We live in a time where technology - particularly Artificial Intelligence (AI) - is reshaping nearly every aspect of life and business. In this environment, enterprises are continually striving to innovate in ways that enhance efficiency, agility, and competitiveness. To that end, Business Process Management (BPM) is an approach to create, manage, analyze, and constantly improve business processes within an organization. Over the decades, BPM has evolved significantly, adapting to the changing needs of organizations and the technologies that support them.Moving from traditional BPM to agentic AI works best as a phased programme: assess which processes suit autonomy, run a contained pilot, prepare data and integrations, set governance, then scale. This article compares the approaches and walks through each step.
How traditional BPM works
Traditional Business Process Management (BPM) focuses on analyzing, modelling, and improving well-defined processes within an organization. Its main objective is to streamline workflows, automate tasks, and improve overall efficiency, especially in predictable and ongoing operations. It is very well suited for processes that are repetitive, have clear steps, and lead to predictable outcomes. In order to reduce manual efforts and errors, many of the tasks within these processes can be automated. Traditional BPM is primarily set up to improve overall business performance by enhancing efficiency and reducing costs.
However, a drawback of traditional BPM is that the systems operate within predefined rules and lack the autonomy to make complex decisions independently. This is where Agentic AI really comes to the rescue and makes a difference for the better!
What is agentic AI?
Agentic AI refers to a class of AI systems that are capable of acting autonomously. With minimal human intervention, they set and pursue goals, make context-aware decisions and adapt based on feedback. Our guide to what Agentic AI systems is and how it works covers the concept in more depth.
The evolution of BPM
Over the years, BPM began integrating technologies like Robotic Process Automation (RPA), Machine Learning (ML), and Natural Language Processing (NLP). This blend of technologies (often termed Intelligent Automation) brought new capabilities such as faster execution of repetitive tasks through RPA, predictive insights using ML models, and improved customer interaction via NLP-based chatbots. Despite these advances, these systems still operate within predefined rules and lack the autonomy to make complex decisions independently. BPM has now been enhanced by the arrival of Agentic AI which can reason, adapt, and act with a greater degree of independence. This next stage is often called agentic process automation: AI agents that plan and carry out multi-step work across systems, rather than bots that repeat fixed steps.
Traditional BPM vs RPA vs agentic AI: what changes?
The shift is less about replacing earlier tools and more about who decides the next step. The table below shows how each approach handles the same work.
| Traditional BPM | RPA and intelligent automation | Agentic AI | |
| How work is defined | Process models designed in advance | Scripted bots follow fixed steps | Goals set by people; the agent plans the steps |
| Decision-making | Rules and human approvals | Rules, with ML for narrow tasks | Context-aware decisions within set limits |
| Exceptions | Routed to people | Bots stop and hand over | Resolved where allowed, escalated otherwise |
| Change effort | Remodel the process | Rebuild or fix the bot | Update goals, rules or data access |
| Best suited to | Stable, well-defined processes | High-volume, repetitive tasks | Variable, multi-step, judgement-heavy work |
| Human role | Run and approve steps | Handle bot exceptions | Supervise agents and own outcomes |
How agentic AI helps improve traditional BPM
The integration of Agentic AI into BPM transforms it into a dynamic, self - optimizing framework. There are several benefits from this integration - the main one being the fact that rather than waiting for triggers, these systems anticipate needs and thus take up proactive decision making. Leveraging data, these systems are capable of continuous learning and improve over time without the need for manual intervention. They understand and react to the business context in real time, adjusting processes dynamically. They possess the capability to align processes not just to static rules but to evolving business goals. While they can make autonomous decisions, they can also seek human input when necessary and strike a balance between autonomy and control. For typical BPM use cases and the challenges to plan for, see agentic AI: the next frontier in business process management.
Real - world applications of agentic AI
Agentic AI is finding its use in several industries, whether it is in supply chain management to autonomously manage inventory, predict demand, and adjust delivery routes in real-time, or in cybersecurity to detect threats by analyzing network activity and automatically responding to potential breaches.In banking, a traditional BPM workflow routes a loan application through fixed steps, while an agentic system can gather missing documents, check them against lending policy and escalate only the cases that need a credit officer.
Considering the slew of advantages that agentic AI brings to BPM, top BPM providers are using this state-of-the-art technology to drive end-to-end workflow automation. This significantly enhances efficiency, creates new AI-driven job opportunities, and supports a shift toward value-based pricing and workforce reskilling.
How to implement agentic AI: a five-step roadmap for enterprises
Each step below builds on the one before it, so the organisation earns trust in agents gradually rather than switching everything at once.
Step 1: Assess processes and choose the right candidates
Start with a process inventory. Good candidates run at high volume, involve frequent exceptions or judgement calls, change often and draw on data the organisation can already access. Processes where a wrong action cannot be reversed are better left for later, once controls are proven.
Step 2: Run a contained pilot with clear baselines
Pick one process and record its current cycle time, exception rate, cost per transaction and accuracy before the pilot begins. In the first phase, a person approves every action the agent proposes, so the team can see where the agent is reliable and where it is not.
Step 3: Prepare data, integrations and process knowledge
Agents can only act on what they can reach. Connect them to the systems they need through application programming interfaces (APIs), give them access to clean, cross-functional data and document the policies and business rules they must follow. Existing BPM process models are a useful starting point for this knowledge.
Step 4: Set governance and human-in-the-loop rules
Decide which decisions an agent may take on its own, which need human approval and which it must never take. Log every action for audit, set thresholds that trigger escalation and review them regularly. Frameworks such as the US National Institute of Standards and Technology (NIST) AI Risk Management Framework offer a structured way to map, measure and manage these risks.
Step 5: Scale, measure and reskill
Extend proven agents to adjacent processes, track results against the pilot baselines and retire manual steps only when the data supports it. Teams move from doing the work to supervising agents and handling the exceptions they escalate, which makes reskilling part of the roadmap rather than an afterthought.
The way forward
With rapid advancements in Agentic AI, the traditional model of Business Process Management (BPM) is being fundamentally redefined. Agentic AI is driving a tectonic shift in how processes are managed - infusing them with intelligence, autonomy, and adaptability. To truly leverage the potential of this transformative technology, organizations must enable these autonomous agents to access high-quality, cross-functional data. Only then can Agentic AI deliver on its promise of optimizing decision-making and elevating process performance across the enterprise.
How can Infosys BPM help with your agentic AI roadmap?
At Infosys BPM, we help businesses build intelligent, autonomous processes that drive efficiency, agility, and long-term value. Our generative and agentic AI services combine a human plus AI operating model with a focus on responsible AI, and our work with Americana on agentic AI for accounts payable shows how this applies in a live finance process.
Frequently asked questions
Usually not. Most enterprises layer agents on top of their existing BPM and workflow platforms through APIs, and keep the process models as the guardrails the agents work within. Replacing the platform is worth considering only when it cannot expose the data, events or integrations that agents need.
A Business Process Model and Notation (BPMN) workflow is a fixed sequence designed in advance, so every path has to be modelled before it runs. An agent decides its next step at runtime based on its goal and the situation in front of it. Many enterprises combine the two, using BPMN to define approvals and boundaries and letting agents handle the variable steps inside them.
Processes with fixed regulatory rules, actions that cannot be reversed without review and simple, high-volume tasks with little variation are often better served by rules or RPA. They are cheaper to run, easier to audit and do not need an agent's judgement. Agentic AI adds the most value where work varies from case to case.
It depends mostly on data readiness and process complexity. A contained pilot on one well-documented process can show results relatively quickly, while scaling across functions is a longer programme, because integrations, governance and team roles all change along the way.
