In the rapidly evolving digital age, the correlation between cloud maturity and the successful implementation of Artificial Intelligence (AI) is becoming increasingly evident, particularly in the banking sector. As organisations strive to incorporate AI into their daily operations, the readiness and maturity of their cloud environment becomes a critical factor determining the success of such endeavours. This is particularly true for Financial Service Institutions (FSIs) where AI is being layered on top of complex legacy estates, fragmented data, and compliance-heavy operations.
Research by Amdocs suggests that nearly 68% of enterprises perceive the use of AI for cloud operations as a competitive edge. However, the challenge lies in ensuring that the cloud environment is mature enough to support AI at scale. If this maturity is lacking, the successful implementation and scaling of AI remain elusive, leading to fragmented AI initiatives and a failure to convert experimentation into measurable business impact.
Cloud maturity: A critical enabler of AI
The role of cloud maturity extends beyond being a simple IT milestone. It is now evolving into a decisive factor that impacts how far and how fast AI can be integrated within an organisation. This becomes especially crucial with the advent of agentic AI and multi-agent systems, which rely heavily on real-time data access, orchestration across various systems, and infrastructure that can support autonomous execution under governance.
The cloud as the execution layer for AI
Cloud technology is increasingly being recognised as the environment where AI models are trained, deployed, and optimised continuously. Conversely, AI is improving how cloud environments are managed, secured, and optimised. The shift towards agentic AI systems that operate across cloud environments and automate workflows is visible across industries, signalling a rapid move from experimentation to production-scale deployment.
Research commissioned by Amdocs and conducted by Coleman Parkes indicates a sharp rise in enterprises expecting to run multiple AI agents in production, from 26% in Q4 2025 to 71% by Q4 2026. This leap underscores the changing role of the cloud and its inseparability from AI. As AI becomes more agentic, cloud operations become more adaptive, efficient, and responsive.
Bridging the readiness gap
Despite the growing recognition of the importance of AI in banking, there exists a significant gap between AI ambition and operational readiness. While many institutions are investing in cloud technology, it does not necessarily translate into an environment where AI can scale. Amdocs’ research reveals a readiness gap with only 56% of organisations reporting agentic-ready data and cloud platforms. This indicates that nearly half are not yet ready for agentic AI at scale.
The role of cloud maturity in AI deployment
Without a modern cloud and data foundation, the scalability and operationalisation of AI remain a challenge. For successful AI deployment, banks need to invest deliberately in cloud capabilities that enable agentic operations. These include legacy modernisation, interoperability, governance, automation, and data readiness. These capabilities not only help in transitioning from AI pilots into production but also in moving from experimentation to execution, thereby creating measurable business impact.
As AI continues to revolutionise the banking sector, the importance of cloud maturity cannot be overstated. It is the critical backbone that enables AI deployment at scale and allows institutions to leverage AI’s full potential.
Deborah Koens, Global Go-To-Market Leader, Cloud Studio, Amdocs
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