AI for Regulated Industries

In regulated industries, AI adoption is not a question of ambition. It is a question of control. Healthcare organizations, financial institutions and life sciences companies often know exactly where AI could create value, but the environments they operate in leave little room for error. Core workflows are tied to legacy platforms. Critical business logic is buried in undocumented code. Sensitive processes require role-based access, auditability and clear accountability. And every change must protect continuity as much as it improves speed.

Publicis Sapient helps regulated enterprises modernize and operationalize AI in environments where compliance is non-negotiable. We combine strategy, engineering, data and AI delivery to help organizations uncover hidden logic, document dependencies, embed governance from day one and deploy AI into sensitive workflows with the controls required for production. The goal is not experimentation at the edge. It is governed transformation inside the systems and processes that matter most.

Why regulated environments need a different AI approach

Most AI pilots stall for predictable reasons. Ownership is fragmented. Data lineage is unclear. Controls are added too late. Legacy systems hold business rules that no one wants to disturb, but no one can fully explain. In regulated industries, those issues are magnified. AI cannot operate as a black box inside claims, payments, medical review or regulated content workflows. It has to be explainable, traceable and built to work within enterprise policy from the start.

That is why Publicis Sapient focuses on governed execution. We help organizations define the systems that constrain growth, identify where AI can operate safely and clarify governance before deployment. We build role-based controls, traceable lineage, observability and audit-ready workflows into the operating foundation so AI can move from pilot to production with less risk and more confidence.

Modernization with traceability, not guesswork

Many regulated enterprises still depend on decades-old systems to run the business. Claims engines, mainframe batch feeds, payments modules and approval workflows continue to perform mission-critical work, even when they were never designed for APIs, cloud-native deployment or AI-enabled orchestration. Replacing them blindly creates risk. Leaving them untouched creates drag. The challenge is to modernize without losing the logic, controls and continuity those systems contain.

That is where Sapient Slingshot plays a critical role. Slingshot helps teams read existing code, extract hidden business rules, map dependencies, generate verified specifications and automate testing across the software development lifecycle. Instead of treating legacy code as a barrier, it turns that code into a usable, traceable source of enterprise knowledge. This makes modernization faster, safer and more accountable, especially in environments where undocumented logic can become a compliance and operational risk.

For regulated organizations, traceability matters as much as speed. Slingshot preserves embedded business logic while helping teams generate modern software with full visibility into what the legacy system does, how requirements map forward and where validation is required before change reaches production.

AI inside governed workflows, not outside them

Once the foundation is visible and testable, AI can be embedded where it creates measurable value. But in regulated industries, that requires more than a standalone model or assistant. AI has to operate inside real workflows with the right context, the right permissions and the right oversight.

Sapient Bodhi is Publicis Sapient’s enterprise AI platform for building, deploying and orchestrating AI solutions in production environments. Bodhi connects agents to governed data, role-based access and enterprise context from day one. It is designed to bring AI into workflows that demand accountability, including regulated content operations, approval processes, research tasks and other sensitive decision-support journeys. With built-in controls and observability, organizations can move faster without losing confidence in how outputs are produced, reviewed and governed.

This matters because regulated AI is not just about generating answers faster. It is about creating an orchestration layer where AI can contribute safely, where outputs can be traced back to approved inputs and where teams can govern who sees what, who validates what and how performance is monitored over time.

Human-in-the-loop where risk and judgment matter most

In high-stakes environments, trust is built through validation. Publicis Sapient uses a human-in-the-loop delivery model to ensure AI acceleration is matched by expert review at critical points in the workflow. Product owners, engineers, business stakeholders and compliance teams can validate generated specifications, confirm business logic, review outputs and guide exceptions before downstream action is taken.

This model allows organizations to automate the heavy lifting without automating away accountability. Human judgment stays where it matters most: validating high-impact decisions, governing exceptions and ensuring changes align to operational, regulatory and business requirements. The result is a more practical balance of speed and control, especially in environments where missed requirements or undocumented changes create unacceptable risk.

Proven across healthcare, financial services and life sciences

Publicis Sapient’s approach is already delivering outcomes in regulated environments where continuity, compliance and execution quality all matter.

In healthcare, a leading U.S. healthcare company modernized decades-old claims systems running on mainframe using Sapient Slingshot. After years of limited progress, the organization achieved 3x faster migration speed, modernized 10,000 screens and reduced modernization costs by 30 percent. Legacy COBOL was transformed into maintainable Java and React, functional specifications and test cases were auto-generated, and human-in-the-loop validation helped support quality and compliance throughout the program.

In banking, a major British retail and commercial bank used Publicis Sapient’s modernization approach to analyze more than 350 files and nearly half a million lines of complex Unisys COBOL code across critical programs. In just eight weeks, the team delivered program overviews, flowcharts, detailed field mappings and fan-out diagrams that allowed product owners to validate functionality quickly. The result was a 70 percent reduction in manual effort for code-to-spec work, 95 percent accuracy in generated specifications and a 40 to 50 percent increase in migration speed.

In life sciences, a global pharmaceutical company used Bodhi to transform regulated content creation at scale inside its own environment. The solution streamlined data ingestion, MLOps and creative workflows so teams could generate compliant-ready copy and imagery in seconds, while supporting localization, reuse and secure integration with existing systems. The program delivered 75 percent faster content production, up to 45 percent cost reduction and faster time to market. In healthcare marketing, AI agents trained on brand, regulatory and medical context helped teams scale content across more than 30 markets while maintaining governance controls.

What regulated AI should deliver

For regulated enterprises, success is not measured by how many pilots launch. It is measured by how well AI holds up in production. That means legacy logic surfaced and preserved. Governance embedded before deployment. Role-based controls and audit logs in place from day one. Human validation applied at the right moments. And operational continuity protected as modernization moves forward.

Publicis Sapient helps organizations build that foundation and activate the right platform against the right bottleneck. With Sapient Slingshot, buried logic becomes visible, documented and ready for modernization. With Sapient Bodhi, AI can be orchestrated inside sensitive workflows with observability, controls and enterprise context built in. Together, they help healthcare, financial services and life sciences organizations move faster without compromising trust.