Move AI from pilot to production in regulated industries

In regulated industries, AI success is not defined by how quickly a pilot gets approved. It is defined by whether AI can operate safely inside high-stakes workflows, stand up to scrutiny and keep delivering value after launch. Healthcare organizations, financial institutions and life sciences companies all face the same reality: the workflows that matter most are tied to legacy systems, sensitive data, strict controls and business rules that cannot be broken in the name of innovation.

That is why moving AI from pilot to production in these environments requires a different approach. It is not enough to prove that a model works in isolation. Enterprises need clear ownership, governed data, role-based controls, auditability, human validation and the ability to trace outputs back to trusted systems and business logic. They need to design for speed and control from day one.

Publicis Sapient helps regulated enterprises do exactly that. By combining Sapient Slingshot and Sapient Bodhi within a broader enterprise operating model, we help organizations modernize the systems beneath AI, orchestrate governed workflows around it and bring human expertise into the loop where risk, accountability and compliance matter most.

Why regulated AI programs stall

A pilot may perform well in a controlled setting, but production introduces harder questions. Which data is authoritative? How are permissions enforced? Can outputs be explained and audited? Where do embedded business rules actually live? Who owns the workflow, the controls and the business outcome after go-live?

These questions become even more urgent in healthcare, financial services and life sciences, where core processes often run on decades-old platforms. Claims engines, batch feeds, payments modules and regulated content workflows still power the business, but they were rarely designed for APIs, real-time orchestration or AI-enabled decision support. Critical logic is often buried in legacy code, manual workarounds or institutional knowledge. When controls are added late and traceability is incomplete, promising pilots stall before they ever become trusted production capabilities.

What production-ready AI looks like in regulated environments

Production readiness in regulated industries requires more than model performance. It requires an operating foundation built for governance.

This is the difference between AI that demos well and AI that can operate inside claims processing, banking modernization, medical review and regulated content creation.

Sapient Slingshot: make buried business logic visible and usable

In regulated industries, modernization cannot be based on guesswork. If hidden rules are lost, compliance risk rises. If teams try to preserve them manually, transformation slows to a crawl.

Sapient Slingshot helps solve this by turning legacy code into usable enterprise context. It extracts buried business logic, maps dependencies, generates verified specifications and supports testing and modernization with full traceability. Instead of treating legacy environments as a barrier, Slingshot turns them into a source of insight that can guide safer, faster transformation.

That matters because AI can only scale if the systems underneath it are understood. When code becomes traceable, rules become testable and dependencies become visible, regulated organizations gain a more reliable foundation for modernization and future AI deployment.

This approach has already delivered measurable outcomes. In healthcare claims modernization, Publicis Sapient used Slingshot to help a leading U.S. healthcare company accelerate migration speed by 3x, modernize 10,000 screens and reduce modernization costs by 30 percent. Legacy COBOL was transformed into maintainable Java and React, functional specifications and test cases were generated automatically, and human-in-the-loop validation helped ensure quality and compliance throughout the process.

In banking, Publicis Sapient applied the same principle to a major retail and commercial bank’s complex mainframe environment. By analyzing more than 350 files and nearly half a million lines of code across critical programs, the team produced program overviews, detailed mappings and modern target-state specifications that product owners could validate 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.

Sapient Bodhi: orchestrate AI inside governed workflows

Once legacy logic and enterprise context are surfaced, the next challenge is operationalizing AI safely. In regulated industries, that means embedding AI into workflows with the right controls, permissions and oversight already in place.

Sapient Bodhi is Publicis Sapient’s enterprise AI platform for building, deploying and orchestrating agentic workflows in production environments. Bodhi connects agents to governed data with role-based access, observability and auditability from day one. It gives organizations a way to move beyond isolated tools and point solutions toward AI that works inside real business processes.

This is especially important in high-stakes environments, where AI cannot operate as a black box. Whether the workflow involves regulated marketing content, knowledge-intensive review or sensitive operational tasks, Bodhi helps organizations enforce control without losing speed. Governance is not bolted on after the fact. It is designed into how the workflow runs.

That approach is already proving its value in life sciences. For a global pharmaceutical company, Publicis Sapient deployed Bodhi in the client’s environment to transform regulated content creation at scale. 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 a broader healthcare marketing context, Bodhi also helped scale content production across more than 30 markets by using AI agents trained on brand, regulatory and medical context. The result was dramatically faster creation while maintaining governance controls needed for regulated communication.

Human-in-the-loop validation is not a fallback. It is a design principle.

In regulated industries, trust is built through oversight. That is why human-in-the-loop validation remains essential, even as AI accelerates more of the workflow.

Publicis Sapient applies human expertise at the points where judgment matters most: validating specifications, confirming business logic, reviewing generated outputs, governing exceptions and approving high-impact decisions before they move downstream. This balance allows organizations to automate the heavy lifting without automating away accountability.

The result is a more practical model for regulated transformation. AI increases speed, consistency and scale. Human experts provide the control, review and confidence needed to make that acceleration usable in production.

Designed for both speed and control from day one

The path to production in regulated industries is not about choosing between innovation and governance. It is about designing for both at the same time.

Publicis Sapient helps healthcare, financial services and life sciences organizations build AI programs on a stronger foundation: legacy systems understood, dependencies documented, governance embedded, workflows orchestrated and oversight built in before deployment begins. With Sapient Slingshot, buried logic becomes visible, testable and ready for modernization. With Sapient Bodhi, AI becomes operational inside governed workflows with traceability, role-based controls and observability built in.

That is how regulated enterprises move AI from pilot to production without compromising governance: by making control part of the architecture, not a constraint added later. The outcome is faster modernization, safer scale and AI that can stand up to the realities of the enterprise.