At a glance
- Even with strong AI momentum across financial services, many organizations are only scratching the surface of what’s possible.
- Agentic AI can help financial institutions reduce administrative burden and focus more on long-term client and member value.
- Discover AI use cases across banks, credit unions, insurers, and pensions and benefits organizations.
- A leadership-led AI strategy, robust governance, and a people-focused approach are critical to scaling AI responsibly across financial services.
Many financial institutions have moved beyond AI experimentation to realizing value through scalable, enterprise-wide adoption based on their operating models, priorities, and resources. They are no longer asking whether AI matters. The real question now is whether they can embed it into core operations quickly enough to improve productivity, strengthen decision-making, and defend relevance in a more digital, data-rich market.
Large banks and insurance companies are using AI in areas like fraud prevention, credit analytics, underwriting, customer service, and compliance, and are working to extend these capabilities across their organizations. Meanwhile, mid-sized banks, credit unions, and insurance companies see AI as a way to level the competitive playing field; they’re adopting proven AI solutions to enhance their operations and productivity.
Even with strong AI momentum across financial services, many organizations are only scratching the surface of what’s possible. Over the next four years, we expect leading institutions will increasingly turn to agentic AI to transform their businesses so they can better focus on building long-term client value to fuel their growth.
Where are financial institutions on their AI transformation journey today?
We surveyed 113 financial services organizations across Canada to better understand the current state of AI adoption and digital transformation across the sector. Here’s what we found:
A deeper analysis of Canadian AI trends can be found in our AI Vision Report: Past the Pilot to the Agentic Future of Work.
Moving beyond digital finance: How AI can power more value-focused business models
As competition intensifies and financial institutions work to build stronger relationships with digital-first customers, agentic AI is poised to become a major differentiator by 2030.
AI agents can help your organization turn data into action at a scale that was previously difficult to achieve—but only if you establish the right foundation for AI success. This enhances client or member value while reducing the administrative burden on your people, so you can focus less on one-time, product-driven interactions and more on building trusted, high-value, long-term relationships with your clients or members.
What will distinguish AI leaders across financial services in 2030?
Banks use AI to:
- Simplify operational complexity, reduce process friction, and support scale.
- Create a more connected decision environment across risk, operations, and client-facing functions.
- Coordinate credit, treasury, fraud, and service decisions—with appropriate human oversight.
- Strengthen cybersecurity, fraud detection, and real-time monitoring and response.
Credit unions use AI to:
- Strengthen personalization, deepen trusted member relationships, and grow responsibly.
- Support real-time risk management while enabling more tailored member experiences.
- Elevate human-led decision-making while preserving the relationship-based trust credit unions are known for.
- Better align products, service, and engagement with evolving member expectations.
Insurance companies use AI to:
- Enhance underwriting and claims processes end-to-end.
- Support intelligence-led risk management and pricing decisions.
- Strengthen productivity and support growth by equipping teams with better insights and AI-powered supports.
- Use AI agents to help deliver more responsive customer service.
Pension and benefits organizations use AI to:
- Support asset allocation, scenario modelling, and long-horizons risk forecasting.
- Inform investment and operational decision-making with human oversight and accountability.
- Improve insight into risk, performance, and funding decisions.
- Strengthen governance through better transparency, reporting, and decision support.
All financial institutions use AI to:
- Support more client-centric operating models that enable personalized engagement and value delivery across customer segments and channels.
- Improve operational efficiencies, elevate human productivity, reduce organizational friction, and improve the client experience.
- Equip employees with insights and support so they can focus on establishing and building trusted relationships with their clients or members of all ages.
- Enable a human-led, hybrid workforce where AI agents help accelerate tasks, generate insight, and support human decision-making.
Using agentic AI to support a new financial services operating model: Four pillars for success
Through our client work and our own AI transformation as client zero, we’ve identified four foundational pillars that can help financial institutions get the most from their AI efforts over the long term.
While many financial institutions are already using AI to some degree, their activities are often fragmented by function or product. This makes it harder to build the trust, explainability, and accountability needed to scale AI effectively and confidently integrate outputs into the core decision-making.
Developing an AI strategy that’s aligned to business value can give you a strong foundation for success. That strategy should include a realistic view of your current state, a clear vision for the future state operating model, measurable objectives (such as accelerating decision-making or reducing manual work), and a change road map that reflects regulatory requirements.
Given the velocity at which AI technologies and capabilities are changing—including agentic AI and multi-agent orchestration—the strategy should also address how your organization will monitor, govern, and adapt to ongoing change.
Trust is critical when it comes to embedding and scaling AI, particularly AI agents, across your financial institution. It helps improve operational efficiencies, enhance workforce productivity, and develop the deep insights needed to differentiate the organization from competition.
However, many financial institutions are looking to deploy AI within complex technology environments characterized by legacy systems, fragmented data architectures, and information silos. These challenges can hinder scalability, increase operational and compliance risk, and erode trust in AI-generated insights or agentic decision-making.
Before organizations can trust AI, they must be able to trust the data feeding it—and that starts with strong governance, from how data is managed and used, to the frameworks, controls, and human accountabilities put in place to oversee AI systems and agents. Tactics like readiness assessments, data mapping exercises, and data lineage reviews can provide the visibility needed to understand where critical data resides, how it flows across your organization, and where quality or governance gaps may impact AI outcomes.
At BDO, we work with financial institutions to strengthen AI governance across three key dimensions:
- Evaluations—Establishing processes to test that AI systems and agents are performing as intended in use cases such as fraud detection, underwriting support, customer service, and compliance workflows, including both pre-deployment testing and in-production testing.
- Guardrails—Creating mechanisms to keep AI within defined boundaries across security, privacy, ethics, and regulatory compliance to build trust and reduce risk.
- Observability—Building the structures needed to track performance, monitor agent behaviours, and understand what AI systems are actually doing in practice. This is critical for auditability, traceability, and supporting regulatory compliance.
Prioritizing governance as AI scales across your organization can help build confidence that AI outputs and the data behind them are accurate, consistent, secure, and compliant.
Financial institutions need the right governance, guardrails, and operating environment to support the responsible use of AI. This includes helping employees understand when and how AI can be used, providing training so they can be confident in their use of AI, and offering ongoing opportunities to build their skills and capabilities as AI evolves. Without this foundation, organizations can face fragmented adoption, compliance challenges, increased security risks, and, ultimately, difficulty in maintaining trust in AI-driven outcomes.
- Ziad Akkaoui, Partner, National Practice Leader—Risk Advisory, BDO Canada
There’s no getting around it: change is hard. Without establishing trust early and making people feel like genuine stakeholders in your AI transformation (rather than bystanders bracing for disruption) even the most well-resourced strategy can stall. Anxiety about what AI means for individual roles does more than slow adoption; it can quietly undermine long-term transformational goals.
With the right support, your people can become your organization’s greatest transformation asset. This includes providing clear communications, training and upskilling programs, safe environments to experiment, and practical insights on how AI can help them be more productive, creative, and focused on client and member relationship activities.
Our AI Vision 2030 is grounded in a simple belief: the future is human-led and AI-embedded. As AI becomes increasingly woven into the fabric of work, people should remain at the centre—guiding decisions, exercising judgment, and shaping outcomes.
Financial services are well positioned for agentic AI and AI-embedded functionality, given the breadth and depth of data available across the enterprise. But not every organization has the internal resources, technology capabilities, or capacity needed to manage AI transformation and the shift to a new AI-enabled operating model.
This is where an experienced partner support you, from helping you understand how AI is being used today to establishing or strengthening the foundational pillars needed to grow and scale AI over time.
Powering AI transformation in financial services
Wherever your organization is on its AI transformation journey, we can help you scale AI responsibly while unlocking greater value from your data, people, and processes.
We’ve worked with banks, credit unions, insurance companies, and pension and benefits organizations across all aspects of AI enablement, from end-to-end AI transformation initiatives to data and digital engineering, cybersecurity, and governance and regulatory compliance. We bring a practical, outcome-driven, and human-led approach to AI transformation, informed by both client work and our own AI journey, to help you build the foundation needed to scale safely in an increasingly competitive market.