The Rise of the Intelligent Worker
In 2015, a mid-level finance manager at a Fortune 500 bank found himself routinely spending his mornings reconciling thousands of transactions across spreadsheets and systems. His work was accurate but time-consuming. Then came RPA—robotic process automation. Within months, that painstaking reconciliation was handled by bots, giving the manager back his mornings and helping the bank save thousands of man-hours.
Fast-forward to 2025, and that story is repeating on a grander, smarter, and more autonomous scale. Welcome to the age of Agentic AI.
Agentic AI represents the next leap forward in intelligent automation. Unlike traditional RPA bots, which are rule-bound and rigid, agentic AI systems are self-guided problem solvers. They learn from data, adapt in real time, and make complex decisions with context and nuance.
Agentic automation goes beyond rules-based RPA to
- learn from the data it ingests
- adapt its processes using what it learns, and
- take the best possible steps in changing situations.
Those capabilities mean that agentic AI can handle the kind of dynamic and complex processes that RPA cannot. Operations leaders and other decision makers who are considering how to automate processes need to know what agentic AI brings to the intelligent automation space — and how they might use it to improve efficiency, productivity, and user experience even more than standard RPA has already done. Think of RPA as a calculator. Agentic AI is the data analyst who uses that calculator, learns patterns in the data, and recommends actions before you even ask.
Where does agentic AI fit in the intelligent automation landscape?
Demand for intelligent automation is growing fast. The RPA market has a projected CAGR of just under 44% through 2030, when it’s forecasted to be worth more than $30 billion. That growth prediction is underpinned by the fact that even basic RPA can drive efficiency improvements and reduce costs — and by the fact that AI is already starting to build on and expand process automation use cases and ROI.
RPA uses structured data to decide among a small range of possible next steps, which makes it ideal for automating simple, repetitive, high-volume processes. AI agents can use unstructured data and continuously evolving context to make more complicated decisions based on current conditions. This makes agentic AI a good fit for processes with many inputs across multiple systems that require rapid, accurate completion. Organizations that deploy agentic intelligent automation can dramatically improve efficiency of those more complex processes while building a foundation to scale this more advanced automation technology.
Agentic process automation for heavily regulated businesses
There are use cases for RPA and agentic intelligent automation in virtually every industry, but the banking, financial services, and insurance (BFSI) sector was by far the largest user of RPA solutions in 2024, followed by information technology and telecommunications, pharmaceuticals and healthcare, and logistics.
When BFSI companies add agentic AI to their intelligent automation deployments, they have the opportunity to speed up transaction processing, reduce errors, and improve compliance with regulations, often in multiple jurisdictions. For example, one major investment bank with a global customer base needed a way to automate the manual processing of several thousand cash flow transactions each day. It needed to do so in a way that complied with daily currency cut-off deadlines to avoid settlement delays and risk exposure.
The bank selected an AI-enabled automation solution that could affirm cashflow, reconcile transactions, and review settlement instructions before releasing payments. It also made the settlement process faster for better currency cut-off compliance, which reduced the bank’s risk exposure and improved customer satisfaction. Integrating this solution with the bank’s over-the-counter settlement workflow saved the bank more than 450 hours per month, equivalent to 3 FTEs.
Agentic AI automation for complex customer billing processes
The telecommunications industry has a longstanding customer experience challenge. Compiling data from multiple systems into customer billing statements that are timely and accurate requires intensive manual work by employees. Toggling among different systems to gather and input equipment and usage data increases the likelihood of data entry errors, which require more manual work across systems to resolve on behalf of customers.
A major global cable, phone, and internet service provider sought to streamline their billing processes to minimize errors, improve customer experience, and reduce costs. With an AI-based process automation solution integrated across their billing systems and extracting relevant data from the web, the company was able to remake the billing audit process. Now, with data collection and entry handled automatically, the company’s billing process is faster, customers find fewer mistakes on their bills, and the company saves an amount of time equivalent to 80% of their FTEs.
Other popular use cases for agentic AI-powered process automation include:
- Contract risk analysis
- Document verification and tracking
- Employee onboarding
- Regulatory compliance, such as KYC and AML processes
- Invoice processing and payment
- Predictive maintenance for manufacturing equipment
A powerful new tool for process automation
One of the most important aspects of agentic AI automation is that it gives businesses like the telecommunications company the flexibility to improve their workflows without having to rearchitect them. Integrating AI agents into existing systems and training them on requirements and goals can take the place of a legacy-style replatforming. Using these faster processes to handle repetitive tasks as well as complex ones can save a tremendous amount of employee hours and increase accuracy.
Agentic AI can use the data it works with to make real-time decisions that make the processes it manages even more efficient. That can create a flywheel for continuous improvement. Agentic AI automation tools can also make it easy to securely add new users, connect with additional cloud resources, and tailor settings for different groups, all of which support scalability. This combination of always-evolving efficiency, accuracy, speed, and scalability will be a game-changer for organizations that seek to optimize complex and regulated processes.
