Why cloud modernisation is the missing link to AI adoption

In the last decade, enterprises migrated thousands of workloads to the cloud for elasticity and lower infrastructure costs.

Nevertheless, most workloads behave as they did on-premises: tightly coupled, batch-oriented, and blind to data in motion. This creates high virtualised technical debt with few systems ready for AI. IT leaders should identify and prioritize refactoring critical applications. Early steps include adopting containerization, implementing DevOps, and exploring data integration for real-time flow — establishing an agile, AI-ready ecosystem.

This growing intent to re-architect for AI highlights a critical gap between cloud adoption and true cloud modernisation. To better understand what is getting in the way, it’s important to recognize a fundamental insight:

Migrations ≠ Modernisations, where Migration offers quick lift-and-shift cloud hosting, but not the value of true Modernisation.

From Lift & Shift to AI-Native

Moving your servers from a data center to the cloud gives you the capacity to scale, but not the capability to respond to new needs driven by AI. An AI system requires more than capacity; it demands an environment that is:

Elastic by design, not by manual scaling script
Data-centric, not just storing data in multiple silos.
Automated, as AI models learn continuously
Observable, as failures must be self-healing
Governed, as compliance, security, and privacy need to be part of the architecture, not an afterthought

The bridge between the two words is modernisation. When done properly with microservices, event streaming, externalised state, and robust CI/CD and MLOps pipelines, AI can evolve alongside your business and code. For instance, consider a retail enterprise that integrates microservices architecture and event streaming. By breaking down its monolithic applications into microservices, the company can quickly deploy new features, such as personalized AI-driven product recommendations, to optimize the shopping experience in real time. Event streaming further enhances this by continuously processing customer data, allowing the AI system to refine its recommendations as shopping trends evolve.

5 Main Drivers

There are 5 main drivers of modernisation: speed, data, cost, resilience, and innovation. When you tackle all of them in a cloud modernisation exercise, you obtain a cloud-native solution, ready for AI that has continuous deployment (including integrated MLOps), event stream fabric, FinOps, and AI for FinOps in place, predictability via AIOps, a self-healing mechanism, and the capability to ship new AI features easily.

IT leaders can prioritize these drivers based on specific business needs and strategic goals. For example, they might begin with data integration to ensure real-time analytics, then enhance resilience to minimize downtime, focus on cost optimization to improve financial efficiency, and finally drive innovation to create a competitive advantage. This prioritisation approach enables a structured, strategic sequence of modernisation efforts that aligns technology with business objectives.

Cloud Modernisation is NOT Optional

To become AI-Native, an enterprise needs to learn, adapt, and optimise the current IT ecosystem. When cloud is part of your technical stack, this journey does not start with an LLM or a large AI component. It starts with automation, refactoring, PaaS adoption, and data readiness.

Depending on your cloud ecosystem, it means fewer EC2 (Elastic Compute Cloud)/Azure VMs (Virtual Machines) and more EKS (Elastic Kubernetes Service), AKS (Azure Kubernetes Service), Fabric (cloud integration service), and EventBridge (event bus for event-driven apps), with EventStream and EventHub (real-time data streaming platforms) becoming the backbone of your enterprise architecture.

When selecting technology services, leaders should consider scalability, integration capabilities, cost, and specific business requirements. For instance, if your operations demand high scalability and flexibility, services like EKS or AKS might be more suitable. On the other hand, if seamless data integration across multiple platforms is your priority, solutions like Fabric or EventBridge can offer significant advantages. It’s crucial to align technology choices with not only immediate technological needs but also long-term strategic goals.

Conclusion

Intelligence demands architecture, not just compute. Tomorrow’s AI can’t run on yesterday’s systems. Cloud modernization is the essential link, enabling us to innovate and fully capitalize on cloud-native intelligence.

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