Cloud maturity determines whether AI can move beyond pilots and into production. Organisations with stronger cloud maturity typically have the data access, integration, scalability, and governance to run AI reliably across the enterprise. Without that foundation, AI initiatives often remain fragmented, confined to isolated use cases, with disconnected data architectures, or proofs of concept that are difficult to scale.
That matters a lot in banking, where AI is being layered on top of complex legacy estates, fragmented data and compliance-heavy operations. Financial Services Institutions (FSIs) now need to validate that their cloud environments are mature enough to support AI at scale. If that maturity is missing, it becomes much harder to embed AI into day-to-day operations or turn experimentation into measurable business impact.
Cloud maturity is becoming a competitive advantage as an AI enabler
Cloud maturity becomes even more important with agentic AI and multi-agent systems, which depend on real-time data access, orchestration across a wide variety of systems, and infrastructure that can support autonomous execution under governance. What used to be seen as an IT milestone is now something much more consequential: it shapes how far and how fast AI can go inside the organisation.
Go deeper with GlobalData
Access deeper industry intelligence
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
This is already perceived as important. Amdocs research shows that 68% of enterprises view the use of agentic AI for cloud operations as a competitive advantage.
Cloud is becoming the execution layer for AI
Cloud provides an environment where AI models are trained, deployed, and continuously optimised, while AI is increasingly improving how cloud environments are managed, secured, and optimised.
Across the industry, we’re seeing a shift toward agentic AI systems that operate across cloud environments to automate workflows, optimise performance, and reduce the manual burden of operations. Adoption is accelerating quickly. Research commissioned by Amdocs conducted by Coleman Parkes indicates that enterprises expecting to run multiple AI agents in production will rise from 26% in Q4 2025 to 71% by Q4 2026, underscoring how quickly organisations are moving from experimentation toward production-scale deployment. This rapid shift is redefining the role of the cloud.
You can’t separate AI from the cloud anymore. It is the environment that lets AI operate at scale and connect to core systems in a usable way.
As AI becomes more agentic, cloud operations themselves become more adaptive, efficient, and responsive.
More agentic operations will be felt by both bank employees and customers in many use cases. For example, behind the scenes, agentic AI will help banks streamline compliance checks, optimize risk management and orchestrate end-to-end workflows with less manual coordination. At the customer level, it could be underwriting agents pulling credit data and documents in real time, fraud agents triggering safeguard actions, or a relationship manager preparing with an AI-generated brief – all coordinated across cloud-connected systems.
Closing the readiness gap
Across banking, there is still a meaningful gap between AI ambition and operational readiness. Many institutions are investing in cloud, but that does not always translate into an environment where AI can scale. The result is a disconnect between the desire to innovate with AI and the technical foundation required to operationalise it.
Amdocs research also points to banks’ readiness gaps, with only 56% of organisations reporting agentic-ready data and cloud platforms – meaning nearly half are not yet ready for agentic AI at scale. In practice, institutions with more mature, cloud-native architectures are better positioned to move from AI pilots into production and then into core workflows. By contrast, fragmented or under-optimized environments tend to keep AI efforts siloed, making it harder to show repeatable value or scale adoption across the enterprise.
Cloud maturity is the backbone of AI deployment
Without modernised cloud and data foundations, AI remains difficult to scale and even harder to operationalise.
Banks need to invest deliberately in cloud capabilities that make agentic operations possible including legacy modernisation, interoperability, governance, automation, and data readiness. These capabilities are what turn AI from experimentation into execution and help institutions move from pilots to production with greater confidence, speed, and measurable business impact.
Deborah Koens, Global Go-To-Market Leader, Cloud Studio, Amdocs
