AI is evolving, from answering questions to taking action. For years, most AI systems have been reactive: they interpreted input and returned predictions or classifications. But the latest generation of models can go further. Beyond answering questions, they can interpret goals, plan multi-step actions, call external tools and adapt their behaviour over time. This marks a critical shift: from AI as a passive assistant to AI as an active agent.
Organizations across industries are exploring how agentic AI systems can take on meaningful tasks with minimal oversight. Focused on the financial industry, this white paper explores the current adoption of agentic AI in this field, the benefits, adoption challenges and real-world use cases. In addition to this, it offers a practical framework help organizations adopt and scale agentic AI effectively in customer service.
Key takeaways
- Who it’s for. CIOs, CTOs, CFOs, Chief Risk & Compliance Officers, Heads of Digital Transformation, Innovation & AI Leaders, Data Science Directors, and Finance Program Owners in banks, fintech and enterprise financial organizations.
- Signals from the market and current agentic AI adoption across fintech industry.
- Agentic AI. Definition, key capabilities, real-world use cases and agentic AI systems in banking vs. traditional automated banking tools.
- Trust crisis and uncertain economic climate as cornerstone framing today’s financial landscape.
- Challenges of agentic AI adoption and golden questions that help navigate them.
- A practical agentic AI adoption framework. By applying three distinct lenses (Complexity, Sensitivity, Volume) and asking targeted questions, you can map a relevant use case to one of four adoption modes which provide a practical, deliberate way to align AI deployment with business goals, risk tolerance and resource priorities.
