AI Freed Up Your Time, Who Decided Where It Went?
Written by Harriet Bodö, Executive Coach & Mentor
Harriet Bodö has spent years on both sides of the relationship, as a client and today as an executive coach and mentor, across programs in leadership, business development, and change management. That vantage point shaped Coach Me & More, where she is also a certified board member, advisory board member, and keynote speaker.
New research shows AI often intensifies work instead of freeing up time. Here is why it happens, what it costs your people, and three ways leaders can make the freed hours actually count. We were promised time. Fewer reports to compile. Fewer emails to draft. Fewer hours lost to coordination. The tools delivered. The time didn’t. So here is the honest question: where did the hours AI freed up in your organization actually go, and who decided? For most leaders, the answer is nobody. That is exactly the problem.
Does AI actually save time?
An eight-month UC Berkeley study, published in Harvard Business Review earlier this year, followed about 200 employees at a tech company that offered AI tools but never mandated them. The researchers didn’t find freed-up time. It was intensified work. People worked faster, took on a wider range of tasks, and let work spill into hours that used to be their own, often without anyone asking them to.
You will recognize the patterns. The “last prompt” before closing the laptop, so the AI can keep working overnight. The prompt sent off in the middle of a meeting, so two things run at once instead of one. That isn’t laziness. It’s the opposite. It is what makes AI so seductively efficient, and so easy to misuse.
In March, a Boston Consulting Group study of nearly 1,500 workers gave the cost a name: AI brain fry, the mental fatigue that comes from using or overseeing AI tools beyond what the brain can carry. One in seven AI users reported it. Those who did made significantly more major errors, experienced more decision fatigue, and were more likely to be thinking about leaving. Productivity rose with up to three AI tools in parallel. Beyond that, it fell.
But here is the finding that matters most for leaders. When AI replaced repetitive work, burnout went down. When it was simply piled on top of everything else, burnout went up. Same tools. Different leadership.
AI isn’t the variable. How we lead it is.
Why AI drains our focus
Our brains weren’t built for the high-tech working life we have created. Every interruption costs energy. Research by Gloria Mark at UC Irvine has shown that getting fully back into a task after an interruption can take well over twenty minutes. Her work has also tracked how long our attention stays on a single screen before we switch: from around two and a half minutes some twenty years ago to roughly 47 seconds today.
We can multitask. The brain still only does one thing at a time. Drafting a text while asking AI for ideas, while another AI works on something entirely different in the background, feels fast. It costs focus. Add up those switches over a day, and the time AI was supposed to free is gone, spent on the switching itself.
So the time AI frees needs to go somewhere on purpose. Into rebuilding focus, with hands busy away from the screen: sketching, knitting, taking notes by hand. Into meetings with real room for discussion and problem-solving, rather than more and shorter meetings just because the calendar suddenly allows it.
And into a new skill we all need to practice: prompting well, delegating clearly to AI, and then daring to leave the task alone. Give the AI time to work. Do something else in between.
Come back with focus, instead of waiting or switching back and forth. Leaders who have spent years leading teams often already have this muscle: the habit of delegating and trusting that the work gets done without doing it themselves. That habit is worth its weight in gold now that one of your colleagues is AI.
When AI becomes your only colleague
Much of today’s work is done alone, not least from home. We don’t always dare, or know how, to ask for help, feedback, or collaboration. So AI becomes the easiest colleague to turn to. It’s always there. It makes no social demands. It never sighs.
AI can be a genuinely good colleague, the one you can ask the question nobody else wants to ask out loud. The risk begins when it becomes the only colleague. Replace human collaboration with quick AI answers, and we lose both the time we gained and our ability to work together, the very foundations of psychologically safe, innovative workplaces.
There is a second risk. AI hallucinates. It also tends to tell us what we want to hear, unless we explicitly ask it to challenge us. Research from Microsoft and Carnegie Mellon found that the more knowledge workers trusted generative AI, the less critical thinking they reported applying. Use AI too often, for things that are too simple, and we start doubting ourselves.
We ask it about things we are already experts in. We second-guess our own writing because the AI raised a question. One thing doesn’t move, however good the tools get: responsibility. We cannot hand that over to AI, even when it would be the easiest and fastest thing to do.
Who learns judgment now?
Critical thinking is built through experience. Which raises a question every leadership team should be asking right now: where will the next generation get that experience?
In August, Stanford’s Digital Economy Lab updated its research on AI and early-career work. Employment for 22- to 25-year-olds in the most AI-exposed occupations now sits 19% below where it would be had it kept pace with their peers, up from 15% a year earlier. The researchers attribute the gap primarily to reduced hiring, rather than increased separations. Experienced workers in the same roles show no comparable gap.
The researchers point to a reason. AI reproduces the kind of knowledge you learn from textbooks and written procedures. What it doesn’t reproduce is judgment: the tacit knowledge built over years of getting things wrong and learning from it. Either you win, or you learn. Take away entry-level work, and you take away the place where that learning used to happen. Today’s mid-level talent came from yesterday’s junior roles. Tomorrow’s will have to come from somewhere.
That is why I am increasingly convinced that two-way mentoring is a win-win for younger and more experienced people alike, and perhaps one of the best ways to use the time AI frees.
Cut costs or reinvest time?
What an organization does with the time AI frees says a lot about its leadership. Research from Workday earlier this year found that large companies are more likely to reinvest AI gains in technology than in their people, and close to a third simply fill the saved time with more work. This summer, a Careerminds survey of 600 HR leaders went further. More than half said their company would accept, or had already accepted, slower skill development in exchange for bigger AI budgets.
It’s easier to fall back on what gives quick results than to stop and think about what really matters. Cut a cost, and it shows next quarter. Build judgment, and it shows in three years.
I keep coming back to two leaders I heard speak this spring. Emma Storbacka, CEO of the consultancy Avaus, described a clear divide between two Nordic neighbors. In Sweden, the time AI frees tends to go into innovation. In Finland, the reflex is still costs and cutting staff. That reflex isn’t unique to one country. Most of us recognize it.
At a keynote in Helsinki, Petter Stordalen showed what the other choice looks like. At one of the hotels in his group, AI in operations saved 460 hours in a single month. Instead of cutting staff, they used those hours to be more present. To notice what was happening around them. To see each other, help guests, and fix small things in passing. The result: top scores for both guest satisfaction and staff engagement, and an effect that shows on the bottom line.
The research now points the same way. The organizations getting the most value from AI treat saved time as a strategic resource and reinvest it in their people. Because AI isn’t only about efficiency, about saving as many hours as possible. It’s about optimizing impact for the long run.
Same hours. Different decisions.
3 ways to make freed time count
If you really want to benefit from the time AI frees, it comes down to building the team that will take you into the future. Here is where to start.
1. Build two-way mentoring
Let younger colleagues teach more experienced ones how to use AI. Let the experienced show the younger that a fast conclusion isn’t always a sound one, and share what years of critical thinking look like in practice. Done well, it also builds understanding across communication styles, and a shared respect for why collaboration matters.
2. Set clear rules for AI
Which tools do we use? What data and information can we upload? When is AI the best colleague, and when do we create the most value together, as humans? The Berkeley researchers call it building an AI practice: a shared sense of what is reasonable. The BCG study points the same way. Brain fry dropped when teams built AI into shared workflows and helped each other, and productivity rose with it. Start with why, as Simon Sinek has taught us. When the frame is unclear, time and energy go into debating what’s acceptable instead of into what AI could actually free up.
3. Prompt well, delegate with confidence
Build shared agents, skills, and templates that the whole team, and the whole organization, can use. That moves AI from everyone’s private trial and error to a shared way of working: one you can measure, and one that gets better every time someone improves it.
Then protect the time it frees. Workshops where knowledge is shared, a common vision, and recurring sessions where people learn from each other. Use the time for brainstorming, product development, and innovation. Above all, use it to think critically together.
What doesn’t change
This isn’t about people lacking the skills or ability to do their jobs. It’s about learning to work in a new context, under new conditions, with a new colleague called AI, one that arrives in many forms and versions and keeps changing. Which is exactly why it’s worth remembering what doesn’t.
We are still social beings. Herd animals. We need interaction, collaboration, and time for the brain to recover. There are three books I keep returning to, and I’d recommend them to you as well. Cal Newport’s Deep Work and Slow Productivity show the value of undisturbed focus in a world built for interruption, and offer concrete ways to do fewer things better rather than more things faster. For those who read Finnish, neuroscientist Mona Moisala’s Tyhmä työelämä explains why today’s knowledge work wears down the brain, and what a more brain-friendly work culture actually requires. Together, they say what we need to hear right now.
Make it a leadership decision
AI will free up time. That part is already happening. Whether your people ever get that time back is not a technology question. It’s a leadership decision. Make it on purpose. Make it together. Or it will be made for you, one last prompt at a time.
If you are working out how to lead your organization through this, as a leadership team, a board, or a leader carrying more of it alone than you should have to, that is exactly the work I do: building the clarity, trust, and psychological safety that let strategy hold up under pace, and turn new tools into better results rather than faster exhaustion.
Read more from Harriet Bodö
Harriet Bodö, Executive Coach & Mentor
Harriet Bodö became an executive coach and mentor after experiencing, as Head of Department, what a real difference that support makes for leaders who often carry decisions alone. In 2018, she turned that conviction into Coach Me & More, a Finland-based coaching and leadership development practice working with leaders, leadership teams and boards across the Nordic region. She holds a Master's degree in Economics from Hanken and is an ICF-certified executive coach, with 700 hours of coaching experience and more than 200 leaders coached and mentored. She is a certified board member (HHJ), holds board and advisory board assignments, and is a keynote speaker on leadership, transformation, and psychological safety.
