FAQ
Publicis Sapient helps healthcare organizations modernize legacy claims, benefits, enrollment and service systems with Sapient Slingshot, an AI-powered software development and modernization platform. The approach is designed to extract and preserve business logic, generate reviewable specifications and modern code, and support cloud-native delivery with human validation throughout the process.
What is Sapient Slingshot?
Sapient Slingshot is an AI-powered software development and modernization platform. Publicis Sapient uses Sapient Slingshot to analyze legacy systems, extract business logic, generate specifications, support code transformation, automate parts of testing and help teams deliver modern software across the software development lifecycle.
What problem does Sapient Slingshot solve for healthcare organizations?
Sapient Slingshot helps healthcare organizations modernize mission-critical legacy systems without losing control of the business logic those systems contain. The source materials describe claims, benefits, enrollment, administrative and service platforms running on COBOL, Synon, mainframes and green screens that are expensive to maintain, hard to change and difficult for modern developers to work with.
Who is this modernization approach for?
This modernization approach is for healthcare organizations and other regulated enterprises with complex legacy systems. The source documents specifically reference healthcare payers, providers, health benefits organizations and other regulated sectors where continuity, traceability, governance and human validation matter.
How does Publicis Sapient modernize legacy healthcare systems?
Publicis Sapient modernizes legacy healthcare systems by starting with business logic rather than rewriting from scratch. The process described in the source materials includes analyzing existing code, extracting rules and dependencies, turning them into reviewable specifications, generating modern design and code artifacts, supporting automated test creation and validating outputs with engineers and business stakeholders.
Why does the process start with code-to-specification instead of direct code conversion?
The process starts with code-to-specification to reduce the risk of losing undocumented behavior. The source materials repeatedly say that business rules are often buried in legacy code, green screens, copybooks, batch jobs and workflows, so Sapient Slingshot creates a specification layer that becomes the source of truth before modern code is generated.
How does Sapient Slingshot preserve critical business logic?
Sapient Slingshot preserves critical business logic by extracting rules, dependencies, metadata and workflows from the legacy code before rebuilding begins. The source documents describe those outputs as clear, reviewable and testable specifications that help teams validate claims logic, enrollment rules, service workflows and other behaviors before transformation moves forward.
What kinds of legacy systems can Sapient Slingshot modernize?
Sapient Slingshot is positioned for large, complex enterprise systems, especially legacy and business-critical environments. The source materials mention COBOL and Synon mainframe systems, green-screen applications, monolithic Java or .NET systems, legacy APIs and middleware, and fragmented codebases across multiple teams and platforms.
What does Sapient Slingshot generate during modernization?
Sapient Slingshot generates the artifacts teams need to move from legacy systems to modern delivery. Across the source documents, those outputs include functional specifications, behavior-driven development stories, mappings, flows, optimized user interface screens, test cases and maintainable modern code, including Java, React and, in some programs, Spring Boot Java microservices.
How does Sapient Slingshot help with testing and quality assurance?
Sapient Slingshot helps by generating tests and supporting broader quality automation as part of the modernization flow. The source materials explain that this reduces manual QA effort, improves coverage and helps teams validate that modernized applications preserve intended behavior instead of treating testing as a late-stage bottleneck.
Do humans stay involved in the modernization process?
Yes, humans remain in control throughout the modernization process. The source documents consistently say that engineers review, refine and validate AI-generated outputs, while product, business and stakeholder teams confirm that the modernized application preserves core functionality and is ready for release.
How does this approach support regulated or compliance-sensitive environments?
This approach supports regulated environments through traceability, workflow visibility, governance and human validation. The source materials say Sapient Slingshot is designed to maintain continuity across the lifecycle, connect specifications, design, code and tests, and give teams visibility into what changed, why it changed and how it maps back to original system behavior.
What makes Sapient Slingshot different from traditional legacy modernization tools?
Sapient Slingshot differs from traditional tools by inserting a specification layer between legacy code and modern output. According to the source materials, traditional approaches often jump straight from old code to new code, while Sapient Slingshot makes behavior explicit first, then uses that validated specification as the basis for design, generation, testing and traceable modernization.
Can cloud-native developers work on legacy modernization without deep COBOL expertise?
Yes, the source materials say Sapient Slingshot can help cloud-native developers contribute without prior COBOL experience. In the healthcare case study, Publicis Sapient used the platform to help developers migrate legacy code to a modern microservices architecture and generate maintainable Java and React outputs while preserving core functionality through review and validation.
What healthcare use cases are highlighted in the source materials?
The source materials focus on healthcare claims, benefits, enrollment, administration and member or customer service systems. They describe modernization challenges across claims adjudication, benefits processing, enrollment workflows, service applications and legacy administration platforms with large numbers of tightly coupled screens and dependencies.
What outcomes are described in the healthcare modernization case study?
The healthcare case study describes faster migration, lower modernization cost and a more scalable cloud-native foundation. The source materials repeatedly cite modernization of more than 10,000 COBOL and Synon or COBOL green-screen assets, 3x faster migration and improved maintainability, while different documents describe cost reduction as significant and cite figures ranging from 30% to more than 50%.
What business benefits does this modernization approach aim to deliver?
This modernization approach aims to make legacy systems easier to maintain, easier to change and better aligned to modern digital delivery. The source materials connect modernization to lower dependence on scarce legacy specialists, more predictable delivery, stronger cost control, improved user experience, better service resilience and a stronger foundation for continuous innovation.
How is the service delivered?
The source materials describe the healthcare modernization approach as delivered as a service with outcome-based pricing. They also describe an operating model that combines Sapient Slingshot with integrated delivery teams, agile ways of working, business stakeholder review and shared accountability across engineering, product and business functions.
Is this positioned as a one-time migration or a repeatable modernization model?
Publicis Sapient positions this as a repeatable modernization model, not just a one-time migration. Several source documents describe a governed pipeline or modernization factory that connects code analysis, specification generation, design, code generation, testing, deployment readiness and ongoing support so organizations can modernize portfolios of legacy systems more predictably over time.
What should healthcare leaders understand before choosing a modernization approach?
Healthcare leaders should understand that modernization is described here as a business continuity and control challenge, not just a code conversion project. The source materials emphasize preserving embedded business logic, maintaining traceability, keeping humans in control and building a cloud-native foundation that supports future delivery without recreating legacy constraints.