We keep saying AI will give us more time for the human side of work. More time to listen, build relationships, and have the conversations we keep putting off.
I like that idea. I also wonder what makes us think we’ll use the time that way.
We can fill it with more work. We can use it to take on another responsibility. And if we’re uncomfortable having a difficult conversation, we now have something that can write a very thoughtful message while we remain just as uncomfortable—and just as absent.
This week, Colette Molteni joined Kelly and me to talk about empathy, efficiency, and the human work happening around our systems. I came into the conversation worried about what organizations might give up in pursuit of speed. Colette helped me sharpen the question: do we understand which effort we’re trying to remove, and what that effort contributes?
The News Beat
What leaves with the people?
We opened with the corporate layoff announcements and the familiar explanation that organizations are shifting toward AI. My concern was the experience those people leave with. Someone may know the process, but they also know where it breaks, which customer needs another conversation, and what to do when the instructions stop being useful.
Colette pushed that concern further. If the expected AI gains underdeliver, an organization may already have removed the people who could help recover the work. The people who remain are watching, too. Uncertainty about their own positions can affect their willingness to take risks, invest in relationships, or commit to the next change.
An efficiency gain can look different once you account for what happens afterward.
Kelly introduced an alternative: IKEA’s approach to reskilling. In 2023, Ingka Group reported that 8,500 call center coworkers had gained skills in areas including remote interior design, digital sales, relationship building, and complex customer inquiries as its Billie chatbot handled simpler questions. That is an older example, but a useful one for this discussion. It shows that adopting AI can include a deliberate decision about where people contribute next.
Kelly also noted that people need opportunities to learn how their work is changing. That puts responsibility on both sides. Employees need to develop skills, and organizations need to decide whether they are willing to invest in the people who already understand the business.
What is happening to empathy in management?
The second story covered research in HR Dive that found managers scored below the general population on measures including empathy. The research draws on personality assessments spanning almost twenty years; it does not establish that AI has recently made managers less empathetic.
That distinction matters because Colette immediately questioned the explanation. Are organizations promoting people who demonstrate less empathy? Is the experience of management wearing it down? What expectations and pressures are shaping the behavior?
Those are different problems. They need different responses.
Our preparation also included Gallup’s reporting on declining engagement, with managers accounting for much of the recent downturn. Gallup connects the discussion to the scope of managers’ responsibilities and notes their importance in supporting AI adoption.
The person we expect to guide everyone else through change may be struggling with it themselves. Asking that person to demonstrate more empathy is reasonable. We also need to look at what we’re asking them to carry.
The Gravity Audit
The work around the system
Human Middleware is the work people do to connect what an organization’s systems, processes, and ways of working leave disconnected.
I originally understood that in a very tangible way. A person takes information from one place and puts it somewhere else. Someone reconciles two systems. Someone knows how to get a request through because the official process doesn’t quite do the job.
I wanted to fix those gaps. I still do.
But the conversation with Colette brought us into the relationships around that work: the trust that makes someone willing to raise a concern, the explanation that helps another team understand a request, and the attention that lets a manager notice someone is having a hard time.
Kelly and I had a current example. As we prepared to launch a system, we were still discovering conversations and background work. The system could accept more information and included an AI component, but people were still using back channels to get things done.
We hadn’t accounted for everything.
That is an uncomfortable admission when you’re the person talking about Human Middleware. It is also exactly why I keep testing the idea against actual work. A new system can make the visible process easier while leaving people responsible for the connections around it.
Before counting that as an efficiency gain, I want to know what those people are still doing.
Some of the labor is the point
Colette gave us the example that became the center of the conversation.
A neighbor had brought her homemade chicken noodle soup when she was sick. Some efficiency was involved—the soup had been prepared and frozen—but its meaning included the effort and attention behind it. Bringing a can of soup to the doorstep would have been a different gesture.
Kelly connected that to a child making a gift. Its value is bound up in the child having made it for you. The finished object is only part of what you receive.
That distinction gets harder at work, because we’ve trained ourselves to look for effort we can eliminate.
Some effort is unnecessary. Some exists because a process is broken. Some helps us understand another person well enough to make a sound decision or offer useful support.
We need to tell those apart.
An AI tool can help a manager organize their thoughts before a conversation. It can suggest clearer wording. But the manager still needs to understand what happened, consider the person involved, and take responsibility for what they say.
A warm message can be useful. The relationship still needs someone in it.
The Conversation with Colette
Connection does not look the same to everyone
We spent part of the episode talking about remote work, messages, and meeting people in person.
Kelly described how her impression of someone had changed after meeting them. Over chat, she had read a particular tone into their messages. In person, she found someone she connected with. I recognized the value of those encounters, too.
Colette offered an important qualification. She prefers text to phone calls in many situations, and receiving a message can feel like connection to her. The medium itself does not tell us whether someone cares.
That challenged an assumption running through parts of our discussion. We can mistake our preferred way of connecting for the correct way.
If empathy involves understanding another person, we have to leave room for their preferences. Someone who wants a message rather than a call is telling us how to reach them.
Does AI make room for people?
Kelly asked Colette whether AI gives leaders more time to connect with their teams or allows them to avoid difficult conversations.
Colette’s answer depended on how the person uses it.
Earlier that day, she had used AI to compare code between environments. Saving time on that task could leave her with more attention for a later conversation. She also described another possibility: using AI to summarize a conversation and send an email without absorbing what was discussed.
The tool can support either pattern. The person still chooses how involved they want to be.
This is where my concern about saved time keeps returning. I’m quite capable of finding another task to put in it. Saving ten hours can become an invitation to accept ten hours of new commitments.
We should be specific when we say AI will create more time for relationships. What work is going away? What will we do with the capacity? Will people actually be more available to one another?
Empathy under pressure
Colette described middle managers being asked to absorb rapid change, model new behavior, drive AI adoption, and support teams while expectations around speed keep shifting.
Empathy can get squeezed in that arrangement. The immediate demand has a deadline. Paying attention to how people experience the change is easier to postpone.
But those relationships affect whether the work succeeds. People have to trust someone enough to ask questions, admit confusion, and say when something isn’t working.
Colette argued that leaders need to develop empathy deliberately. Treating it as a personality trait leaves too much to chance. It takes practice, especially when the person or situation is difficult to understand.
She brought the discussion back to a practical consequence: people will use the technology we build. Leaving their experience out of the design eventually creates problems for the work itself.
Where Gabe Got It Wrong
Kelly turned the question toward me. Did moving into more senior positions change how much empathy I felt expected to demonstrate?
I had plenty of material.
There were times when I believed I could do everything myself. I needed to delegate, hire, or give someone else room to contribute, and I held on. I also admitted that I had sometimes treated my ability to do the work as a reason to dismiss someone else’s input.
That’s difficult to acknowledge while arguing that organizations need to preserve the human side of work. I have helped create the very problem I’m worried about.
My understanding of Human Middleware had the same limitation. I could see a structural task and imagine a better way to perform it. I was slower to understand what the work meant to the person doing it, what relationships existed around it, or what would need to change before they could safely put it down.
Then I declared that empathy was hard because there was no checklist.
Colette informed me that she does, in fact, have a framework.
Of course she does.
She introduced her PACE framework, while emphasizing that applying it still requires nuance. A structure can help someone practice. They still have to pay attention to the person in front of them.
As someone who loves a system, I appreciated the correction.
The Close
I left this conversation thinking more carefully about the effort we ask AI to remove.
I want fewer people spending their days compensating for systems that should work better. I also want us to recognize when someone’s attention, judgment, or care is part of what makes the work valuable.
Those can exist in the same task. Untangling them takes more care than deciding the whole thing is inefficient.
Before handing something over, ask what the person doing it knows, who relies on their involvement, and what would have to change for the work to succeed without them. Then ask where AI can help.
And when we say the technology will give us more time for people, we should be willing to explain how we intend to spend it.
I’m including myself in that question.
