Month: July 2026

The promise of agentic process automation is compelling: autonomous systems that reason, plan, and execute complex workflows without constant human intervention.

However, most automation investments fail not because of technology limitations, but due to siloed data and systems architecture, a lack of organizational readiness, and an inability to scale pilots into production. Agents rely on robust infrastructure, data foundation, governance, and other factors that demand a fundamentally different design philosophy from the point-solution stacks enterprises have spent years assembling.

The question for enterprise leaders is no longer how to upgrade the automation stack, but how to fundamentally reimagine the enterprise execution model.

Beyond Point Solutions: Why Enterprises Need a System of Execution (SoE)

As market volatility intensifies and time-to-market compresses, piecemeal automation has reached its limit. Faster tasks are no longer enough. What organizations truly need is an execution engine that is intelligent, adaptive, and coordinated end-to-end—a System of Execution (SoE) that translates insights into coordinated action(s) across processes, systems, and teams in near real time.

Isolated point solutions may improve efficiency, but they rarely transform the underlying operating model. Some of the key challenges include:

This is precisely where agentic process automation marks a turning point. Rather than automating tasks in isolation, agentic systems orchestrate work across the enterprise, dynamically, contextually, and with far greater resilience.

What is Agentic Process Automation and Why Does it Matter

Agentic Process Automation Platforms (APAPs) represent a fundamental rethinking of how enterprise work gets done. At their core, they combine deterministic execution with agentic intelligence: systems that go beyond following instructions to plan, decide, and act in context.

Rigid, rule-bound processes become adaptive, with decisions embedded directly within workflows and work coordinated seamlessly across functions in real time. This marks a move away from siloed point solutions toward unified platforms where rule-based execution and agentic reasoning coexist. Agentic platforms align execution with business goals, continuously adapting to changing conditions, and acting with a level of judgment traditional automation cannot achieve.

This shift sets the stage for a new architectural foundation designed to support intelligent, outcome-driven execution at scale.

The Architecture Behind Agentic Process Automation Platforms

APAPs redefine the game by embedding agentic intelligence, enabling enterprises to transition from automating tasks to orchestrating outcomes. They combine deterministic and probabilistic intelligence by using rule-based workflows for reliability alongside LLM-powered agents for adaptive, real-time decision-making. This hybrid model retains the governance of Intelligent Process Automation Platforms (IPAP) while introducing agentic flexibility. Through multi-agent orchestration and unified data fabrics, they transform automation into an execution fabric, enabling enterprises to evolve from reactive and siloed to adaptive and intelligent by design.

This is what enables the shift to platform-based execution, unifying technology, processes, and people to drive transformation at scale.

Reimagining Enterprise Transformation Across the Three Pillars

To fully realize the value of agentic process automation, enterprises must rethink transformation across three interconnected pillars: technology, process, and people. A platform-based approach ensures these pillars evolve in sync, enabling a cohesive SoE rather than isolated improvements.

On the technology front, this means elevating enterprise IT from modernization to execution readiness by integrating legacy and modern systems into a unified, composable execution layer. From a process standpoint, it transforms execution from linear, rule-bound workflows to adaptive, outcome-driven operations. This evolution enables context-aware, situational decision-making that extends beyond traditional automation. For people, it unlocks human potential through platform-driven collaboration, automating routine tasks and surfacing AI-led insights, allowing teams to focus on higher-value decisions and tasks.

However, organizations that treat agentic platforms as another tool to bolt onto existing architecture will extract only marginal gains. While those that redesign around a unified, composable, and outcome-driven platform can unlock and enable enterprise-wide transformation. This is where Infosys Topaz AI Next (earlier called EdgeVerve AI Next), a unified AI platform, comes into play.

Platform-Based Approach: Infosys Topaz AI Next

Infosys Topaz AI Next is a comprehensive agentic process automation platform (APAP) that helps enterprises leverage AI to unlock digital-native performance at speed and scale. Built from the ground up to tap the power of Generative and Agentic AI, the platform bridges silos in people, processes, data, and technology and reimagines an organization’s operating model through an AI-first approach.

Why Topaz AI Next?

Topaz AI Next delivers three capabilities enterprises need the most right now: elevate performance to digital-native levels without needing constant redesign; faster time-to-value by moving from AI pilots to production with less fragmentation and higher ROI; and a sustainable foundation for growth through lower total cost of ownership, higher adaptability, and continuous learning embedded into execution.

The platform delivers digital-native outcomes through agentic orchestration across complex, cross-functional processes where traditional automation reaches its limit. Topaz AI Next enables organizations to leapfrog directly to outcome-driven execution, combining faster transformation and time-to-value with lower total cost of ownership to build a sustainable foundation for growth.

Enabling Intelligent Enterprise Transformation: Real-World Use Cases

Here are a few use cases where agentic platforms solve critical enterprise problems and deliver positive impact.

Intelligent booking and invoicing orchestration:

HR efficiency:

Conclusion: The Agentic Execution Imperative

The enterprises that will lead the next decade will not necessarily be those with the most advanced AI models or highest volume of AI initiatives, but those that can execute with them consistently, intelligently, and at scale on a unified and adaptive platform foundation. Agentic Process Automation Platforms make this possible by embedding execution intelligence into the operational fabric, transforming automation from a tactical tool into a driver of tangible business value. The enterprises that act now won’t just close the efficiency gap; they’ll build an execution advantage that compounds.

Ready to move from automating tasks to orchestrating transformation?

References:

Everest Group, Enabling Enterprise Transformation through Agentic Process Automation Platforms (APAPs), 2026

As enterprises race to move generative AI from experimentation to enterprise-wide deployment, a critical reality is setting in: AI is not a one-time investment; it is an ongoing operating model. And at the heart of this shift lies a new imperative: Cost Discipline.

What begins as a manageable proof of concept often evolves into a complex, multi-layered cost structure at scale. Without a structured approach, organizations risk losing visibility, accountability, and ultimately, control over their AI investments.

This is why cost discipline is the missing link in an enterprise’s scaled AI strategy and why establishing a robust AI financial operations (FinOps) framework is critical to sustaining long-term success. Forrester validates this in its report, highlighting FinOps as a foundational capability for governing AI investments, controlling costs, and maximizing business value at scale.

Why AI Cost Control is a Board-Level Mandate

AI spending is accelerating faster than anticipated across industries. Costs are no longer confined to simple model inference; they have expanded to include infrastructure, data storage, transfer, observability, governance, security, training, and organizational transformation. This cost footprint combined with opaque cloud billing models and unpredictable usage patterns has elevated AI cost management to a board-level concern.

Today’s business leaders face a dual mandate:

The challenge is clear: without disciplined cost governance, enterprises risk delayed ROI, inefficient spending, and stalled innovation.

The Pivot: From Isolated AI Experiments to a Managed Portfolio

Most organizations begin their AI journey with isolated pilots: team-specific use cases built on fragmented tools and data. While this fosters rapid experimentation, it is fundamentally unscalable. As AI workloads (such as high-volume inference, continuous model fine-tuning, and robust agentic data pipelines) grow, expenses increase non-linearly.

To unlock real value, enterprises must shift from project-centric execution to a managed AI portfolio, one that standardizes platforms, centralizes governance, and enables reuse.

Enter Infosys Topaz AI Next (earlier called EdgeVerve AI Next), a unified platform that integrates data, process, AI, and experiences, intelligently orchestrating workflows while providing end-to-end observability to optimize both operational costs and business outcomes.

Topaz AI Next helps organizations maximize ROI from AI initiatives by addressing the key cost drivers outlined in Forrester’s AI cost-optimization research through the following capabilities:

AI Cost Optimization: What Leaders Do Differently

Effective AI cost optimization starts with the right foundation: clear business use cases, a holistic view of the AI stack, and a commitment to continuously refine the cost model as both technology and the organization evolve.

A few things leaders do differently that give them a substantial edge over laggards include:

Organizations that get this right, treat AI as a core business capability, not a set of experiments. They align investments to outcomes, embed governance early, and continuously optimize as they scale.

The result is lower costs, better outcomes, and AI that delivers sustained business value.

The Way Forward: Embedding FinOps into Your AI DNA

Treating AI as a collection of disjointed experiments will inevitably lead to financial drain. In contrast, successful organizations treat AI as a core business capability governed with strict financial discipline from day one. FinOps is the framework that transforms AI from a reactive cost center into a managed, value-driven capability.

To establish a financial backbone that links AI costs to measurable ROI, organizations must:

Ultimately, AI cost optimization is about the strategic balancing of cost, performance, and risk. Cost discipline is the operating model that enables organizations to scale AI confidently, delivering impact, controlling risk, and maximizing their return on investment.

Looking to embed cost discipline directly into your AI operations?

Global Capability Centers (GCCs) are at a pivotal crossroads. Traditionally viewed as cost-efficient operational hubs, they are now transforming into engines of strategic innovation in the AI era.

As enterprises demand greater agility and faster delivery, GCCs are expanding beyond back-office functions into high-value roles like R&D, digital transformation, analytics, and product engineering. This shift is already underway. The fast-paced AI evolution is putting additional pressure on GCCs to drive innovation, accelerate decision-making, and deliver measurable business outcomes, within shorter timeframes and budgets.

To fully realize the value of their GCC investments, leaders must address two critical questions:

The answer lies in adopting an AI-first operating model built on unified platforms that enable GCCs to move beyond incremental efficiency gains and become intelligence-driven engines of transformation.

Overcoming Barriers: From Cost Centers to AI-Native Innovation Engines

Historically, GCCs followed a slow maturity curve—from low-cost operations centers to process excellence hubs, then to innovation centers, and ultimately to strategic global offices. Increasingly, however, many greenfield GCCs are bypassing the early stages altogether, launching directly as innovation hubs focused on advanced engineering and digital capabilities.

The catalyst enabling this leap is AI. However, tapping AI capabilities is far from easy unless organizations adopt a strategic and unified approach addressing challenges spanning people, process, data, technology, and others. This is where AI-first platforms, specifically curated to address these challenges and accelerate AI-led transformation, truly shine.

With AI-first platforms, GCCs can unify fragmented operations, intelligently automate repetitive tasks, augment decision-making with real-time intelligence, and scale capabilities without linear headcount growth. In doing so, GCCs move from support functions to core drivers of enterprise innovation. This shift frees teams to redirect their efforts toward higher-value work, such as design, customer experience, and other strategic initiatives, creating a more agile, intelligence-led operating model.

At the core of this transformation is Infosys Topaz AI Next (earlier called EdgeVerve AI Next), a unified AI platform that integrates predictive, generative, and agentic AI with data transformation and intelligent automation to reimagine enterprise operating model with an AI-first agenda.

Powering AI-Native GCCs and Unifying AI-Driven Operations

Despite their potential, many traditional GCCs struggle with fragmented systems, manual workflows, and siloed operations that dilute their strategic impact. To truly act as an enterprise engine, GCC operations must be seamless and business outcomes-oriented.

Operations & Service Management (OSM)—a curated solution for GCCs powered by Topaz AI Next platform—addresses business operations’ inefficiencies through a unified, AI-driven architecture by:

The result is faster decision-making, smarter operations, and scalable value creation.

Data Intelligence as the New Currency

As GCCs take on more strategic mandates, high-quality data becomes their most critical asset. Fabric, an intelligent data integration and transformation capability within the Topaz AI Next platform, supports this shift by allowing GCCs to integrate, cleanse, and contextualize data from disparate enterprise systems at scale, making it AI-ready.

This continuous feed of real-time inputs into AI models creates a powerful closed-loop system:

Data → AI Models → Insights → Actions → Improved Outcomes -> Feedback loop

Over time, this continuous cycle drives higher accuracy and stronger business impact.

The Path Forward: AI-Native, Enterprise-Embedded GCCs

The divide between GCC leaders and laggards is widening in the AI era. GCCs relying on legacy models, fragmented systems, and “experiment-heavy” approach to AI risk stagnation, while those boldly rewiring their service models with a more holistic and unified platform-based approach are leaping ahead, redefining enterprise-wide transformation and gaining competitive edge.

Ready to unlock the full potential of your GCC?

Enterprises today manage vast volumes of documents — legal contracts, commercial insurance papers, SOPs, images, handwritten notes, PDFs, and emails — each varying in layout and format. Processing these at scale while maintaining accuracy, scalability, and compliance remains one of the most persistent operational challenges.

Traditional document AI solutions are no longer sufficient. Today’s enterprises are looking to unlock the insights hidden within unstructured documents to drive real business value.

This is where AI powered Intelligent Document Processing (IDP) platforms change the game. By going beyond basic extraction and classification, modern IDP solutions accelerate workflows, intelligently orchestrate document driven processes, and enable enterprises to achieve measurable outcomes.

In this blog, we explore how Infosys Topaz AI Next’s Document AI capabilities are helping organizations transform documents into actionable intelligence. Read on.

What is Intelligent Document Processing (IDP)?

IDP is a technology driven approach that automates the extraction, classification, and processing of data from documents using advanced AI techniques. IDP leverages technologies such as Optical Character Recognition (OCR), Machine Learning (ML), Natural Language Processing (NLP), Generative AI, and Computer Vision to automatically extract, classify, and process data from diverse document types, including structured, semi-structured, and unstructured formats.

High Impact Intelligent Document Processing Use Cases Across Industries

Financial Services: Banks and financial institutions struggle to process huge volumes of documents, including KYC forms, contracts, and compliance filings. Manual processing often creates bottlenecks: increased errors, data inaccuracies, and slow turnaround times.

Document AI enables organizations to ingest both structured and unstructured data, digitize documents, and extract deeper insights for better decision-making and enhanced operational efficiency.

KYC (Know Your Customer): Banks are under immense pressure to process KYC documents faster and at scale. Yet fragmented workflows, manual data extraction, validation, and compliance checks continue to slow operations.

Topaz AI Next’s Document AI capability helps transform KYC operations with speed, scale, and accuracy. Our platform’s AI-driven document processing automates the extraction, processing, and validation of data embedded in KYC documents. It integrates seamlessly with clients’ KYC applications, creating a robust data pipeline that leverages supervised and unsupervised learning for document classification.

Investment Guidelines & Compliance: Compliance teams struggle with managing and scaling complex workflows. Moreover, extracting data from multiple systems and manually reviewing documents to identify relevant compliance rules results in errors and operational delays.

Our AI-powered documentation analysis, with AI-driven workflows can help systematically orchestrate the guideline review process, accelerating decision-making, account onboarding, improving productivity, while reducing downstream compliance risks with thorough and systematic reviews.

Insurance: Automating and verifying document-heavy workflows presents significant operational challenges. Traditional document processing is highly manual, relying on rule-based automation, which can lead to frequent errors and processing delays.

AI-driven Intelligent Document Processing (IDP) platforms can streamline document workflows by automating classification, extraction, and validation—significantly reducing risks, eliminating inefficiencies, and enhancing regulatory compliance.

Claims Processing: Manually processing claims requests is often cumbersome and time-intensive, leading to errors, data inconsistencies, and longer cycle times. How can insurers scale effectively while maintaining accuracy?

Our IDP solution streamlines claims processing by automating repetitive, manual tasks based on pre-defined rules. The platform intelligently processes incoming customer requests, extracts and validates claims data, and helps reduce backlogs.

Underwriting: Underwriters are required to manually review hundreds of documents in varying unstructured formats and languages, deal with fragmented data, and rely on outdated tools, which negatively impact risk assessment and customer satisfaction.

Topaz AI Next, our modular Intelligent Document Processing platform, helps improve the overall underwriting productivity by enabling data identification, extraction, and classification of data elements at scale from unstructured documents, thereby improving operational efficiency, and reducing underwriting turnaround time.

Manufacturing (MFG): Handling a vast array of complex contracts in various file types and formats can be challenging. Automated document processing enables intelligent data extraction from structured and unstructured documents, identifying key information, clauses, obligations, and exceptions—significantly reducing manual intervention and errors.

Intelligent Contract Analysis: Manual contract review impacts both team efficiency and information accuracy, often leading to errors. Document AI helps address these inefficiencies by identifying hidden contract risks and enabling better business decisions, thereby allowing teams to focus on higher value work.

O2C (Order-to-Cash): Dependence on multiple ERP systems and manual processes creates obstacles in the order-to-cash (O2C) process, leading to inefficiencies, delays, and errors. Topaz AI Next seamlessly integrates AI-driven orchestration and document AI capabilities, combined with exception-only human involvement to accelerate cycle times and enhance the customer experience.

CRL: CRL’s operations generate an enormous volume of customer documentation every day—booking requests, invoices, emails, and RFI responses arriving in every format imaginable. Manual review, from cross-checking details across systems to re-entering data introduces inconsistencies that lead to invoicing disputes and booking errors. IDP makes accurate, dispute-free container booking possible at scale.

Intelligent Container Booking & Automation: Managing container bookings across multiple disparate workflow systems combined with manual invoicing leads to inaccuracies, disputes, and processing errors.

Our platform’s AI-powered document processing capability enables efficient extraction of data and remarks, automates the processing of emails and Request for information (RFI) responses, supports AI-driven partner searches, and reads unstructured data from PDFs.

The Way Ahead: Accelerating AI Document Intelligence for the Enterprise

As enterprises evolve toward AI-native operations, the ability to intelligently process documents becomes a cornerstone of digital transformation. From contracts and invoices to research papers and regulatory filings, enterprise documents hold vast amounts of untapped value. Traditional extraction methods often fall short in handling the complexity, variability, and scale of modern document workflows.

Topaz AI Next’s IDP solution capabilities are purpose built to streamline document workflows, improve data accuracy, and enable scalable automation across a wide range of business scenarios.

Ready to simplify document processing at scale?

Agentic AI is redefining how industries operate and insurance is no exception. These intelligent, autonomous agents, capable of reasoning and judgment, bring autonomy to the insurance process, automating complex workflows‌, ‌from underwriting to claims resolution‌ to deliver significant benefits.

With changing customer expectations and the growing pressure to deliver seamless, reliable, and personalized experiences, enterprises today are going beyond chatbots to enhance operational efficiency, deepen customer engagement, and gain richer insights.

Generative AI and agentic AI are game changers, enabling insurers to rethink operating models, orchestrate end-to-end processes, and scale AI across use cases, while complying with the evolving regulations and strengthening governance.

Here are two high-impact use cases where agentic AI is transforming the insurance landscape.

High-Impact Agentic AI Insurance Use Cases

Augmented Claims Processing

Looking for faster resolutions and lower costs? AI agents can respond in real time—reducing manual effort, improving accuracy, and accelerating resolution times, without the need for manual intervention every step of the way.

Our commercial claims processing agents are a suite of collaborating agents designed to help automate and augment the commercial claims lifecycle, including:

For instance, a large US-based healthcare insurance company leveraged our unified AI platform to automate more than 10,000 transactions, substantially reducing backlogs.

Smarter, Faster Underwriting

If you’re looking to improve underwriter productivity and response time, adopting an agentic platform can provide a unified view of the right information across the new business and underwriting lifecycle.

Our AI agents help bring consistency to the submission process, enabling a transition from fragmented workflows to responsive underwriting by intelligently prioritizing submissions and reducing iterations for missing or incomplete information.

Bringing these capabilities together cohesively is where Infosys Topaz AI Next comes in.

Topaz AI Next, an enterprise-grade unified AI platform, helps transform point solutions into an intelligent insurance workforce, enabling trusted and controlled operationalization of agentic AI.

Transform End-to-End Insurance Operations with a Unified Platform

Topaz AI Next provides the foundational capabilities required to design, deploy, govern, and scale agentic AI across complex insurance ecosystems.

Built for the scale, complexity, and regulatory rigor of the insurance industry, Topaz AI Next enables insurers to deploy intelligent AI agents that can help autonomously execute workflows, collaborate seamlessly with human teams, and continuously optimize outcomes ,all under robust governance and control.

Insurers can:

The Road Ahead: Scaling Agentic AI for the Future of Insurance

Agentic AI holds immense potential for the insurance industry, but scaling it enterprise-wide is not without challenges—fragmented systems, manual processes, siloed data, governance complexities, security risks, and talent shortages.

This is where a unified AI platform becomes critical. By enabling seamless Agentic AI orchestration and workflow optimization, insurers can gain a deeper understanding of the underlying risks while delivering hyper-personalized services, ultimately enhancing customer experience, compliance, and decision-making.

Insurers today are looking to move beyond automation; agentic AI helps reimagine operating models, empowering enterprises to lead the next era of insurance transformation.

Reimagine insurance document processing with Agentic AI!

Financial services are embracing a new wave of innovation: Agentic AI.

Financial services are embracing a new wave of innovation: Agentic AI. Legacy processes are complex. Regulatory demands keep shifting. Customer expectations are higher than ever. Traditional automation— bound by rigid, rule-based constraints—simply can’t keep up. Agentic AI can. It thinks, acts, and learns autonomously, adapting to dynamic environments with minimal human oversight.

The era of Agentic AI has arrived, and it’s poised to redefine the future of finance. From KYC onboarding to fraud detection and mortgage processing, Agentic AI is transforming every stage of the financial operations. Let’s explore four high-impact use cases where Agentic AI is transforming financial services.

Real-World Use Cases: Revolutionizing Financial Services with Agentic AI

Transform KYC & Customer Onboarding

High volumes of KYC documents, fragmented processes, and diverse data mean operational inefficiencies, customer onboarding delays, errors, and inaccuracies. Want to reduce customer churn and make onboarding a smoother experience?

AI agents help reimagine the KYC journey by automating data discovery, classification, and indexing–eliminating silos, reducing operational costs, boosting productivity, and delivering a superior customer experience.

For instance, our Agentic AI KYC solution helped a leading US Bank standardize its KYC process, reducing turnaround time from weeks to days, while enhancing speed, scale, and accuracy.

Streamline Mortgage Processing

Processing manually intensive, high-volume applications often results in delays, errors, missed opportunities, and dissatisfied customers. Expedite loan processing and mortgage approvals by leveraging the power of Agentic AI. AI agents automate document processing and streamline every stage of the lending process, accelerating workflows, improving accuracy, and enhancing compliance.

A large UK bank realized ~45% operational efficiency and cost savings across its business processes by implementing AI-powered solution, enabling straight-through-processing, and delivering first-time-right, accurate, and compliant operations.

Accelerate Effective Collections Management

If you’re looking to reduce delinquencies, minimize write-offs, and improve efficiency in your collections process, Agentic AI-powered solution is the answer. It enhances the lending value chain by intelligently augmenting the collections process across asset classes and loan types.

Our unified AI platform offers a web-based unified dashboard, contextual assistance, automated document processing, and insights that help employees serve customers quickly and effectively.

Manage Complex Investment Guidelines

Managing complex investment guidelines across numerous documents can be tedious and time-intensive. Moreover, failure to adhere to these guidelines can lead to financial risks, affecting credibility and operational efficiency. Automate, simplify, and ensure compliance with Agentic AI.

One investment management firm transformed customer onboarding with AI-powered, reimagined experience.

To realize these use cases, enterprises require a unified AI platform that can drive operational transformation and unleash endless possibilities in financial services.

Advantages of Adopting an Agentic-Powered Platform

Infosys Topaz AI Next helps connect end-to-end banking processes, creating frictionless experiences.

Agentic AI, powered by Topaz AI Next, delivers measurable impact across the board. Teams benefit from faster, more compliant KYC processes and highly accurate loan reconciliation. Customers experience smoother hardship withdrawals and more responsive service. And compliance teams gain automated, reliable reviews of complex investment management guidelines.

Embracing the Future: The Transformational Role of Agentic AI in Financial Services

Although Agentic AI is overhauling workflows across financial operations, eliminating manual intervention and enabling teams to focus on higher-value work, they also present unique challenges, from data privacy to transparency and talent shortages.¹

A unified agentic platform helps overcome these obstacles, ensuring scalability, agility, improved governance, enhanced customer experience, and accelerated decision-making.

Banks and financial institutions are gearing up to embrace autonomous financial solutions to rapidly process data, enhance accuracy, streamline operations, and drive innovation.

Take the next step towards a smarter future in finance!

Enterprise transformation today is driven by competing demands and has become essential for survival. Organizations must respond to rapidly changing customer expectations, manage talent disruption, improve resilience, and continue innovating in an increasingly commoditized digital landscape. Addressing these challenges calls for a continuous, adaptive, and scalable transformation approach.

Today, automation is no longer just a lever for back-office efficiency; it is a cornerstone of broader transformation and AI strategies. The pressing question for enterprises is how they can effectively embed adaptability and intelligence into their core operations. The answer lies in shifting from fragmented automation to an execution model designed for resilience, adaptability, and growth.

The Evolution of Integrated Automation Platforms

The first wave of enterprise automation focused on standalone intelligent automation solutions, such as:

While the above solutions delivered quick wins, they also introduced new challenges:

Automation improved certain tasks but did not fundamentally rewire enterprise execution, limiting scalability.

This led to the rise of Intelligent Process Automation Platforms (IPAPs). They go beyond task-level efficiency to enterprise-grade, value chain-wide execution with structured governance and human oversight. However, they are not without limitations.

While data-driven and insight-rich, IPAPs remain largely deterministic, excelling at automating predefined, rules-based processes but less effective in dynamic, exception-heavy environments. Additionally, integration constraints with legacy systems can slow down deployment and scalability; governance and compliance introduce further challenges; and performance is highly dependent on data quality, making inconsistencies or gaps in data a significant risk to outcomes.

How can enterprises scale their automation efforts and drive value in business environments that are increasingly dynamic and complex?

The Shift to Outcome-Driven Platforms

To unlock the next phase of value, enterprises must move beyond fragmented point solutions toward composable, adaptive, and outcome-driven platforms that integrate data, enhance transparency, and accelerate decision-making.

The implication is clear: Enterprises need a System of Execution (SoE) that converts data and insights into coordinated action across processes, systems, and teams in near real time.

The Rise of Agentic Automation: Reimagining the Enterprise Execution Model

While intelligent automation platforms have streamlined structured workflows, the next frontier in enterprise transformation is Agentic Process Automation.

It brings together AI agents, nondeterministic flows, and traditional deterministic control flows to achieve business goals and enable more autonomous decisions.

Agentic Process Automation: Redefining Boundaries

Agentic Process Automation Platforms (APAPs) embed autonomous, goal-oriented capabilities such as planning, decision making, and action across workflows and systems, strengthening the foundation of IPAPs with agentic intelligence.

Traditional automation programs were often measured primarily by efficiency gains, such as cost reduction, cycle time improvement, and labor optimization. In contrast, APAPs enable multi-dimensional transformation outcomes, including operational resilience, faster decision-making, improved customer and employee experience, stronger compliance, and greater business agility.

How can enterprises unlock the full value of APAPs? How can they adopt agentic workflows and position themselves as AI-first digital enterprises? This is where a unified, platform-based approach comes in.

Why a Platform-Based Approach Matters More Than Ever

As enterprises transition from fragmented deployments to scalable adoption, they need a unified platform that brings together data, processes, and workforce (human + digital).

Infosys Topaz AI Next is a purpose-built platform to operationalize this agentic paradigm. With next-generation data-to-experience and agentic AI orchestration capabilities, it unifies data, people, processes, and technology to help enterprises reimagine their operating model.

Business Outcomes

The Road Ahead: From Automation to Agentic Execution

The direction is clear: the next phase of enterprise transformation will not be driven by isolated automation or incremental gains, but by platforms that continuously learn, adapt, and execute at scale.

The future belongs to enterprises that embed agentic automation into their operating DNA, driving adaptability, intelligence, and scale.

Scale beyond efficiency. Unlock outcome-driven enterprise execution.

References

Everest Group, Enabling Enterprise Transformation through Agentic Process Automation Platforms (APAPs), 2026