Key takeaways from the blog
As the market requirements shift dynamically, automation solutions, which sufficed till yesterday, fail to support business requirements today. However, businesses that have invested heavily in automation solutions feel discouraged to leverage new technologies that can fulfill the new requirements. The fear of increasing the IT-spend on technologies that become obsolete with each passing day is more than the fear of missing out or FOMO on the evolving technologies. In such cases, businesses should rest assured that the automation continuum always uses the earlier technology investments as a springboard to launch higher automation initiatives for productivity gains. Artificial Intelligence, which has already been around for some time, is such a springboard that launches Agentic AI, the latest in the automation continuum. Agentic AI leverages your data and intelligently orchestrates the existing automation investments to achieve higher levels of speed and productivity, which was unimaginable in the past, with increasing levels of autonomy.
Robotic Process Automation (RPA), a rule-based automation technology, is still relevant for processes that require human intervention. The distance between RPA and Agentic AI is traversed using a series of supervised AI-models that exhibit increasing degrees of AI-led Intelligent Automation. Both Intelligent Automation (IA) and Agentic AI make holistic use of the rule-based RPA to start where the RPA purview ends. In other words, both IA and Agentic AI intelligently orchestrate RPA bots (and other automation solutions such as workflows and API integrations) to achieve pre-defined goals with an increasing degree of autonomy, respectively. As such, RPA is very much in use and is leveraged by modern automation solutions to automate more complex scenarios.
The primary difference between RPA and Agentic AI is that Agentic AI leverages both structured and unstructured data (along with existing automation solutions, such as workflows) to make autonomous decisions to achieve a predefined goal. Whereas RPA requires human intervention at each decision-making node. Agentic AI feeds on data inputs from the environment at run-time and adapts to the changing working conditions by adjusting the workload along dynamic workflows. So, the RPA to Agentic AI migration starts with having a well-structured data extraction and data management backbone.
The migration from RPA to Agentic AI includes the following main steps -
Agentic AI driven automation enables businesses to make significant productivity gains by automating complex workflows. For instance, the CFO back office is an ideal candidate for Agentic AI-driven automation. Here, the AI agent not only extracts data from invoices, but also analyzes it for discrepancies, performs 3-way matching, and more. It also goes so far as to communicate with vendors to resolve the highlighted issues and initiate payment without manual intervention. Agentic AI thus provides superior process autonomy, improving cycle times.
Traditional automation solutions throw exceptions when they face unforeseen scenarios. Agentic AI-driven solutions are built to be truly adaptive. They are built to plan and reason to find a solution in a changing environment. They leverage the existing technology infra and reason through the context of an exception to figure out a solution. For example, when an AI agent processes a purchase order and encounters that the item is out-of-stock, it checks for similar products, informs the customer about the issue, suggests appropriate alternatives, and updates the order based on the customer’s preference.
Agentic AI offers a composite RoI. Firstly, it generates direct cost savings from end-to-end workflow automation. Secondly, it improves accuracy and efficiency, resulting in a better customer experience and loyalty. Thirdly, it automates tasks, freeing up human capital to focus on high-value work that drives innovation and revenue growth. Globally renowned analysts predict that Agentic AI will increase workforce capacity by 30% in the next two years.
Businesses using traditional automation solutions should start off with an Agentic AI pilot with small steps. They should select one high-impact use case for the pilot. Low-hanging Agentic AI automation projects include customer service processes, such as responding to queries and routing query tickets to the appropriate team. On successful completion of the pilot project, the business can roll out the Agentic AI initiative across the organization.
As technology matures along the automation continuum, the question arises whether the earlier automation investments have become obsolete. However, technology propagates by using the earlier automation as a springboard to launch itself. It uses the earlier automation capabilities as tools to navigate and chart the pre-defined goal by holistically using trained AI-models and data feedback loops at run-time. Agentic AI is one such technology in the automation continuum that uses erstwhile automation capabilities, such as RPA and workflows, to achieve higher speed and productivity with a greater degree of autonomy.