For years, enterprises have relied on legacy systems to power their core operations. These systems earned trust through reliability, data stability, and predictable performance. From streamlining well-defined repeatable tasks to automating code scans and accelerating test cycles, traditional AI has shown tremendous success with speed & consistency. But as technology evolved, these once AI assets failed to account for the evolving complexities of modern applications demanding an approach that is adaptive and autonomous, capable to answering unknown & unseen barriers to agility & innovation.
As per a recent survey, 31% of IT infrastructure is still reliant on legacy systems and only 33% is transformed into modern technology.
Why Should Today’s Businesses Modernize?
Modern businesses are no longer defined by simple transactions — they thrive on dynamism, experiences, and loyalty. Every customer interaction is a chance to build trust and deliver value, but as enterprises evolve and operations become increasingly complex, reliance on legacy infrastructure has become both cumbersome and costly, threatening agility and business continuity.
Across industries, modernization has now shifted from a good-to-have to a must-have strategic imperative. Global enterprises are realizing that outdated systems not only limit innovation but also expose them to operational risks and inefficiencies.
For instance, SAP has mandated that all its customers migrate to S/4HANA by 2030, underscoring how modernization has become essential to future-proof enterprise systems, enable seamless integration, and ensure business resilience.
But modernization isn’t just about replacing old systems or cutting costs. In fact, legacy applications were once reliable, predictable, and required minimal training. So why are enterprises racing to modernize today?
1. Staying Ahead of the Curve: In today’s rapidly evolving digital landscape, legacy systems can act as growth bottlenecks. To outpace competition, enterprises need modern, agile applications that can innovate faster, integrate dynamically, and scale effortlessly with emerging technologies.
2. Real-Time Adaptation: The market is globally evolving rapidly and businesses that can’t adapt risk losing loyalty overnight. Modern applications enable real-time responsiveness, allowing enterprises to adjust to market changes, customer needs, and business priorities without delay — ensuring continuity and retention.
3. Performance & Productivity: Modern systems are smarter, faster, and more efficient. They leverage AI-driven decision-making, automation, and cloud scalability to optimize performance — helping teams move from reactive operations to proactive innovation.
4. Security & Compliance: With increasing cyber threats and stricter regulations, data security has become a core business concern. Modern architectures embed advanced security frameworks and compliance standards to safeguard sensitive data while maintaining agility.
5. Innovation & Growth: Modernization isn’t just a technology upgrade — it’s a growth catalyst. By embracing new-age tools, architectures, and intelligent automation, enterprises can experiment, innovate, and evolve faster than ever before.
6. Cost Efficiency & Value Creation: While modernization requires upfront investment, it delivers long-term savings by eliminating technical debt, reducing maintenance costs, and streamlining workflows — freeing up resources to focus on innovation, customer experience, and strategic growth.
Success Stories: How Leading Brands Modernized for the Future
Global leaders are rewriting their digital playbooks through application modernization — reducing technical debt, improving performance, and accelerating innovation integrating with modern architectures. Here’s how some of the world’s most influential brands transformed their systems to stay ahead:
1. Twitter Ads: Modernizing to Boost Agility
Twitter Ads embarked on a modernization journey to tackle growing technical debt and performance bottlenecks.
By re-architecting its legacy systems using a hybrid cloud strategy, Twitter successfully migrated key components to the cloud while keeping sensitive user data secure and anonymized.
The result? Faster release cycles and improved agility — delivering new capabilities effortlessly.
2. Airbnb: Transforming Release Cycles with Modern Frameworks
Airbnb realized that legacy frameworks were slowing its pace of innovation. By adopting modern cloud-based architectures and streamlined frameworks, Airbnb accelerated its release timelines, reduced errors, and improved overall platform stability.
This modernization not only decreased tech debt but also empowered developers to innovate faster, enabling seamless user experiences across the platform.
3. NBA: Reinventing Fan Experience with Azure
The NBA partnered with Microsoft Azure to reimagine how it delivers experiences to fans worldwide. By automating critical operations like scaling, repository management, and security, the NBA reduced manual effort and enhanced time-to-market for new digital features.
This shift enabled real-time data processing, immersive virtual experiences, and a new level of engagement that was once impossible with legacy systems.
4. Spotify: Refactoring for Seamless Multi-Channel Experiences
Spotify faced challenges maintaining a consistent user interface across incompatible platforms. Through code refactoring and modularization, Spotify unified its UI framework and significantly boosted performance and reliability.
This modernization not only improved scalability but also ensured a consistent multi-device experience, keeping millions of users connected effortlessly.
Modernization: A Business Imperative, Not an Option
Enterprises that modernize today aren’t merely upgrading their systems — they’re future-proofing their businesses. The shift from legacy to modern frameworks represents a transformation in mindset, capability, and opportunity — turning technology into a strategic advantage.
To simplify & catalyze this shift, Agentic AI — the new force behind enterprise modernization.
Why Enterprises Are Turning Agentic
Agentic AI goes beyond automation. It enables AI agents that can act, decide, learn, and collaborate autonomously—a leap from traditional technology which merely reacts to prompts. These intelligent agents are emerging as the very solutions enterprises have long been searching for: adaptable, context-aware, and business-outcome-driven.
Business leaders now recognize that while legacy infrastructures once ensured continuity, migrating to modern platforms is no longer optional—it’s imperative. In this transformation journey, Agentic AI plays a pivotal role by:
- Minimizing operational disruption during migration,
- Simplifying complex workflows, and
- Reducing costs significantly—without compromising customer experience.
A recent Accenture report reveals the magnitude of this shift: the enterprises which went through complete modernization and AI-led processes nearly doubled—from 9% in 2023 to 16% in 2024. These organizations reported 2.5x higher revenue growth, 2.4x greater productivity, and 3.3x more success in scaling their operations.
How Agentic AI Simplifies Business Processes
Agentic AI operates on Large Language Models (LLMs) that understand and generate human-like language. Agents can interpret complex business queries, process vast datasets, and extract actionable insights—all in real time.
But the real power lies in Agentic Architecture — a system where multiple AI agents collaborate seamlessly, much like a beehive.
Just as each bee plays a distinct role within a hive, each AI agent performs specialized functions:
- One may standardize workflows by analyzing and optimizing routine tasks.
- Another could integrate documents and data systems, eliminating silos.
- A third might respond to customer queries instantly, improving satisfaction and retention.
- And a central agent oversees the entire network, ensuring that every function aligns with the end business objectives.
These agents in an architecture work together to ensure complex processes are tackled and simplified which were beyond the capabilities of any single agent.
Why Enterprises Need to Integrate Agentic AI into Their Business Frameworks
Here’s why integrating Agentic AI into existing enterprise frameworks is becoming a strategic necessity rather than a futuristic ambition:
1. Tackling Complex Business Problems at Scale
From analysing financial data and public records to detecting fraud and maintaining customer satisfaction —Agentic AI helps enterprises achieve faster, more precise decision-making. The result? Smarter insights, improved accuracy, and accelerated revenue growth — all without slowing down operations.
2. Adapting to an Evolving Market Landscape
Markets evolve. Consumer behaviour shifts. Technology transforms overnight.
But agents can help you analyse buying patterns, behavioural cues, and market signals to help enterprises anticipate demand, personalize experiences, and stay ahead of disruption.
In short, Agentic AI doesn’t just respond to change — it adapts and evolves with it.
3. Driving Scalability and Operational Autonomy
Scalability is no longer about adding more resources — it’s about doing more intelligently.
Agentic AI helps you transform and optimize your business with end-to-end efficiency, responsiveness, and ensuring business continuity at scale.
4. Reducing Operational Costs While Transforming Operations
Legacy modernization has always been a tightrope walk — high costs, data loss risks, and technical debt make it daunting.
AI agents enable seamless transitions from old systems to modern platforms. The payoff: lower operational costs, minimal disruption, and accelerated transformation.
In Essence
Agentic AI is not a replacement — it’s an enhancement.
It empowers enterprises to go beyond automation, enabling a self-optimizing ecosystem where systems think, collaborate, and grow alongside the business.
The Tech Behind Agentic AI
Powered by Natural Language Processing, Machine Learning, and Reinforcement Learning, Agentic AI creates self-learning agents that collaborate intelligently across systems and teams.
- Reinforcement Learning enable agents to learn from experience, adjust strategies dynamically, and adapt to real-time feedback. Picture a supply chain agent that analyzes live weather patterns, market trends, and customer demand to prevent delivery delays and minimize costs—all without human intervention.
- With Machine Learning, the agents uncover hidden trends across massive datasets, transforming raw data into actionable insights that enable faster, smarter, and more confident decision-making.
At the core, Agentic AI transforms enterprises into intelligent, adaptive ecosystems—where every process evolves autonomously to meet business objectives and deliver measurable outcomes.
How is Agentic AI Enabling Application Modernization
Ever since AI has taken a historical leap and market has been shifting with an unseen growth, modern enterprises are under a constant pressure to transform their legacy applications & systems into a modern architecture to make agile, cloud-ready, & intelligent ecosystems.
In the agentic approach of application migration, the modernization cycle isn’t isolated but connected to a series of intelligent & self-optimizing processes. By combining these capabilities with data-driven insights, the agents remove eccentricates, streamline decision-making to unlock faster delivery, higher quality & a simplified modernization journey.
Here’s how Agentic AI is enabling different modernization phases:
1. Intelligent Discovery & Assessment: Agentic AI agents have the potential to assess legacy systems, prioritize migration opportunities, analyze their interdependencies, and seamlessly identify modernization opportunities without extensive human intervention. The result? Reduced manual efforts & accelerated modernization by 60%.
Example: An AI agent evaluates a bank’s core applications, identifies outdated APIs, and recommends modern solutions which modules to refactoring, updating, or retiring old applications.
2. Automated Code Refactoring & Optimization: AI agents work on large language models which can read, understand, & refactor legacy code- transforming old monolithic systems into modern microservices or API-based architectures.
Example: AI agents can seamlessly rewrite primitive code like COBOL to cloud-native architectures, improving performance and reliability.
3. Data Migration & Integration Intelligence: Agentic AI enables continuous data migration by automatically mapping workflows & cleaning data across legacy systems to avoid data disintegration, thereby detecting digital gaps across systems to ensure minimal process disruption
Example: During ERP migration, AI agents monitor real-time data flow between legacy databases and cloud environments to ensure integrity and zero data loss.
4. Continuous Monitoring & Optimization: Agentic AI just doesn’t stop at modernization; it monitors the efforts continuously by learning from user behaviour & real-time feedback and optimizes systems to avoid setbacks & inefficiencies.
Example: AI agents proactively adjust cloud resource usage based on demand patterns, optimizing costs without human intervention.
5. Human-AI Collaboration for Strategic Transformation: Despite the popular notion, Agentic AI doesn’t replace humans. Rather AI agents rely on humans to understand the nuances & get expert opinions to provide best customer experience & transform enterprises with excellence.
Example: AI handles dependency mapping and code conversion, while teams focus on designing new business capabilities.
The Bottom Line:
Agentic AI understands the challenge. With its proactive approach and quick thinking, it is rearticulating the transformation game with ease. With Agentic AI, turning a once-manual, high-risk process into an intelligent transformation journey. By combining automation with strategic reasoning, enterprises can modernize faster, reduce costs, and build adaptive systems that evolve & scale as their business grows.
Navigating the Risks of Data Privacy
At its core, Agentic AI thrives on autonomy — enabling intelligent agents to act, decide, and learn independently. While this autonomy drives unprecedented speed, scalability, and precision, it also introduces a growing challenge: risk to data privacy & security.
To truly build trust and alignment with enterprise goals, transparency in how AI agents operate is essential. However, transparency can sometimes expose sensitive data, creating a delicate balance between visibility and vulnerability.
That’s why enterprises must invest in robust governance frameworks and ethical AI guidelines that emphasize responsible data handling. By combining seamless system integration with human oversight, businesses can ensure collaboration without compromise—protecting privacy, maintaining compliance, and driving innovation responsibly.
Data privacy isn’t a constraint; it’s the foundation of trustworthy AI. When businesses embed security and governance at every stage of AI deployment, they not only mitigate risks but also enable Agentic AI to realize its full potential—securely, transparently, and ethically.
Indium’s Leading the Way with LIFTR.AI
At the intersection of AI and innovation, global enterprises are reimagining how they modernize — and Indium is at the forefront of this transformation with LIFTR.AI, our Agentic AI-powered modernization platform.
With smarter, reliable, and future-ready technology, Indium’s engineers are redefining application modernization — blending cloud-native architectures, AI-driven automation, and microservices to unlock agility, scalability, and seamless user experiences.
Indium’s approach ensures a smooth, disruption-free transformation from legacy to modern applications — enabling enterprises to gain real-time insights into their application portfolios and IT landscapes. Beyond modernization, we empower businesses to assess, analyze, and prioritize their modernization roadmap to maximize value and minimize risk.
LIFTR.AI- The Future of Enterprise Modernization
Unlike traditional approaches that stop at code upgrades, LIFTR.AI goes further — leveraging Agentic AI to orchestrate the entire modernization lifecycle.
Our AI agents collaborate autonomously to bring domain expertise into every phase — from code analysis and architecture redesign to cloud migration, optimization, and continuous improvement.
With LIFTR.AI, modernization isn’t a one-time project — it’s an ongoing evolution toward efficiency, intelligence, and innovation.
Here are the reasons why Indium is the way to go:
1. Data Privacy & Security: For Indium, Data privacy is non-negotiable. Indium embeds enterprise-grade encryption, zero-trust frameworks, and continuous monitoring into every stage of modernization — ensuring complete data integrity without compromising performance.
2. Scalability & Agility: With a flexible, AI-optimized architecture, enterprises can scale effortlessly — managing exponential data growth, rising engagement, and complex workflows with confidence.
3. Continuous Modernization: Through API-led connectivity, cloud-native adoption, and microservices, our platform enables frictionless modernization — reducing downtime, minimizing risk, and accelerating digital transformation across the enterprise.
The Art of Possible
Indium and LIFTR.AI aren’t just helping enterprises modernize — we’re choreographing a new era of intelligent transformation.
Because modernization isn’t about keeping up — it’s about leading with efficiency.
