Saturday, 5 September 2026
The banking industry’s artificial-intelligence debate is moving from how much money institutions are investing towards a more difficult question: where is the return actually appearing? At Ai4 2026 in Las Vegas, BMO Financial Group’s Chief AI & Quantum Officer Kristin Milchanowski argued that AI is entering a new stage in financial services in which access to technology itself will become less differentiating. As banks increasingly obtain similar foundation models and cloud infrastructure, competitive advantage will depend more heavily on how effectively those capabilities are embedded into business processes, client relationships and decision-making.
That represents an important change from the first phase of generative AI adoption. Banks spent heavily on experimentation, employee tools, pilots and infrastructure, but a successful demonstration is not the same as an application capable of producing repeatable value across a large institution. Milchanowski described one of BMO’s central tests as whether AI has been connected to a workflow that genuinely matters to the business. If employees constantly need to be reminded that a particular system is using AI, the technology may not yet be sufficiently integrated. The objective is for AI increasingly to disappear into everyday banking processes rather than remain a separate product employees or customers have to consciously operate.
BMO has formalised much of this strategy through its Institute for Applied Artificial Intelligence & Quantum, created in 2026 as an enterprise-wide centre covering implementation, governance and emerging quantum capabilities. Milchanowski’s role therefore combines two responsibilities that are sometimes separated inside large organisations: building technology and establishing the framework under which that technology can operate. The economic challenge is that most banks do not have an accounting line showing AI revenue or AI profit. AI contributes to other activities, meaning the return has to be identified inside broader business outcomes such as faster credit processing, stronger client engagement, improved win rates or lower operating costs.
That makes baseline measurement critical. A bank needs to know how long a process took, what it cost and how effectively it performed before AI was introduced. Without that starting point, it becomes difficult to demonstrate whether the technology actually created value. Milchanowski described BMO’s work in commercial credit as one example, where generative AI is being used to collect information and help bankers prepare credit analysis more quickly. The bank has also developed Aura, a GenAI-powered assistant supporting commercial underwriting by retrieving information from approved systems and helping prepare parts of credit submissions while human underwriters remain responsible for the final assessment.
The underlying challenge is familiar across financial services. Large banks have accumulated technology over decades, leaving employees working across numerous applications and databases to complete a single task. A banker or financial-crime specialist may need to search several systems, review multiple documents and assemble the results into a memo before making a decision. Generative AI and agents can increasingly act as an information layer across those fragmented environments, retrieving relevant material and bringing it together for the employee. The human may continue making the consequential decision, but considerably less time is spent locating, comparing and reorganising information.
This is why many of the most credible banking applications currently involve document interpretation, information retrieval and the restructuring of content from large bodies of unstructured material. AI can locate clauses in legal documents, identify risk factors, compare information between reports or assemble material scattered across internal systems. These applications may appear less dramatic than autonomous banking, but they address workflows that consume significant employee time across lending, compliance, legal, operations and risk and can therefore produce measurable operational value.
The larger economic opportunity, however, may not come from efficiency alone. Milchanowski cautioned against treating AI as another cost-reduction programme. A strategy concentrated exclusively on doing the same amount of work with fewer resources could overlook the greater opportunity to generate revenue. Her argument was that banks should concentrate on improving client experience, increasing adoption and strengthening relationships, with efficiency emerging partly as a consequence of better-designed processes rather than becoming the sole objective.
That distinction matters because efficiency gains can quickly become part of the industry’s new cost base. If every major bank reduces the cost of producing a credit memo or answering a routine customer question, competitors will eventually be forced to make similar investments. The more durable advantage could come from using AI to improve how quickly institutions identify opportunities, respond to clients and make decisions. Milchanowski described this as decision velocity: reducing the administrative distance between information and the people responsible for acting on it.
Large organisations still rely heavily on periodic reporting processes in which information moves through multiple departments before senior management receives an aggregated picture. Forecasting can require several teams to prepare spreadsheets, reconcile assumptions and roll information upwards before a final view reaches leadership. If AI can continuously access approved information from core systems and assemble it into useful analysis, management could move towards a more continuous operating model. The value would not come from allowing AI to determine strategy independently, but from enabling people to make informed decisions sooner.
Before technology is introduced, however, BMO’s approach places responsibility on business teams to simplify existing processes. Milchanowski’s warning was straightforward: automating a poorly designed workflow simply allows the organisation to perform unnecessary work more efficiently. Banks therefore need to remove obsolete approvals, duplicated steps and bottlenecks before applying AI. Technology should then be used to redesign the remaining workflow rather than reproduce decades of accumulated complexity in automated form.
This principle also affects how AI programmes are organised and funded. Instead of building hundreds of unrelated applications, central technology teams can develop reusable capabilities that individual business lines adapt for their own products and workflows. BMO’s model combines enterprise-wide technology and governance with authority retained by the individual profit-and-loss businesses, which remain responsible for identifying client needs and allocating investment. Common infrastructure can therefore be developed centrally without requiring every business unit to create its own AI platform.
Governance becomes particularly important as banks move from assistants towards agents capable of performing actions. Milchanowski identified identity, orchestration and accountability as some of the main challenges. An institution needs to know who created an agent, why it exists, what information it used, which systems it may access and who is responsible for the results. As multiple agents begin interacting, banks will also need mechanisms determining which systems can communicate and under what circumstances.
Well-designed governance can eventually accelerate deployment rather than simply slow it. When responsibilities, thresholds and escalation procedures have already been defined, teams do not need to reconsider the same fundamental questions every time a new application is proposed. The institution can move more quickly because the boundaries are clearer. This also means employees need sufficient AI literacy to understand how tools are being used and remain accountable for decisions made with their assistance.
The technology strategy reflects a similar desire to avoid adding another fragmented layer to an already complicated banking architecture. Rather than buying large numbers of disconnected applications, AI increasingly needs to sit inside established infrastructure and data environments if banks want to improve decision speed across the organisation. Buying a platform does not eliminate development work because the technology still needs to be integrated with the bank’s own data, controls and operating processes.
A related shift may eventually favour smaller and more specialised language models. Milchanowski predicted that banks could increasingly use smaller models where their capabilities are sufficient because running the largest available model for every task can make little economic sense. A bank may need advanced reasoning for some activities but not for routine extraction, classification or internal Q&A. The return on AI can therefore improve partly through matching model capability and cost to the complexity of individual workloads.
Quantum computing occupies a much earlier position in this development cycle. BMO is researching quantum applications through its work with IBM and has explored areas including optimisation, risk and environmental modelling. One example involves earthquake forecasting, where the bank is researching ways to process extremely large and complex datasets. The broader logic is not that earthquake prediction will suddenly become a banking product, but that methods developed in demanding scientific problems could eventually have applications in capital markets and financial risk.
For financial institutions, the important contrast is therefore between today’s AI investment cycle and the much earlier quantum research cycle. Banks are already being asked to demonstrate measurable returns from AI, while quantum investments can still reasonably be justified through research, readiness and long-term capability building. Confusing those stages could lead organisations either to demand commercial returns too early from quantum research or to accept experimentation for too long in mature AI programmes.
The broader message from the Ai4 discussion is that banking’s AI race may ultimately become less about access to technology. Major financial institutions can increasingly obtain similar models, cloud platforms and development tools. What they cannot purchase as easily is the operating discipline required to redesign workflows, integrate proprietary data, establish governance, train employees and connect AI investment to commercially meaningful outcomes.
That is why AI may increasingly become a margin and execution issue rather than simply a technology issue. Banks using the same underlying models could still produce very different financial results depending on how those models are deployed. The winners may not be the institutions with the largest number of AI pilots, the highest token consumption or even the most advanced individual models. They are more likely to be the banks capable of turning intelligence into faster decisions, stronger client relationships and repeatable economic value across the organisation.
Source: CIJ.World Research & Analysis Team
