Accelerate Application Modernization with Generative AI on AWS

For many enterprises, the most valuable early use case for generative AI is not a chatbot. It is application modernization.

Legacy systems still power critical business processes, but they also slow delivery, increase maintenance costs, deepen technical debt and make it harder to respond to new market, customer and regulatory demands. Traditional modernization programs often face the same obstacles: incomplete documentation, scarce legacy skills, unclear business logic, long testing cycles and limited traceability from current-state systems to future-state architectures.

Publicis Sapient helps organizations use generative AI on AWS to address those challenges in a practical, engineering-led way. By combining Amazon Bedrock, Amazon CodeWhisperer, AWS-native governance controls and Sapient Slingshot, we help enterprises modernize faster, reduce delivery risk and improve long-term maintainability.

A practical AI use case with immediate business value

Many leaders are still asking where generative AI can create measurable value now. Application modernization is one of the clearest answers because it targets a problem organizations already need to solve.

Modernization is expensive, complex and business-critical. It affects cost structure, speed to market, software quality and future agility. When generative AI is applied with the right context, controls and human oversight, modernization becomes more structured, more traceable and less dependent on manual analysis alone.

The goal is not AI novelty. It is better business outcomes: lower modernization cost, faster migration, fewer defects and a stronger software foundation for future change.

How Publicis Sapient applies AI across the modernization lifecycle

Publicis Sapient uses generative AI as an accelerator across the full modernization journey. Our approach combines three critical inputs: code, context and prompts.

Code provides the source material for analysis and transformation. Context helps models understand business logic, intent and target outcomes. Prompt libraries improve repeatability and guide models toward useful outputs across common modernization tasks. Together, they turn generative AI into an engineering asset rather than a black box.

Understand legacy code at scale

One of the first barriers in modernization is understanding what the legacy estate actually does. Documentation is often fragmented, outdated or missing altogether. Critical logic may be buried across millions of lines of code, making dependency mapping and migration planning slow and risky.

Generative AI can help teams analyze legacy applications, deconstruct functionality and establish a clearer baseline for modernization planning. This is especially valuable when organizations are dealing with large code estates and limited access to specialists who understand the old environment deeply.

Publicis Sapient has demonstrated this kind of acceleration at scale, including work that turned 3 million lines of COBOL into clear specifications in just eight weeks. That kind of early clarity improves planning quality and reduces downstream rework.

Generate functional specifications from existing systems

Modernization is not about rewriting code blindly. It is about preserving the business logic that still matters, identifying what should change and creating confidence in the move from current state to target state.

Generative AI can convert legacy code into feature summaries and functional specifications that are easier for engineering and business stakeholders to review. This reduces dependence on tribal knowledge and improves traceability between what the legacy system does today and what the modernized application needs to deliver tomorrow.

That traceability matters because modernization decisions are rarely technical alone. They affect compliance, operations, customer experience and future roadmap choices.

Create test assets and strengthen validation

Testing is often one of the most time-consuming and risk-sensitive phases of modernization. Documentation gaps make it harder to confirm expected behavior, and expanding test coverage manually across aging applications can be expensive.

Publicis Sapient uses generative AI to help generate test cases, unit tests and automation scripts that improve validation while reducing manual effort. Earlier and broader test support helps teams move into refactoring and migration with more confidence. It also helps improve software quality in the target environment by increasing coverage of requirements and test scenarios.

Support refactoring and future-state architecture

Once teams understand the current estate, generative AI can help accelerate refactoring and support the creation of more maintainable target-state architectures on AWS.

That includes assisting with code transition, documenting future-state designs and helping teams move beyond brittle, high-maintenance legacy environments. The objective is not simply to move old problems into a new cloud environment. It is to create applications that are easier to enhance, govern and evolve over time.

Why Amazon Bedrock and Amazon CodeWhisperer matter

Amazon Bedrock provides a flexible foundation for enterprise generative AI on AWS. It offers a unified, serverless interface to foundation models from Amazon and third-party providers, allowing organizations to integrate model capabilities into modernization workflows without assembling fragmented infrastructure.

For modernization programs, Bedrock supports practical enterprise patterns such as fine-tuning, retrieval-augmented generation and guardrails. Knowledge Bases for Amazon Bedrock can automate ingestion, retrieval and prompt augmentation so models can work from relevant internal content instead of generic context alone. This is especially useful when teams need outputs grounded in proprietary application knowledge, migration decisions and current project documentation.

Amazon CodeWhisperer adds another important layer by improving the developer experience. As an AI coding companion, it helps developers build faster with code suggestions across multiple languages. Its customization capabilities can also reflect internal APIs, libraries, best practices and architectural patterns. That matters in modernization because generated outputs need to align with enterprise standards, not just produce syntactically correct code.

Together, Bedrock and CodeWhisperer make generative AI more usable across analysis, specification, testing and refactoring activities in the modernization lifecycle.

Sapient Slingshot: stronger traceability, oversight and governance

Sapient Slingshot strengthens this approach by bringing purpose-built acceleration to legacy modernization and the software development lifecycle.

More than a code assistant, Sapient Slingshot helps organizations turn legacy code into verified specifications and modernization outputs with stronger human oversight and governance. It supports activities such as code migration, testing, deployment, documentation and modernization planning while maintaining a clearer connection between source systems, generated artifacts and target-state outputs.

This is a critical advantage for enterprise modernization. Leaders need to know how current-state functionality maps to future-state systems, where key decisions were made and how important business logic is being preserved. Modernization is far more credible when it is reviewable, explainable and measurable.

Human-in-the-loop delivery makes modernization work

Generative AI can accelerate modernization, but it does not replace engineering judgment. Publicis Sapient’s approach is explicitly human in the loop.

Architects, engineers and domain specialists provide context, refine prompts, validate outputs, assess tradeoffs and ensure alignment to business priorities and governance requirements. This balance helps organizations capture the productivity benefits of AI without sacrificing accountability, architectural integrity or quality.

AWS-native security and governance services strengthen that model. Across enterprise AI programs, Publicis Sapient draws on capabilities such as IAM, KMS, CloudTrail, CloudWatch, Macie, Security Hub, Bedrock Guardrails and SageMaker Model Monitor to support access control, encryption, observability, auditability and responsible AI safeguards.

Outcomes that matter to the business

Across more than 100 end-to-end experiments using generative AI modernization accelerators together with internal tools, models and AWS solutions, Publicis Sapient has seen the potential for more than 50% reduction in modernization costs, 50% fewer defects with significantly expanded coverage of requirements and test cases, and a 50% increase in migration speed.

In one modernization effort for a leading benefits provider, Publicis Sapient designed a process using private models and large language models to deconstruct legacy mainframe applications. The result was a migration that moved three times faster, improved traceability through functional specifications and produced code that was easier to maintain and enhance in a modern technology stack.

A smarter first move for enterprise AI

For CIOs, CTOs and engineering leaders, application modernization is one of the most credible ways to move generative AI from experimentation to production value. It addresses a high-cost enterprise problem, creates measurable impact across speed, quality and cost, and helps establish a more maintainable digital core on AWS.

With Publicis Sapient, Amazon Bedrock, Amazon CodeWhisperer and Sapient Slingshot, organizations can modernize with a business-led, engineering-grounded approach that combines AI acceleration, cloud-native architecture and disciplined human oversight.

The result is a more practical path from legacy complexity to modern, maintainable systems built for long-term change.