Month: October 2025

Transition of RPA to Agentic AI Systems

Organizations are now transitioning to the fifth industrial revolution, which is redefining the way of working, with a focus on the convergence of human, workflow automation, state machine-based automations, and Agentic Artificial Intelligence (Agentic AI). Robotic Process Automation (RPA) focuses on workflow and state machine-based automations for rule-based tasks, along with event-based tasks and those that are repetitive or mundane. In contrast, Agentic AI enables machines to make decisions, adapt, and learn autonomously. RPA focuses on attended or unattended automations that provide output by automatically performing a predetermined sequence of actions depending on a sequence of events or designed business rules.

RPA is primarily suited to structured, rule-based processes; the success rate decreases with increasing workflow complexity and dynamicity. Recently, most vendors have added features like self-healing of robots, applications, systems, and process-level complex exception handling scenarios to increase the automation success rate. Despite these enhancements, pre-defined rule-based decisions in RPA workflows aren’t accurate for dynamic scenarios. Hence, there is a need to integrate Agentic AI into RPA workflows to increase the success rate and stability of automation solutions. Agentic AI integrations with RPA bots can operate beyond pre-defined rules; they can leverage Large Language Models (LLMs) to address complex and unpredictable scenarios.

The powerful combination of RPA and Agentic AI can automate a broader range of complex processes. Due to its deterministic nature, RPA provides accuracy that can be further amplified by Agentic AI’s intelligence and self-healing capabilities. Moreover, a powerful combination of RPA and Agentic AI can handle dynamic and complex scenarios with ease.

Drivers of RPA to RPA + Agentic AI Transitions

  • Dynamicity and Complexity in Business Processes

    Business process scenarios involve data in both structured and unstructured formats, and their complexity increases with an increase in business rules in processes; most of these rules are dynamic and decision-based. This complexity can be easily addressed by integrating RPA with Agentic AI systems to automate end-to-end processes, enabling ‘self-healing’ and ‘self-adjustments’ in case of dynamicity in the business process. Self-heal agents are designed to address dynamicity by handling unexpected failures. In complex, ever-changing systems, processes may fail due to changes in process flow, whereas self-adjustment capability enables AI Agents to learn from their own experiences and modify their behavior to improve performance and achieve the goal more effectively.

  • Industry Shift from Automation to Autonomous Systems

    Traditional automation systems are now transitioning to making them semi-autonomous, requiring the convergence of humans and Agentic AI . The goal of the industry is to have autonomous systems handle business processes monitored by humans.

  • Personalized Customer Experiences

    There is a customer need for real-time analytics, personalization, and dynamicity in the business processes. Automation streamlines personalized experiences, reduces manual effort and operational costs, driving value creation.

Few Use Cases of RPA and Agentic Artificial Intelligence

  • Logistics Operations

    RPA is powering GBS logistics operations, like in processes of intermodal freight operations, vehicle operating and service agreements, invoicing, shipment scheduling and tracking, document and data management, order fulfillment, etc. Traditionally, RPA works based on a pre-defined set of mundane process steps and rules; here, Agentic AI can easily integrate to predict demand, enable document management, make decisions on advanced scheduling of shipments based on available data, and even manage order fulfillment with a supervisor and workers Agentic AI.

  • Customer Services

    RPA attended automation has alleviated mundane tasks within customer services in GBS by utilizing personal AI assistants. These RPA personal assistants, now enhanced with AI chatbots, improve customer service through personalized chat interfaces and personalization with retrieval augmented generations provides relevant information from knowledge bases prior to generating texts, tool calling, i.e., external application calls. The combination of RPA and Agentic AI in customer service helps agents resolve customer inquiries faster, thereby reducing average handling times.

  • Accounts & Banking

    Traditionally, RPA has automated mundane processes such as batch bank reconciliation, customer onboarding, KYC & AML. Now complex processes can be automated by integrating RPA with Agentic AI, like personalized recommendations—tailored financial advice based on individual behaviors, predictive modeling to anticipate future financial needs, anomaly detections, automated regulatory compliance, analysis of financial behavior & spending patterns.

Key Challenges

The transition from RPA to RPA + Agentic AI offers the advantage of Intelligent Automation, but it also introduces inherent problems to solve. Here are some key challenges that companies may face:

  • Complexity in Disparate Applications

    Business applications and their associated data often exist in silos; disparate systems are designed to operate in a way that is fundamentally different than the systems with which Agentic AI works. There should be integrated and connected systems to leverage and upgrade Agentic AI capabilities effectively.

  • Data Privacy and Security Compliance

    Agentic AI involves processing vast amounts of sensitive data that requires adherence to data regulations. To ensure robust safeguard mechanisms, it’s necessary to monitor and protect the AI models and systems from risks and threats. There is a need for a responsible AI framework that includes a combination of accelerators and solutions designed to drive responsible AI adoption across enterprises and ensure strong AI governance, ethics, and security.

  • Infrastructure Requirements

    Agentic AI implementations require high-performance computing resources and scalable cloud services, which most organizations in the banking & finance, public sector enterprises, or operations domains lack. Lack of infrastructure readiness is a key challenge for transformations with RPA + Agentic AI.

  • Talent Shortage

    The transition from RPA to Agentic AI is creating a talent shortage, as it demands a new skillset that goes beyond automation. While RPA requires rule-based workflow development, Agentic AI necessitates expertise in problem-solving and prompting. EdgeVerve AI Next, with native RPA and Agentic AI capabilities, helps bridge this gap by offering a unified low-code, no-code platform. This empowers existing teams to upskill and transition from building simple bots to designing intelligent, self-adjusting Agents.

An Approach to Accelerate Agentic Process Automation

True enterprise-grade Agentic AI is rarely implemented. There are different levels of AI maturity, ranging from RPA to Copilots or AI Agents, Multi-Agentic Systems, and Autonomous workforces. Most organizations are currently using Copilots or RPA, often adding minimal intelligence by integrating with Agentic systems. However, a shift is needed from RPA to RPA + Agentic AI, Multi-Agent Systems, and Autonomous systems; these combinations are seldom implemented.

As next steps, we can address these key challenges in transitioning from RPA to RPA + Agentic AI by upgrading new systems, investing in technology and talent, ensuring robust data & security compliance, and enhancing infrastructure. The transition from RPA to RPA + Agentic AI systems, i.e., Intelligent Automation, marks a crucial landscape of digital transformations for organizations. While RPA successfully delivered ROI by automating mundane, rule-based tasks, with increasing complexity and dynamicity, it requires the Agentic AI fabrication within RPA workflows to build more adaptive and self-healing automation workflows.

Transition to RPA + Agentic AI can help organizations tackle the broad spectrum of unstructured and complex processes, where RPA is the foundation for rule-based tasks and Agentic AI enhances the ability of automation workflow to manage complex and unstructured process flows, make context-aware decisions, adapt, and learn continuously. Finally, the goal is not to replace RPA with Agentic & AI, but to transition to RPA + Agentic AI that creates a symbolic relationship where these technologies complement each other.

The Platform Advantage: Powering Agentic Process Automation

Realizing these benefits requires a unified platform approach, such as EdgeVerve AI Next, to streamline AI adoption and ensure long-term success. This approach minimizes project overruns, accelerates deployments, and delivers consistent outcomes through:

  • Unified Data Layer: Seamlessly integrate new AI agents with a curated knowledge base, eliminating messy retrofitting.
  • Modular Models: Select and update specialized models for each use case without disrupting the entire system.
  • Orchestrated Agents: Deploy new agents as independent yet interconnected mini services, maintaining system stability.
  • Built-in Governance: Proactively manage biases, audits, and compliance with platform-level guardrails.
  • Intent-Driven, Contextually Aware Execution: Enable agents to understand the desired outcome and adapt to process nuances, allowing them to autonomously determine the optimal path for enhanced decision-making and automation efficacy.

Further, the value of the platform for specific organizational setups/delivery models such as Global Business Services (GBS) could be enhanced through curated platform-based offerings. For instance, EdgeVerve AI Next Operations & Service Management (OSM) is an Agentic-powered platform built on AI Next to drive AI-led transformation of GBS at speed and scale. It’s packed with powerful capabilities—from AI-driven service request handling and intelligent case management—to dynamic workforce allocation and insightful dashboards.

From the above benefits, it’s clear that a unified platform approach can help accelerate the transition from RPA to RPA + Agentic AI and ensure optimal ROI on automation investments.

Navigate the Shift: Migrate from Legacy Systems to AI-Powered iPaaS

Legacy B2B integration often creates bottlenecks, impeding seamless operations and communication across the value chain. In today’s evolving business environment, flexible integration platforms are essential to optimize overhead costs, address growing technical debt, and navigate the complex B2B landscape.

In our previous blog, we discussed how traditional B2B integrations hinder growth and the significance of adopting integration solutions at neutral cost, which not only helps orchestrate advanced business processes but also enables both real-time and batch-based data integration and transformation. This blog dives deep into how organizations can successfully migrate from legacy platforms to modern iPaaS solutions without incurring any migration costs.

Common Migration Challenges and Why Modernizing B2B Integration is Crucial

The increasing complexity of global supply chains necessitates businesses to move beyond legacy B2B integration tools for several reasons, including the need to connect with an expansive network of partners and onboard new partners with speed through modern integration solutions. However, meeting B2B integration requirements and migrating to a new platform is no easy task!

Here are a few challenges enterprises face when transitioning from legacy B2B systems to AI-powered solutions:

  • Data silos: Legacy platforms are characterized by data silos—where systems do not talk to each other, making data accessibility and integration from disparate sources cumbersome.
  • Data loss and discrepancies: Migrating vast amounts of connections and data integrations across systems and applications increases the risk of data loss and discrepancies, resulting in inaccuracies, downtime, and disruption to day-to-day operations.
  • Data compatibility: Multiple data formats, structures, and communication protocols can lead to data distortion. E.g., Converting flat files into XLS, XML, CSV, etc., to enable integration into ERP or CRM systems with the new integration platform.
  • Scalability: Middleware solutions aren’t designed to manage high-velocity, high-volume data streams, resulting in increased latency and system failures.
  • Hidden costs: Maintaining and supporting legacy systems involve dedicated hardware, frequent patches and upgrades, which require substantial amounts of space, time, and resources.

From scaling data storage to streamlining processes, enterprises today are looking to transition to modern, cloud-based integration platforms with easy-to-use interfaces and AI copilots to manage integration, enable smart data orchestration, and reduce the burden of infrastructure management, ultimately resulting in cost savings.

Steps to Successfully Migrate from Legacy to an AI-first iPaaS at no additional Cost

  • Comprehensive system audit: Enterprises need to begin by evaluating the existing IT landscape and the current application architecture to spot data dependencies and potential disparities in data formats to mitigate the risks associated with disruptions, address compliance issues, and any integration complexities head-on.
  • Assess and plan: A comprehensive plan with phased approaches, including migration, testing, and deployment, is essential to minimize downtime and ensure a smooth transition. It’s also vital to have a backup plan to avoid unexpected delays, errors, risks—operational, financial, and security.
  • Leverage Automation and AI: Leverage automation and AI tools to reduce manual effort and errors.
  • Training & change management: Optimize adoption with minimal disruption through systematic employee enablement and support.
  • Select the right platform: Select an AI-powered platform that can manage all your EDI, non-EDI, and API requirements, reducing frequent manual interventions and dependency on multiple tools, enhancing end-to-end visibility, secure data exchange, and customer satisfaction.

This is where EdgeVerve AI Next Supply Chain (TradeEdge) Integration comes in, a single, comprehensive solution for all enterprise-wide integration needs.

AI Next Supply Chain Integration: Supporting Your B2B Integration Journey with Cost-Neutral Migration

AI Next Supply Chain iPaaS provides a comprehensive integration platform that connects all three critical aspects of the supply chain:

  • Trade Partner Network: Securely connect with suppliers, distributors, retailers, and logistics providers.
  • Internal Systems: Seamlessly integrate ERP, WMS, TMS, and other enterprise applications at no incremental cost.
  • Data Sources: Connect to both structured and unstructured data across your ecosystem.

Why AI Next Supply Chain Integration?

  • Provides a single platform for any-to-any integration—whether they’re on-premises, cloud-based, or hosted by partners.
  • Includes 300+ pre-built connectors for data, integration, AI and automations, dramatically reducing implementation time and complexity.
  • Bridges the gap between traditional EDI (Electronic Data Interchange) processes and modern API-based integration through single unified platform.
  • Offers comprehensive monitoring and management tools, enhancing visibility into all integrations from a single dashboard.
  • Configure and optimize integration in a budget friendly way, reducing the need for manual intervention.
  • Automate and integrate processes, ensure seamless data flow, and minimize disruption.
  • Drive affordable, fast, and risk-free migrations.

Discover how EdgeVerve AI Next Supply Chain Integration can enable cost-neutral AI-powered migration by optimizing workflows and streamlining processes, while ensuring reliability, flexibility, and scalability.

From Legacy to Leading Edge: AI iPaaS That Won’t Break the Bank

The traditional B2B integration landscape, characterized by disparate applications, partners, and data streams, is undergoing a transformative shift. Today, enterprises are moving beyond legacy integration platforms and embracing AI-powered solutions that are scalable, flexible, and secure, reducing overall operating costs and enabling faster innovation.

However, the biggest roadblock is migrating from legacy integration platforms to modern, cloud-based, and AI-led platforms, without impacting operational efficiency, visibility, and customer experience.

What does it take to transition to a modern and flexible integration landscape? How can enterprises avoid costly software licensing and maintenance costs for legacy upgrades that usually come with no innovation? This blog explores the need for cost-neutral migration to a modern integration platform and the impact of such AI-powered integration solutions on enterprise future-readiness.

Typical Challenges in the Partner Ecosystem and Enterprise Integration

Legacy middleware and point-to-point solutions are rigid, time-consuming, and prone to errors, requiring complex workflows and substantial infrastructure. Costly, inflexible middleware and outdated EDI systems slow down innovation, impede agility, and create technological blind spots. Moreover, IT teams often spend time maintaining complex integrations, which consequently delay new partner onboarding, as well as managing existing customer change requests, leading to maintenance overheads, increased security risks, and a lost competitive edge.

With digital transformation accelerating at a rapid pace, migrating to modern integrated platforms such as Integrated Platform as a Service (iPaaS) with minimal disruption is the need of the hour.

Inflexibility and lack of scalability High costs Maintenance demands Scarcity of IT resources
Heterogeneity of systems and applications Point-to-point integrations Code-dependent data mapping and transformation Multiple solutions for B2B and internal application integrations

AI-Powered iPaaS: Necessity of Modern Integration

From trouble connecting disparate data sources to orchestrating business processes, enterprises today are shifting to cloud-native solutions with AI-driven features and seamless API integrations.

Modern integration encompasses cloud-ready architecture, API-driven connectivity, microservices, and real-time data synchronization. In other words, modern platforms empower businesses to carry out integrations with low-code/UI-based configurations that can be reused, eliminating the need for point-to-point integrations.

With EdgeVerve AI Next Supply Chain (TradeEdge), enterprises can transition from inflexible legacy integration systems to a modern, AI-powered platform that simplifies and streamlines the entire integration lifecycle, eliminates technological barriers, and enables building scalable business solutions for next-generation growth.

AI Components in TradeEdge Supply Chain Integration

Cognitive Connect and Mapper

These are AI-enabled automation tools that simplify and accelerate integration setup by automatically configuring connections between systems and mapping source and destination entity attributes, minimizing errors in data mapping, especially in complex B2B ecosystems.

Gen AI-Based Data Harmonization

The platform utilizes GenAI to harmonize data across disparate systems, multiple formats, and sources, including EDI, APIs, and spreadsheets. It breaks down data silos and transforms fragmented inputs into unified, actionable insights.

AI for BI (Business Intelligence)

The platform provides real-time data insights through AI-powered dashboards, enabling faster decision-making, continuous operational improvement, and enhanced visibility across partner ecosystems. AI also allows self-healing workflows, which proactively detect and resolve integration issues, reducing IT overhead.

Unstructured Data Processing

The platform utilizes Agentic AI and GenAI Copilots to interpret and process unstructured data, including emails, PDFs, and logs. These tools extract relevant information and convert it into structured formats for integration.

Advancements in integration technologies, particularly iPaaS solutions, empower businesses to overcome the challenges of legacy infrastructure and provide a range of benefits, as outlined below.

Unlocking Business Benefits with iPaas

  • OOB support for EDI ANSI X12 and EDIFACT, all message types and versions, including 997s
  • No-code, low-code configurators with assisted co-pilots for expediting design time configurations
  • Reduced time to onboarding new partners with canonical-based integration for re-usability
  • Unified platform for both B2B and internal application integrations
  • Pre-built skills across Data, Integration, AI, Automation (300+ OOB skills and connectors)
  • An extendible platform to build and leverage Agentic and business-centric solutions on the same data foundation
  • Helps achieve operational excellence, flexibility, and scalability
  • Provides real-time data insights and facilitates decision-making
  • Optimizes the total cost of ownership while achieving the integration modernization agenda

Why Cost-Neutral Upgradation is a Game-Changer

Typical migrations are slow and expensive, locking organizations in legacy cycles. Supply Chain B2B Integration disrupts this with a frictionless, cost-neutral upgradation model that allows enterprises to migrate quickly and risk-free, with:

  • Minimal business disruption through AI-driven migration tools that facilitate quicker migrations and streamlined testing
  • Instant time-to-value; enterprises can deploy new workflows and partner connections in a fraction of the time, from months to a few days
  • Business and operational dashboards for real-time control and continuous improvement provide end-to-end message visibility and tracing with the ability to bulk retry, retransmit, and replay messages
  • Agentic error resolutions with automated RFIs to request missing information to reduce manual overheads

Embrace Seamless, Future-Ready Integration with Supply Chain B2B Integration

As an AI-powered Integrated Platform as a Service (iPaaS), Supply Chain B2B Integration delivers an intelligent, future-ready approach to enterprise connectivity and a cost-neutral upgrade from legacy platforms. With support for API, EDI, and MFT, it offers a unified environment to seamlessly connect, orchestrate, and scale digital ecosystems, while also optimizing the broader supply chain network.

At its core, Supply Chain B2B Integration employs Generative AI Copilots and Agentic AI, managing every stage of enterprise integration with advanced intelligence. The platform powers:

  • API Integration: Rapid, scalable connectivity for cloud services and modern SaaS apps
  • EDI Integration: Fast, easy onboarding for B2B trade partners across legacy and cloud systems
  • MFT Orchestration: Secure, automated large-scale data movement
  • Supply Chain Data Harmonization: Breaking data silos for unified, actionable insight
  • Cognitive Connect and Mapper: AI-enabled automated configurations in setting up connection, Source, and Destination entity attributes mapping
  • Self-Healing Workflows: Automated Agentic-led troubleshooting and resolution with minimal IT overhead

Learn more about EdgeVerve AI Next Supply Chain Integration a purpose-built supply chain integration platform trusted by global enterprises, and explore how businesses can move from legacy to leading edge, at neutral cost.