As organisations accelerate their AI ambitions, success depends on far more than deploying new technologies. Trusted data, strong governance, resilient systems and modern digital infrastructure are becoming the foundations of meaningful transformation. Sudheer Prabhu, Group Chief Technology Officer at Cim Finance, explains why these elements are essential to unlocking AI's business value, while also sharing his views on legacy modernisation, cybersecurity and the evolving role of the CTO as a strategic business leader.

Artificial intelligence has captured the attention of business leaders worldwide, yet its success ultimately depends on an organisation’s digital maturity. From your perspective, what foundations must organisations establish before AI can deliver meaningful business value? 
Artificial intelligence is not a shortcut to transformation. It amplifies the strengths and weaknesses that already exist within an organisation. If the foundations are weak, AI will simply magnify existing inefficiencies. 

Everything begins with strong governance. Organisations need clear policies, ethical principles, robust security standards and well-defined accountability for how AI is developed, deployed and managed. In a highly regulated industry such as financial services, governance is not optional, it is fundamental. 

Equally important is ensuring that AI is embedded within the broader business strategy, with active sponsorship from executive leadership. The most successful AI initiatives are never driven by technology teams alone. They bring together business, technology, risk, compliance, operations and customer-facing functions to solve meaningful challenges collaboratively. 

Thirdly, and perhaps most importantly, organisations should start with the business problem, not the technology. AI should never be adopted simply because it is available. It must address a genuine customer need, improve operational efficiency or create measurable business value. We experienced this first-hand at Cim Finance when we digitised our collections process. At the beginning of each month, long queues would form outside our offices as customers made repayments in cash. By building a complete digital payment stack through our MoFinans mobile application, integrated with the national MAUCAS payment infrastructure, we now process over 100,000 payment receipts digitally each month, at over 80 percent lower collection cost. The starting point was never the technology; it was a very real customer pain point. 

Finally, none of this can succeed without cultural readiness. Employees need to understand, trust and embrace AI as a tool that enhances human judgement, accelerates decision-making and empowers people to deliver better outcomes, rather than replacing them. 

Financial institutions have been investing in digital transformation for many years. How has the emergence of AI changed the priorities of technology leaders? 
In my view, digital transformation and the emergence of AI are not competing priorities. They are complementary. Digital transformation builds the foundation, while AI unlocks the true value of those investments. Organisations that have invested in modern platforms, quality data and digital capabilities are naturally better positioned to realise AI’s full potential. 

Our ambition is not simply to be a financial institution that uses technology, but to build a business where technology is central to how we create value for customers. Digitising payments through MoFinans and building fully digital lending journeys were never the end goal. Every digital interaction enriches the data ecosystem that enables faster credit decisions, stronger fraud prevention and increasingly personalised customer experiences. That is where AI begins to create meaningful business value. 

The biggest shift is that digital transformation is no longer about putting existing processes online. It is about making those processes intelligent. AI enables us to move from digital channels to intelligent financial services, where credit decisions become faster, risk is assessed dynamically and customer experiences evolve continuously based on insight rather than assumption. 

Another major shift is governance. AI has elevated discussions around ethics, explainability, regulation and risk from the technology function to the boardroom. These are strategic business issues that require executive oversight and clear accountability. 

Customer-facing innovation still matters, but AI’s biggest opportunities often lie behind the scenes: better decisions, smarter automation and giving our people more time to focus on higher-value work. At Cim Finance, our automation programme using Robotic Process Automation and workflow technologies has already delivered efficiency gains equivalent to approximately 125 full-time employees. Our next phase is to build on that foundation through Agentic AI, enabling increasingly autonomous workflows while maintaining appropriate governance and oversight. 

Legacy systems remain a significant challenge across the financial services industry. How can organisations modernise their technology landscape while continuing to serve customers without disruption? 
Modernisation should be evolutionary rather than revolutionary. Attempting to replace an entire core system in a single, big-bang event introduces significant operational risk. A more resilient approach is incremental transformation, where new capabilities are built alongside existing platforms, gradually assume greater responsibility and are proven under real operating conditions before legacy components are retired. 

An API-first architecture is fundamental to this approach. APIs enable legacy platforms to integrate seamlessly with modern digital channels such as mobile applications, online services and AI-powered customer interactions. This provides the flexibility to innovate rapidly at the front end without repeatedly rebuilding the core. This architecture gives us the flexibility to introduce new digital capabilities rapidly while maintaining the resilience and stability expected of a regulated financial institution. Our recent launch of MoPay illustrates this well. It is the first product of its kind in Mauritius, allowing customers to purchase merchandise at zero percent interest through a fully digital journey. Behind that seamless experience sits an integrated digital platform, combining digital KYC with embedded OCR, facial recognition for identity verification, machine learning for fraud detection and robotic process automation for credit underwriting. This allows customers to enjoy a simple digital experience while complex processes happen securely in the background. 

Equally important is execution discipline. Rigorous testing, robust rollback strategies and meticulous migration planning are essential to maintaining business continuity. The best technology upgrade is one the customer never notices.

Many companies are eager to deploy AI solutions quickly. What are the risks of implementing AI before modernising core systems, data infrastructure and governance? 
The greatest mistake organisations can make is believing that AI can fix weak foundations. It cannot. AI does not solve underlying problems; it amplifies them. 

Feed a model fragmented, inconsistent or poor-quality data and the outcomes will inevitably be unreliable. The principle of “garbage in, garbage out” has never been more relevant than in the age of AI. 

In financial services the stakes are especially high, because AI increasingly sits inside decisions that directly affect people’s financial lives: whether a loan is approved, whether a transaction is flagged as fraud, how a customer in difficulty is treated. That is why Cim Finance deliberately sequenced its journey: digital payments and customer journeys first, then consolidated, well-governed data, and only then AI at scale in sensitive areas such as fraud detection and credit underwriting. 

Governance is equally critical. Organisations need clear policies covering data privacy, security, model governance, regulatory compliance and human oversight. Without these safeguards, AI can expose the business to operational, regulatory and reputational risks simultaneously. 

Trust is another defining factor, and in financial services trust is the product. A single high-profile failure, whether a hallucinated response, a biased lending recommendation or a credit decision that cannot be explained to a customer or a regulator, can quickly undermine the confidence of both customers and employees, and rebuilding it can take years. That is why responsible AI must be embedded from the outset, with explainability, transparency and accountability treated as fundamentals rather than afterthoughts. Innovation and speed are important, but they should never come at the expense of trust. 

Technology leaders are increasingly expected to drive business strategy rather than simply manage IT. How has the role of the Chief Technology Officer evolved in the era of AI and digital transformation? 
The role of the CTO has evolved more dramatically than at any other point since the position was created. It has shifted from being a technology leader focused on execution to becoming a strategic business leader shaping the future of the organisation. 

In the past, the CTO’s primary responsibility was to answer the question, “Can we build and operate this reliably?” Today, the question is far broader: “Should we build it? How will it strengthen our competitive position? What risks does it introduce, and how do we manage them?” That shift reflects the growing recognition that technology decisions are, fundamentally, business decisions.

This is particularly evident in organisations where technology increasingly defines the customer proposition. When we launched platforms such as MoFinans and MoPay, they were never viewed simply as technology initiatives. They represented a different way of delivering financial services: digital-first, data-driven and increasingly intelligent. Decisions about our payment ecosystem, data capabilities and AI roadmap influence our competitive position just as directly as product design or pricing. 

In financial services, decisions around core platforms, cloud architecture, AI capabilities, cybersecurity and data are no longer technical implementation choices. They directly influence customer experience, operational resilience, regulatory readiness and long-term competitiveness. As a result, the CTO has become a strategic partner in shaping the future of the organisation, ensuring technology investments create measurable business value while enabling innovation, resilience and long-term competitiveness. 

Data is often described as the fuel that powers artificial intelligence. What distinguishes organisations that effectively leverage their data from those that continue to struggle? 
The organisations that will lead in the AI era will not necessarily be those with the most advanced algorithms. They will be the ones with the highest-quality data. 

Data is often described as the fuel that powers AI, but I would take that analogy one step further. Fuel only creates value once it has been refined. Similarly, AI can only deliver meaningful outcomes when it is powered by data that is trusted, well-governed and fit for purpose. 

The organisations that succeed are those that build what I would call a “golden” data foundation: data that is organised, standardised, labelled, governed and continuously maintained to provide a single source of truth across the business. Only then can AI generate reliable insights and support better, faster decision-making. 

For a consumer finance business like ours, this is a strategic asset. Every month, customers generate more than 100,000 digital payment transactions through MoFinans. Beyond simplifying repayments, these interactions create valuable behavioural insights that, when used responsibly, help us make better lending decisions, strengthen improve customer experience. Better data literally enables greater financial inclusion. 

Ultimately, I believe competitive advantage will not come from having the most AI models. It will come from having the best data feeding those models. 

Cybersecurity threats continue to evolve alongside advances in artificial intelligence. How is AI changing both the defensive and offensive sides of cybersecurity? 
Artificial intelligence is transforming both the offensive and defensive sides of cybersecurity. It has become an arms race, with attackers and defenders leveraging the same technology to gain an advantage. 

On the offensive side, cybercriminals are using AI to launch more sophisticated and scalable attacks. We are already seeing highly convincing phishing emails, hyper-realistic social engineering campaigns and advanced deepfake technologies being used to impersonate individuals, facilitate fraud, bypass identity verification and authorise fraudulent transactions. AI enables attackers to automate these activities at scale, making cyber threats more target For organisations delivering digital financial services, these are very real risks. Technologies such as deepfakes and synthetic identities increasingly target digital onboarding and identity verification. That means fraud prevention cannot remain static. Our AI models must continuously learn, adapt and evolve alongside emerging threats so customers can continue to transact with confidence. 

On the defensive side, AI has become an indispensable capability. It can detect behavioural anomalies across networks, user activity and financial transactions by learning from patterns that would be impossible for traditional rule-based systems to identify. 

Ultimately, cybersecurity is becoming as much an intelligence challenge as a technology challenge. The organisations that will be most resilient are those that combine AI-driven capabilities with strong governance, skilled cybersecurity professionals and a culture of continuous vigilance. AI is not replacing human expertise; it is enabling faster, smarter and more proactive cyber defence. 

Finally, if you were advising the board of an organisation beginning its AI journey today, what would be the three most important strategic priorities to ensure long-term success? 
The first priority is to get the foundations right before scaling. Strong data, governance, cybersecurity and risk management must come before ambition. Boards should be asking fundamental questions: Do we have the right data infrastructure to support AI? Are our models explainable and auditable before they are deployed to customers? Is AI model risk managed with the same rigour as our financial and operational risks? Without these foundations, even the most sophisticated AI initiatives will struggle to deliver sustainable value. That principle has guided our own transformation at Cim Finance. We first invested in modern digital platforms, secure payment infrastructure and robust data governance before scaling AI across customer journeys, fraud detection and credit decisioning. That sequencing allows us to innovate with confidence while maintaining the trust and resilience expected of a regulated financial institution. 

Secondly, AI must be treated as a strategic business capability, not a delegated IT initiative. It belongs at the heart of the organisation’s long-term strategy, with active board oversight and clear accountability. AI should feature in executive and board discussions not simply as a technology update, but as a driver of business performance and competitive advantage. 

Finally, organisations should build for adaptability rather than fixed, multi-year technology plans. AI is evolving too quickly for rigid roadmaps. Instead, organisations need flexible technology platforms, vendor independence where possible, governance frameworks that can evolve with regulation and a culture that encourages experimentation, continuous learning and course correction. 

This matters all the more in Mauritius, where national payment infrastructure such as MAUCAS and a maturing regulatory environment are accelerating the shift to digital finance. This is particularly relevant in markets such as Mauritius, where digital infrastructure, customer expectations and regulation continue to evolve rapidly. Organisations that succeed will not necessarily be those investing the most in AI, but those combining trusted data, resilient technology and responsible governance to solve real customer problems. Ultimately, technology is not the differentiator in itself. The real differentiator is how intelligently it is applied to make financial services simpler, faster, more inclusive and more secure. They will be the ones that successfully combine trusted data, responsible governance, resilient technology and an adaptable culture to create lasting value for their customers, employees and shareholders.

Article published in Investors Magazine - Issue No 36