Enterprise AI Transformation in North America

Across North America, enterprise ambition around AI is no longer the question. Adoption is moving fast. Investment is real. Teams are already using AI in daily work. But for many large U.S. enterprises, the harder truth is setting in: the real barriers are not model availability or pilot activity. They are organizational design, disconnected workflows and aging technology estates that were never built for the speed, scale and autonomy AI now makes possible.

That shift is especially visible in the U.S., where advanced adopters increasingly recognize that the constraint is not AI itself, but the way the enterprise runs. Organizations want returns from AI now, yet too often the foundations beneath those ambitions remain fragmented. Data is scattered. core systems are hard to change. Governance arrives late. Operations teams are already stretched keeping complex environments stable. The result is familiar: pilots succeed, but enterprise impact stalls.

Enterprise AI transformation in North America means closing that gap. It means modernizing the systems AI depends on, connecting AI to real business workflows and running increasingly complex environments with resilience. That is how AI moves from promising experimentation to measurable execution.

North America’s AI challenge is now a readiness challenge

Many North American enterprises do not need more proof that AI can work. They need a better enterprise environment for AI to work inside. In practice, that means addressing three connected barriers at once:

These are not separate issues. They compound. Legacy technology slows workflow redesign. Disconnected workflows limit AI value. Fragile operations make every new deployment riskier. North American enterprises need a transformation model that addresses all three together.

1. Modernize the legacy estate without losing what makes the business work

For many U.S. enterprises, especially in healthcare and banking, legacy modernization is no longer a background IT program. It is the foundation of enterprise AI readiness. Critical business rules are often buried in decades of undocumented code. Teams know those systems are expensive to maintain and hard to adapt, but replacing them without preserving what matters most introduces risk the business cannot accept.

This is where modernization has to become more intelligent. Sapient Slingshot is built to read existing code, recover the logic inside it and turn that knowledge into verified specifications and modern software with traceability. That allows enterprises to move faster while preserving the business rules, dependencies and operational knowledge that cannot be lost.

The impact is already clear in complex, high-stakes environments. A healthcare organization used Slingshot to modernize critical legacy systems and move from COBOL gridlock to cloud-native delivery, achieving 3x faster migration of legacy applications and cutting modernization costs by more than 50 percent. In financial services, a major bank used Slingshot to turn nearly three million lines of legacy COBOL into audit-ready specifications in eight weeks, reducing manual code-to-spec effort by 70 to 85 percent and reaching 95 percent specification accuracy.

For North American buyers, this matters because AI cannot become core to the enterprise if the enterprise core remains trapped in systems that resist change. Modernization is what clears the path.

2. Move AI from isolated pilots into agentic workflows

In North America, enthusiasm for AI is high, but many enterprises are discovering that deploying a model is not the same as redesigning a workflow. The value gap usually appears in the handoffs: between teams, between systems and between insight and action. An agent may generate output, but without business context, governance and orchestration, that output never becomes reliable enterprise execution.

Sapient Bodhi is built for that next step. It helps enterprises design, deploy and orchestrate agents across systems, data and workflows, with centralized governance and built-in business context. Rather than forcing organizations into a rip-and-replace model, it works with existing enterprise environments and supports a more practical path from pilot to production.

This is especially relevant in regulated North American industries, where organizations need speed without losing control. In healthcare and pharma, for example, content, compliance and approval workflows are too complex for generic AI tools alone. A global pharmaceutical company used generative AI to streamline content creation, improve speed and consistency and maintain regulatory compliance, delivering 75 percent faster content production and up to 45 percent cost reduction. In another large-scale content transformation, a global CPG leader used Bodhi to create more than 700 assets in two months while achieving 60 percent reuse across brands.

The lesson for North American enterprises is direct: AI scales when it is connected to real workflows, grounded in enterprise context and governed as part of the operating model. Otherwise, organizations accumulate tools instead of capabilities.

3. Run complex environments with autonomous resilience

As modernization accelerates and AI spreads across the business, IT operations become even more important. North American enterprises are managing hybrid estates, growing integration complexity and rising expectations for uptime, speed and service continuity. In that environment, traditional operations models spend too much time reacting to incidents and too little time improving the system.

Sapient Sustain is designed to shift IT from reactive support to context-aware, increasingly autonomous operations. It works on top of the tools enterprises already use, helping teams detect issues earlier, resolve known problems automatically and reduce the human-heavy effort required to keep technology running well.

That change can produce meaningful business results. In a large automotive environment, AI-powered monitoring, self-healing automation and real-time visibility helped achieve 62 percent same-day issue resolution, maintain 99.9 percent platform uptime and reduce operational costs by 40 percent. For enterprises with complex estates, this kind of resilience is not an operational nice-to-have. It is what makes broader AI transformation sustainable.

Why this matters now in North America

North America is a market defined by urgency. Enterprises want faster returns, stronger resilience and clearer paths from AI ambition to enterprise value. But the organizations that pull ahead will not be the ones running the most experiments. They will be the ones that modernize the systems beneath the business, embed AI into the workflows that create value and keep the whole environment operating with less fragility and lower operational drag.

Publicis Sapient brings together more than 30 years of enterprise transformation experience, deep industry knowledge and three purpose-built platforms to help organizations make that shift. For healthcare organizations modernizing critical claims and service systems, for banks recovering logic from aging COBOL estates and for large enterprises trying to reduce operational debt while scaling AI, the goal is the same: turn AI investment into real returns by transforming the enterprise around it.