The support and product teams are seeing a significant change. AI assistants now handle routine tasks quickly, but people are still needed for decisions that require context and nuance. For example, a friend of mine saw a teammate use an assistant to close five support tickets in ten minutes. The tool wrote replies in the Teams style and included the right product details, so editing was fast, and the queue cleared quickly. But when a ticket revealed a subtle expectation mismatch from a past sales call, it required human judgment to resolve. The assistant brought speed, but the person kept the relationship strong. This is the future. Where AI helps, but people are essential when context matters.

AI now handles routine, repetitive work like clicking, copying, and rewriting, which used to wear us out. As these tasks go away, we’re left with deeper work that needs judgment, focus, and courage. This shift can feel unsettling. People aren’t just afraid of losing jobs. They’re worried about facing the work we’ve been putting off.

Shallow work seems productive on a sprint chart as you see tasks get checked off, progress lines drop, and we feel like we’re moving forward. Deep work is different. It’s hard to schedule, needs long stretches of time, and often gives slow or uncertain results. But deep work is where real value comes from: fewer mistakes, better product choices, and lasting ideas. We only get these benefits if we stop multitasking, which isn’t a skill but a distraction.

Product teams are often the first to notice this shift. AI can summarize research calls and draft release notes, which is useful. But it can’t decide whether a feature aligns with the bigger product vision. That decision needs judgment, experience, and understanding. Things only people have.

A tool can fill out a document, but only a person can say no to a feature that won’t last, even if the data suggests adding it. Making those calls is deep work.

Development cycles are changing, too. AI assistants can write tests, set up services, patch functions, and help code pass linters. That’s helpful. But key decisions still need people. Should we simplify a dependency tree now or wait? Should we use a clever pattern that might not be reliable? Should we choose speed or clarity? These are choices, not just tasks. The assistant can show options, but the team has to weigh the consequences.

Sustainability efforts show this clearly. A tool can measure energy use and point out easy ways to save energy and money in a pipeline or data store. But the bigger move is to design systems that do less from the beginning. By using smaller models, fewer transformations, more caching, and cutting out unnecessary jobs. The assistant can help us find these things, but people make the smart choices about resources.

You can sense a culture shift when shallow tasks disappear. Meetings lose their fake agendas. People face big problems and sometimes reach for their phones. Attention needs to be rebuilt like a muscle. It doesn’t come back on its own. Teams that do well choose a few good habits and stick to them, just as athletes do.

The first habit is protecting time for deep work. Set aside two hours in the morning, three times a week, and treat it as untouchable. No chat, no email. Don’t use this time to catch up. Instead, use it to make progress on the most important task of the week. The benefits show up quickly, and your mind gets used to longer periods of focus.

The second practice is making small, clear bets. Deep work is easier when the goal is focused and the outcome is clear. We write a short paragraph that states the bet, the evidence we’ll use to check it, and the time limit. We don’t write long documents just to feel safe. We make a plan and follow it. Staying focused helps us stay brave.

The third practice is making maintenance a regular habit. We set aside a ninety-minute block each week to clear up friction and keep the system running smoothly. We fix flaky tests, trim noisy logs, and close alerts that keep popping up. This habit cuts down on distractions so deep work can stay deep. Nothing kills focus faster than lots of small problems.

People often ask where AI fits into all this. The answer is simple: it handles the surface layer that keeps deep work clear. AI drafts, summarizes, and brings up important points. It watches metrics and flags changes. But it doesn’t have taste, doesn’t make the big decisions, and doesn’t define a product’s spirit. It supports the parts that help people do their best work.

Some people think deep work is a luxury and not realistic for busy teams. But the truth is, deep work leads to less rework, fewer incidents, a better reputation, and fewer changes in direction each year. The benefits are real, even if the first day feels quiet.

Let’s talk about screens for a minute. People worry we’ll be stuck behind glass forever. AI can help by taking routine tasks off the screen. You don’t have to click through endless menus when an assistant sends you a summary that fits your needs. You don’t need ten tabs open when the interface is calm by default. This gives you more time to think without distractions.

Here’s a pop culture example: in a heist movie, the crew handles the small tasks so the mastermind can focus on the big plan. The excitement isn’t in the chores, but it’s in the key moves that drive the story. Our work should be like that, too.

We should be clear about the risks. Some people will fill deep work time with fake deep tasks like long documents with no decisions, research without a clear goal, or meetings that talk about thinking instead of actually thinking. The fix is simple: decide the question, decide the bet, and decide when to stop. Anything else should be returned to the assistant or dropped.

Managers often ask how to lead in this new way. The role shifts from tracking status to protecting attention and good judgment. You remove distractions and guard time for deep work. You guide with examples and clear limits. You measure results, not just activity. You reward the person who makes a brave choice that saves the team from wasted effort.

Engineers sometimes find the word ‘taste’ intimidating. It sounds unclear, but it’s not. Taste means knowing what has worked before and what lasts. It’s the instinct that tells you a screen will confuse users, even if the data looks good in the short term. Taste can be taught. Pair people with good judgment on small tasks and have them explain their choices. Don’t turn it into a lecture. Make it a shared learning experience!

I look for a few signs that show a team is getting the benefits of deep work: fewer status meetings, more short notes that end with decisions, fewer urgent messages, more quiet days, fewer incidents, and more small wins that shape a quarter. These things might not look exciting in a presentation, but they feel good in real life.

The key change is that jobs are being redesigned around what people do best: judgment, taste, courage, and steady focus. While AI handles the busywork, teams need to decide what matters and say no to the rest. The future isn’t empty because of automation. It’s a chance to build on our unique human strengths.

Some people will claim that deep work can be automated if we just write better prompts. But it’s like getting bathing suits from a vending machine. You can’t learn to swim by buying more suits. You need the pool, the practice, the time, the fear, and the joy.

So where do we start? Pick one day a week for deep work. Set a clear goal at the start of the week. Let the assistant handle routine tasks. Schedule a maintenance block and keep your tools simple. Teach good judgment by pairing people up. Cut out fake deep work with a positive approach. Then deliver the one thing that matters. The benefits add up, the quarter feels different, and the team grows.

AI isn’t our destiny. It’s just a tool that clears the way and gives us back the work we’ve been avoiding. Take your time. Think things through. Make fewer mistakes. Build things that help people, not hurt them. The assistant will handle the surface tasks, but the deeper work is still up to us. Let’s get better at it.