From pilot to production: the enterprise operating model for AI at scale

AI is already showing up across teams, tools and workflows. In Publicis Sapient’s 2026 Global Enterprise AI Report, 73% of decision-makers said AI is used regularly or across most business processes. Yet only 10% said AI is core to how their business operates. Nearly half said AI is already capable of meeting today’s business needs, while 42% said their organizations are not set up to capture its value.

That gap matters. It explains why so many organizations can point to promising pilots, productive teams and isolated wins, yet still struggle to generate enterprise-wide returns. Adoption alone does not equal transformation. AI creates measurable value only when the business changes around it: the systems it runs on, the workflows it supports, the governance that controls it and the operating model that turns local activity into scalable execution.

The real barrier to AI ROI is organizational, not technical

Most enterprises have already proven that AI can work. What they have not done is redesign the enterprise so AI can work consistently, safely and at scale.

In many organizations, the same barriers keep resurfacing. Legacy systems trap critical business logic in aging code. Data remains fragmented across functions. Teams adopt different tools and frameworks with no shared orchestration. Governance is added late, after pilots are already underway. And ownership becomes unclear once a model or agent moves into production.

The result is predictable: AI remains active, but not compounding. Gains stay trapped inside functions instead of improving how the enterprise runs as a whole. One workflow gets faster, but downstream decisions, approvals and operational processes do not change with it. The enterprise absorbs activity, not advantage.

What an enterprise operating model for AI actually requires

For AI to create returns inside the business, organizations need more than use cases and model access. They need a coordinated way to connect strategy, product thinking, engineering execution, governed data and operational oversight.

That starts with clear priorities and clear ownership. Leaders need to define the business outcomes that matter, identify the systems and workflows constraining them and decide where AI can operate safely and effectively. From there, they need the delivery model to turn those decisions into production systems: governed architectures, traceable lineage, embedded controls, model monitoring and continuous operational accountability.

The People + Products model: combining human expertise with enterprise-scale platforms

The principle is simple: the greatest value comes from combining human expertise and judgment with intelligent technology. Human teams bring business understanding, transformation experience, delivery discipline and change leadership. Products bring speed, repeatability, orchestration and enterprise context. Together, they help organizations move beyond experimentation and redesign how work gets done.

This matters because enterprise AI is never just a tooling decision. It requires choices about priorities, workflows, governance, systems integration and how people and machines work together. Publicis Sapient’s model brings those decisions into one execution framework so organizations can modernize foundations, embed AI in real workflows and keep operations resilient after deployment.

That model is strengthened by Publicis Sapient’s SPEED capabilities: Strategy, Product, Experience, Engineering, and Data & AI. Rather than treating AI as a standalone initiative, SPEED connects business ambition to product design, technical implementation, user adoption and governed intelligence. It helps ensure that AI is not only launched, but operationalized.

Why context is the difference between activity and return

AI underperforms when it operates without the context of the business around it. Models may generate outputs, but they cannot create durable value if they are disconnected from business rules, systems, approvals, dependencies and decision paths.

That is why enterprise context is so important. Publicis Sapient’s approach connects systems, data, rules and workflows so AI can operate against the realities of the business rather than in abstraction. Shared context reduces duplication, improves traceability and allows intelligence to compound across deployments instead of resetting with each new initiative.

It also supports governance where it matters most: in production. As AI scales, leaders need visibility into what is running, what it can access, how it performs and whether it follows policy. Governance cannot be a separate workstream. It has to be embedded into the platform and operating model from day one.

Three execution paths for enterprise AI transformation

Most organizations trying to capture value from AI face three barriers at once. Their technology foundation is too old to build on. Their AI initiatives struggle to move from demo to production. And their operations become harder to manage as complexity grows. Publicis Sapient addresses those barriers through three connected execution paths.

1. Modernization with Sapient Slingshot

AI cannot become core to the business if the core is locked inside fragile legacy environments. Sapient Slingshot helps organizations modernize legacy systems and build software across the development lifecycle by reading existing code, recovering buried business rules and rebuilding on top of them with traceability and lower risk.

This is foundational work for AI at scale. It frees critical logic from undocumented systems, reduces technical debt and creates a technology estate that can support APIs, real-time data and intelligent workflows. Results already show the impact: a global bank turned nearly three million lines of COBOL into audit-ready specifications in eight weeks, cutting manual code-to-spec effort by 70% to 85% and reaching 95% specification accuracy. In healthcare and energy environments, modernization has also delivered faster migration, lower costs and quicker generation of production-ready software.

2. Agentic workflows with Sapient Bodhi

Many enterprises know where AI could add value. The harder problem is making it work across real workflows with the context, orchestration and governance required for production. Sapient Bodhi is built for that shift.

Bodhi enables organizations to design, deploy and orchestrate intelligent agents across systems, functions and workflows. It is cloud-agnostic, multi-model and designed to work with existing enterprise systems rather than forcing rip-and-replace change. Most importantly, it helps enterprises move from isolated pilots to coordinated, repeatable operating capability.

That is how AI begins to create compounding value. A global CPG leader used Bodhi to automate content creation and produce more than 700 assets in two months, with 60% reuse across brands. In regulated health environments, Bodhi has helped accelerate content production, improve compliance and reduce costs while embedding AI inside real production workflows rather than leaving it at the experimentation stage.

3. Resilient operations with Sapient Sustain

As enterprises modernize and scale AI, operational complexity rises. Traditional support models are too reactive, too manual and too costly to keep pace. Sapient Sustain helps organizations shift toward more autonomous, resilient IT operations.

Sustain works across existing tools and environments to anticipate issues, resolve known problems automatically and improve reliability without heavy human oversight. It brings context-aware monitoring, self-healing automation and real-time visibility into live operations so teams can spend less time firefighting and more time improving systems.

The business impact is direct. Nissan used Sustain to add AI-powered monitoring and automation without disrupting its existing stack, achieving 62% same-day issue resolution, maintaining 99.9% platform uptime and reducing operational costs by 40%.

From AI adoption to enterprise execution

The next phase of AI will not be defined by how many pilots an organization launches. It will be defined by whether the enterprise can absorb AI into how it works.

That means modernizing the systems beneath the business, embedding AI into governed workflows and building operations that can sustain growing complexity. It means shifting from fragmented tools to coordinated capability. And it means designing an operating model where strategy, product, engineering, data and governance work together from the start.

Publicis Sapient is built for that challenge. With more than 30 years of enterprise transformation experience, 20,000 people worldwide and platforms shaped by deep business and industry context, Publicis Sapient helps organizations move from experimentation to measurable execution.

The organizations that win with AI will not be the ones that adopt it first or talk about it most. They will be the ones that redesign work, technology and operations so AI becomes part of the enterprise core. That is how pilots turn into production. And that is how AI starts delivering real returns.