Across the GCC, banks and financial institutions have embraced generative AI with speed and enthusiasm. Developers are using code assistants, leaders are demanding productivity gains, and transformation programmes increasingly include “GenAI enablement” as a visible workstream.

Across the GCC, banks and financial institutions have embraced generative AI with speed and enthusiasm. Developers are using code assistants, leaders are demanding productivity gains, and transformation programmes increasingly include “GenAI enablement” as a visible workstream.

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But the truth is clear: high adoption has not yet converted into high impact. Most organisations are still stuck in pilot mode — achieving small pockets of efficiency without translating them into material business value.

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This briefing outlines what the market is learning, and what banks and regulators in the region need to do next.

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The Productivity Myth: Tools Alone Don’t Deliver Value

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While two-thirds of software organisations now use GenAI tools, the measurable gains remain modest — typically 10–15% efficiency improvements at the individual
developer level.

“The challenge isn’t the technology. The challenge is the operating model around it.”

Most banks still run legacy development processes, heavy governance cycles, and disconnected teams. This means any time saved through AI is simply absorbed into existing inefficiencies — never flowing through to faster delivery, reduced risk, or improved compliance outcomes.

GenAI applied to old processes yields old results.

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Why Gulf Banks Feel This Pain More Intensely
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Financial institutions in the region operate under three pressures that magnify the problem:

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Regulatory expectations are rising.]

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Basel, cyber resilience, AI governance, model risk, and new supervisory frameworks require faster, more consistent software releases — not sporadic AI-assisted coding.

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Change programmes are large, complex, and multi-vendor.

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GenAI cannot generate value if upstream architecture, testing, security reviews and change controls still operate in slow, manual ways.

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Security and confidentiality requirements remain uncompromising.

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Banks must ensure any AI usage is tightly governed, explainable, and fully auditable — not a free-form experiment at the developer’s desk.

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These realities mean the GCC cannot rely on tool-level adoption alone. The sector must move towards AI-native engineering, where GenAI is woven into processes, not layered loosely on top.

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What Leading Organisations Are Doing Differently

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The institutions beginning to see meaningful payoff treat GenAI as a transformation catalyst, not a tactical enhancement.
They commit to:

End-to-end AI enablement

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Embedding AI across design, architecture, coding, testing, security, documentation, and deployment.

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Workflow redesign

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Removing redundant handoffs, compressing review cycles, and structuring delivery around rapid iterations supported by AI accelerators.

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Governance and compliance by design

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Introducing AI firewalls, secure model access, granular entitlements, audit logs, and risk-aligned controls — enabling safe adoption at scale.
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A cultural shift toward augmented delivery

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Reskilling teams, redefining roles, and creating norms where AI is a permanent co-worker, not a novelty.

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The result isn’t just faster output — it’s better engineered, more secure, regulator-ready software, delivered consistently.
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The Opportunity for GCC Banks and Regulators
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For banks, this is a chance to unlock real transformation:

• Faster release cycles for regulatory updates
• Higher-quality code with fewer defects
• Reduced pressure on overstretched development teams
• Stronger cyber and data-governance alignment
• More capacity for innovation and new products

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For regulators, the shift is equally strategic:

“GenAI-enabled engineering creates more transparent, traceable, and testable software — supporting real-time supervision, better stress- testing, and improved assurance over AI-driven decisioning.”

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Impact Team Recommendations: Moving From Pilot to Payoff

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• Establish an AI-Native Engineering Blueprint
• Anchor GenAI across the full software lifecycle — including
  requirements, QA, security, and release management.

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• Implement Secure AI Access & Controls
• Adopt LLM firewalls, entitlement-based model access, and data-
  protection measures to safely open GenAI to developers and business
  teams.

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• Redesign Delivery Workflows for AI Acceleration
• Move away from linear, document-heavy processes. Replace them with agile, AI-augmented, compliance-aligned flows.

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• Create a Regulatory-Ready AI Governance Framework
• Ensure every AI-supported output is explainable, reviewable, logged, and compliant with emerging GCC supervisory expectations.

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• Track ROI at the Business Outcome Level
• Measure value through delivery speed, risk reduction, audit readiness, and customer impact — not just developer productivity.

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In Summary

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Generative AI will not transform your organisation because you have tools. It will transform your organisation when you redesign how you deliver software, how you govern technology, and how you align development with regulatory expectations. The Gulf’s most forward-looking banks are now moving decisively from pilots to payoff. With the right architecture, controls, and operating model, GenAI becomes not an experiment — but a competitive advantage.

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