When Catherine Desgagnés-Belzil left her hometown to work in the kitchens of Lake Louise, she didn’t imagine she’d one day oversee a nationwide technology transformation at one of Canada’s major mutual insurers.
It’s a cinematic origin story, but also a reminder that the people leading Canada’s AI efforts didn’t arrive through a linear path.
Desgagnés-Belzil’s mother, a self-taught computer enthusiast, encouraged her daughter to study computer science.
“She was the first one to get a computer,” recalls Desgagnés-Belzil. “She said, ‘Technology and computer science are the future.’”
It feels a little obvious in hindsight, but it’s the kind of thing that makes you think there’s something to that old adage about moms always being right.
Across Canada, chief information officers (CIOs) are being measured not by how smoothly systems run, but how efficiently technology moves the business forward. They are watching how it drives growth, improves competitiveness, and delivers financial results.
AI threw gasoline on that fire.
Testing is omnipresent in almost every business, but the jump from pilot to real value is bigger (and messier) than a press release suggests.
Experimenting is the easy part. The hard part is building the systems and habits that turn those trial runs into something leaders can point to in a quarterly meeting.
As the executive vice president of business performance and IT at insurer Beneva, and recently named the CanadianCIO Private Sector CIO of the Year, Desgagnés-Belzil has seen that challenge up close.
AI has become a test of how well leaders can connect technology, data, and business strategy. Some organizations are beginning to see returns, while many are running into barriers that technology alone can’t fix.
Turning pilots into performance
Canadian companies are pouring money into AI, but most are still figuring out how to make it pay off.
A recent study from IT Brief Canada and Snowflake found that firms report an average 43% return on investment from generative AI projects, yet almost half haven’t moved past those early experiments.
Part of the problem is structural. Many enterprises have invested in AI tools without updating the systems that support them. Companies can’t scale AI on top of fragmented systems and hope it magically works.
Data lives in silos, teams are running around with disconnected projects, and business leaders often see innovation as an experiment rather than an operating model. The result is that proof-of-concept initiatives never make it past the lab.
Gartner’s 2025 CIO priorities highlight “demonstrating business value from AI” as one of the most pressing concerns for global technology leaders.
And the numbers support that claim.
McKinsey’s latest Global AI Survey found that only one in five organizations has the data readiness to scale AI profitably. Those that do tend to invest earlier in data governance, cloud modernization, and cross-functional alignment between IT and business teams.
These organizations are more likely to report financial gains from AI adoption, but those that see positive returns tend to approach AI as a long-term operational shift rather than a series of disconnected trials.
Desgagnés-Belzil experienced that shift at Beneva during the company’s transformation, taking a hard look into how much of the work takes place in operating models and team behaviour rather than in algorithms.
Building the foundation for scale
When Beneva was formed in 2020 through the merger of La Capitale and SSQ Insurance, the company decided to rebuild its core systems rather than modernize what it already had.
“We got the great opportunity to create a brand new ecosystem and really start from fresh so we can have really modern use of our technologies,” she says.
“In many cases, our old legacy systems took 30 to 40 years to develop,” she explains. “We knew the first version wasn’t going to be as perfect in every single function as the old system, but it would be sustainable for the future.”
That choice echoes a broader shift taking place inside the Canadian market.
Many enterprises are realizing that AI performance depends less on the model and more on the maturity of the systems underneath it.
Organizations that have invested in cleaner data, modern infrastructure, and integrated workflows are the ones able to move faster when new AI capabilities emerge.
Jim Keller, managing director at Quantiphi, sees this gap every day. He describes data readiness as one of the biggest hurdles to realizing AI’s full potential.
“Organizations appreciating that their data estate needs to be in order, in order to actually benefit from the true value of AI across the organization,” he says. “That chasm between where organizations are with their data readiness and quality, versus where they need to be, is pretty large in some cases.”
Quantiphi works with enterprises to bridge this divide, aligning technology with the business outcomes leaders are seeking.
“We really think about how to help organizations work backwards from the business outcomes they’re seeking,” says Keller. “AI and data are the foundational elements that drive that.”
Across Canada (and beyond), the same pattern is emerging. Organizations that have invested in modern data architecture, governance, and system alignment are finding it easier to scale AI across operations, product teams, and customer channels.
By contrast, those that haven’t are grappling with fragmented legacy systems, which is a gap more than 60% of companies have cited as a top barrier.
Beneva’s experience is only one example, but it points to a trend we see nationwide.
System work is becoming the real competitive edge, and the companies treating it as a strategic priority are the ones turning early AI efforts into performance that lasts.
Leadership through alignment and accountability
AI at scale is no longer a technical exercise.
It requires coordination across strategy, data, operations, and talent. Scaling AI only works when it reaches the places where decisions are made and is built into everyday work.
According to Accenture’s The Art of AI Maturity research, companies with tight alignment between IT and business teams are almost twice as likely to report financial benefits from their AI initiatives, largely because they make faster decisions and deploy models in real workflows rather than isolated innovation projects.
The shift is changing what real technology leadership looks like. The CIOs gaining traction are the ones treating AI as a shared responsibility instead of an IT initiative.
Gartner’s research shows that CIOs who work closely with executive peers on strategy are three times more likely to achieve business growth that can be measured.
Cross-functional governance, clear decision-making authority, and transparency about risk are emerging as baseline requirements for transformation.
Among companies moving fastest in Canada, enterprises making progress tend to adopt a coordinated approach across product, data, and operational teams rather than centralizing responsibility.
At Beneva, Desgagnés-Belzil has seen how alignment accelerates transformation.
“The culture at Beneva is really on collaboration and very much on collective performance,” she says. “It is not about who really wins or who makes it better. It is about how we all work together in order to achieve our goal.”
Other leaders are observing the same shift. Keller says that the CIO role increasingly centres on strategy and innovation.
“CIO now should stand for Chief Innovation Officer,” he says. “You are really charged with innovating and choosing the foundational technical capabilities and tools that the enterprise needs to do innovation.”
Industry research supports this evolution. Deloitte’s State of AI in the Enterprise report found that companies that treat AI as an enterprise program rather than a technology project see faster returns, because alignment cuts repeated efforts and speeds up deployment.
AI isn’t a mystery at this point. The companies that make it work are the ones that treat alignment like an operating requirement instead of a team-building exercise.
When strategy, data, and technology move in the same direction, pilots turn into products.
When they don’t, nothing scales, no matter how shiny the model is.
Responsible growth as a competitive advantage
As organizations scale AI, governance and transparency are becoming differentiators. Keller told Digital Journal that doing nothing is not an option.
“We’re helping many of our customers think about responsible AI for everything we do with them. Quantiphi has our own Responsible AI Committee,” he said. “We take the work we do for our customers very seriously.”
As for Desgagnés-Belzil, she says accountability begins with execution.
“Everybody knows what to do,” she says. “The difference is, there are those who know what to do and do it, and those who know what to do and don’t do it. You need to be part of those that do it.”
AI is changing the tech, sure, but it’s changing leadership even faster.
CIOs are now growth executives who shape markets, customer experiences, and business models.
The ones who make it work are those who can turn experiments into numbers that boards and leadership teams care about.
Canada’s innovation economy will depend on how well organizations close the distance between experimentation and scale. Beneva’s progress is a reminder that Canada wins when companies focus on execution.
Desgagnés-Belzil’s mother saw the direction the world was heading with technology. Now, her daughter’s work is making the case that leaders moving Canada forward are the ones who can turn that technology into results.
Final shots
- Many Canadian enterprises have proven AI’s potential but struggle to scale it profitably.
- Data readiness and alignment across business and IT are the biggest barriers to adoption.
- The CIO role now extends beyond operations to driving growth and measurable results.
- Governance and responsible AI practices are becoming sources of advantage.
Digital Journal is the national media partner for the CIO Association of Canada.
