Canadian insurers haven’t adopted shorter work weeks, but as the pressure to implement AI intensifies, many are beginning to rethink how time is structured to make room for learning and experimentation.
According to Joe O’Connor (pictured), CEO of global consulting firm Work Time Revolution, time – not talent or technology – is quickly becoming the biggest bottleneck in achieving AI goals. And while traditional work models persist, some insurers are quietly asking a new kind of question: how can we make space for employees to actually learn, experiment, and integrate these tools into their day-to-day jobs?
“We’ve been seeing growing interest from insurers – not necessarily to improve the employee value proposition through a four-day work week, but to create more capacity in the workweek for learning, training, upskilling, and experimentation,” O’Connor told Insurance Business.
He said the ambition from leadership is often clear. Organizations talk a big game about digital transformation, and many are pouring significant investment into AI infrastructure and platforms. But that enthusiasm doesn’t always translate to action on the ground, where employees are often left to upskill “off the side of an overflowing desk.”
Research from the European Union supports this concern, O’Connor added, highlighting that lack of time – not lack of interest – is the number one factor slowing down AI adoption among employees. The result is a rise in “shadow AI”: informal, often unsanctioned use of generative tools outside of work hours, without oversight, training, or shared learning.
“In many cases, employees are using their own personal time to experiment with these tools, which leads to inconsistent usage, inadequate governance, and missed opportunities for collective learning,” O’Connor said.
Rather than opting for sweeping structural changes, some insurers are exploring lighter-touch options to carve out focus time – often at the end of the week.
For insurers seeking to integrate AI meaningfully across their organizations, the greatest challenge may not be technical – it’s human. According to O’Connor, any initiative aimed at boosting productivity or enabling transformation must start by addressing the daily realities of employees’ workloads and perceptions.
“One of the most basic principles in any project I work on is thinking about this from the employee’s perspective,” he said. “You can’t create capability unless you first create the capacity.”
AI poses a unique challenge, he explains, because it isn’t just another tool – it’s a technology that many employees view with suspicion or fear. Unlike past productivity initiatives, which might have been met with apathy, AI demands significant effort and emotional investment at a time when many are worried about job security. That anxiety has already led to resistance in some organizations – and in more extreme cases, even quiet sabotage.
“Trying to entice and motivate the workforce to enthusiastically get behind these initiatives is a tricky proposition,” O’Connor said. “That’s why we’re seeing high levels of AI resistance in the data.”
The path forward, he argued, lies in freeing up time and shifting the narrative. That starts with eliminating busywork and low-value tasks, so employees can focus on learning and experimenting with AI tools in a supported environment – not during evenings and weekends. Only 11% of workers surveyed in a recent European Union study said they had dedicated time during the week to focus on AI upskilling, and O’Connor said that anecdotal data from Canadian insurance events shows even fewer hands go up when that question is asked.
“If we want people to transition from being efficient in a world without AI to being efficient in a world with AI, there’s a learning curve – and space needs to be made for that,” he said.
But time alone isn’t enough. O’Connor said that many companies are falling short in how they communicate the purpose and value of AI adoption to their employees. When layoffs and uncertainty dominate headlines, workers need to hear a compelling, personal case for how AI can enhance (not endanger) their careers.
“There’s a major perception and positioning problem right now,” he said.
Without that kind of clarity and commitment, insurers risk investing in technology that their workforce neither trusts nor has time to learn.
While the insurance industry has traditionally equated productivity with efficiency – faster processing, higher task volumes, and tighter timelines – O’Connor believes that mindset is quickly becoming outdated. As AI tools become embedded in everyday workflows, the real question isn’t how much more can human employees do, but what should they focus on instead?
“In a world where repeatable, efficient processes can be scaled almost infinitely using AI, squeezing an extra hour or two out of your workforce becomes redundant,” he said. “The traits that will matter most are decision-making, judgment, creativity, and problem solving – which are fundamentally human capabilities.”
That shift, O’Connor argued, will require a redefinition of productivity across the sector. Instead of measuring output alone, insurers will need to prioritize energy, focus, and well-being as critical inputs. And in that emerging reality, conversations about flexible models – like a four-day work week – may finally start to make more sense.
