Agentic artificial intelligence (AI) refers to systems that can pursue objectives independently, without step-by-step human instructions. These systems assess context, decide on actions, and deliver outcomes independently. In banking, this means a system can approve a transaction, trigger a trade, or send a compliance alert without any manual intervention. A relationship manager might begin the day with an AI assistant that monitors market conditions, detects a price drop, identifies affected clients and drafts personalised outreach with revised strategies. Although the manager approves the content, they did not initiate the process. Responsibility, however, still rests with the human.
This shift from human-led execution to autonomous action introduces a new kind of risk. Banks may be held liable for decisions made by systems they neither configured nor controlled directly. Ms Sina Wulfmeyer, chief data officer at Unique AI, helps regulated institutions deploy such systems. “We are moving from systems that wait for commands to systems that drive results. This changes how we think about process, trust and talent,” she said. Supervisory models must now focus on defining decision boundaries and validating outputs, rather than micromanaging individual steps. Institutions must rethink governance to match systems that act with initiative.
New system architecture must support outcome-driven AI behaviour
Earlier banking technologies automated routine steps using hardcoded rules. These tools required human prompts and followed predictable sequences. Generative AI introduced conversational interaction, but still depended on instruction. Agentic AI now accepts a goal and builds the necessary process to achieve it.
Wulfmeyer explained that most banks sit in the middle layer of a capability pyramid. Their systems complete tasks in a fixed sequence, such as document retrieval and due diligence reporting. At the top of the pyramid, agentic systems receive only an objective, such as onboarding a client, and construct the workflow to achieve it. “The agent builds the flow; it does not follow a flow,” she said. Banks adopting such systems must redesign decision-making flows, reassess control mechanisms and define who remains accountable at each step. Architecture, governance and compliance must align with outcome-driven execution. These architecture-level decisions now reshape day-to-day roles, especially in frontline operations.
Daily roles shift from execution to supervisory accountability
The shift to autonomous systems redefines how work is performed in banking. The relationship manager scenario illustrates this clearly: what was once a manual, time-intensive process becomes a system-led, responsive workflow. Know Your Customer reviews now follow the same logic. An AI system can extract data from client submissions, populate regulatory fields and prepare compliance reports. The manager reviews, but no longer compiles. Wulfmeyer noted that human work increasingly centres on reviewing outcomes, correcting exceptions and providing final approvals.“People become outcome validators, not process drivers,” she said. This transition changes more than efficiency. It reshapes how institutions assign responsibility, how staff utilise their time, and how clients experience services.
Institutional readiness requires infrastructure, compliance and leadership clarity
Adopting agentic AI safely depends on making informed choices across infrastructure, risk, compliance, and internal capability. Many banks are moving toward hybrid models that keep sensitive data on-premise and run scalable workloads in the cloud. These require secure system architecture and skilled engineering teams. Talent remains a major constraint. “Few people understand both how to build AI systems and how to audit them,” said Wulfmeyer. Banks need leaders who can evaluate whether outputs are traceable, explainable and aligned with internal policy. AI governance frameworks are still evolving. “They do not trust what they cannot audit,” she said. “Most agents are not fully auditable right now.”
Agentic systems often follow probabilistic paths. This makes their decisions less predictable and harder to track. In regulated environments, that creates serious compliance risks. Wulfmeyer warned that delays in adapting can be costly. “What you build today might already be outdated in six months,” she said. “Leadership cannot wait six months to form a committee; technology will already lag by then.” These challenges reveal a deeper issue. Most institutions lack the structural capacity to govern systems that operate autonomously at scale.
Workforce roles and institutional trust must evolve in parallel
Agentic AI transforms how institutions utilise their personnel. Manual, rule-following roles face rapid automation. “Assistant relationship managers who spend their day filling out forms are particularly vulnerable,” said Wulfmeyer. This change requires new functions in AI supervision, compliance interpretation and exception handling. Institutions that fail to invest in these areas risk degradation in service quality and regulatory alignment. Trust in system outputs is now a key variable. “Without trust in the system, there is no adoption, and without adoption, there is no return on investment,” said Wulfmeyer. Trust must be engineered into the system from the start. It depends on transparent models, reliable performance and operational readiness to collaborate with machine-led processes.
Agentic AI marks a test of institutional maturity and leadership focus
Agentic AI is already reshaping the financial services landscape. Institutions that treat it as an automation tool will fall behind those that integrate it as a shift in operational logic. Human expertise remains essential. Relationship managers, analysts and compliance officers will work alongside systems that enhance speed, accuracy and foresight. “People who use AI effectively will outperform those who do not, and that performance gap will grow,” said Wulfmeyer. AI expands rather than replaces judgment. Future managers will rely on intelligent systems to surface risks, model options and support decisions previously made by teams of analysts.
Agentic AI reshapes not only operations but also institutional strategy. Leaders must decide where autonomy supports their competitive edge and where it introduces exposure they cannot yet manage. Agentic AI eliminates the separation between operations, risk and strategy. Systems now act across all layers simultaneously. Leadership, institutional agility and structural clarity have become prerequisites for safe deployment. This is not a technical upgrade. It is a test of readiness.
The world of AI is evolving at an unprecedented pace, and at the forefront of this revolution is Agentic AI. Unlike passive automation, this advanced intelligence actively reasons, plans, and takes initiative. Its next-generation capabilities are set to transform industries, and in banking, its impact will be nothing short of revolutionary.
Join us as we explore:
- Understanding Agentic AI and how it represents a paradigm shift in its capabilities.
- Exploring how Agentic AI enhances internal operations by streamlining workflows, automating repetitive tasks, and improving decision-making to create more efficient and responsive retail banking services.
- How Agentic AI is transforming customer interactions by delivering hyper-personalised experiences, anticipating individual needs, and providing proactive, tailored solutions that deepen trust and loyalty.
- Real-world applications of Agentic AI, such as hyper-personalised customer engagement, advanced fraud detection, autonomous wealth management, and dynamic risk modeling.
Agentic AI is not just a tool, it’s a transformative force reshaping the competitive landscape of banking. This is your opportunity to gain tactical insights, anticipate the future, and position yourself at the forefront of AI-driven finance.
Agenda:
- 5:30 PM – 5:40 PM: Opening Remarks by Urs Bolt, Chairman, The Banking Academy
- 5:40 PM – 6:10 PM: Presentation by Sina Wulfmeyer, CDO, Unique FinanceGPT
- 6:10 PM – 7:00 PM: Q&A Session followed by Closing Remarks
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