Legacy modernization is no longer a back-office cleanup project.

CIOs and engineering leaders know the pattern well. Critical operations still depend on aging core systems built long before APIs, real-time data flows and AI-enabled workflows became central to growth. The software still runs the business, but it also slows it down. Business rules live inside undocumented code. Dependencies are hard to see. Releases take too long, testing is manual and fragile, and every change introduces risk. In that environment, AI initiatives struggle to move beyond pilots because the systems they need to connect to are opaque, brittle and expensive to change.

That is why modernization matters now. Not because legacy is inherently bad, but because enterprise AI depends on clarity, control and execution speed. If teams cannot confidently understand how core systems work, they cannot safely expose workflows to agents, connect models to governed processes or scale automation across operations. AI needs context. It needs traceability. It needs systems that can evolve without destabilizing the business.

Publicis Sapient helps DACH organizations solve exactly this problem. With more than 30 years of experience solving complex operational challenges, we work side by side with enterprises in highly regulated, high-stakes environments to modernize how software gets built, reduce time trapped in legacy systems and create operations that are reliable, secure and built to last. Our teams combine strategy, engineering and data and AI expertise to help organizations move from stalled modernization and scattered pilots to technology foundations that are ready for production-scale AI.

At the center of this approach is Sapient Slingshot. Slingshot modernizes legacy systems by turning existing code into verified specifications and generating modern software with full traceability. Instead of forcing a blind rewrite, it helps teams surface the logic already embedded in their systems, preserve critical business rules and accelerate delivery with greater confidence. That matters because most enterprise risk in modernization does not come from the code alone. It comes from what the code is hiding: years of undocumented decisions, exceptions and operational dependencies that the business still relies on every day.

Slingshot is designed to make those hidden constraints visible. It helps extract business logic from legacy systems, map dependencies and make that knowledge testable. It uses enterprise context to uncover how systems, rules and workflows connect, creating a clearer path from old architecture to modern software. This is not modernization for its own sake. It is modernization that protects continuity while making future change faster, safer and more measurable.

The impact is practical and immediate. When undocumented logic becomes visible, teams reduce the risk of breaking critical operations. When dependencies are mapped, leaders can prioritize what to modernize first and avoid creating new complexity. When verified specifications guide code generation, delivery accelerates without losing control. And when modernization is traceable, engineering leaders can explain what changed, why it changed and how it connects back to business requirements.

RWE’s modernization story shows what this looks like in practice. Facing an aging application with no documentation, RWE used Slingshot to modernize the software in two days rather than two weeks. The work restored reliability and reduced operational risk in days instead of months. The results also showed measurable engineering gains, including roughly 40 percent time savings in automated code generation and roughly 35 percent efficiency gains in test creation and unit test setup. More importantly, the effort proved that even systems with buried logic and limited documentation can be modernized without losing the business knowledge they contain.

That is a powerful lesson for DACH enterprises. In sectors such as energy, financial services, mobility and retail, core systems often carry years of hard-won operational logic. Replacing them carelessly creates unacceptable exposure. Leaving them untouched creates a different risk: slower delivery, rising maintenance cost and an AI agenda that never reaches production. Modernization offers a third path. By surfacing rules, validating dependencies and preserving what matters, organizations can reduce operational risk while improving the speed and quality of change.

This foundation is also what makes enterprise AI genuinely scalable. Publicis Sapient’s platform approach is built around three hard enterprise problems that block AI at scale: legacy modernization, production-ready agentic solutions and resilient operations. Slingshot addresses the first by modernizing and building software. Bodhi addresses the second by building and orchestrating enterprise-ready AI agents with the context, governance and controls needed for real workflows. Sustain addresses the third by keeping enterprise technology running efficiently and resiliently once it is live. Together, they create a path from legacy constraint to AI-enabled execution.

But the sequence matters. Agentic AI cannot deliver meaningful value if the workflows it touches are trapped inside brittle, undocumented systems. Better operations cannot scale if every release remains slow and risky. Governance is difficult to enforce when lineage is unclear and business logic is invisible. Legacy modernization creates the conditions for all of that to change. It gives enterprises a living map of systems, rules and workflows. It gives engineering teams modern software with traceability. And it gives business leaders a more stable platform on which AI and operations improvements can compound over time.

For CIOs in DACH, the message is straightforward: the fastest route to enterprise AI is often through the systems that seem furthest from it. Modernizing legacy architecture reduces risk, accelerates delivery and creates the foundation for governed, production-scale AI. For engineering leaders, it replaces uncertainty with visibility and brittle release cycles with a more executable path forward.

AI that delivers is not built on top of guesswork. It is built on systems teams can understand, change and trust. With Sapient Slingshot, Publicis Sapient helps organizations uncover what legacy systems are really doing, preserve the business logic that matters and generate modern software with the traceability enterprise transformation demands. That is how legacy modernization becomes more than a technology initiative. It becomes the foundation for agentic AI, stronger operations and a business that can keep moving forward with confidence.