AI Leader ยท UAE Banking

Pradeep
Dhanapalan

Building enterprise AI capability that compounds

Enabling banks turn AI ambition into production reality โ€”
from strategy and architecture through governance to measurable outcomes.

LinkedIn
๐Ÿ†
Middle East Banking Award 2025
Best AI Deployment ยท Fraud Prevention
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About

Two decades at the frontier
of banking technology.

I've spent the last five years building AI capability inside UAE banking from the ground up โ€” turning fragmented pilots into a governed, production-grade AI delivery engine spanning fraud detection, revenue optimisation, and customer experience.

My edge is operating at the intersection of business value and engineering reality. I speak fluent management and fluent engineering โ€” and I translate between them every day. That means AI initiatives don't stall at the POC stage; they get through architecture review, risk governance, and into production where they generate returns.

Before AI, I led the delivery of the UAE's first fully digital bank โ€” a project that shaped my conviction that technology's value is only realised when it changes how people work and live, not just when it ships.

I hold an MBA from the University of Illinois Urbana-Champaign and a Post-Graduate Program in AI from UT Austin. I'm based in Dubai, UAE.

18+
Years in UAE Banking Technology Digital banking, AI delivery, transformation, program leadership
5+
Years Leading Enterprise AI GenAI, ML, MLOps, governance in production
3
Core AI Domains in Production Fraud prevention, revenue optimisation, CX automation
๐Ÿ†
Middle East Banking Award 2025 Best AI Deployment for Fraud Prevention & Detection
Expertise

Where I operate.

End-to-end AI delivery โ€” from framing the use case to value realization.

๐Ÿงญ
AI Strategy & Roadmapping
Translating business ambition into executable AI strategy. Use case prioritisation, ROI modelling, build vs. buy decisions, and portfolio governance aligned to organisational maturity.
Strategy ROI Roadmap
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GenAI & ML Delivery
Taking models from POC to production. RAG pipelines, agentic AI patterns, MLOps, LLMOps, feature engineering, integration architecture, monitoring, and retraining governance.
GenAI RAG MLOps Agentic AI
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AI Governance & Responsible AI
Model risk frameworks, documentation standards, audit readiness, regulatory alignment (UAE AI regulations, CBUAE guidelines), and operational controls that keep AI trustworthy at scale.
Governance Risk Compliance
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Banking Domain Depth
Fraud prevention & detection, credit risk, AML, trade finance, RM copilots, customer 360, digital channel automation โ€” built with an understanding of how banks actually operate.
Fraud Credit AML CX
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Stakeholder & Executive Leadership
Driving AI steering committees, translating technical progress for C-suite and board audiences, managing vendor relationships, and building cross-functional AI delivery teams.
C-Suite Vendors Steering
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AI COE Design & Capability Building
Architecting centres of excellence: operating models, team structures, intake processes, tooling stacks, and AI literacy programmes that scale AI adoption beyond the data team.
COE Capability Culture

Points of view worth holding.

On enterprise AI, what works, and what doesn't โ€” drawn from real delivery, not theory.

POC โ†’ Production
"Most AI projects don't fail at the model. They fail at the handover."
The graveyard of enterprise AI is full of impressive pilots that never shipped. The gap isn't technical โ€” it's organisational. Production readiness is a discipline: data contracts, monitoring, model governance, operational runbooks. Build that muscle early or pay for it later.
AI Governance
"Governance isn't the enemy of speed. A bad governance framework is."
The instinct to treat governance as friction is understandable but wrong. A well-designed model approval process doesn't slow delivery โ€” it prevents the costly delays that happen when a poorly documented model gets challenged by risk at the last mile. Design governance to enable, not police.
GenAI in Banking
"GenAI's first real value in banking is internal, not customer-facing."
The temptation is to build customer-facing GenAI first because it's visible. The better bet is internal: RM copilots, document processing, compliance Q&A, policy lookup. Lower risk, faster value, and it builds the capability base needed for customer-facing use cases later.
AI Leadership
"The CAIO role isn't a technical job. It's a translation job."
The most important skill for AI leadership isn't knowing the best model architecture. It's the ability to make a board understand why responsible AI governance is a competitive advantage, and make a data scientist understand why their model needs to survive a regulatory audit. Context-switching at that level is rare and underrated.
Confidential Assets

AI Strategy Blueprint

A comprehensive 3-year AI transformation blueprint covering strategy, architecture, governance, COE design, use case roadmap, and projected ROI

๐Ÿ”’ Access Blueprint
Password protected ยท Invitation only

Let's talk about
what AI can do for your organisation.

Whether you're exploring an AI strategy, building a COE, or looking for senior AI leadership โ€” I'm open to the right conversation.