Technology is evolving exponentially and so are customer expectations. Today, customers are expecting businesses to deliver instant, seamless and hyper-personalized service and support.
Traditional customer service models in the past had often led to anger and frustration due to long hold times, the constant need to repeat personal information and dealing with slow, inefficient resolutions. Businesses today are modernizing and turning to artificial intelligence (AI) to drive better customer experience by shifting from reactive responses to proactive engagement.
Although businesses are investing in AI-driven customer experiences, many are failing to see returns from their pilot projects. In fact, according to Informatica’s latest CDO Insights 2025 survey, nearly half of the APAC & Japan data leaders surveyed cite poor data quality as the biggest roadblock to AI success, while 42% say AI ethics is a top concern.
Why? When AI uses poor-quality data, it can lead to inaccurate, outdated information and bias. This can result in misunderstandings, damage customer relationships and hurt brand loyalty in the long run.
When AI gets it wrong
Imagine a banking customer with an excellent credit score applying for a loan online. Their bank’s AI-powered chatbot, designed to speed up approvals, pulls from an outdated dataset and incorrectly flags them as ineligible. Instead of a quick approval, they receive a rejection or a lengthy manual review. The customer, confused and frustrated, takes their business to a competitor.
Now consider e-commerce. A returning customer who just purchased a smartphone is shown advertising to buy the same phone again. This is a case of data inconsistency where different AI systems fail to communicate properly due to siloed data. The result is a missed opportunity to drive more sales and increase customer satisfaction, as well as wasted marketing budgets.
These are clear examples of how AI is only as good as the data behind it. When businesses fail to maintain accurate, consistent and high-quality data, AI-driven customer experience is hence challenged.
To truly leverage AI for better customer experience, businesses must first get their data foundation in order.
Three steps to eliminate bad data and unlock AI’s potential
- Ensure data consistency across all channels
Too often, businesses store data in siloed systems, leading to inconsistent or conflicting records that make AI-driven interactions unreliable. AI can only deliver seamless, omnichannel experiences if it has a unified view of the customer.
With modern data management, businesses can capture data from multiple touchpoints to build a 360-degree view of the customer. This allows AI and machine learning models to analyze customer behavior, identify key attributes and predict life events; allowing businesses to optimize offers and deliver highly personalized experiences. Coupled with an understanding of demographics, device preferences, service history and more, businesses can become well-equipped with data-backed insights to better serve their customers.
There are already some great examples of businesses putting this into action. Union Bank of the Philippines, for example, adopted Informatica’s AI-powered master data management cloud-based solution to eliminate duplicate data records and improve real-time insights. By achieving 100% data accuracy for the bank’s Know-Your-Customer initiatives, the bank optimized loan approvals, personalized customer interactions and improved compliance reporting in just one year, proving that trusted data is the key to delivering business outcomes. - Build customer trust through secure and ethical data use
While customers increasingly expect personalized experiences, they must also be aware of how their data is collected, stored and used. With evolving data privacy and protection laws across ASEAN countries like Singapore, Malaysia and the Philippines, businesses need to ensure that their data practices are secure and ethical. Non-compliance, privacy breaches and data misuse damage brand reputation and brand loyalty.
To address this, businesses require a robust data management foundation that delivers high-quality, trusted data sets and identifies whether they contain personally identifiable information. They also need to know who has access to the information and for what purpose. This ensures that organizations are able to track and protect sensitive information, maintain compliance with privacy regulations and minimize risks; all while leveraging data for better decision-making and enhanced customer experience. Recognizing the importance of responsible AI use, 85% of APAC data leaders we surveyed are prioritizing workforce training in 2025 to ensure employees can safely and ethically use generative AI in daily operations. - Secure executive buy-in for better customer experiences
AI-driven customer experience initiatives can only succeed when they have strong leadership support and business-wide collaboration. Nearly all APAC organisations (98%) using or planning to adopt GenAI find it difficult to demonstrate the business value of their efforts.
To truly transform the customer experience, AI strategies must align with business goals and customer expectations, requiring cross-functional collaboration from IT, operations, marketing and sales to digital teams. Without this, AI projects risk working in siloes, failing to deliver consistent, personalized and secure customer experience. AI project failures are occurring due to rushed implementations that do not take data quality and governance into account.
Businesses that prioritise advanced data management and have a strong data governance strategy will lead the next wave of successful AI-powered customer experience. AI is only as good as the data it learns from. To reap the returns from your GenAI investments, it is crucial that your organisation invests in a data management platform that delivers a holistic, relevant and governed view that contains high-quality, trustworthy data.
