From Legacy Modernization to Autonomous Run: A Continuous Model for Enterprise AI Transformation
Enterprise AI value rarely comes from isolated use cases alone. Pilots can prove technical possibility, but they often fail to change how the business actually operates. The bigger opportunity comes when modernization, orchestration and IT operations are connected as one transformation model—so AI can move from point solutions to enterprise execution.
That shift matters because many organizations are already using AI, yet only a small minority see it as core to how their business runs. The gap is not simply model performance. It is enterprise readiness: legacy systems, fragmented workflows, disconnected data, inconsistent governance and operating models that were never designed for the speed and autonomy AI now makes possible.
Publicis Sapient addresses that gap with a connected platform approach built for the full lifecycle of transformation. Sapient Slingshot helps organizations modernize core systems and extract the business logic embedded inside them. Sapient Bodhi provides the orchestration, context and governance needed to design and run intelligent agents and workflows at scale. Sapient Sustain extends the lifecycle beyond deployment, helping enterprises keep digital operations resilient through predictive, self-healing IT operations. Together, these platforms support a continuous path from legacy modernization to autonomous run.
Why enterprise AI needs a lifecycle model
Many enterprises do not lack AI ambition. They lack the foundations that allow AI to scale safely and create durable business outcomes. When business logic remains trapped in legacy applications, when workflows are disconnected across functions and when post-launch operations remain reactive, AI stays fragmented. Results may appear inside a team or a use case, but they do not compound across the enterprise.
A lifecycle model changes that. It treats AI transformation not as a sequence of disconnected projects, but as a system: modernize the estate, connect the workflows, govern the intelligence and sustain performance after go-live. This is how organizations move from experimentation to execution—and from execution to resilience.
Sapient Slingshot: modernize with context, speed and lower risk
For established enterprises, modernization is often the first barrier to AI value. Critical systems may be decades old, costly to maintain and difficult to evolve, yet they still contain the rules, dependencies and institutional knowledge the business depends on every day. Replacing them without understanding that logic introduces risk. Leaving them untouched limits speed.
Sapient Slingshot is designed to break that tradeoff. It uses AI to read, interpret and extract business rules from legacy systems, turning existing code into verified specifications with full traceability. Its enterprise context graph creates a living map of code, architecture, data, business rules and operational dependencies, enabling teams to make more informed decisions while preserving continuity across the software development lifecycle.
The impact is measurable. In healthcare, Sapient Slingshot helped a large healthcare benefits provider modernize more than 10,000 legacy COBOL screens, delivering migration work three times faster while reducing modernization costs by 50%. In another healthcare example, a major organization compressed what had been a ten-year COBOL modernization effort into three years. In retail, the platform helped a major Latin American retailer standardize code generation and built-in testing during re-platforming, improving consistency, reducing rework and cutting development effort by 20% to 30%.
These outcomes show why modernization is more than a technical prerequisite. It is the stage where enterprises recover the context AI will need later—business rules, process logic and system relationships—while reducing the risk and cost of change.
Sapient Bodhi: turn AI capability into governed enterprise execution
Once the foundation is in place, the next challenge is scaling intelligence across real workflows. Enterprises need more than access to models. They need a way to build, orchestrate and govern agents that can operate across business processes, tools and teams. They need security, observability and the flexibility to work across major cloud environments and frameworks.
Sapient Bodhi is the orchestration layer for that next phase. It enables organizations to design, build and run enterprise-ready AI agents and workflows with the governance and business context required for production use. Bodhi is built to help enterprises move beyond experimentation and deploy autonomous systems that can reason, collaborate, use tools, execute tasks and improve over time—while remaining secure, observable and aligned to enterprise controls.
The value becomes clear when AI is applied to high-volume, high-complexity work. A leading pharmaceutical company used Bodhi to transform its content production process, accelerating content creation by 75% and reducing costs by up to 45%. In another global pharmaceutical use case, Publicis Sapient deployed a scalable generative AI solution for personalized marketing content generation, supporting localization, repurposing and faster time to market while reducing content creation costs by an estimated 35% to 45%.
Bodhi also reflects a larger truth about enterprise AI transformation: governance cannot be bolted on later. Intelligent workflows need context, guardrails and orchestration from the start if they are going to scale across regulated, distributed and business-critical environments.
Sapient Sustain: keep operations resilient after deployment
Transformation does not end at launch. In many enterprises, the post-go-live period is where value erodes—through rising operational debt, slower incident response, fragmented support models and inconsistent customer experiences. As AI and digital systems scale, IT environments become more distributed and harder to manage. Traditional automation often lacks the context and coordination to keep pace.
Sapient Sustain is built for this phase of the lifecycle. It brings agentic AI into IT operations to help enterprises detect issues early, resolve incidents autonomously and prevent recurring failures. Its foundation includes an enterprise context graph that correlates signals across tickets, logs and systems, self-healing workflows built on a service map, a consolidated knowledge base for faster action and predictive models that surface problems before they affect the business.
The automotive sector provides a strong example of what this looks like in practice. A global auto manufacturer used Sapient Sustain to reduce operational costs by 40% and achieve a same-day issue resolution rate of more than 62%. In another automotive deployment, Nissan used AI-driven capabilities to proactively identify and remediate high-impact issues in business-critical application maintenance, helping shift operations from reactive support toward predictive, continuously improving performance. That work contributed to a 40% reduction in operational costs, a 62%+ same-day resolution rate, an 80% shift from reactive to proactive operations and 99.99% platform uptime.
This is the final step in continuous transformation: not just building digital systems faster, but keeping them reliable, efficient and ready to evolve.
One connected architecture for enterprise change
The power of this approach is not in any single platform by itself. It is in the way the lifecycle connects. Slingshot helps recover and structure business logic from complex legacy environments. Bodhi uses that enterprise context to power intelligent agents and governed workflows. Sustain extends the same logic into operations, where context-aware AI can protect uptime, reduce operational debt and improve service continuity.
That is how AI begins to compound. Modernization informs orchestration. Orchestration improves execution. Execution feeds more resilient operations. And operations create the stability needed for the next wave of change.
For healthcare, pharma, automotive and retail organizations facing pressure to move faster while reducing cost and risk, this continuous model offers a more practical path forward. Enterprise AI delivers its full value when it is connected to how the business is built, how work is executed and how systems are sustained over time. That is the journey from legacy modernization to autonomous run—and the foundation for enterprise transformation that lasts.