Agentic AI for regulated sectors in the UAE and emerging markets

In public sector, healthcare and other regulated industries, the promise of AI is no longer theoretical. Leaders are under pressure to modernize legacy systems, improve service quality and reduce friction across complex workflows—while still meeting strict expectations for privacy, governance, traceability and operational resilience. In the UAE and across emerging markets, that challenge is even more strategic. Organizations are moving quickly toward AI-enabled operations, but they must do so on infrastructure and operating models that respect sovereignty, local policy requirements and the realities of enterprise-scale delivery.

This is where agentic AI becomes meaningful.

Agentic systems can do more than generate content or answer questions. When grounded in enterprise context, they can reason across workflows, coordinate actions, use tools, interact with systems and support people in executing work end to end. For regulated organizations, that opens a path to automate high-friction processes, improve experiences for citizens, patients and employees, and unlock value from fragmented data and legacy technology estates.

But success depends on more than models. In these sectors, AI only works when it is connected to modern data foundations, embedded in real operating processes and deployed with governance from the start.

In the UAE and many emerging markets, digital transformation is happening alongside growing requirements around data localization, privacy, resilience and control. Public institutions, healthcare organizations and regulated service providers cannot treat AI as a generic layer added on top of existing complexity. They need architectures that support secure deployment, trusted data handling and auditable decision flows.

That is why sovereign infrastructure matters. It gives organizations greater confidence over where data resides, how models are operated and how services are governed. But infrastructure alone is not enough. Enterprise context matters just as much.

An AI agent deployed in a hospital, public service workflow or regulated operations environment must understand business rules, policy constraints, exceptions, dependencies and the systems around it. Without that context, automation remains shallow. With it, organizations can move beyond pilots and build AI systems that help handle real work in production.

For public sector organizations, agentic AI can help orchestrate services that often span departments, systems and manual review steps. That can include intake and triage, case routing, exception handling, document-heavy workflows, knowledge support for frontline teams and more responsive citizen service experiences. When paired with the right data and platform foundation, AI agents can help reduce administrative burden, improve service consistency and create more transparent operating models.

In healthcare, the opportunity is equally significant. Many organizations still depend on fragmented applications, manual handoffs and legacy systems that slow everything from claims and benefits administration to patient communications and internal operations. AI can support faster modernization of those core systems while also improving how healthcare organizations create, localize and govern regulated content. It can help teams move faster without losing compliance discipline.

The same is true across regulated services more broadly. Financially sensitive, operationally critical and compliance-heavy environments all share a common need: automate what is repetitive, augment what is complex and preserve human oversight where judgment matters most.

Most AI programs stall for a familiar reason: the use case gets attention before the foundation is ready. In regulated sectors, AI value depends on clean data access, strong architecture choices, system interoperability and an operating model that can sustain change.

A modern data foundation allows organizations to qualify the highest-value opportunities, assess readiness and connect AI into business workflows instead of isolating it in experiments. It also supports the governance layer that regulated sectors need—clear controls, observable processes, protected data and confidence in how outcomes are produced.

This is especially important in the UAE and emerging markets, where organizations are often balancing rapid digital ambition with varied regulatory environments, cross-border considerations and different levels of technology maturity across agencies, enterprises and ecosystems. The right foundation enables local compliance without sacrificing speed.

In regulated environments, speed alone is not a differentiator. Trusted execution is.

That means AI systems must be designed with human-in-the-loop validation, policy-aware orchestration and full traceability across the workflow. Organizations need to know how outputs were created, what rules were applied, which systems were involved and where human review took place. For many use cases, explainability and observability are not optional features. They are operational requirements.

This is also why responsible AI principles, safeguards and data protections need to be built into the platform layer, not bolted on later. Enterprise AI should support security, privacy and accountability by design so that teams can innovate with confidence.

Publicis Sapient brings more than 30 years of digital business transformation experience to this challenge, combining strategy, product, experience, engineering, and data and AI capabilities to help organizations identify, build and scale the right AI solutions. The focus is not on experimentation for its own sake. It is on measurable business outcomes.

That work spans enterprise strategy and roadmap development, readiness assessment, implementation and the creation of self-sufficient AI operating models. It also includes the delivery depth needed to take complex programs from concept to production in large, regulated organizations.

Publicis Sapient’s platform approach is central to that execution.

Sapient Bodhi is an enterprise-scale agentic AI platform designed to build and run AI agents with the orchestration, context and governance required for real business workflows. It supports secure, scalable deployment while helping organizations simplify complex processes, accelerate solution delivery and maintain compliance-sensitive controls. With specialized agents, multi-agent architecture and enterprise-grade privacy and security measures, Bodhi is designed to help regulated organizations move beyond narrow automation toward intelligent, governed orchestration.

Sapient Slingshot addresses another critical challenge in public sector and healthcare transformation: legacy modernization. Many organizations cannot scale AI effectively because core systems remain too brittle, opaque or expensive to evolve. Slingshot helps modernize legacy environments by transforming existing code into verified specifications and generating modern software with traceability across the software lifecycle. That preserves institutional knowledge while reducing risk and accelerating delivery.

This matters in regulated sectors where legacy systems often contain decades of embedded business rules and operational logic. Rather than treating modernization and AI as separate agendas, Publicis Sapient helps organizations connect them.

This approach has already delivered results across enterprise environments where governance and complexity matter.

In healthcare, Slingshot helped a large healthcare benefits provider modernize more than 10,000 legacy COBOL screens, accelerating delivery by 3x and reducing modernization costs significantly. In financial services, Publicis Sapient used a GenAI-driven modernization approach to analyze hundreds of files and nearly half a million lines of code for a major bank, improving migration speed and reducing manual effort in code-to-spec work. In regulated content operations, Bodhi enabled a global pharmaceutical company to automate the creation and localization of marketing collateral, reducing content creation costs by 35% to 45% and accelerating time to market while maintaining compliance-sensitive workflows.

These are not isolated pilots. They reflect a repeatable model for modernization, orchestration and enterprise AI deployment in environments where risk, scale and accountability are inseparable.

For leaders in the UAE and emerging markets, the next chapter of AI is not about adding more tools. It is about building the right foundation for trusted execution.

In public sector, healthcare and regulated services, the organizations that create value first will be those that treat sovereignty, governance and enterprise context as core design principles—not constraints. With secure infrastructure, modern data foundations, agentic orchestration and deep delivery expertise, it becomes possible to automate complex workflows, improve citizen and patient experiences and reduce friction across the enterprise without compromising control.

That is the opportunity Publicis Sapient helps clients capture: AI that is not only innovative, but governable, traceable and built to deliver in the real world.

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