In its report "Seizing the agentic AI advantage," McKinsey identifies agentic AI as banking's next frontier: autonomous agents that reason, plan, and execute end-to-end workflows, not chatbots that only answer. The message for banks is clear: those who experiment today will lead tomorrow.
The revolution of autonomous artificial intelligence agents has already begun. Is your bank ready? In the banking industry, the race for operational efficiency and frictionless experiences never stops. We have seen the evolution from digital banking and mobile apps to the adoption of generative AI in the form of chatbots and assistants ; technology has redefined how customers interact with their banks. But what comes next? A new wave is arriving: agentic AI . According to a recent report by McKinsey , titled "Seizing the agentic AI advantage," autonomous generative AI agents are the next major disruption. This technology goes far beyond chatbots. And for banking leaders, understanding it is no longer optional, it is a strategic priority. Below, we break down the key ideas from the McKinsey report and what they mean for the future of your institution. What is agentic AI and why is it key for banking? Unlike traditional AI, which responds to specific commands or questions, agentic AI is made up of autonomous agents that can reason, plan, and execute complex tasks proactively, in order to reach a defined goal. Example: Chatbot : A customer asks for their balance. The bot queries a database and answers. AI agent : A customer wants to dispute a charge and understand how it affects their loan application. The agent could: Identify the suspicious charge in the transaction history. Initiate the dispute process autonomously, using the bank's internal tools. Access the status of the loan application in another system (CRM or origination system). Analyze the potential impact of the dispute on the customer's risk profile. Communicate a complete summary to the customer with the next steps and possible implications. As McKinsey notes, these agents act as "digital workers" that can orchestrate tools, data, and other APIs to complete a job from end to end. 3 key insights from the McKinsey report for the banking industry From McKinsey's analysis we draw three points that resonate directly with the challenges and opportunities of the financial sector: 1. Automating complex processes Agentic AI makes it possible to automate entire workflows. The real promise of AI agents is not in automating simple tasks, but in orchestrating end-to-end workflows that today require considerable manual intervention. Examples for banking : Customer onboarding Claims processing Fraud management Debt renegotiations An AI agent could handle document collection, verification across multiple systems, customer communication, and rule-based decision making, escalating to a human only in exceptional cases. This speeds up response times, reduces costs and improves operational efficiency. 2. Exponential improvement in productivity and customer experience (CX) McKinsey predicts that agentic AI can "supercharge human performance" and reinvent CX. By freeing teams from complex procedural tasks, it lets them focus on high-value relationships and strategic problem solving. For banking: your customer service agents, instead of spending time navigating five different screens to resolve a query, can supervise the work of a team of AI agents, stepping in to add empathy and critical judgment. Result : better-served customers, with greater speed, proactivity, and personalization, and employees with more time to contribute human capabilities. 3. The time to act is now McKinsey is emphatic: those who experiment today with agentic AI by building and deploying AI agents for specific use cases will be the ones leading the market tomorrow. Waiting for the technology to be "perfect" will leave them behind. For banking: it is not just about technology: governance, security, and compliance are key. You need a platform that lets you build, test, manage, and scale these AI agents securely and with governance, especially in an environment as regulated as finance. How to prepare for the era of agentic AI? The McKinsey report is a wake-up call. The conversation about AI in banking is no longer "how do we answer questions" but "how do we solve problems autonomously." To capitalize on this advantage, banking leaders must start asking the right questions: Which customer service processes are the most costly and inefficient? Where could we reduce costs and improve CX with autonomous agents? The key to making this vision real is having a robust and specialized SaaS platform. We help banks make this leap At Delto we developed a solid and secure platform to design, test, and scale AI agents so that banks and financial institutions can: Design their own AI agents Test them in controlled environments Scale them with governance and compliance Transform customer operations for this new era Agentic AI is already transforming the future. Is your bank ready?
What is the difference between a chatbot and an AI agent in banking? A chatbot answers specific commands (for example, checking a balance). An AI agent reasons, plans, and executes complex tasks end to end: it identifies a suspicious charge, initiates the dispute autonomously, checks the status of a loan in another system, and communicates the summary to the customer.
What does the McKinsey report say about agentic AI in banking? The report "Seizing the agentic AI advantage" states that autonomous generative AI agents are the next major disruption. It highlights three axes for banking: automating complex processes, an exponential improvement in productivity and CX, and the urgency to experiment now with governance and compliance by design.
Why should banks act now? According to McKinsey, institutions that experiment today by building agents for specific use cases will lead the market tomorrow. Waiting for the technology to be "perfect" leaves the bank behind. The key is a platform that lets you build, test, and scale agents with governance and compliance.
What is agentic AI? Agentic AI is an approach where autonomous AI agents reason, plan, and execute complex tasks end to end to reach a defined goal, orchestrating tools, data, and APIs. Unlike a chatbot that only answers specific questions, an agentic system acts like a digital worker that completes the entire process.
What is agentic banking? Agentic banking is the application of agentic AI to banking operations: autonomous agents that run workflows such as customer onboarding, claims processing, fraud management, and debt renegotiation, escalating to humans only in exceptional cases. Because finance is highly regulated, it requires a platform with governance, security, and compliance built in.