Modernizing Legacy Systems from India to Unlock Enterprise AI at Scale
For many enterprises, the biggest barrier to AI at scale is not model choice. It is the technology estate underneath. Decades-old systems still run claims, payments, customer records, product data and core operations across industries. They contain the business logic that matters most, but that logic is often buried in undocumented code, fragmented dependencies and manual workarounds. Until those systems are understood, documented and rebuilt safely, AI remains difficult to scale with confidence.
That is why modernization is not a side project. It is the foundation for enterprise AI. From India, Publicis Sapient helps organizations take on that foundation work at scale—surfacing hidden business rules, generating verified specifications, automating testing and reducing the risk that usually slows legacy transformation. The result is not modernization for its own sake, but a clearer path to integration, agility and production-ready AI.
Why legacy systems block AI adoption
Many organizations are trying to move AI from pilots into production while core systems still were never designed for APIs, real-time data or intelligent workflows. Critical processes may depend on COBOL, Synon, batch feeds, hard-coded rules or aging applications with little to no documentation. Teams know the systems are important, but not always how they truly work. That creates a serious problem: if the business logic is unclear, every modernization effort carries risk, and every AI initiative inherits the same uncertainty.
In practice, that means AI pilots stall because the data lineage is unclear, the rules are trapped in code, the interfaces are brittle and governance is added too late. It also means engineering teams spend too much time translating legacy behavior manually instead of building modern platforms that can scale. Before enterprises can safely deploy AI across real workflows, they need a way to make their existing systems legible, testable and traceable.
Start with enterprise context, not guesswork
Publicis Sapient approaches modernization through enterprise context: a living map of business systems, rules and workflows. This context helps connect code, architecture, data and operational dependencies so transformation starts from what the business actually runs on, not from assumptions. When teams can see how systems interact and where logic lives, they can modernize with greater speed and far less uncertainty.
This matters especially in large enterprises and Global Capability Centers, where complexity compounds over years of platform changes, regional customization and team turnover. In these environments, undocumented behavior is often the real source of delay. Publicis Sapient uses enterprise context to uncover buried logic, clarify dependencies and create the visibility needed to move from fragile legacy estates to modern, AI-ready platforms.
How Sapient Slingshot reduces modernization risk
Sapient Slingshot is Publicis Sapient’s AI-powered modernization and software delivery platform. It is designed to do more than help individual developers write code faster. It modernizes legacy systems by turning existing code into verified specifications and generating modern software with full traceability across the software development lifecycle.
That distinction matters. Enterprise modernization cannot rely on a black-box rewrite. It requires a system-level approach that preserves critical business rules, documents how applications behave and validates what should carry forward. With Slingshot, teams can read legacy code, extract business logic and dependencies, generate verified specifications, automate test creation and produce modern code with traceability back to the source. This helps reduce the risk of rewrite-from-scratch programs while accelerating delivery.
It also creates a stronger foundation for AI. Once hidden rules are surfaced and systems are made testable, enterprises are better positioned to integrate data, expose services, embed governance and connect AI to real workflows. Modernization becomes the enabler for AI at scale, not a separate track competing for budget and attention.
Proof in complex, high-stakes environments
The value of this approach is clearest in environments where legacy complexity is highest. In healthcare, Publicis Sapient used Slingshot to help modernize more than 10,000 COBOL and Synon mainframe screens, enabling a 3x faster migration speed while reducing modernization costs. By uncovering hidden business rules and dependencies and automating test generation, teams were able to move faster with greater confidence.
In financial services, Publicis Sapient helped a major bank modernize complex mainframe batch feeds and payments modules by analyzing more than 350 files and nearly half a million lines of code. The work reduced manual effort for code-to-spec by 70 percent, achieved 95 percent accuracy in specification generation and increased migration speed by 40 to 50 percent. Instead of relying on slow manual interpretation, teams were able to validate functionality quickly and define a clearer modernization roadmap.
In energy, RWE used Slingshot to modernize an aging application with no documentation, restoring reliability and reducing operational risk in just two days rather than two weeks. The program delivered roughly 40 percent time savings in automated code generation and about 35 percent efficiency gains in test creation and unit test setup. For enterprises facing decades-old applications that still matter to the business, that speed can materially change the economics of modernization.
Why India is a strategic base for modernization at scale
India plays a central role in this story not simply as a delivery location, but as a strategic base for enterprise engineering, platform modernization and AI activation. Publicis Sapient has worked with organizations in India and beyond for over 30 years, and its teams across eight offices combine engineering depth with the scale needed to support global transformation programs.
That makes India especially relevant for enterprises operating or building Global Capability Centers. As GCCs evolve from operational support units into engines of innovation and growth, they need more than execution capacity. They need the ability to modernize complex core systems, accelerate software delivery and create the technical foundations for AI adoption across the enterprise. Publicis Sapient’s focus in India is built to support that shift—helping organizations establish, scale and transform GCCs into future-ready value centers.
For global clients and GCC leaders alike, the opportunity is significant. India offers the talent, digital ambition and delivery scale to run large modernization programs that connect business strategy, engineering execution and AI readiness. When combined with enterprise context and platform-led delivery, that scale becomes a way to reduce legacy drag without sacrificing control.
From pilot pressure to production confidence
Strong engineering starts with clear systems. Dependencies must be visible. Business rules must be documented. Testing must be automated. AI must be built in from the beginning, not layered on top of unstable foundations later. Publicis Sapient helps enterprises fix the foundations first so they can move faster after that—with modern platforms that integrate cleanly, improve over time and support AI in production.
This is the shift many organizations need most: away from isolated experiments and toward technology estates that can actually support change. With Sapient Slingshot and an enterprise context approach, modernization becomes more traceable, more testable and more reliable. From India, Publicis Sapient brings the engineering capability, platform mindset and delivery scale to help enterprises modernize safely and unlock the full value of AI across global operations.
Because in enterprise AI, the question is rarely whether there is enough ambition. The question is whether the core systems are ready. We help make sure they are.