ByJennifer Friesen
DIGITAL JOURNAL
March 26, 2026
In 2020, organizations handed their employees a laptop and called it a remote work strategy.
The meetings that used to happen in boardrooms started happening on screens, back to back to back, but nobody took anything off the proverbial plate.
They just moved everything to a smaller one.
Sam Jenkins has watched organizations repeat that mistake with AI.
“We changed location without redesigning work,” says Jenkins, managing partner and co-founder of Punchcard Systems, an Edmonton-based software and transformation company. “With AI, we’re kind of making the same mistake. We’re changing tools without redesigning the work.”
The data is starting to show the cost. ActivTrak’s 2026 State of the Workplace report, which analyzed more than 443 million hours of work activity across 1,111 organizations, found that AI adoption has reached 80% of employees and productive hours are up 5%. Focus efficiency, though, fell to a three-year low of 60%.
The average focused work session now lasts just 13 minutes. Something is eating the rest of the day, and it isn’t the work getting done faster.
AI is adding to workloads instead of redistributing them.
Adoption without redesign
Jenkins has spent three years watching organizations celebrate AI adoption while leaving the underlying work untouched.
The tools arrived faster than the thinking did.
“AI doesn’t reduce work by default,” he says. “It reduces work when you redesign how work happens. If AI is just making our teams busier, that means we didn’t adopt AI. It means we adopted another piece of software.”
ActivTrak research followed a subset of users in the 180 days before and after AI adoption and found that time spent across every measured work category went up, with email increasing 104% and chat and messaging jumping 145%.
Researchers at UC Berkeley’s Haas School of Business found the same pattern in a February 2026 study, with employees working faster, taking on broader scope, and extending into more hours without being asked. Instead of shrinking workloads, the work got spread out.
The average organization now runs seven AI platforms, up from two in 2023. That means more to manage, more decisions to make, more places for focus to fragment.
“Organizations that are leaving that space are generally going to be more successful because they’re leaving the space for growth,” says Jenkins. “This is all about balancing good strategy with enough resources and time to enact that strategy, both short and long term.”
The signal leaders are missing
The burnout numbers look good right now. ActivTrak found risk fell 22%, to just 5% of employees, with healthy work patterns at a three-year high.
But disengagement risk jumped 23% in the same period, affecting nearly one in four employees.
These are workers with capacity that have nowhere to go.
As AI absorbs routine tasks, that time tends to get filled with more meetings, more tools, more low-value activity rather than work that moves anything forward.
Jenkins sees a version of this play out when leaders move fast without bringing their teams along. For example, a leader experiments with AI tools on evenings and weekends, builds something, then brings it to the team and tells them to start using it.
He calls it the “slingshot effect.” Pull too far ahead of your people and the whole thing snaps back.
“It cannot be built in a vacuum,” he says. “It has to be built in partnership with folks in the organization.”
Staying connected to where people actually are, he says, is the harder discipline.
When asked how he stays connected to what employees need at every level, he laughs, but catches himself.
“I laugh because it’s uncomfortable,” he says, “not because it’s funny.”
For Jenkins, that discomfort is worth paying attention to. The leader who has six AI agents running by 6 a.m. and the one who hasn’t touched the tools yet are both telling him where the work is happening.
“I’m making space to listen to the people who are very comfortable [with AI], and making space for the people who aren’t,” he says. “Because what AI and transformation strategy can feel like is unsafe.”
Punchcard saw this firsthand in its work with the College of Registered Nurses of Alberta (CRNA). When the company took on redesigning the CRNA’s regulatory affairs and permitting processes, they started by mapping where the existing process was breaking down.
What was breaking down, and where? AI entered the conversation only after those questions had answers, built onto a foundation that had been rebuilt to receive it.
Jenkins sees it in his own shop at Punchcard, where the ratio of writing code to reviewing code has changed. Same hours. Different work.
Getting that sequence right, he says, is where most organizations are still struggling.
Less tools, more thought
Jenkins isn’t prescriptive about this, but he has a three-step framework that keeps coming up with clients.
Start small. Stay anchored to something real.
The first move is picking a single metric that matters to the business (like a deal cycle, cost per hire, or nurses validated through a regulatory system) and asking how work should be redesigned to move it, assuming AI already exists.
From there, he says, pick one high-impact workflow and rebuild it from scratch rather than running a tool pilot.
Map what steps could disappear with AI, what could be automated, and where a human being still needs to be in the loop.
Then cut the AI footprint in half.
“Organizations have too many tools, too many licenses, things that are potentially inconsistent,” Jenkins says. “Do less, implement fewer tools, but focus on more business value, more repeatable patterns. That constraint can help drive adoption.”
He calls it small experiments with radical intent.
“The smaller the segments we can learn, adapt, understand, and deploy, that means we’re not going to upset our teams,” he says. “We can think about how we’re going to make that incremental benefit within our organization, and those one-per cents certainly stack up over time.”
In 2020, organizations moved fast and figured out the work later. Most of them are still paying for it in some form. The meeting that started in a boardroom and moved to a screen is still the same meeting.
The plate’s already full. Loading AI on top isn’t a strategy.
Final shots
The organizations pulling ahead started by asking what the work should look like. Most are still asking which tools to buy.
The leaders pulling ahead are asking how work needs to change to make all the tools they’ve invested in, worth using.
Written ByJennifer Friesen
Jennifer Friesen is Digital Journal's associate editor and content manager based in Calgary.
March 26, 2026
In 2020, organizations handed their employees a laptop and called it a remote work strategy.
The meetings that used to happen in boardrooms started happening on screens, back to back to back, but nobody took anything off the proverbial plate.
They just moved everything to a smaller one.
Sam Jenkins has watched organizations repeat that mistake with AI.
“We changed location without redesigning work,” says Jenkins, managing partner and co-founder of Punchcard Systems, an Edmonton-based software and transformation company. “With AI, we’re kind of making the same mistake. We’re changing tools without redesigning the work.”
The data is starting to show the cost. ActivTrak’s 2026 State of the Workplace report, which analyzed more than 443 million hours of work activity across 1,111 organizations, found that AI adoption has reached 80% of employees and productive hours are up 5%. Focus efficiency, though, fell to a three-year low of 60%.
The average focused work session now lasts just 13 minutes. Something is eating the rest of the day, and it isn’t the work getting done faster.
AI is adding to workloads instead of redistributing them.
Adoption without redesign
Jenkins has spent three years watching organizations celebrate AI adoption while leaving the underlying work untouched.
The tools arrived faster than the thinking did.
“AI doesn’t reduce work by default,” he says. “It reduces work when you redesign how work happens. If AI is just making our teams busier, that means we didn’t adopt AI. It means we adopted another piece of software.”
ActivTrak research followed a subset of users in the 180 days before and after AI adoption and found that time spent across every measured work category went up, with email increasing 104% and chat and messaging jumping 145%.
Researchers at UC Berkeley’s Haas School of Business found the same pattern in a February 2026 study, with employees working faster, taking on broader scope, and extending into more hours without being asked. Instead of shrinking workloads, the work got spread out.
The average organization now runs seven AI platforms, up from two in 2023. That means more to manage, more decisions to make, more places for focus to fragment.
“Organizations that are leaving that space are generally going to be more successful because they’re leaving the space for growth,” says Jenkins. “This is all about balancing good strategy with enough resources and time to enact that strategy, both short and long term.”
The signal leaders are missing
The burnout numbers look good right now. ActivTrak found risk fell 22%, to just 5% of employees, with healthy work patterns at a three-year high.
But disengagement risk jumped 23% in the same period, affecting nearly one in four employees.
These are workers with capacity that have nowhere to go.
As AI absorbs routine tasks, that time tends to get filled with more meetings, more tools, more low-value activity rather than work that moves anything forward.
Jenkins sees a version of this play out when leaders move fast without bringing their teams along. For example, a leader experiments with AI tools on evenings and weekends, builds something, then brings it to the team and tells them to start using it.
He calls it the “slingshot effect.” Pull too far ahead of your people and the whole thing snaps back.
“It cannot be built in a vacuum,” he says. “It has to be built in partnership with folks in the organization.”
Staying connected to where people actually are, he says, is the harder discipline.
When asked how he stays connected to what employees need at every level, he laughs, but catches himself.
“I laugh because it’s uncomfortable,” he says, “not because it’s funny.”
For Jenkins, that discomfort is worth paying attention to. The leader who has six AI agents running by 6 a.m. and the one who hasn’t touched the tools yet are both telling him where the work is happening.
“I’m making space to listen to the people who are very comfortable [with AI], and making space for the people who aren’t,” he says. “Because what AI and transformation strategy can feel like is unsafe.”
Punchcard saw this firsthand in its work with the College of Registered Nurses of Alberta (CRNA). When the company took on redesigning the CRNA’s regulatory affairs and permitting processes, they started by mapping where the existing process was breaking down.
What was breaking down, and where? AI entered the conversation only after those questions had answers, built onto a foundation that had been rebuilt to receive it.
Jenkins sees it in his own shop at Punchcard, where the ratio of writing code to reviewing code has changed. Same hours. Different work.
Getting that sequence right, he says, is where most organizations are still struggling.
Less tools, more thought
Jenkins isn’t prescriptive about this, but he has a three-step framework that keeps coming up with clients.
Start small. Stay anchored to something real.
The first move is picking a single metric that matters to the business (like a deal cycle, cost per hire, or nurses validated through a regulatory system) and asking how work should be redesigned to move it, assuming AI already exists.
From there, he says, pick one high-impact workflow and rebuild it from scratch rather than running a tool pilot.
Map what steps could disappear with AI, what could be automated, and where a human being still needs to be in the loop.
Then cut the AI footprint in half.
“Organizations have too many tools, too many licenses, things that are potentially inconsistent,” Jenkins says. “Do less, implement fewer tools, but focus on more business value, more repeatable patterns. That constraint can help drive adoption.”
He calls it small experiments with radical intent.
“The smaller the segments we can learn, adapt, understand, and deploy, that means we’re not going to upset our teams,” he says. “We can think about how we’re going to make that incremental benefit within our organization, and those one-per cents certainly stack up over time.”
In 2020, organizations moved fast and figured out the work later. Most of them are still paying for it in some form. The meeting that started in a boardroom and moved to a screen is still the same meeting.
The plate’s already full. Loading AI on top isn’t a strategy.
Final shots
Disengagement risk now affects nearly one in four employees, up 23% in a single year.
Leaders who reduced overload without redirecting capacity have solved half the problem.
The organizations pulling ahead started by asking what the work should look like. Most are still asking which tools to buy.
The leaders pulling ahead are asking how work needs to change to make all the tools they’ve invested in, worth using.
Written ByJennifer Friesen
Jennifer Friesen is Digital Journal's associate editor and content manager based in Calgary.
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