Agentic Process Automation (APA): The Power of Combining RPA & Generative AI
For over two decades, Robotic Process Automation (RPA) has been used to automate mundane and complicated business processes. RPA uses software robots to automate the boring stuff done by humans such as data entry, transaction processing, web scrapping and so forth. Automation technology has evolved throughout history to liberate human beings from arduous tasks. Yet, RPA struggles with tasks needing human-like intelligence, especially in elaborate design of workflow construction and dynamic decision-making in workflow execution.
Introducing APA, which represents a groundbreaking shift in automation technology, leveraging the capabilities of large language models (LLMs) to manage and execute complex tasks that require human-like intelligence. APA uses AI agents to design and oversee workflows dynamically, allowing for sophisticated decision-making that adapts to changing conditions. This approach not only increases efficiency but also expands the potential applications of automation in business and technology sectors.
Given the potential agentic AI has demonstrated in a business-to-consumer context, it is intriguing to consider how this technology could be applied in an enterprise setting. At the lower end of the realization spectrum, it could more effectively automate mundane or repetitive tasks with limited human supervision. On a larger scale, agentic AI could be leveraged to have fully autonomous agents that think through and achieve outcomes and goals communicated in natural language. Regardless of the promise eventually realized, agentic AI will likely boost employee productivity and provide greater end-to-end automation.
For businesses, APA offers transformative advantages. It can lead to more efficient process management, reduce human error, and free up human workers to focus on more creative and complex tasks. Additionally, APA’s adaptability makes it suitable for a range of industries, from manufacturing to services, providing a versatile tool for integrating intelligence into various aspects of operation. As AI technology continues to evolve, APA is likely to become a fundamental component of enterprise automation strategies, pushing the boundaries of what can be automated and how automation is implemented.
The Benefits of APA
The advantages of APA will be significant:
- Autonomy: The ability to take goal-directed actions with minimal human oversight.
- Reasoning: Contextual decision-making to make judgment calls and weigh tradeoffs.
- Language understanding: Comprehending and following natural language instructions.
- Enhanced Efficiency: Intelligent agents powered with Generative AI could work tirelessly, completing tasks faster and with fewer errors and less dependency on IT Operations.
- Improved Decision-Making: APA will be able to analyze data and identify patterns using the power of Generative AI, leading to better decision-making throughout your workflows.
- Increased Agility: Adaptive agents will be able to handle exceptions and unexpected situations which RPA usually struggles with, making your operational processes more flexible.
- Reduced Costs: By Automating tasks and improving efficiency, APA could lead to significant cost savings with limited project investments.
- Empowered Workforce: APA will free human employees from repetitive tasks, allowing them to focus on higher-value activities.
- Democratized Offering — APA will empower significant scale of Democratization of implemented solutions there by provisioning Process Automation solutions and products to a wider set of users.
Agentic AI and its potential impact on enterprises
The limitations of RPA systems mean they cannot reliably achieve complex objectives or operate independently across diverse environments. People are still required to heavily oversee their work.
Agentic AI promises to radically reshape organizational workflows, roles, and relationships. As RPA assistants gain advanced reasoning and planning abilities, they unlock the capacity to take on responsibilities previously reserved for humans.
Agentic AI promises to unlock new benefits for enterprises:
- Increased efficiency by automating complex workflows end-to-end by connecting to external systems and tools
- Freed up employee time from mundane tasks for higher impact work
- Optimized operations that dynamically respond to shifting conditions
Within IT departments, agentic AI could automate up to most service desk tickets through self-service resolutions. Help desks would shift from performing repetitive tasks like password resets and device provisions to managing intelligent automation.
For HR, agents could own end-to-end onboarding and offboarding processes, seamlessly completing workflows spanning dozens of systems with zero human involvement. HRBPs would be freed to focus on strategic priorities and employee engagement.
Across functions like facilities, finance, marketing, and more, agentic AI could optimize operations in real time. Agentic copilots can adjust goals, adapt plans, and handle exceptions as conditions change without continuous oversight.
Agentic AI also has the potential to redefine the relationship between humans and AI at work. Rather than replacing employees, digital coworkers would augment human abilities and handle routine work so employees can focus on high-judgment responsibilities.
What does this mean for existing automation tools?
In the near future, Agentic AI will likely augment existing automation tools, not replace them. Agentic AI can work with automation tools in certain situations to enable smoother execution. For instance, data and insights gained through task mining can be used to train and refine the agentic AI model for workflow generation. This model, embedded within a process orchestration tool, can then be used to build workflows that are subsequently executed using application programming interfaces (APIs), robotic process automation (RPA), intelligent document processing (IDP), and other automation tools.
References
See the November 2023 research paper on this subject — ProAgent: From Robotic Process Automation to Agentic Process Automation by Yining Ye, Xin Cong, Shizuo Tian, Jiannan Cao, Hao Wang, Yujia Qin, Yaxi Lu, Heyang Yu, Huadong Wang, Yankai Lin, Zhiyuan Liu, and Maosong Sun.
