Bank of Montreal is creating a dedicated hub for next‑generation technology, launching a new institute focused on applied artificial intelligence and quantum computing as it works to scale innovation across the bank and sharpen its competitive edge.
The BMO Institute for Applied Artificial Intelligence and Quantum will be headed by Kristin Milchanowski, who has shifted from her former position as chief AI and data officer into a broader mandate overseeing both AI and quantum initiatives. A quantum mathematician by training, Milchanowski will be responsible for unifying the bank’s research, experimentation, and deployment efforts across these two rapidly advancing fields.
According to Milchanowski, the institute is designed to significantly widen the scope of BMO’s research agenda. Its goal is to create a formal environment where artificial intelligence and quantum computing can intersect, accelerating the development of practical applications while the underlying technologies continue to mature. She describes the institute as a “platform for convergence,” one that should keep BMO at the front of the pack as financial institutions race to modernize.
The bank is not starting from scratch. BMO has been using AI for years to improve operational efficiency and strengthen risk management, but the new institute formalizes and centralizes those efforts, while layering in a parallel track dedicated to quantum technologies. Governance, ethics, and commercialization strategies are being built into the institute’s mandate from the outset, reflecting the bank’s recognition that technical progress must be paired with robust oversight and clear business value.
Quantum computing, though still largely experimental, is widely expected to transform how complex problems are solved. Unlike classical computers, which process information in bits that are either 0 or 1, quantum systems exploit quantum states to evaluate many possibilities at once. This makes them especially suited to tasks like optimization, cryptography, and simulation. Recent industry data suggests that momentum is accelerating: a growing share of quantum‑focused companies now employ more than 100 people, signaling an industry moving from pure research toward early commercialization.
Within that context, Milchanowski says her team has already reached what she calls “quantum utility.” By this she means BMO can execute a real business function within a quantum environment and obtain a result that is not just academically interesting but practically useful. She stresses, however, that this does not yet equate to full‑scale production deployment. The work remains primarily research‑driven, and the systems involved are still fragile, expensive, and highly specialized.
“I don’t know of any institution that is actually putting it into production,” she notes. “It’s still early, but not too early to be invested in and making sure we are prepared for the near future.” For BMO, that near future involves being ready to switch on quantum‑enhanced processes once the technology becomes stable and scalable enough to integrate with core banking infrastructure.
The bank is already exploring concrete use cases where quantum computing could confer an advantage. Portfolio optimization is a prime candidate: large financial institutions routinely face vast combinatorial problems when choosing the most efficient mix of assets under multiple constraints. Quantum algorithms promise to evaluate these choices far more effectively than traditional techniques. Similarly, risk analysis-especially in areas like market risk and credit risk-could benefit from quantum methods capable of modeling complex, high‑dimensional systems.
Another focus area is financial crime prevention. Anti‑money‑laundering (AML) systems require intense computation to trace hidden patterns in millions of transactions. Quantum‑inspired or quantum‑enabled techniques could, in the long run, help banks identify suspicious activity faster and with greater precision. These explorations remain in pilot and research phases, but they are laying down the intellectual and technical groundwork for future adoption.
While quantum is still on the horizon, AI is already deeply integrated into daily banking operations. Across the industry, machine learning models are used in fraud detection, customer service, credit scoring, and internal process automation. BMO has been explicit that AI will be central to meeting its financial and efficiency targets over the coming years, treating the technology less as an experiment and more as a core productivity engine.
Piyush Agarwal, BMO’s chief risk officer, has pointed to AI’s tangible impact on compliance functions, especially in AML. The bank’s algorithms already generate large volumes of alerts when potential red flags arise. By applying AI to refine and prioritize these alerts, BMO has managed to cut the number of false positives by about 10 percent. This reduction translates into less manual review work for compliance teams and quicker focus on truly suspicious cases.
AI has also dramatically shortened the time needed for key investigative tasks. For instance, adverse media searches-screening news and other content for negative information about clients or counterparties-have been compressed from roughly three hours to about 20 minutes. Automating and accelerating this work reduces bottlenecks in onboarding and monitoring, while improving the consistency of risk assessments.
BMO is not alone in this transformation. Other major Canadian banks are aggressively scaling their AI efforts. Toronto‑Dominion Bank has publicly emphasized the use of AI to simplify processes and drive substantial cost savings, from back‑office automation to smarter customer engagement. Royal Bank of Canada has characterized the current period as an “AI arms race,” underscoring the fear that lagging in AI adoption could translate into long‑term competitive disadvantage in pricing, speed, and service quality.
What sets BMO apart is the decision to bind its AI journey to a structured quantum strategy. The launch of the institute coincides with the bank deepening its ties to the broader advanced‑computing ecosystem. BMO recently became the first Canadian bank to join the IBM Quantum Network, a move that gives it privileged access to cutting‑edge quantum hardware, software tools, and collaborative research opportunities with quantum specialists.
This membership allows BMO’s teams to experiment on real quantum processors rather than relying solely on simulations, helping them test algorithms, benchmark performance against classical methods, and identify where quantum advantage might genuinely emerge. It also positions the bank to influence-and benefit from-ongoing advances in quantum architecture, error correction, and software frameworks.
BMO’s work in AI has already attracted external recognition. The bank has been ranked among the top ten global banks for AI innovation by an independent analytics firm and has received an award for its use of artificial intelligence and advanced analytics in commercial banking. These accolades suggest that its strategy is not just aspirational but is producing measurable outcomes in how it serves clients and manages risk.
In parallel, BMO is extending its interest in advanced technologies into the realm of digital infrastructure. The bank is preparing to launch a 24/7 tokenized cash platform, developed in partnership with a major derivatives marketplace operator and a leading cloud provider. This initiative will mark the first implementation of that partner’s tokenized cash solution on a cloud‑based universal ledger, enabling around‑the‑clock settlement and potentially transforming how liquidity is managed across markets.
Tokenized cash-digital representations of deposits or cash balances that can move instantly across a shared ledger-complements BMO’s AI and quantum agenda. AI can help optimize liquidity flows and detect anomalies on such platforms in real time, while quantum computing, over the longer term, could support the optimization of collateral, intraday funding, and cross‑border settlement paths.
Strategically, the establishment of the BMO Institute for Applied Artificial Intelligence and Quantum signals a broader shift in how large banks approach innovation. Instead of treating technology purely as a set of tools purchased from vendors, BMO is positioning itself as a co‑creator of new methods and infrastructure. By embedding researchers, data scientists, and engineers inside a dedicated institute, the bank can experiment more freely, prototype quickly, and transfer successful concepts back into business lines in a controlled way.
The institute is also likely to play a critical role in talent development. AI and quantum computing both suffer from acute skills shortages, and competition for top experts is intense. By offering a clear research mission, advanced infrastructure, and the chance to work on real‑world financial problems, BMO can attract and retain specialists who might otherwise gravitate to pure tech companies or academic institutions. Over time, this talent pipeline could become one of the bank’s key strategic assets.
Governance and ethics are another pillar of the institute’s work. As AI models become more powerful and opaque, and as quantum computing begins to touch sensitive areas like encryption and data security, banks must demonstrate that they can deploy these technologies responsibly. BMO’s structured approach-explicitly combining experimentation with governance and commercialization disciplines-suggests that it is trying to pre‑empt regulatory and reputational risks rather than respond to them after the fact.
For customers, the impact of these initiatives will likely show up first in more personalized services, faster decision‑making, and improved security. AI‑driven insights can tailor product offerings to individual needs, while advanced fraud and AML systems can protect customers from evolving threats. Over a longer horizon, quantum‑enhanced analytics might enable more sophisticated investment solutions and risk‑managed products that are currently too complex to design or manage at scale.
For the broader financial sector, BMO’s move raises the bar on what it means to be technologically prepared. As more institutions invest in AI, the differentiation will come not just from who uses machine learning, but from who can integrate AI with emerging paradigms like quantum computing and digital ledgers. Banks that build this multi‑layered capability early may be better positioned to adapt when quantum hardware becomes practical and when digital asset infrastructures become standard.
In effect, BMO is betting that AI and quantum computing will not evolve as separate tracks but as complementary forces shaping the next generation of financial services. The new institute is its way of turning that bet into a structured strategy-one that blends research, partnerships, and real‑world experimentation in pursuit of long‑term advantage.