Using Generative AI on AWS to Accelerate Application Modernization
For many enterprises, the most valuable early use case for generative AI is not a chatbot or a standalone assistant. It is the difficult, expensive and business-critical work of modernizing legacy applications.
Core systems still run essential processes across industries, but they also create friction. They slow release cycles, increase maintenance costs, deepen technical debt and make it harder to respond to changing customer expectations or regulatory demands. Traditional modernization programs often struggle with the same issues year after year: incomplete documentation, scarce legacy skills, unclear business logic, long testing cycles and limited traceability from current-state systems to future-state architectures.
Generative AI offers a more practical path forward when it is applied with discipline. On AWS, Publicis Sapient helps organizations use generative AI to speed up application modernization, improve quality and reduce delivery risk. By combining Amazon Bedrock, Amazon CodeWhisperer and broader AWS-native capabilities with Sapient Slingshot, we help clients modernize legacy estates with a human-in-the-loop approach that emphasizes business outcomes over technical novelty.
A high-value Gen AI use case with immediate business relevance
Many organizations are still looking for a generative AI investment that can move quickly from promise to measurable value. Application modernization is one of the clearest answers because it addresses problems leaders already understand: rising operational cost, aging platforms, slow delivery, migration complexity and maintainability concerns.
Applied well, generative AI can help transform modernization from a largely manual, document-heavy effort into a more structured and traceable engineering program. The result is not simply faster code conversion. It is a stronger path to improved software quality, lower modernization cost, better migration confidence and a more maintainable application landscape on AWS.
How generative AI supports modernization on AWS
Publicis Sapient applies generative AI across the modernization lifecycle by combining three critical inputs: code, context and prompts. Code provides the raw material for analysis and transformation. Context helps models understand business intent, domain logic and target outcomes. Prompt libraries improve repeatability and help guide models toward useful outputs across common modernization tasks.
This approach turns generative AI into an engineering accelerator rather than a black box. It helps teams address some of the hardest parts of legacy transformation more efficiently while keeping architects, engineers and domain experts in control.
1. Understanding legacy code at scale
One of the first barriers in any modernization program is simply understanding what the legacy estate does. In many organizations, documentation is outdated, fragmented or missing altogether. Business logic may be buried across large, aging codebases, making dependency mapping and migration planning slow and risky.
Generative AI can help teams analyze legacy code, deconstruct existing functionality and create a clearer understanding of the system baseline. This is especially valuable when organizations are dealing with millions of lines of code and limited access to specialists who know the old environment deeply. Better understanding at the start improves planning quality and reduces downstream rework.
2. Generating functional specifications from existing systems
Modernization is not about rewriting everything blindly. It is about preserving the logic the business still needs, identifying what should change and building confidence in the transition from current state to target state.
Generative AI can help convert legacy code into feature summaries and functional specifications that are easier for engineering and business stakeholders to review. This improves traceability between what the old system does today and what the modernized solution needs to deliver tomorrow. It also helps organizations reduce dependency on tribal knowledge and establish a stronger foundation for migration decisions.
3. Creating test cases and improving validation
Testing is one of the most time-consuming and risk-sensitive phases of modernization. Expanding test coverage manually across aging applications can take significant effort, especially when documentation gaps make expected behavior difficult to confirm.
Publicis Sapient uses generative AI to help generate test cases, unit tests and automation scripts that strengthen validation and reduce manual effort. This can improve coverage of requirements and test scenarios earlier in the lifecycle, giving teams more confidence as they move into refactoring and migration. Stronger testing support also helps reduce defects and improve software quality in the target environment.
4. Supporting refactoring and migration to more maintainable architectures
Once teams have a reliable understanding of the current estate, generative AI can support refactoring and target-state generation. This includes helping teams transition code, document future-state designs and move toward architectures that are easier to maintain and evolve on AWS.
The value here is strategic. Modernization should not end with a technical lift and shift into a new environment that preserves the same inefficiencies. It should produce applications that are more adaptable, easier to enhance and better aligned to long-term business priorities. Generative AI can help accelerate that transition when combined with sound architecture, strong engineering discipline and cloud-native thinking.
The role of Amazon Bedrock, Amazon CodeWhisperer and AWS-native controls
Amazon Bedrock provides a flexible foundation for enterprise generative AI on AWS. It offers access to multiple foundation models through a serverless interface, allowing teams to integrate model capabilities into modernization workflows without assembling a fragmented toolchain. It also supports important enterprise patterns such as fine-tuning, retrieval-augmented generation and guardrails.
For modernization programs, Bedrock can help teams ground outputs in proprietary enterprise content and current project context through retrieval-augmented generation. Knowledge Bases for Amazon Bedrock can automate ingestion, retrieval and prompt augmentation, helping models work with relevant internal information rather than relying on generic responses alone.
Amazon CodeWhisperer adds another important layer by improving the developer experience. As an AI coding companion, it can support faster software development with code suggestions across multiple languages. Its enterprise customization capabilities can also reflect internal APIs, libraries, best practices and architectural patterns, which is especially relevant when modernization programs need generated code to align with target standards rather than generic defaults.
AWS-native governance and security capabilities matter just as much as the models themselves. Services such as IAM, KMS, CloudTrail, CloudWatch, Macie, Security Hub, Bedrock Guardrails and SageMaker Model Monitor support the access control, encryption, monitoring, auditability and safety controls needed for enterprise AI adoption. In modernization, these controls help organizations move faster without weakening governance.
Sapient Slingshot: accelerating modernization with traceability
Sapient Slingshot is a key differentiator in Publicis Sapient's approach. It is an AI-powered platform built to accelerate legacy modernization and the software development lifecycle. Its role extends beyond code assistance alone. It helps automate code migration, testing, deployment, documentation and broader modernization activities in a way that connects engineering acceleration to business confidence.
In modernization programs on AWS, Sapient Slingshot helps turn existing code into verified specifications and supports the generation of modern software with stronger traceability. That traceability is critical in enterprise transformation. Leaders need to understand how current-state functionality maps to future-state systems, where decisions were made and how important business logic is being preserved. Modernization becomes far more credible when it is measurable, reviewable and explainable rather than opaque.
Human-in-the-loop delivery is what makes enterprise modernization work
Generative AI can accelerate modernization, but it does not remove the need for experienced human judgment. Publicis Sapient's approach is explicitly human in the loop. Engineers, architects and domain experts guide context, validate outputs, refine prompts, assess architectural tradeoffs and ensure alignment to business priorities.
This balance is essential. Enterprises need the speed and productivity benefits of AI, but they also need accuracy, accountability and trust. Human oversight helps turn AI-generated outputs into production-ready modernization assets and ensures that quality and maintainability are not sacrificed in pursuit of speed.
Business outcomes that matter
The case for generative AI in modernization is strongest when framed around outcomes, not models. Across more than 100 end-to-end experiments using generative AI modernization accelerators alongside internal tools, models and AWS solutions, Publicis Sapient has seen the potential for more than 50 percent reduction in modernization costs, 50 percent fewer defects with significantly expanded coverage of requirements and test cases, and a 50 percent increase in migration speed.
In one modernization effort for a leading benefits provider, Publicis Sapient designed a process that used private models and large language models to deconstruct legacy mainframe applications. The effort helped achieve a migration that was three times faster, improved traceability through functional specifications and made the resulting code easier to maintain and enhance in a modern technology stack.
These outcomes point to a broader shift. Generative AI is not just helping teams write code faster. It is helping organizations reduce technical debt, improve migration confidence, strengthen software quality and create a more maintainable foundation for future growth on AWS.
From modernization challenge to transformation opportunity
For CIOs, CTOs and engineering leaders, application modernization is one of the most credible ways to realize near-term value from generative AI. It targets a high-cost, high-friction enterprise problem that already demands action. And when approached with the right operating model, it can deliver benefits across speed, quality, cost and maintainability.
With Publicis Sapient, AWS and Sapient Slingshot, organizations can modernize with a business-led, engineering-grounded approach that combines cloud-native architecture, AI-powered acceleration and human expertise. The result is a more practical path from legacy complexity to modern, maintainable systems built for long-term change.
If your organization is looking for a high-value place to apply generative AI now, application modernization may be the smartest place to start.