AI is evolving quickly and the next major innovation, agentic AI, is already taking shape.
Just as most companies are ramping up their generative AI efforts, agentic AI is quickly gaining momentum. According to a report from Deloitte, a quarter of companies currently using generative AI will experiment with agentic AI pilots this year, and they expect half will do so by 2027. “Some organizations,” Deloitte writes, “will even begin deploying agentic AI into day-to-day workflows by the end of 2025.”
For most organizations, especially those in highly-regulated industries like financial services, broad deployment of agentic AI is likely a long way off given the significant technical, compliance, and operational effort it requires. Still, it’s important to start building awareness and taking proactive steps to lay the groundwork for this new tool. Early action will not only make future adoption smoother but also strengthen your current AI capabilities.
Generative AI vs. Agentic AI
Generative AI is reactive, requiring prompts to create new content or analyze vast quantities of data to unearth trends or patterns. Although it can learn to personalize its output based on its knowledge of the user, it is still dependent on prompts to provide content.
Conversely, agentic AI can act and learn independently while leveraging large language models (LLMs), machine learning, and natural language processing (NLP). It works autonomously in an iterative manner to train itself to solve complex problems that require multiple steps to address.
Agentic AI can make decisions and take action, with little to no human supervision. In fact, agentic AI can plan and execute complex strategies to achieve a goal, gathering data from the external world which it processes and acts on in real-time, such as a self-driving car traveling to a destination across town.
The impact of agentic AI in financial services
Let’s take financial services as an extended example. Markets move fast, but agentic AI could help firms stay ahead of the curve, analyzing trends and trading patterns to change investment strategies on the fly. AI agents could enable real-time compliance risk assessments as they find and assess anomalies on their own, growing in accuracy as they learn. Agentic AI could also handle mundane, repetitive activities like processing transactions and data entry faster and, ultimately, more accurately, than humans.
In another example, while a generative AI-powered virtual agent can provide human-like answers to customer questions, a chatbot that employs agentic AI can do much, much more. The agentic chatbot could analyze the customer’s investment history, appetite for risk, current financial position, and current sentiment to determine if an opportunity exists to sell an additional financial product. If agentic AI determines it’s appropriate, it can make personalized upsell suggestions.
“This kind of personalization is critical for remaining competitive—a recent Salesforce report found that 53% of financial customers would switch providers for better digital experiences.”
