Article By: Zane Ulhaq, Head of MENA, Endava.
When PwC projected that artificial intelligence could contribute as much as US$320 billion to the Middle East economy by 2030, it identified financial services as the sector with the greatest upside. Half a decade on from the surge of generative AI, that prediction does not look misplaced. Banks across the GCC have deployed AI at scale, from customer service chatbots and agent augmentation, to increasingly sophisticated fraud detection and risk modelling capabilities. Vast datasets are being analysed at unprecedented speed, surfacing insights that would have been inconceivable just a few years ago.
And yet, for all this progress, AI has not fundamentally transformed banking. The underlying business models of banking remain intact, and the way institutions generate revenue has changed far less than the hype might suggest. For all its promise, AI has delivered optimisation, not reinvention. And for this reason, when it comes to the critical outcome of revenue generation, AI has been a refiner, not a creator of value.
Two Blueprints for Breakthroughs: What History Tells Us
To understand why this gap persists, it is useful to look backwards. The internet remains the clearest example of a technology that did not merely enhance banking but redefined it. Its impact was not limited to efficiency gains; it fundamentally altered distribution. By enabling “always-on”, digital access to financial services, it reduced geographic and temporal constraints, gave rise to entirely new customer expectations, and opened the door to new entrants.
Online banking, mobile-first experiences, and eventually digital-only banks, were not incremental improvements, but entirely new ways of engaging with financial services. As the foundation, the internet first enhanced revenue streams and then unlocked new concepts entirely (i.e. tokenisation, Account Information Services). Its success lay not in what it improved, but in what it made possible.
Cloud computing, by contrast, tells a more nuanced story. It has undeniably transformed how banks build and operate technology, enabling scalability, and pushing the bank to focus on customers rather than becoming inward-focused on their technology estate.
In many cases, it created the conditions for innovation without compelling it. Institutions modernised infrastructure and improved agility, but often stopped short of reimagining their products or revenue models. The result was progress, but not transformation.
AI Sits at the Inflection Point of Optimisation or Reinvention
AI today risks following a similar trajectory. Despite its sophistication, it is largely being applied within existing workflows, optimising processes rather than redefining them. Fraud detection becomes more accurate, onboarding becomes faster, and customer interactions become smoother, but the core proposition remains unchanged.
This is the paradox at the heart of AI in banking. The technology is powerful, the investment is significant, and the use cases are compelling, yet the industry finds itself in a familiar position: achieving incremental gains without unlocking fundamentally new sources of value. AI, in its current form, is reinforcing existing models rather than disrupting them.
History suggests that this moment is not unusual, but pivotal. Technologies do not become transformative by default; they become transformative when organisations choose to apply them differently. AI now sits at a crossroads, with one path leading towards continued optimisation, and the other towards genuine reinvention.
The distinction lies in how banks choose to deploy it. If AI remains confined to back-office efficiency and incremental improvements, it will follow the path of cloud — valuable, but ultimately limited in its impact on revenue creation. If, however, it is used to rethink how value is created and delivered, it has the potential to follow the path of the internet, reshaping business models and unlocking entirely new markets.
Unlocking AI’s Potential for Truely Transformative Change
Real transformation begins when AI changes how financial systems operate and make money. This requires moving beyond internal optimisation and embedding intelligence directly into customer-facing propositions, creating services that would not have been viable without it. Agentic AI, with its ability to act autonomously, learn continuously, and make context-aware decisions, provides a foundation for this shift.
Consider wealth management, traditionally reserved for high-net-worth individuals due to the cost of human advisory. With agentic AI, sophisticated financial guidance can be delivered at scale, tailored to individuals with modest means and evolving alongside their financial circumstances. This is not simply a matter of efficiency; it represents the creation of a new, previously underserved market, with its own revenue potential.
The implications extend further. AI agents capable of orchestrating decisions across multiple domains could redefine the role of financial institutions within broader ecosystems. Rather than existing as standalone service providers, banks could become embedded within complex decision-making processes, where financial considerations are seamlessly integrated into everyday choices. In such a model, revenue is generated not just through transactions, but through continuous, value-adding interactions.
Even in core areas such as credit and risk, the shift is profound. By moving from static, historical assessments to real-time behavioural analysis, agentic AI can expand access to credit while improving risk accuracy. This does not simply enhance existing processes; it expands the addressable market, creating new opportunities for growth that were previously out of reach.
Technology Alone Won’t Deliver Transformation
Yet, realising this potential is not primarily a technological challenge. The constraint is organisational. Many banks remain structured around products rather than outcomes, incentivised to minimise risk rather than pursue innovation, and inclined to treat AI as an extension of IT, rather than a driver of business strategy.
Without a corresponding shift in culture, product thinking, and risk appetite, even the most advanced AI capabilities will remain trapped in optimisation loops. The history of cloud illustrates this clearly: capability alone does not guarantee transformation. It must be matched by intent.
The Choice That Will Define the Next Decade
The history of technological change in banking suggests that the defining moments are not driven by the emergence of new tools, but by how they are applied. The internet forced a rethinking of distribution and customer engagement, unlocking new revenue models in the process. Cloud, for all its strengths, often stopped short of that leap.
AI now stands at a similar inflection point. The question is no longer whether it can deliver value, but whether banks are willing to use it to fundamentally rethink their business models. At this crossroads, it’s becoming increasingly clear that the next phase will not be defined by more powerful models or larger datasets, but by bolder choices about how value is created.
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