For CIOs, cloud is now the enterprise operating layer, says Infosys’ Anant Adya

Cloud adoption has moved beyond migration and cost optimization to shape how enterprises build, run, and scale their core systems. In a recent conversation with TechCircle, Anant Adya, EVP and Service Offering Head at Infosys, outlines how cloud has evolved into a foundational operating model, enabling composable architectures, AI readiness, and industry-specific transformation. He also explains why governance, regulation, and digital sovereignty are becoming central considerations for CIOs as cloud matures across regions and sectors.

Edited Excerpts:

How has cloud shifted from being an enabler to an operating model for enterprises, especially for CIOs dealing with legacy systems and technical debt?

When we launched Infosys Cobalt in 2020, the positioning was clear that cloud was not a single product or platform but a collection of services, solutions, and platforms designed to accelerate enterprise cloud adoption. At that stage, most enterprises were focused on what was effectively a “save and optimize” agenda. This involved exiting data centers, migrating workloads to cloud environments, shifting from capital expenditure to operating expenditure, and beginning to build applications using cloud-native technologies. A large part of this phase also involved legacy modernization, with enterprises looking to move away from platforms such as mainframes, Solaris, AIX, and AS400.

As enterprises progressed, the focus gradually shifted from cost and efficiency to growth and disruption. Cloud began to be used not just to optimize IT operations but to drive revenue, build new products, and enter new markets. Between 2020 and 2025, the question for enterprises shifted from whether they were using the cloud to where it could be applied across the organization.

Today, the cloud has become pervasive across enterprise operations. It functions as the foundational operating fabric for enterprises that want to innovate, build products, and scale digital capabilities. In the current AI-driven environment, enterprises are focused on accelerating value from AI. To do that, they first need their data to be AI-ready. That, in turn, requires composable, cloud-native applications running on secure and scalable hybrid cloud environments. From infrastructure and networking to data platforms and AI workloads, the cloud has become the foundation that enables all these layers to work together. In that sense, the cloud has become the enterprise operating system.

From a client standpoint, how has adoption evolved from early technology transformation to business-driven cloud programs?

The earliest cloud engagements were largely technology-focused. These were centered on transforming IT and application landscapes using cloud infrastructure, and this continues to represent a significant portion of cloud work today. Over time, enterprises began to expect more than infrastructure transformation. They wanted partners that understood their industries and business processes.

This led to the development of industry-specific cloud solutions aligned to sectors such as financial services, healthcare, retail, consumer goods, manufacturing, media, and high tech. These solutions were further refined into sub-segment offerings, for example, within financial services, where banking, capital markets, and investment banking have different requirements.

In parallel, Infosys began leveraging its industry platforms, such as Finacle for banking, Helix for healthcare, and Equinox for supply chain and commerce. These platforms were deployed on the cloud to co-create solutions and drive innovation with clients. At the same time, enterprises were independently investing in cloud, data, AI, IoT, 5G, and edge technologies, often without a clear way to integrate them.

The next phase of evolution focused on bringing these technologies together into unified solutions designed around business outcomes. The approach combined technology transformation, industry solutions, and platform-based innovation, with an explicit focus on delivering measurable business results rather than isolated technology implementations. This combination defines how cloud offerings have evolved over the past five years.

Composable architecture is often discussed abstractly. What are the most tangible benefits for enterprises?

Composable architecture is rooted in the idea of building systems that do not become obsolete over time. The fundamental principle is that enterprise architectures—applications, infrastructure, and platforms—should be designed so they do not reach an end-of-life state or require large-scale replacement.

At the foundation is a hybrid cloud environment that combines public, private, and edge infrastructure. This layer focuses on scalable compute, high-performance storage, security built into the architecture, and cloud-based networking that does not rely on physical appliances or frequent hardware refreshes. This establishes a stable and high-performing digital infrastructure layer.

Above that, applications are built using cloud-native technologies such as Kubernetes, managed container platforms, and platform-as-a-service offerings from hyperscalers. This abstracts dependencies on specific databases or middleware, ensuring applications remain current and portable. The next layer consists of data platforms and data fabrics, which can be built using technologies from providers such as Microsoft, Databricks, or Snowflake. Above the data layer sit business processes and AI capabilities.

In practical terms, composable architecture means systems are built from modular, interchangeable components. Enterprises can replace or enhance individual parts without re-architecting the entire environment. This makes scaling, modernization, and innovation more manageable. In some cases, clients have moved the majority of their applications to platform-as-a-service and software-as-a-service models, reducing operational complexity and increasing their ability to launch new products and services.

Does composability reduce vendor lock-in, or does it risk increasing architectural sprawl if not governed properly?

If cloud is used primarily as another data center, with most workloads running on infrastructure-as-a-service, enterprises can still face vendor lock-in and architectural sprawl. This often happens when multiple services are adopted without a coherent architectural framework or governance model.

When composability is applied using cloud-native principles, the outcome is different. Architectures designed to be modular and portable reduce dependence on any single platform. This makes it easier to move workloads across public and private clouds, select best-of-breed services, and orchestrate data across environments. It also provides enterprises with greater flexibility from a procurement perspective, as they are not tightly bound to a single hyperscaler. In this context, composability reduces long-term risk rather than increasing it.

In regulated industries like financial services and healthcare, how do compliance requirements shape composable cloud frameworks?

In regulated industries, security and compliance need to be addressed as part of the design rather than added later. Traditionally, enterprises built systems first and addressed security, audits, and compliance after deployment. In cloud-based industry solutions, these considerations are embedded into the architecture from the start.

Industry-specific cloud offerings are designed with regulatory requirements, security controls, and risk management frameworks built in. Whether it is a core banking platform, a mortgage solution, or an industry-specific API framework, compliance checks and certifications are part of the deployment process. This ensures that regulatory requirements are met before systems go live, reducing the need for post-deployment remediation. For these industries, compliance and security are treated as foundational design principles.

Looking ahead, what are the biggest barriers to enterprises realizing the full value of cloud and composability?

Cloud platforms have reached a level of maturity where they can support everything from core systems of record to systems of engagement. Enterprise applications, SaaS platforms, workflow tools, and data systems can all be supported within cloud environments. As a result, there are a few technology-related barriers remaining.

The primary challenges tend to be related to trust, security, and digital sovereignty, particularly in regions such as Europe. In these cases, concerns extend beyond data residency to include operational and digital sovereignty. Some enterprises require data and cloud operations to remain within specific national boundaries. While these constraints can slow adoption, they are increasingly being addressed through localized cloud deployments and governance frameworks. Overall, the remaining barriers are diminishing as both cloud capabilities and regulatory models continue to evolve.