Modernizing Legacy Systems to Accelerate Enterprise AI in Oman and MENA

Across Oman and the wider MENA region, enterprise interest in AI is no longer the issue. Ambition is high. Investment is growing. Leadership teams across telecom, energy, financial services and government increasingly understand the opportunity to improve operations, launch new services and deliver better customer experiences. Yet many organizations still struggle to move from isolated pilots to enterprise-wide results. The reason is often not a lack of AI ideas. It is the reality of aging technology estates, fragmented data, undocumented logic and delivery models that were never designed for the speed, scale and coordination AI now demands.

That is why legacy modernization should not be treated as a separate IT agenda running alongside AI. It is the foundation for AI that delivers. When core systems are difficult to change, when business rules live inside old code, and when teams rely on manual workarounds to keep critical operations running, AI use cases remain constrained. They can demonstrate promise in pockets of the business, but they struggle to scale across real workflows, real governance requirements and real customer journeys.

In Oman and across MENA, this matters especially in sectors where complexity is highest. Telecom providers manage vast operational environments and customer touchpoints. Energy companies run mission-critical systems across distributed assets and supply chains. Banks and public sector organizations operate in regulated environments where traceability, control and resilience are essential. In each case, AI depends on a technology foundation that can expose business context clearly, connect systems reliably and support change without introducing unacceptable risk.

Many enterprises already use AI in parts of the business, yet only a small minority describe it as core to how the business operates. That gap between adoption and readiness is where modernization becomes decisive. Organizations may have capable models, strong use cases and executive sponsorship, but still lack the operating foundation required to capture value. Legacy systems slow delivery. Siloed architectures fragment data. Undocumented applications make it hard to understand what can be changed safely. And reactive IT operations create friction that undermines confidence in scaling new capabilities.

These barriers are common in enterprises that have grown through years of investment, customization and integration. The result is often a patchwork of platforms, tools and processes that keep the business running but make transformation harder and more expensive than it should be. AI then gets layered on top of operational complexity rather than being embedded into the fabric of the enterprise.

For leaders in Oman and MENA, the implication is clear: the path to enterprise AI is not just about model selection or pilot design. It is about making core systems, software delivery and operations fit for an AI-driven future.

Publicis Sapient approaches this challenge by treating modernization as a business acceleration strategy, not simply a technical clean-up exercise. The goal is to reduce fragility, remove delivery bottlenecks and create a foundation on which AI can operate with more context, control and speed.

A central part of that approach is Sapient Slingshot, Publicis Sapient’s AI-powered modernization and software development platform. Sapient Slingshot modernizes legacy systems by turning existing code into verified specifications and generating modern software with full traceability. This matters because many enterprises know their legacy systems are important, but not always how they work in sufficient detail to modernize them safely. By creating clearer specifications from existing applications, organizations can move faster with greater confidence, preserving critical business logic while reducing dependency on brittle legacy environments.

The result is not just refreshed technology. It is a more reliable foundation for software delivery itself. Publicis Sapient positions Sapient Slingshot as a way to accelerate the end-to-end software development lifecycle and modernization effort, enabling enterprises to ship faster and reduce the drag of technical debt. That faster delivery becomes strategically important when AI priorities change quickly and when organizations need to move from one-off prototypes to repeatable enterprise implementation.

Modernization creates value downstream in several ways. First, it makes business rules, workflows and dependencies more visible. That visibility is essential for AI systems that need trusted context to act effectively inside enterprise processes. Second, it improves the ability to connect data and workflows across teams, which is critical when organizations want AI to do more than assist individual users and instead support coordinated, cross-functional execution. Third, it gives engineering teams a faster and more governed path to building, testing and deploying new solutions.

This is where modernization and enterprise AI begin to reinforce each other. Publicis Sapient’s broader platform portfolio is designed around that progression. Sapient Bodhi enables organizations to build and orchestrate enterprise-ready AI agents and workflows with the context and governance needed to scale across real business operations. Sapient Sustain extends the value after go-live by helping enterprises automate and improve IT operations in increasingly complex environments, using AI to detect issues early, resolve incidents autonomously and reduce operational debt.

Together, these capabilities support a practical model for transformation: modernize the systems that constrain growth, build AI into workflows where it can create measurable value, and keep the environment resilient and efficient once those capabilities are live.

The opportunity is particularly relevant for organizations pursuing ambitious national and enterprise transformation agendas. In Oman, the collaboration being explored between Otech and Publicis Sapient reflects a shared focus on accelerating AI adoption, technology modernization and measurable business outcomes. It connects regional technology capabilities and local market understanding with global experience in complex transformation programs. That combination is important because enterprise AI works best when it is grounded in how organizations in the region actually operate.

Across MENA, Publicis Sapient’s work is built around helping ambitious organizations spend less time stuck in legacy systems, deliver faster and build operations that hold up under pressure. Its enterprise context graph, described as a living map of business systems, rules and workflows, strengthens that approach by giving AI deeper context into how the enterprise functions. For regulated and operationally complex sectors, that context is not optional. It is what helps make AI more precise, more governed and more useful in production.

For transformation leaders, modernization should ultimately be judged by what it unlocks. In practice, that means faster time to market for new products and services, lower operating costs, greater system resilience and better experiences for customers, citizens and employees. It means fewer manual handoffs, less dependence on undocumented systems and a clearer path from strategy to execution.

In banking, that can support more agile product innovation and more intuitive, AI-enabled customer experiences. In telecom, it can enable more responsive service delivery and more intelligent operations. In energy, it can reduce complexity across critical systems while improving reliability and operational efficiency. In government, it can help agencies modernize citizen services on foundations that are more secure, adaptable and scalable.

AI may be the headline, but modernization is what determines whether that headline becomes reality. For enterprises in Oman and MENA, the leaders who move first on legacy transformation will be better positioned to turn AI from experimentation into a core operating capability. The question is no longer whether AI matters. It is whether the enterprise is ready to support it. Modernizing legacy systems is how that readiness is built.

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