Sri Lanka: Is Your Digital Product Ready for AI?
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The conversation in every leadership meeting today seems to circle back to the same question: how do we bring AI into our product? Artificial intelligence is no longer a future consideration. It is a present reality reshaping how digital products serve customers, how businesses operate, and how organisations compete. The pressure to act is real. Competitors are making moves, customers are expecting smarter experiences, and the promise of efficiency, personalisation, and scale is difficult to ignore.
But here is what I have observed across multiple digital product implementations, particularly in financial services and banking: organisations that rush to integrate AI into products with unresolved user experience issues rarely see the returns they expect. Instead of unlocking value, they compound existing friction. Users who already struggle with basic tasks are now presented with intelligent features they neither asked for nor understand. The result is not innovation. It is confusion layered on top of frustration.
Nisal Tharanga.
This is not an argument against AI. AI will be essential to the next generation of digital products, and organisations that delay too long risk falling behind. But the question is not simply whether to invest in AI. It is whether your product has earned enough user trust to carry that investment forward. When the foundation of user experience is solid, AI amplifies value. When it is not, AI amplifies problems.
The business case for fixing UX first
Before discussing AI readiness, it is worth understanding what poor user experience is already costing your organisation. Research by Forrester shows that every dollar invested in UX can return up to a hundred dollars. On the other side, a study by AWS estimates that poor user experience costs businesses approximately 1.4 trillion dollars annually in lost revenue. These are not design statistics. They are business performance indicators that directly affect customer acquisition cost, retention, and lifetime value.
McKinsey confirms that organisations improving their customer journey satisfaction can increase cross-sell rates by 15 to 25 percent and boost share of wallet by 5 to 10 percent. That is not a marginal gain. It is a strategic revenue opportunity sitting inside the experience your users already have.
A pattern I have observed across the industry compounds this problem: organisations launching multiple apps under a single brand to serve different functions. In banking, a customer might be expected to use one app for everyday transactions, another for investments, and yet another for insurance or digital wallets. From a user perspective, it creates confusion and unnecessary complexity. Users recognise and trust their bank by name. When they are asked to navigate between products with unfamiliar names and different interfaces, it fragments their experience rather than enhancing it. Based on user research I have conducted across multiple products, users consistently express discomfort when they encounter product names that differ from the brand they originally trusted. PwC reinforces this reality: 32 percent of customers will leave a brand they once valued after just one poor experience.
Why AI on top of poor UX makes things worse
When a user interacts with a digital product, they are extending trust at a fundamental level. Can I navigate this? Will it do what I expect? Is this reliable? AI introduces a second, distinct layer of trust on top of these baseline questions. Is this recommendation accurate? Is the system making decisions on my behalf? What happens when something goes wrong?
A 2025 study by KPMG and the University of Melbourne, surveying over 48,000 people across 47 countries, found that only 46 percent of people globally are willing to trust AI systems. Nearly half said they do not feel they understand AI or when it is being used. This is not a technology gap. It is a trust and literacy gap. Through user studies I have carried out as part of AI-powered product work, people have shared mixed feelings about AI-driven features. Some see the potential but hesitate because they are unsure how the system arrives at its suggestions. Others disengage when the experience feels unpredictable or difficult to control.
In a market like Sri Lanka, where a significant portion of the population is still building confidence with digital products and many users have recently transitioned from branch-based interactions to mobile platforms, this gap is even more pronounced. Consider a user who already struggles to locate a specific service within their banking app. Now introduce an AI assistant that proactively suggests financial products or automates transaction categorisation. For a user who has not yet built confidence in the basics, this does not feel like innovation. It feels overwhelming.
Five UX foundations to address before introducing AI
Based on patterns across successful digital product implementations and validated through user research, there are five foundational experience areas organisations must address before introducing AI.
Performance and interface optimisation. Slow loading screens, unresponsive interactions, and lag during critical tasks weaken user patience and trust before any feature gets a chance to prove its value. Research shows that 88 percent of users will not return to a product after a poor experience, and load time is frequently the first point of failure.
Information architecture and navigation. How services, features, and content are structured must reflect how users think, not how the organisation is structured internally. Many digital products mirror their company’s departmental structure rather than the user’s mental model. Industry research suggests that only 20 to 25 percent of mobile app users engage with features beyond the basics, often because those features are buried under vague labels or hidden in secondary menus.
Clear, human language communication. Technical jargon remains one of the most overlooked barriers in digital products. Error messages, notifications, and confirmations should be written in plain language users immediately understand. In the Sri Lankan context, this also means ensuring communication works effectively in Sinhala, Tamil, and English.
Consistent cross-channel experience. Users expect the same functionality regardless of whether they access a service through mobile or web. Inconsistencies quietly damage trust and create unnecessary effort at moments when the experience should feel seamless.
Onboarding and first-time experience. The first interaction sets the tone for everything that follows. The most effective approach is progressive onboarding: ask only what is necessary at each stage, with deeper verification reserved for advanced features. If users cannot get through the front door comfortably, nothing else you build, including AI, will matter.
The path forward
Once these foundations are in place, introduce AI in phases. Start with AI working passively in the background through analytics and personalisation. Then introduce it as an optional, assistive layer. From there, move to interactive features like chatbots or guided workflows, always with human support accessible and AI interactions clearly labelled. Finally, let AI become fully embedded into the product experience.
Across every phase, user education is non-negotiable. In Sri Lanka, where AI literacy remains limited, each phase must include clear, simple explanations of what AI is doing and why.
The most strategic AI investment an organisation can make right now may not be in AI at all. It may be in strengthening the experience users already have. Trust is cumulative. The organisations that invest in user trust first and introduce AI with intention and patience will be the ones that lead, not just in technology adoption, but in genuine customer loyalty and long-term growth.
(Nisal Tharanga is a Senior Product Designer specialising in AI-driven design with over a decade in user-centred,
inclusive experiences and 20 years
in software development.)
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