Simply handing employees AI tools won’t solve Canada’s productivity problem. Despite rising AI access across Canadian enterprises, a lack of clear processes in place is keeping companies from translating experimentation into meaningful adoption.

Deloitte’s State of AI in the Enterprise report released in March 2026 found that while over 60 per cent of organizations report widespread AI access across their workforce, 74 per cent admit that usage remains superficial and isn’t driving real business gains.

Glenda Crisp, president and CEO of Vector Institute, says that adoption lags when employees view AI as a “lose-lose” proposition for job security, rather than a tool for solving previously unaddressable problems.

“If you can frame it as this is a growth opportunity for the employee and the company, then you start to get the engagement within your employee base,” she says.

Taking a people-focused approach to AI

Crisp points to BMO, a founding supporter of the Vector Institute, as an organization that has designed its AI strategy with people in mind, instead of the other way around. “Their thought is, it’s not about replacing people; it’s about augmenting people,” she says.

“In practice, what that means is that we focus on helping all of our teammates spend more time on the work that creates value for our clients, and less time on those activities that are just adding friction to their day,” says Kristin Milchanowski, chief AI and quantum officer at BMO Financial Group. 

The bank published its responsible AI framework in 2025, with core principles of building with empathy, managing with integrity, and optimizing for trust. For Mona Malone, BMO’s chief administrative and human resources officer, that means clearly differentiating between employees and AI. 

“I don’t see AI as the workforce. It’s a way of getting work done,” Malone says.

Equally important, she says, is “thinking about employees not as defined by the tasks they do, but by the skills they have, and their ability to adapt, grow, and develop.” This way, employees aren’t at risk of replacement; instead, it’s about job reconstruction as companies identify different ways of completing tasks.

BMO leaders foster agility among their workforce by making the organization’s AI For All training program available company-wide, ensuring no one is left out, as well as providing role-based training programs for specific AI tools.

“And making sure that we understand, not just from a business perspective, but from an HR perspective: How are people feeling about those tools?” she adds. “Having a pulse on your workforce is critically important through this reshaping of how work gets done.”

To foster trust, Milchanowski says they started by making it clear that AI is a discipline that has been around—and used in banking—for decades.

Through “micro-moments of trust,” people grow more comfortable incorporating AI into their daily work, she says. “Adoption happens much faster when people understand how AI is improving that work, rather than feeling like AI is being imposed on them.”

Then, to scale implementation, teams looked for the “traffic jams” to ensure they were embedding technology where it actually solved problems instead of incorporating AI for AI’s sake. 

“We created this environment where experimentation was encouraged, but we were very pedantic that it had to be tied to a business outcome,” Milchanowski says.

Scaling productivity requires custom processes and workflows

Using technology to redesign workflows, as opposed to tackling individual tasks, is what scales productivity at the enterprise level.

For example, Crisp says, in the software development life cycle, much more time is spent on prioritization, testing, implementation, and change management than actual coding—yet coding is often where AI is first implemented.

“If all you’ve done is gain a 90 per cent improvement on what is five per cent of the process, you haven’t gained the value,” she says.

Stephanie Enders, chief delivery officer at the Alberta Machine Intelligence Institute (Amii), works with executive teams to develop a process for quickly analyzing whether AI can effectively be applied to the case at hand.

They use a ‘Five Ps’ framework, evaluating whether the challenge is a problem of projection, production, prevention, promotion, or personalization. “If so, then AI might be one of the tools that we can deploy to accelerate the solution,” she says.

Enders says this quick evaluation helps reframe the conversation around AI from “how are we implementing AI?”, which is a big question, to instead applying an AI lens check that helps frame subsequent conversations.

The key role leaders play in taking AI action

Every expert emphasizes that AI adoption hinges on a strong vision and execution plan from organizational leaders. “Technology can accelerate change, but it’s the culture that really determines whether or not that change sticks,” Milchanowski says.

Malone identifies various tactics that leaders can successfully deploy to influence AI adoption among their teams, such as absolute leader role modeling, where leaders demonstrably use the tools in their daily tasks, and provide opportunities for their teams to do the same.

“And say what you don’t know,” Malone adds. “You’re demonstrating that you know you don’t have it all figured out. That’s how you create the safety for other people to also do that, wherever they are on that learning continuum.”

To facilitate effective AI use, people leaders need to make the expectations for their teams clear, Enders says. 

She encourages enterprises to develop AI personas, each with defined parameters for using AI assigned to team members for a particular project, role, or time horizon. In Amii’s stack, for example, you can be AI curious, an AI user, an AI builder, or an AI strategist. 

“You’re giving people the tools to understand the expectations in their given role or in a specific project,” Enders says.

Guardrails and governance grant permission for innovation

Enders encourages leaders to approach AI how they would any other business development: by mapping it to your organization’s existing strategy and culture.

By visibly making decisions about advancing projects based on how well they align to governance frameworks, she says, you reinforce trust in the guardrails you’ve put into place.

Milchanowski credits strong governance with BMO’s ability to innovate at scale. “People move faster when they understand the expectations, who’s accountable, and the parameters in which they can operate,” she says. “Clear frameworks reduce uncertainty and help teams focus their energy on the execution.”

Through BMO’s AI For All training, it’s made clear that no matter what tools an employee uses, they’re still accountable for the work product. That means understanding the data inputs, how the model comes up with recommendations, and how those recommendations are implemented.

To ensure accountability, Crisp says, you need solid measurement practices, with automated governance principles that are scalable, and monitored by people who understand how the models and data work.

Measure value, not volume

You also need to be measuring value, Crisp says. When working in banking, she had five categories of value for every use case: revenue increase, expense reduction, customer engagement, employee engagement, and risk.

Crisp suggests using measurement proxies: for example, expense reduction can be captured by looking at the number of minutes per call in your contact center. She also emphasizes the need to measure data quality metrics. “Everything has always been dependent on data, but AI just magnifies that,” she says.

Milchanowski agrees that value metrics are more useful than adoption metrics when aligning to business outcomes.

“I’ve watched people measure the number of pilots or prompts or licenses, but that really tells you very little about the business value that you’re creating,” Milchanowski says. “[At BMO], we focus on decision velocity as a metric. That’s the ability to detect change, interpret information, or act with confidence where it increasingly influences competitive advantage.

“It’s when you’re able to improve your decision quality while still accelerating your execution.”

For Milchanowski, it’s about leveraging AI as a competitive advantage. “What excites me most about AI is the ability to surface insight that would otherwise remain hidden,” she says.

“That’s where the real value is, and I think more firms need to start adopting that mindset so that they also start getting more value out of these new tools.”

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