The Hardest Place to Deploy Conversational AI - and Why It Matters
In this blog post, we'll explore how banking will be the defining moment for agentic AI.
Introduction
We’re sure you’ve heard the latest buzzword ‘conversational AI’ floating around. The technology seems tantalizing, but many businesses remain skeptical of its practical application. What about the glaring failures of generative AI - hallucinations, data breaches, or their clear ineptness at answering complex queries?
Interestingly, the industry that perhaps places the most weight on secure and reliable AI practices has their eyes on conversational AI, giving rise to another buzzword — conversational banking.
The testing ground for conversational AI
Banking is a particularly personal affair. Customers want to know that what they say will remain confidential, meaning that they need to have unwavering trust in the agent they are communicating with. For conversational AI to plant its legs in this industry holds great potential in not only transforming banks’ customer engagement, but in spearheading a movement towards conversational AI that is reliable, accurate and trustworthy.
Conversational banking’s success won’t come from the likes of a basic chatbot button on the FAQ page answering pre-defined questions. Rather, conversational banking will redefine entire customer journeys.
AI agents that can detect intent
Our Conversational Banking AI Agent Builder achieves this by embedding behavioral intelligence at every step of the conversational journey. In the agent builder, you can configure guardrails and fact injection at every point, and make use of behavioral algorithms to detect intent.
This way, you can:
Unify customer journeys: AI agents can carry a conversation across channels — chat, voice, human agents, and devices, without forcing the customer to from point A each time. Banks that master this can eliminate previous frustrations and provide a seamless customer experience.
Scale client-level interactions: conversational agents with the right behavioral models can learn from human behavior in real time and dynamically adjust offers, tone and subject-matter. This allows each individual customer to receive what was once only reserved for client-level relationships.
Implement contextual routing: AI agents can detect and redirect customers based on changing context. By setting contextual triggers in your agentic framework, customers can be rerouted to where they need to be, in real-time.
Acquire additional knowledge: Every conversation provides data that humans cannot collect at scale. By letting the customer lead the interaction, AI agents can gain knowledge of behavioral traits and patterns that can assist in perfecting future interactions, or detecting fraud when anomalies arise.
Conversational banking will make or break agentic AI’s ability to perform tasks reliably, accurately and in compliance with privacy policies.