In 2013, a major retailer made headlines for predicting a teenager’s pregnancy before her family knew. How? By analysing purchase patterns; unscented lotion, supplements, cotton balls, and using data-driven AI models to infer what was happening in customers’ lives. The revelation was both astonishing and unsettling, triggering a global conversation about how businesses use data.
Fast forward to today, and companies are still struggling with the same core challenge: data is abundant, but insight remains elusive. Organisations have spent years amassing data lakes, warehouses, and analytics platforms, believing that the more they stored, the more value they would extract. Yet many find themselves trapped in a paradox, drowning in data, yet starving for insight.
“Most organisations assume their biggest challenge is data quality or access,” says Jason Yung, Head of Data Strategy at Amazon Web Services (AWS) for Europe, Middle East, and Africa. “But the real problem often lies in the lack of alignment on why they’re using data in the first place.”
Companies have been operating under the assumption that centralizing data will naturally lead to better decision-making. But Jason argues this is flawed thinking: “It’s like hoarding, just because you have more doesn’t mean you know what to do with it.”
This misalignment is now colliding with a new technological wave: artificial intelligence. The rise of generative AI and AI-driven automation is fundamentally changing how businesses handle data, not just by making storage or computation more efficient, but by shifting the entire approach to data management, access, and utilisation.
AI as a Data Enabler, Not Just an Automation Tool
AI is becoming the key to managing and governing data at scale. “Generative AI isn’t just about creating new content,” Jason explains. “It’s also about summarisation, automation, and helping businesses finally understand the data they already have.”
One of the biggest shifts is AI’s ability to scan and interpret metadata across vast datasets. Instead of manually auditing where data is stored, how it moves, and who is accessing it, AI agents can no w autonomously map entire data ecosystems. “Imagine an AI-powered compliance officer that continuously audits your data, flags potential governance risks, and even suggests optimisations, all in real-time,” Jason explains.
Another major shift is in data collaboration. Historically, businesses faced significant barriers when sharing sensitive data across departments or with external partners due to security concerns. But AI-powered clean rooms and synthetic data technologies now allow companies to analyse and extract value from data without ever exposing the underlying information. “It’s a breakthrough for industries that rely on data-sharing but face regulatory constraints, like financial services.”
Click on these links to register for the AWS Financial Services Symposiums in Oslo (April 8) and Copenhagen (May 15) to learn how industry leaders are turning AI into real business value.
From Data Overload to Intelligent Action in Financial Services
For banks, insurers, and fintech firms, AI is fast becoming an operational necessity.
“The financial services industry has been an early adopter of AI, but many firms still approach it in silos,” says Jason. “AI isn’t just about fraud detection or risk modeling anymore. It’s about enabling hyper-personalised experiences, streamlining compliance, and even reinventing entire business models.”
One of the most compelling applications of AI in finance is Agentic AI. AI systems that autonomously execute workflows. For example, in customer engagement, traditional machine learning models required explicit programming to trigger recommendations. Now, Agentic AI can observe user behaviour in real-time and adapt offers dynamically. “If a customer is hovering over a loan application page, AI can instantly assess their eligibility, generate a personalised offer, and present it in real-time,” Jason illustrates.
Fraud detection is another area where AI is driving transformation. Traditional fraud models rely on static rules, but AI can now analyze real-time transaction patterns across multiple financial institutions without exposing private data. “Imagine an AI system that can detect fraud patterns across different banks without any of them having to share sensitive customer data directly,” Jason says. “That’s the future of collaborative intelligence in finance.”
From Strategy to Execution
The main challenge isn’t in adopting AI, it’s in integrating it into the fabric of an organization’s data strategy. “Many firms approach AI as a bolt-on technology,” Jason notes. “But AI should be embedded into the core data strategy, from governance to decision-making.”
For businesses looking to move beyond experimentation, the key is aligning data strategy with business objectives, aligning AI capabilities with actual needs, and aligning teams around a shared understanding of AI’s role. “The technology is ready,” Jason says. “The question is whether organizations are ready to use it effectively.”
This transformation is a key focus at the upcoming AWS Financial Services Symposiums in Oslo (April 8th) and Copenhagen (May 15th), where industry leaders, including Jason, will discuss how AI is reshaping data management and financial services. The event will explore real-world case studies of businesses that have successfully leveraged AI to overcome data challenges and unlock new opportunities.
Attendees will gain insights into best practices for implementing AI-driven data strategies, from ensuring robust governance to maximizing operational efficiency. Experts will also share forward-looking perspectives on the evolving AI landscape, helping executives prepare for what’s next.
As AI continues to redefine the way organisations manage and utilise data, the companies that proactively embrace these changes will set the standard for long-term success. The question isn’t whether AI will shape the future of business, it already is. The real challenge is ensuring that businesses are strategically positioned to harness its full potential.
Click on these links to register for the AWS Financial Services Symposiums in Oslo (April 8) and Copenhagen (May 15) to learn how industry leaders are turning AI into real business value.
