Banks have long been the GCC’s digital pioneers. In the UAE, Saudi Arabia and Qatar, financial institutions were among the first to embrace mobile banking apps, roll out contactless payments at scale and introduce AI-powered chatbots to handle customer queries in Arabic and English. More often than not, banks set the pace and other sectors followed.

Given this decades-long precedent, you would expect the same pattern to be playing out with artificial intelligence. After all, AI is already embedded in the daily lives of Gulf consumers. Ride-hailing, e-commerce, government, and a plethora of other services across the region have increasingly integrated AI into their systems, to effectively personalise experiences and streamline transactions.

And yet, when we look inside banks themselves, the story is more complicated. According to the latest Riverbed Global Survey, only 40% of organizations in the financial sector consider themselves ready to operationalize AI. Just 12% of AI initiatives are fully deployed enterprise-wide, while 62% remain stuck in pilot or development phases. In a sector known for digital ambition, there is a striking gap between intent and execution.

Stuck in Pilot Purgatory

In most industries, pilots fail because the idea simply does not resonate. Testing reveals a weak product-market fit, limited customer appetite, or unclear commercial value.

That is not what we are seeing in banking AI. Regional banks have successfully piloted AI models that detect fraud in real-time, reduce false positives in anti-money laundering checks, predict liquidity requirements, and power conversational assistants capable of resolving complex service requests. Relationship managers have used AI tools to surface next-best-product recommendations based on behavioral data. And operations teams have leveraged machine learning to optimize payment routing and reduce processing delays.

In controlled environments, these pilots often deliver impressive results. And yet, few ever make it past this stage. The initiative remains confined to a sandbox. Expansion is delayed. Integration becomes “phase two.” Eventually, attention shifts to the next promising experiment. So, if the feature works and the value is clear, what is holding banks back?

AI that Fails to Scale

In my experience working with CIOs across the region, two obstacles repeatedly stand in the way of AI moving from proof of concept to production. The first is operational complexity. Most financial institutions operate in highly fragmented environments. Core banking platforms run alongside decades-old legacy systems, with critical workloads split across on-premise data centers, private clouds, and multiple public cloud providers. Third-party fintech integrations also adds further layers of interdependency.

Deploying AI into this landscape is not as simple as plugging in a model. AI workloads are data-hungry and latency-sensitive. They require reliable pipelines, consistent telemetry, and predictable performance across every layer of the stack. In a hybrid, multi-cloud architecture, even minor configuration mismatches can trigger cascading issues.

The second obstacle is limited visibility. Without a unified view of applications, infrastructure, networks, and user experience, AI-driven services can behave unpredictably. A model may be performing perfectly, but a network bottleneck slows response times. An upstream data source may degrade in quality, subtly skewing outputs, and an infrastructure change in one environment may impact inference speeds elsewhere.

When visibility is fragmented, issues take longer to diagnose and resolve, and Mean Time to resolution increases. Operational risk rises, particularly when customer-facing or revenue-critical services are affected. In a heavily regulated market such as the UAE or Saudi Arabia, that risk has compliance implications as well as reputational ones.

Left unaddressed, this kind of live digital environment leaves very little room for innovation. AI cannot become the transformational force many claim it to be if it is constantly constrained by hidden friction.

Conquering Complexity

Moving AI smoothly from pilot to production requires banks to create as frictionless an operating environment as possible. One of the most effective starting points is unified observability. By consolidating telemetry from applications, infrastructure, networks and end-user devices into a single, real-time view, banks can eliminate blind spots, and decision-makers can gain clarity over performance, dependencies and risk across the entire digital estate.

With this foundation in place, AIOps capabilities can correlate signals, reduce alert noise and automate root cause analysis. Instead of firefighting incidents after customers notice them, IT teams can proactively identify performance degradation and resolve issues before they impact revenue or service continuity.

Standardising on frameworks such as OpenTelemetry can further simplify instrumentation across heterogeneous environments, ensuring consistent data collection and analysis. At the same time, investing in data quality, governance and compliance processes ensures that AI models are trained and operated within regulatory boundaries.

In practical terms, this means rethinking infrastructure as an enabler of AI rather than an afterthought. It may involve accelerating data movement between environments, modernising integration layers or rationalising overlapping monitoring tools. The goal is not perfection, but coherence: a shared, real-time understanding of how systems behave and how AI performs under real-world conditions.

From Optimism to Optimisation

The debate about whether AI belongs in banking is effectively over. Across the Middle East, regulators are publishing AI guidelines, governments are investing heavily in digital transformation, and consumers increasingly expect intelligent, seamless services.

Institutions that continue to treat AI as a series of isolated pilots risk remaining in perpetual experimentation. However, those who address operational complexity head-on will move beyond optimism to optimisation.

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