“The solution would have been very difficult in an ecosystem without high-quality data,” says Aggarwal. “But when you combine a rich data ecosystem with agentic tools, you move from incremental improvements to high-velocity change.”
Aggarwal says the cycle to deploy solutions can be accelerated by investing in AI data and tools and focusing on rapid experimentation. Teams learn that if they meet customer needs and work much faster on a wide range of solutions, the entire innovation cycle becomes faster.
For example, Capital One used customer insights to build a cutting-edge, multi-agent AI framework called Chat Concierge to enhance the customer experience for car buyers and dealers. In a single conversation, the Chat Concierge can perform tasks like comparing vehicles and scheduling a test drive or appointment with a salesperson to help car buyers choose the best option.
Agarwal explains that car buyers can connect directly with the Chat Concierge through participating dealer websites. Dealers can access and handle chat through the Navigator platform. An AI assistant consists of multiple logical agents that work together to mimic human reasoning, allowing it to provide information and take actions based on customer requests.
Elements of an AI-First Mindset
According to a recent MIT Technology Review Insights survey, 70% of leaders say their company uses agentic AI to some extent. Nearly half of executives say agentic AI systems are highly capable of improving fraud detection (56%) and security (51%), reducing costs and increasing efficiency (41%) and improving customer experience (41%).
Looking to the future, the likelihood of achieving these results seems even greater. More than half of banking executives surveyed say they expect to continue to improve fraud detection (75%), security (64%) and customer experience (51%). Agent AI use cases that show strong potential to transform the customer experience in financial services include responding to customer service requests, adjusting bill payments to align with regular pay, or extracting key terms and conditions from financial agreements.
Putting the customer at the center of change requires an AI-first mindset. Companies must move from simply enhancing an existing product to fundamentally reimagining the problem and user needs through the lens of AI’s capabilities.
Some of the best practices suggested by Aggarwal include:
Reimagine AI’s core function to solve a user problem: Agarwal says, “The real value isn’t in chasing AI hype; it’s in solving meaningful customer problems. By focusing on impact, we ensure that our innovation isn’t just fast; it’s transformational.”
Start with high-quality, well-organized data as a foundation: Agarwal explains, “Data preparation and integrated information across all systems are the non-negotiable foundations of AI. A clean data layer orchestrates the agentic loop – enabling the perception, reasoning and execution needed to solve a customer’s problem before they even ask.”
Rebuild workflows with embedded AI from the ground up: “People think of models as black boxes, but agentic systems require tremendous rigor and oversight. Having a data ecosystem that is well-governed and responsible AI standards are essential pillars for building trust in these systems,” says Agarwal.
