Banks are beginning to move artificial intelligence beyond systems that answer questions, generate documents or predict outcomes towards technology capable of taking actions inside financial processes. At Ai4 2026 in Las Vegas, banking and technology executives described an emerging model in which AI agents could analyse information, determine the next step in a workflow and carry out routine actions without waiting for an employee at every stage. The discussion, however, exposed an important distinction between the industry’s ambitions and its immediate reality. The agentic bank is beginning to emerge, but financial institutions remain reluctant to give AI unrestricted authority over consequential decisions involving customers, money and regulatory obligations.

Ash Kaduskar, Head of AI and Advanced Analytics at First Citizens Bank, described the objective as making the organisation faster and more intelligent while redesigning how work is performed. The distinction between conventional automation and agentic AI is important. Traditional systems generally follow predetermined rules, while agentic systems potentially have greater freedom to interpret information, determine what needs to happen next and execute a sequence of tasks. That makes them particularly interesting for banking processes containing large quantities of unstructured information that previously required employees to read documents, transfer information between systems and make repeated assessments.

Third-party risk management provides a useful example. Before a bank introduces a new supplier, extensive information may need to be collected about security, compliance, financial stability and operational risk. Suppliers can face hundreds of questions, while specialists across technology, legal, cybersecurity and risk subsequently review their answers. Generative AI could retrieve information from existing documentation, prepare responses and carry out much of the preliminary assessment, allowing employees to concentrate on exceptions rather than reviewing every routine item manually. The significance is not simply that individual tasks become faster. Processes that previously moved between multiple departments over several months could potentially be compressed substantially if AI handles information gathering, classification and initial assessment continuously.

The same principle can be applied across controls, governance, compliance and other internal processes. These areas may become some of the earliest candidates for agentic AI precisely because the desired outcomes can often be defined relatively clearly. Information must satisfy specific requirements, controls must be present and exceptions must be escalated. First Citizens’ approach is therefore initially concentrated heavily on internal processes rather than placing autonomous agents directly in front of customers. This is an important counterweight to the popular idea of an autonomous bank where AI independently manages customers’ financial lives. The near-term transformation is likely to be much less visible, with customers experiencing faster onboarding, quicker responses and fewer administrative delays while much of the change happens behind the interface.

The central question is how much decision-making authority should be transferred. The panel broadly favoured automating preparation and routine assessments while preserving human intervention for exceptions and high-impact decisions. An agent could collect information, determine whether requirements have been satisfied and recommend that a process continue, but a person might still approve the final action when financial, regulatory or customer consequences become significant. Autonomy therefore becomes a spectrum rather than a simple choice between humans and machines. The value comes from removing unnecessary human involvement from thousands of routine steps without removing human accountability from decisions where judgement remains important.

That distinction also exposes one of the risks of agentic systems. An agent capable of adapting to previous decisions could potentially begin reproducing patterns that were never intended to become formal policy. Systems therefore need monitoring, defined permissions and audit trails so that changes in behaviour can be detected. One anecdote discussed during the panel involved an AI system that allegedly became increasingly willing to approve certain loan cases after observing that human loan officers usually accepted similar recommendations. The example should not be treated as a documented banking incident without independent evidence, but the underlying governance problem is credible: systems that learn from historical human decisions can reproduce existing patterns or develop unintended shortcuts unless their permitted actions remain constrained.

For regulated institutions, governance therefore cannot be added after an AI application has been built. Kaduskar argued that there is effectively no AI use case valuable enough to justify creating a serious regulatory or internal audit problem. Risk, compliance and legal teams need to become partners in development rather than departments asked to approve a completed system at the end. Banks also need standardised methods for assessing generative AI so that applications can pass through repeatable controls rather than requiring the organisation to reinvent governance for every project. Once an institution has clearly defined what data an agent can access, which decisions it can make, what evidence must be retained and when a human must intervene, additional applications can potentially be deployed more quickly. Governance effectively becomes infrastructure.

This also changes the economics of AI investment. Banks initially experimented with large numbers of small applications because generative AI was new and organisations wanted employees to explore what it could do. Attention is now shifting towards larger processes capable of generating measurable financial value. Kaduskar described these as organisational highways: workflows passing through multiple departments where redesign can produce substantially greater returns than automating isolated individual tasks. A manual activity performed once a month may not justify AI, while another performed hundreds of times every day could represent an obvious candidate.

The commercial test remains traditional despite the new technology. An AI investment ultimately needs to increase revenue, reduce costs or prepare the organisation for future competitive requirements. Deploying an agent simply because agentic AI has become fashionable does not create value. This also changes the debate around AI operating costs. Token consumption and computing expenses become a problem when companies cannot demonstrate what they receive in return. If an agent materially reduces processing costs, accelerates revenue or enables substantially more business to be handled without equivalent increases in staffing, its computing expense becomes easier to justify. The important metric is therefore not how much AI an organisation consumes, but the economic output created by that expenditure.

Banco Azteca’s perspective added the revenue side of the equation. Beyond reducing processing times, AI agents could eventually use customer and transaction data to identify financial needs that traditional segmentation misses. An institution could potentially determine that a customer needs a product it does not currently offer, identify a more appropriate credit facility or recognise an opportunity to retain a customer before that person moves elsewhere. Agentic systems could therefore evolve from executing existing banking processes towards helping institutions identify new products and commercial opportunities. The objective would shift from simply making the bank more efficient towards using AI to increase sales, deepen customer relationships and identify previously invisible demand.

Customer interaction could also change substantially. Instead of navigating through an application, selecting a product and completing a series of screens, customers could increasingly interact with a conversational financial assistant. The agent might understand an instruction, identify the relevant banking process and coordinate the necessary systems in the background. Banking would become less dependent on navigating individual applications and more focused on expressing an intended outcome. The customer may not even know that multiple agents are handling identity checks, risk assessments, product selection and administrative processes behind the conversation.

Yet the workforce consequences could be considerably larger than the change in customer interfaces. If agents take responsibility for collecting information, making routine assessments and coordinating processes, many administrative roles will change. Panel participants generally framed this as increasing employee productivity rather than simply eliminating positions, with people moving towards exceptions, supervision and higher-value decisions. Banks frequently need to process more business without increasing staffing at the same rate, making AI potentially valuable as a way of increasing organisational capacity rather than purely reducing headcount.

Kaduskar nevertheless raised a more difficult question: what happens to jobs whose value has historically depended heavily on possessing information that AI can now retrieve instantly? Knowledge itself becomes less scarce when employees throughout an institution can access specialised information through AI. Expertise will increasingly need to involve judgement, experience, accountability and the ability to determine what should be done with information rather than simply knowing where to find it. Banks will consequently need workforce strategies alongside their technology strategies, including retraining employees whose existing responsibilities become increasingly automated.

There is also a competitive dimension. Large banks can invest heavily in proprietary platforms, specialist AI teams, cybersecurity, model testing and governance infrastructure. Smaller institutions may have considerably less capital available for experimentation. If agentic AI materially reduces operating costs, improves customer acquisition or enables faster product development, the technology could widen the gap between institutions able to build sophisticated internal capabilities and those dependent largely on third-party platforms. At the same time, external AI services could eventually give smaller banks access to capabilities that previously required substantial internal technology teams, meaning the competitive outcome is not yet predetermined.

For the banking industry, the immediate future therefore appears more controlled than the term autonomous AI might suggest. Agents are likely to receive progressively greater authority inside narrowly defined processes while people retain control of exceptions, final approvals and decisions carrying substantial financial or regulatory consequences. As systems prove reliable, that boundary may gradually move. The larger transformation is that software is beginning to move from supporting decisions towards participating in them, changing the governance question fundamentally. Banks no longer need only to ask whether an AI system produced the correct information. They increasingly need to know what the system did, why it was permitted to do it, which rules governed the action and who remains accountable when something goes wrong.

The agentic bank is therefore unlikely to arrive through a single autonomous system suddenly taking control of an institution. It will emerge gradually as thousands of individual decisions and administrative steps move from employees towards governed AI agents. The competitive advantage will not necessarily belong to the bank that gives machines the most independence. It may belong to the institution that determines most precisely which decisions should be automated, which should remain human and how the two can operate together at scale.

Source: CIJ.World Research & Analysis Team

Artificial intelligence in banking is moving beyond chatbots and isolated experiments towards a more fundamental redesign of how financial institutions operate. At Ai4 2026 in Las Vegas, banking and technology executives argued that the biggest opportunity is no longer simply adding AI to existing products, but rebuilding workflows around faster document processing, more personalised customer service, automated analysis and closer integration between digital systems and human advisers. Their message was broadly optimistic but also practical: banks are unlikely to hand control of customers’ money to autonomous systems overnight. Instead, the transformation is happening process by process, particularly where institutions already have large volumes of structured information, repetitive administrative work and established regulatory controls.

Banking may in some respects be better positioned for this transition than less regulated industries. Financial institutions have used machine learning for years in fraud detection, credit modelling and transaction monitoring and already operate within extensive frameworks covering model risk, cybersecurity, privacy and compliance. Generative AI introduces new concerns, including inaccurate responses and more complex model behaviour, but it is entering an industry accustomed to testing systems before they are allowed to influence customer outcomes. The challenge is extending those controls into a technology environment developing much faster than traditional banking software. Before AI systems interact with customers, banks need to test not only whether software functions technically but whether its answers remain appropriate across a wide range of scenarios, while synthetic data can help institutions generate additional customer profiles and test cases without relying entirely on historical information.

Data control is equally important. Banks hold financial histories, identity information and other sensitive material that cannot simply be sent indiscriminately to external AI services. That is pushing larger institutions towards controlled environments where models can be tested without exposing core information systems. The concept of an AI sandbox fits relatively naturally into banking because financial institutions have long used segregated environments for experimenting with new technology before connecting it to production systems. What has changed is the speed of development. Previous technology programmes could spend months or years progressing from experimentation through governance and deployment, while generative AI applications can sometimes be prototyped in days or weeks. The bottleneck may therefore increasingly move away from building the application and towards creating an institutional process capable of evaluating and releasing new AI systems safely.

This is also changing how banks think about AI investment. During the early stages of generative AI adoption, many organisations encouraged employees and departments to experiment widely. Banks are now becoming more selective. Not every problem needs a powerful foundation model or an autonomous agent. A straightforward information-retrieval system may be sufficient for some customer-service tasks, while more sophisticated models should be reserved for activities where their additional capability produces measurable value. The economics are becoming important because the costs of running AI can increase rapidly as usage expands. Model choice, the amount of context passed into each request and the volume of interactions all affect operating costs, meaning institutions need to compare the cost of AI with the value of the process being improved rather than treating adoption as an objective in itself.

One of the clearest areas of practical value is customer onboarding. Opening a bank account, applying for a loan or establishing a business relationship often requires identity documents, financial statements and other evidence to be collected and checked. AI can extract information, compare documents, identify inconsistencies and determine which cases require additional human scrutiny. The result is not necessarily the removal of compliance staff, but concentrating human attention on unusual or higher-risk cases while straightforward applications move through the system more quickly. Banorte told the Ai4 audience that a large majority of documents involved in some of its onboarding processes can now be validated automatically, with only a minority requiring manual review. That figure should be understood as a company-reported operating metric, but it illustrates how AI is increasingly being used to reduce friction in digital account opening and lending.

Customer service provides another established use case. Virtual assistants can absorb large volumes of routine enquiries that would otherwise reach contact centres, while increasingly sophisticated systems can also help employees locate answers and resolve customer problems more quickly. This is beginning to connect with a wider strategy of hyper-personalisation. Traditional banking campaigns divide customers into broad segments and send similar offers to large groups, while AI allows institutions to analyse transaction behaviour, product usage and customer preferences much more granularly. Banks can potentially alter not only which offer a customer receives but also when it arrives, through which channel and how it is explained.

That ability to use data in real time could become one of the most commercially significant changes in retail banking. A bank may know that a customer has booked travel, experienced a large expense or reached a particular financial threshold and can use that information to determine whether an already approved credit increase, savings product or other service is relevant. The objective is to move from mass marketing towards contextual financial services that respond to individual circumstances. The same underlying data can also help relationship managers understand customers more holistically, giving human advisers access to insights that would previously have required substantial manual analysis.

Lending represents a more sensitive frontier. AI can already accelerate document processing, underwriting preparation, pre-approvals and the reconciliation of information supplied by borrowers. Smaller banks and credit unions may particularly benefit because these processes have traditionally depended heavily on manual labour. Automation can identify discrepancies, extract information from financial statements, verify documents and prepare cases for review, potentially reducing the time customers spend waiting for decisions. The more consequential question is whether AI should eventually make final credit decisions without human involvement. Technically, increasingly automated lending is possible, but regulation and accountability become much more important when a model directly determines whether someone receives financing.

That distinction helps explain why the most advanced banking applications are likely to continue combining automation with human approval. AI can assemble information, highlight risk factors and recommend an action while the institution retains traceability over how the conclusion was reached. The value lies partly in reducing the amount of time employees spend collecting information, leaving them to concentrate on judgement, exceptions and customer relationships. In areas such as mortgages, investment products and long-term financial planning, customers may continue to want a person involved even if much of the analysis behind the interaction is increasingly automated.

The panel also challenged the assumption that AI will inevitably remove the human element from banking. Routine transfers, balance enquiries and basic administration have already moved towards digital channels, but human employees could increasingly concentrate on complex financial decisions where guidance remains valuable. AI could make those employees more capable rather than simply replacing them. A mortgage specialist, for example, could gain access to tools that explain deposit products, savings options or other areas outside their traditional speciality, allowing financial institutions to broaden the expertise available through existing employees without building entirely separate teams for every product category.

Trust remains the limiting factor. Banks hold a position in the economy that depends heavily on customers believing their money and information are safe. A major AI failure involving discriminatory lending, inappropriate financial advice, a substantial data leak or widespread incorrect customer responses could therefore have consequences extending beyond the individual application. The industry cannot treat accuracy and governance merely as technology-performance measures because failures could damage confidence in the institution itself. This is one reason why traceability, auditability and clear human-review points remain central to most current deployments.

Regulation is developing unevenly between markets, but banks are already subject to extensive financial, privacy and cybersecurity requirements even where dedicated AI legislation remains limited. The practical response is increasingly to bring compliance and legal teams into the development process earlier. Instead of building a system and asking compliance to approve it afterwards, banks can define permitted data, decision boundaries and human-review points when the workflow is first designed. That converts governance from a final barrier into part of the architecture and can help institutions move more quickly without weakening controls.

The most important development may therefore be organisational rather than technological. For several years, financial institutions accumulated proofs of concept without necessarily integrating them into core operations. The focus in 2026 is increasingly shifting towards identifying which applications deserve to be scaled. The emerging model is not fully autonomous banking but a bank in which AI sits inside more workflows: reading documents, preparing applications, assisting employees, analysing customer behaviour, generating personalised communications and directing difficult cases towards people. Human involvement does not disappear, but it moves towards areas requiring judgement, accountability and relationships.

That makes banking one of the more revealing industries in which to observe enterprise AI. The technology is capable of accelerating operations dramatically, but financial institutions cannot separate speed from trust, regulation or economic return. The banks that gain the greatest advantage may therefore not be those deploying the largest models or the greatest number of agents. They may be those that identify precisely where AI produces value, control the cost of operating it and know where the machine should stop and a person should take over.

Source: CIJ.World Research & Analysis Team