The level of cloud maturity influences whether AI can transition from pilot projects to actual implementation. Companies with higher cloud maturity usually possess the necessary data access, integration, scalability, and governance to effectively deploy AI throughout the organization. Without this base, AI efforts often stay scattered, limited to individual use cases, featuring disconnected data structures, or proofs of concept that are hard to expand.
It holds significant importance in the banking sector, where artificial intelligence is being integrated over intricate legacy systems, scattered data sources, and regulations-driven processes. Financial Services Institutions (FSIs) must now ensure their cloud setups are advanced enough to handle AI on a large scale. Without this level of maturity, it becomes considerably more challenging to incorporate AI into daily activities or transform trials into tangible business outcomes.
The advancement of cloud capabilities is emerging as a key differentiator, driven by its role in supporting artificial intelligence.
The significance of cloud readiness grows even more critical with the rise of agentic AI and multi-agent systems, which require immediate access to data, coordination across diverse platforms, and a foundation capable of enabling autonomous operations within regulatory frameworks. What was once considered an IT achievement is now a far more impactful factor: it determines the extent and speed at which AI can advance within the company.
This is already considered significant. According to Amdocs research, 68% of companies see the application of agentic AI in cloud operations as a strategic benefit.
The cloud is increasingly serving as the platform for AI execution.
The cloud offers a setting for training, implementing, and consistently refining AI models, while AI is progressively enhancing the way cloud environments are handled, protected, and improved.
Within the industry, there is a growing trend towards AI systems that act autonomously across cloud platforms, aiming to streamline processes, enhance efficiency, and minimize operational tasks. The uptake of these technologies is increasing rapidly. A study funded by Amdocs, carried out by Coleman Parkes, reveals that the percentage of enterprises planning to implement multiple AI agents in production is expected to grow from 26% in Q4 2025 to 71% by Q4 2026, highlighting how swiftly companies are transitioning from testing to large-scale implementation. This swift change is reshaping the function of the cloud.
AI can no longer be separated from the cloud. It serves as the platform that enables AI to function on a large scale and integrate with essential systems in an effective manner.
As artificial intelligence grows more autonomous, cloud operations also become more flexible, effective, and quick to respond.
Increased autonomous actions will be experienced by both bank staff and clients across various scenarios. For instance, in the background, autonomous AI will assist banks in simplifying compliance processes, enhancing risk control, and managing full-cycle procedures with reduced manual involvement. From the customer perspective, this might involve underwriting agents retrieving credit information and documents instantly, fraud specialists initiating protective measures, or a relationship manager utilizing an AI-created summary – all synchronized through cloud-based systems.
Closing the readiness gap
There remains a significant difference between the aspirations of AI in the banking sector and the actual preparedness to implement it. Several organizations are allocating resources to cloud technology, yet this doesn't consistently create a setting that supports the expansion of AI. This leads to a separation between the wish to introduce AI innovations and the underlying infrastructure needed to put them into practice.
Amdocs research highlights gaps in banks' preparedness, with just 56% of organizations stating their data and cloud platforms are agentic-ready—indicating that almost half are not yet prepared for large-scale agentic AI. In reality, institutions with more advanced, cloud-native structures are better equipped to transition from AI pilots to production and eventually integrate them into core processes. On the other hand, environments that are fragmented or not optimized tend to keep AI initiatives isolated, making it more challenging to demonstrate consistent value or expand adoption throughout the organization.
The foundation of AI implementation is cloud readiness.
Without updated cloud and data infrastructure, AI continues to be challenging to expand and even more difficult to implement effectively.
Banks must intentionally invest in cloud technologies that enable autonomous operations, such as updating legacy systems, ensuring compatibility, establishing governance, automating processes, and preparing data. These elements transform AI from theoretical exploration into practical implementation, allowing organizations to transition from trials to full-scale deployment with increased assurance, efficiency, and clear business outcomes.
Deborah Koens, Global Market Access Leader, Cloud Studio,Amdocs
"Cloud readiness is the key determinant for banks that successfully implement AI" was originally created and published byRetail Banker International, a owned brand.
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