-level integration helps provide a smoother and more consistent user experience, while also providing better stability and performance of the system. However, this integration method may limit the user's need for customization of the AI model, and the technical requirements for the platform are high.
3. Privatization deployment and model fine-tuning
In order to meet the needs of enterprises for data security and personalization, some RPA vendors provide private deployment solutions. Enterprises can deploy and run large models in an on-premises environment while fine-tuning them to specific business needs. Private deployment ensures data security and control, while model fine-tuning makes AI capabilities more suitable for enterprise business scenarios. However, private deployments usually require enterprises to have certain technical capabilities and may incur higher O&M costs.
4. Native model development and customization services
Others focus on developing native large models and providing customized services to meet the automation needs of specific industries or enterprises. With native models, vendors can optimize model performance more deeply and provide more customized services. The advantage of this approach is that it offers highly customized solutions that better meet the individual needs of our customers. However, the R&D and maintenance costs of native models are high, and there are high requirements for the technical strength of manufacturers.
In the process of integrating RPA and large model technology, manufacturers need to comprehensively consider market demand, technical capabilities, cost investment, customer feedback and other factors. Each convergence method has its own specific advantages and limitations, and vendors need to choose the most appropriate convergence strategy based on their own resources and strategic positioning. At the same time, as technology continues to advance and market needs change, manufacturers may need to constantly adjust and optimize their convergence strategies to remain competitive.
What are the remaining issues and challenges?
It should be pointed out that in the process of integrating RPA and large model technology, enterprises and technology providers are facing a series of challenges together, which are not only related to the technical level, but also related to regulations, talent training, and cost-effectiveness.
The complexity of technology integration is the primary problem, and the integration of large models needs to solve the compatibility problem with the existing RPA system, while ensuring the stability and response speed of the system. As iSaiqi pointed out, we need to choose among many large models according to different application scenarios, which may involve considerations such as language understanding, knowledge acquisition, reasoning ability, mathematical skills, code generation, agent design, and licensing. In addition, we need to design and optimize gated network services that integrate large models to achieve efficient model operation and management. The difficulty is to select the appropriate expert model and develop the optimal combination strategy so that the best path and results generated by the model can be fed back to the user.
Large models have extremely high requirements for data processing capabilities, which not only involves the huge amount of data, but also includes the diversity and quality of data. Enterprises must ensure that they have sufficient computing resources and storage capacity to support the training and deployment of large models, which directly affects the feasibility of cost control and large-scale deployment.
The generalization ability of large models is another key point, and while large models excel at specific tasks, they need to be highly adaptable and flexible to remain efficient and accurate in changing enterprise business processes. The same applies to system integration and compatibility issues, as RPA systems must integrate seamlessly with an organization's existing IT infrastructure without compromising the stability and performance of existing systems.
User experience is also an aspect that cannot be ignored, and the introduction of large models aims to improve the user's interaction experience with RPA systems, but it also brings high requirements for system interface and interaction design. At the same time, the response speed and accuracy of the system will directly affect the user's satisfaction and trust in RPA products.
At present, the integration technology of large models and RPA is still in the development stage, which requires continuous technological innovation and system optimization.
In addition to the technical difficulties, there are a number of issues that need to be solved. For example, when it comes to data security and privacy protection, as the number of sensitive data involved in automated processes increases, companies must ensure that their data processing activities comply with laws and regulations, and take effective measures to prevent data leakage and misuse. In addition, the challenges of talent training and education needs cannot be ignored. The development of RPA and large-scale model integration technology is far faster than the existing talent training system, resulting in a demand for such compound talents in the market far greater than the supply. Companies need to invest more resources in talent training and education to fill this gap. Long-term investment and cost-benefit analysis are also important considerations, as the initial investment in convergence technology is large, and business decision-makers need to evaluate the payback period and long-term value of this investment. As technology continues to advance, companies also need to consider ongoing update and maintenance costs.
In the face of these challenges, enterprises need to take proactive measures, such as strengthening cooperation with technology vendors, investing in talent development, and formulating long-term technology development strategies, to ensure that RPA and large model convergence technology can bring maximum value to enterprises. At the same time, policymakers and educational institutions also need to pay attention to the issue of talent training in this field, and provide talent guarantee for the healthy development of the industry through the formulation of relevant policies and educational courses. Through these efforts, the integration technology of RPA and large models will better serve the digital transformation of enterprises and promote the productivity progress of the whole society.