A robust data foundation is critical for effective AI initiatives in financial services
AI only works as well as the data you feed it. In financial services, that means if your data isn’t cleaned, structured, and accessible, you’re not going to get intelligent results. You’ll get noise. Now, that’s not just a technical detail. It’s a strategic decision, whether you’re building smarter credit scoring systems, faster underwriting, or modernizing the claims process, none of it happens without clean and integrated data.
Most financial leaders say they want to personalize experiences using AI. The desire is there. But here’s the reality: According to the Enterprise Strategy Group, only 39% of financial institutions have actually improved customer experience using AI. That’s low. If we’re serious about making AI more than just a buzzword, we need to start with a solid data infrastructure, not just more data, but better data.
Financial institutions already using AI effectively are seeing big advantages. Take credit scoring. With quality data, AI models can process applications and assess risk faster and more accurately than traditional methods. This doesn’t just save hours; it increases approval rates and improves the customer’s experience from the start. Same goes for underwriting, AI speeds up decisions, reduces redundant paperwork, and removes friction for both lenders and borrowers.
Claims processing is another space transforming quickly. Firms using AWS tools like Amazon Bedrock are creating automated, AI-driven systems that review documentation, analyze images of damaged assets, and generate initial reports for human review. It’s efficient, accurate, and scalable, teams still control the final decision, but much of the manual work is eliminated.
For C-suite leaders, this isn’t about trying emerging tech because it sounds impressive. It’s about staying competitive. If your AI project isn’t grounded in solid data, you shouldn’t expect results. Your competitors won’t make that mistake, and neither should you.
Leveraging AI-driven tools to meet evolving customer expectations boosts operational efficiency and satisfaction
Customers today expect speed, accuracy, and relevance. They don’t care whether that experience is powered by humans or AI, what matters is a fast, accurate response. That’s where AI is excelling. Financial institutions deploying tools like intelligent chatbots, natural language interfaces, and sentiment analysis are delivering more efficient customer service, with fewer delays and lower support costs.
The reality is simple: your customers are interacting with intelligent systems every day. If your bank, insurer, or fund isn’t meeting that standard, you’re behind. Tools like Amazon Lex let banks and insurance providers build conversational agents that reduce wait times and resolve issues on the first interaction. This isn’t automation for the sake of removing people, it’s about cutting friction, scaling support, and improving experience.
Here’s a clean example: Australian health insurer nib rolled out an AI assistant called Nimby, powered by AWS. The results were clear, Nimby handled such a large volume of routine inquiries that human agent involvement dropped by 60%. That’s not small. Agent-handled phone calls were also down 15%. That means employees focused on more complex, meaningful tasks and costs dropped without compromising service.
nib continued expanding AI across other services, too, beginning with claims processing and personalization of product recommendations for services like travel insurance. These are high-impact use cases that showed measurable returns, both in terms of customer satisfaction and operational performance.
For business leaders, this isn’t about launching chatbots just to check the innovation box. It’s about deploying focused tools that reduce overhead, improve user experience, and prepare your organization to scale intelligently as customer expectations continue to evolve. Done right, this reduces complexity, not by removing people, but by amplifying their impact where it matters most.
Fostering a culture of continuous learning is essential for maximizing AI’s impact on the customer experience
AI is evolving fast, faster than most organizations can keep up. What you implement today won’t stay relevant tomorrow unless your teams are learning at the same speed. AI isn’t plug-and-play. It requires people who understand how it works, where it fits, and how to use it to improve real outcomes. That kind of talent doesn’t come from hiring alone, it comes from building a learning mindset into the core of your organization.
Executives should prioritize upskilling customer-facing teams, not just technical staff. These are the people who are closest to your customers and most likely to identify how AI can improve the interaction. Give them access to tailored learning resources. Build training time into the workweek. Let them use real tools in real environments. This drives confidence, not just competence, and it accelerates adoption in meaningful ways.
You don’t need formal certifications for every role. What you do need is a clear approach to reskilling and cross-training as tools shift and products change. That might mean training frontline staff on how AI-generated recommendations work so they’re equipped to explain outcomes to customers. Or helping support teams understand machine learning models enough to troubleshoot issues when automation fails.
What’s often missed by leadership is that learning isn’t a one-off initiative. Training programs should evolve as fast as the AI stack you’re building. Leaders who revisit those programs regularly, adjusting for new technologies and skills gaps, place their organizations in a stronger position. The endgame isn’t just better adoption. It’s a workforce that feels confident using AI to solve real problems.
For decision-makers, scaling this culture pays off. Teams work more efficiently. AI tools are used more effectively. And the customer experience improves as your people learn how to combine human strengths with intelligent systems. That’s where differentiation starts, not just from the tools you buy, but how well your people use them.
Key executive takeaways
- Build on clean data: Leaders should prioritize high-quality, well-structured data to ensure AI tools deliver accurate, actionable results in credit scoring, underwriting, and claims automation. Poor data quality remains a top reason why most financial firms haven’t achieved meaningful AI-driven CX gains.
- Deploy AI where customers expect it: Executives should implement AI-powered tools like chatbots, virtual assistants, and sentiment analysis to meet rising customer demands for faster, personalized interactions. Use cases like nib’s AI assistant show clear cost savings and higher satisfaction with the right tools.
- Upskill your people alongside your AI: To maximize ROI from AI investments, leadership must create a culture of continuous learning that empowers teams to confidently use and adapt to evolving technologies. Ongoing training ensures your workforce can integrate AI into real workflows and drive better customer outcomes.
