Westland CIO charts pragmatic path through AI and data transformation
Kanaris Paraskevopoulos, chief information officer at Westland Insurance, has built a reputation for straight talk on transformation: standardize operations first, extract value second, and treat AI as a tool for speed rather than spectacle.
He arrived in insurance by accident. Trained as an electronic systems engineer, Paraskevopoulos entered the job market during the Nortel downturn, when opportunities in his field had dried up. A work term at Saskatchewan Government Insurance opened an unexpected door. What started as a one-year role stretched into roughly a decade. "I fell into insurance somewhat by accident," he said, "but I'm super happy that that's what happened."
His next move took him toward modern software culture. He studied not just the technology of companies like Netflix and Amazon, but how they operated. He joined a software-as-a-service business in retail point-of-sale when it was small, and left after it grew to around 1,000 people and won major enterprise customers including AT&T.
A return to SGI to support a transformation programme led to his own appointment as CIO there. The move to Westland in July 2025 followed naturally-he already knew the business through its partnership with SGI.
Building the foundation
Westland had completed an intense phase of acquisition-led growth and implemented Acturis as its broker management and policy administration platform. It had also built a cloud-based data platform on Databricks. Paraskevopoulos's task was to ensure the company drew full value from these systems.
The strategy is straightforward. First, Westland needed national operational consistency: common workflows, standardized processes and reliable data. Only after that foundation was in place could it improve speed of service, broker experience and customer outcomes.
The next phase focuses on using data to shape frontline decisions: which leads to handle first, which renewals are most at risk, where attention produces commercial value. Paraskevopoulos stresses this is not a reporting exercise. It is about turning information into action.
Data and AI as a single problem
Paraskevopoulos explicitly rejects treating data and AI separately. Data is the substrate; AI is the means of extracting and applying value from it. "I always put AI and data together," he said. The pair are effectively inseparable in any serious modern transformation.
His view of AI itself is notably restrained. Westland is early in its AI journey, but already uses the technology in practical settings. One example: email submission processing. Where submissions would previously require manual extraction and re-entry, AI pulls out relevant information, structures it and enables automated submission.
The gain lies not chiefly in labor efficiency but in responsiveness. "We really see it as a revenue play," he said. "It's really about that speed."
Westland has rolled out Copilot to a large share of staff for back-office work and data analysis, with plans to broaden that deployment. Paraskevopoulos favors augmentation over wholesale substitution. The strongest use case, he said, is a "human in the loop" model, with AI supplying timely data and insight to support employees in real time.
Over time, he expects a more autonomous future, including broader straight-through processing in underwriting and claims. But he describes this as a trajectory to manage with care, not as imminent inevitability.
The orchestration question
Where will the real industry shift occur over the next three to five years? Not in better models, cheaper compute or more pervasive copilots, though Paraskevopoulos expects all of those. Instead, he points to the "orchestration of multiple AI tools" as the likely inflection point.
Large organizations run on sprawling stacks of software: HR, finance, telephony, line-of-business systems and more. If each application embeds its own intelligent assistant, the result may be capability without coherence. The real gain comes when those tools are coordinated through a common layer that can automate whole workflows rather than isolated tasks.
Once confidence and maturity are sufficient, some interfaces may disappear from view altogether as routine processes continue in the background.
His caution is directed less at the technology than at management habits. In a fast-moving AI market, long project cycles carry a new danger: by the time a firm finishes building something, the market may already provide it natively. Technology may "leapfrog" the original idea before the project is complete.
This argues for shorter planning horizons and faster decision cycles. "Planning too far into the future can be risky," he said.
The human side of change
For all the emphasis on systems and models, transformation remains a human problem as much as a technical one. Many Westland employees joined through acquisitions and have absorbed several rounds of change: joining a larger organization, moving onto core platforms, adapting to standardized workflows.
Westland has a dedicated change-management team outside IT to help individuals through what Paraskevopoulos calls the "change curve". Broker reaction has been uneven, in part because acquired firms arrived with varying degrees of technological maturity. Still, he believes the benefits of consistent processes and consolidated data are now becoming visible across the organization.
The hardest decision
Asked about the hardest part of the job, Paraskevopoulos does not cite budgets or legacy technology. Instead, he identifies the challenge of sorting among many worthy demands.
The central difficulty is sequencing and prioritization: choosing the highest-impact changes at the right time, without diluting effort across too many good ideas. It is a revealing answer. In many firms, transformation is still discussed as if the principal problem were access to innovation. At Westland, according to Paraskevopoulos, the harder task is disciplined selection: deciding what to do, what not to do, or not to do yet.
For those in CIO roles navigating similar decisions, structured learning on AI strategy for CIOs can help clarify priorities. Those focused on implementation may find AI agents and automation resources directly applicable to workflow orchestration challenges.
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