HSBC employs over 225,000 people globally. Barclays, 72,000. JP Morgan Chase, 309,926. Banking has always been an industry of scale. Now, Generative AI and multi-agent systems (MAS) are redefining what’s possible.
Klarna’s AI agents famously handle two-thirds of customer service chats — the workload of 700 employees. UBS is rolling out AI avatars of its analysts. Nubank turned a multi-year ETL monolith migration into a weeks-long task — slashing engineering hours 12x and cutting costs over 20x with an army of agents.
Two forces are driving this shift. First, technological developments like agent-to-agent (A2A) communication, which lets these systems collaborate, delegate, and escalate like teams do, rather than acting in isolation.
And second, cost. Agentic systems strip away layers of complexity and overhead. They don’t just work faster — they work cheaper, replacing sprawling teams and brittle processes with something leaner, smarter, and vastly more efficient.
As we highlighted in How to Build the Agentic Enterprise & Planet Agent what this means is a total redesign and restructuring of work itself. Not just a replacement for a worker, but a replacement for an entire business unit.
In this new environment, a ten-person bank is a real, and rapidly approaching, possibility.
Here’s what that might look like:
The CEO
The CEO of an AI-enabled bank leads not just a financial institution but an orchestrated system of AI agents, each operating with autonomy across key functions. Instead of managing large teams, the CEO oversees a lean operation where AI agents handle execution, data analysis, and customer interactions — freeing leadership to focus on strategy, governance, and long-term value creation.
In the agentic bank, the CEO still carries the responsibilities of any bank leader: setting the company’s vision, managing board and stakeholder relationships, and driving innovation to create a frictionless, AI-first banking experience.
AI Governance (Chief AI Officer)
The Chief AI Officer (CAIO) is responsible for the governance and oversight of the bank’s AI-driven agents, ensuring they operate transparently, ethically, and in full compliance with regulatory frameworks. Rather than managing static AI models, the CAIO oversees a dynamic, self-learning system where AI agents continuously refine their decision-making processes.
This role involves setting policies that govern agentic behaviour, ensuring that AI-driven decisions are explainable, auditable, and aligned with the bank’s risk appetite.
Compliance and Legal (Chief Compliance Officer)
In the agentic bank, compliance becomes proactive. The Chief Compliance Officer is supported by a domain-specific AI engine trained on live financial regulations and internal data — surfacing risks, ensuring auditability, and adapting instantly to changes in policy.
LLMs built for the financial sector, like FinLLM, are aligned with frameworks such as the FCA and EU AI Act, enabling continuous oversight without delays. No more manual gap analysis. No more retrospective fixes. Just real-time compliance that scales.
Treasury, Finance and Risk Management (Chief Finance Officer)
In the agentic bank, the CFO delegates core financial operations to intelligent agents — automating accounting, forecasting, treasury, and spend management with precision and control.
In risk management, agents continuously monitor credit, market, and operational exposure in real time — flagging anomalies, surfacing risk insights, and ensuring compliance with internal thresholds and regulatory frameworks.
Customer Support (Chief Experience Officer)
The Chief Experience Officer (CExO) designs an AI-first experience led by the Personal AI Banker — an intelligent agent that adapts to each customer in real time.
No menus. No queues. Just natural conversations. The AI Banker handles onboarding, KYC, transactions, and support with instant, context-aware responses.
It doesn’t just react — it predicts. By understanding intent, the AI Banker surfaces insights, makes recommendations, and evolves the experience around the user. Over time, this moves towards Generative UI: dynamic, real-time interfaces built on user behaviour and intent — not just a chatbot.
Product Development and User Acquisition (Chief Product Officer)
The Chief Product Officer (CPO) oversees an AI-native product engine, where autonomous agents use sentiment analysis to continuously analyse customer behaviour, market trends, and competitor activity to shape the bank’s offerings in real time. Instead of relying on traditional product cycles, this system iterates dynamically, refining products and strategies based on live data.
One of the key advancements in this approach is the use of AI-driven synthetic personas — digital stand-ins for real users that enable continuous, scalable product testing. Rather than relying on focus groups or survey-based insights, these dynamic personas simulate customer responses, helping the bank rapidly test new financial products, refine messaging, and optimise experiences.
Marketing (Chief Marketing Officer)
In the agentic bank, the Chief Marketing Officer (CMO) uses generative AI to produce and personalise campaigns at hyperscale — all aligned to brand guidelines. AI agents can plug directly into internal tools and marketing systems, enabling seamless creation, distribution, and performance monitoring across digital channels.
But they don’t stop at execution. These agents learn — testing creative, analysing funnel data, and refining campaigns in real time. What once took a team of marketers now happens in minutes: fully automated, insight-led, and always optimising.
Engineering (Chief Technology Officer)
The Chief Technology Officer (CTO) is responsible for building the AI-driven backbone of the bank, ensuring that autonomous agents manage infrastructure, security, and continuous development. Engineering agents oversee everything from backend optimisations to real-time system maintenance, allowing the bank to operate with minimal manual intervention.
These AI engineering agents maintain and iterate on the system, identifying inefficiencies, deploying updates, and optimising performance without human bottlenecks. The result is an infrastructure that evolves dynamically, ensuring peak performance, security, and resilience at all times.
Cybersecurity (Chief Security Officer)
The Chief Security Officer (CSO) is responsible for ensuring the resilience of an AI-first bank, where security isn’t an afterthought but a system-wide function embedded into every layer of the architecture. Rather than relying on static defences, the CSO deploys autonomous security agents that monitor, predict, and neutralise threats in real time — working faster than human teams ever could.
These AI security agents operate continuously, adapting to emerging attack patterns, reinforcing data integrity, and securing customer interactions. Instead of waiting for breaches, they proactively identify vulnerabilities, automatically implementing countermeasures before threats escalate.
Operations & Payment Systems (Chief Operations Officer)
The Chief Operations Officer (COO) oversees the core engine of the agentic bank — the models and systems that govern transactions, ledgers, and payments.
Their primary role is to refine and scale the bank’s agentic infrastructure. This includes monitoring agent performance, assessing systemic risks, and evolving the business model to fully leverage AI’s capabilities.
Meanwhile AI agents monitor operations in real time, identifying inefficiencies, flagging anomalies, and adapting workflows on the fly. Payment rails, reconciliation processes, and ledger integrity are constantly audited and optimised — not quarterly, but continuously.
For example, an agent might detect delays in cross-border settlements, reroute transactions through faster corridors, and update compliance logs automatically — all before a human intervenes. The COO no longer reacts to operational issues; they supervise a system that self-corrects.