If you’ve been rolling your eyes at the way people talk about AI lately, you’re not alone.

“Replace your team with a single prompt!”

“Use this tool or get left behind!”

“Why haven’t you automated everything already?”

The problem isn’t AI, it’s the story around it. For people-first teams who care about doing great work and taking care of their people, the current narrative feels misaligned at best and unethical at worst.

But AI adoption can be something else entirely. Used with intention, it has the potential to make work better. It can reduce the busywork, create more space for what matters, and support the kind of team culture you’ve been working hard to build. Here’s what a human-centered approach to AI looks like, and how to adopt it without losing what makes your team great.

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What a Human-Centered Approach Actually Means

Most advice about AI adoption focuses on tools rather than people. But if you lead a team that values autonomy, trust, and sustainable growth, how you introduce AI matters just as much as what you use.

The default rollout usually goes like this: pick a tool, set a goal to reduce hours or costs, and tell the team to start using it. But this kind of top-down, tool-first approach often creates more problems than it solves.

A people-first approach flips the script. Instead of asking, “What roles can AI replace?” it asks:

• “What do we want to protect?”
• “What do we want to free people from?”
• “How can we use AI to give our team superpowers?”

To apply this mindset, it helps to understand what humans are uniquely good at and where AI excels. When you get clear on the strengths of each, you can build systems that let both do their best work.

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What Humans Are Uniquely Good At

AI can summarize. Automate. Scale. It’s fast and tireless. But when you think about what makes a team truly great (not just productive) it’s the human traits that stand out.

Curiosity. Empathy. Judgment. Creativity. Taste. These aren’t just soft skills. They’re the engine behind innovation, problem-solving, and meaningful collaboration.

Humans know how to ask the right question when the problem is unclear. We feel when something’s off. We adapt. We bring in context like personal history, intuition, gut instinct. That’s what helps us connect ideas in ways that feel meaningful.

AI doesn’t feel wonder. It doesn’t sense nuance in a tense conversation or get inspired by a great piece of writing. It doesn’t know what it’s like to be moved by a mission.

That’s your edge.

A people-first approach means designing your workflows around those human strengths. Then letting AI remove the noise, so your team can do more of what they do best.

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What AI Is Uniquely Good At

AI thrives on structure, repetition, and scale. It doesn’t get tired, bored, or distracted. It doesn’t mind doing the same task a thousand times, or doing it in the middle of the night.

While humans shine in ambiguity, AI shines in clarity. Once a problem is well-defined, AI can help solve it faster. It’s great at sorting data, summarizing information, generating variations, and automating anything with a clear pattern.

Where we bring insight, AI brings scale.

Where we navigate emotions, AI follows instructions.

Where we reflect, AI responds.

That’s the sweet spot: using AI to handle the time-consuming, clearly scoped tasks so your team can focus on the work that requires judgment, care, and originality.

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Four Key Characteristics of Human-Centered AI

A human-centered approach to AI isn’t about adding automation for its own sake. It’s about building systems that eliminate busywork, speed up iteration, and help your team focus on their highest-value work. That requires intention.

Here are four traits that set human-centered AI systems apart:

1. Task-Driven Priorities

Human-centered AI starts by solving specific problems. Not leading with the latest shiny new tool, but instead starting from actual pain points.

Maybe your support team is drowning in repetitive ticket tagging. Or your marketers are repurposing content across channels. These are prime candidates for automation. The goal isn’t to remove humans from the process. It’s to free them from tedious steps so they can focus on what matters most.

2. Human-in-the-Loop Safeguards

No AI system is perfect. That’s why human-in-the-loop design matters. It means people are still reviewing critical outputs, whether that’s editing a first draft, validating a recommendation, or checking for tone and compliance.

Think of it like a tandem bike. AI helps with speed and power, but the human steers. This pairing gives you velocity without sacrificing judgment, ethics, or nuance.

3. Transparent, Trust-Building Interfaces

AI shouldn’t feel like a black box. When tools explain how they got to an answer, what sources they used or which steps they followed, it builds confidence. People are more likely to rely on AI when they understand it. Transparency also makes it easier to catch issues early, before they become problems.

4. Iterative Feedback and Continuous Improvement

Human-centered AI systems get better over time. They invite feedback. They evolve. Team members note what worked and what didn’t. Leaders use this feedback to tweak prompts, retrain models, or adjust workflows.

This loop is what turns a generic tool into a custom-fit support system.

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The People-First AI Playbook

Start with Friction, Not Features

Most teams start AI adoption by asking, “What tools should we be using?” But people-first teams flip the question: “Where are we feeling the most friction?”

Maybe your team is stuck rewriting the same updates every week. Or bogged down by notes that never get used after meetings. Or wasting hours hunting for information buried in Slack. These aren’t glamorous problems, but they’re real, recurring pain points that drain energy and focus.

When you start with friction, the tool becomes a means to an end, not the star of the show. You’re not adopting AI for the sake of innovation theater. You’re using it to solve a problem your team actually feels.

This approach also builds trust. Your team doesn’t feel like AI is being imposed on them. They see it as a thoughtful response to their real-world challenges. A single solved problem builds momentum and shows your team you're focused on what actually matters.

Pilot in Public

AI adoption works best when it’s visible and low-stakes. When people have safe ways to experiment out in the open, they learn faster and avoid mistakes in isolation.

Start by creating a dedicated Slack channel for team experiments. Use it as a space to share prompts, celebrate small wins, and most importantly highlight what didn’t work. Making learning public helps spread knowledge faster.

Without a space like this, people often experiment in the shadows. That leads to duplicated effort and security issues.

Instead, set the tone by sharing your own experiments. Don’t wait until something is perfect. Share what you’re trying, what failed, and what surprised you. Curiosity is contagious.

Pair People with AI, Not Against It

Too often, AI tools get handed to teams without guidance. People are told to "figure it out" or left wondering if the tech is meant to replace them.

A better approach is pairing AI with specific roles or responsibilities, and showing people what's possible when they use it well. For example, you might show a project manager how AI can help draft weekly updates using meeting transcripts. Or walk a designer through how AI can generate quick mockups to kickstart a creative sprint or offer alternate concepts in seconds.

In both cases, AI extends the person’s reach instead of erasing their role. It becomes a collaborative assistant, not a competitor.

This framing also helps with adoption. When people see AI as something that makes their job easier (not something that makes them obsolete) they're more open to exploring what’s possible.

Use AI to Protect Focus Time

AI isn’t just about doing more. It’s also about clearing space.

One of the best uses of AI is reclaiming focus time. That means using tools to handle the noisy, fragmented parts of modern work: sorting notifications, summarizing channels, pulling insights from sprawling docs.

You might use AI to:

• Summarize Slack threads at the end of the day
• Draft follow-ups after meetings
• Create “highlight reels” of key project notes

When AI handles these microtasks, your team has more room for deep work. They’re not context-switching all day. They’re not constantly chasing updates. They’re thinking, building, solving.

This also unlocks a critical shift for teams trying to move toward async-first work. For years, disorganized data, scattered communication, and missing documentation have been major blockers. Remote work cracked the dam, but AI can be the force that finally opens the floodgate. When used intentionally, it can remove the friction that's kept teams tethered to sync-heavy habits, and finally create the conditions where async can thrive.

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Give Your Team Superpowers

AI has the potential to make work better, but only if we adopt it with care.

People-first teams already know that how we work is just as important as what gets done. The same should be true of AI.

When you start with your team’s real challenges, protect what makes them great, and use AI to amplify their strengths, you get something rare: lasting progress rooted in what matters.

Use this playbook to start building momentum. And if you’d like support on your people-first AI adoption journey, just reply to this email—I’d love to help.

In Case You Missed It

​May Favorites​
Last week, I shared my favorite lessons, reads, and shares of May.

​AI Recipe: Create Instant SOPs from Meeting Recordings​
In a recent Idea Kitchen recipe, I shared how to turn messy meeting transcripts into polished SOPs using Gemini. It’s a simple but powerful example of using AI to eliminate tedious formatting work. Check it out if you’re looking for a practical, low-stakes way to experiment with AI on your team.

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Work Forward Society Want to meet others who do work differently? Join us in making 2025 your year of less meetings and more real work! Join The Work Forward Society →

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What did you think of this issue? What do you hope to see in the next one? Hit reply and let me know. I read every response. I truly appreciate you taking the time to read this. Hope you have a lovely day! Marissa ​Founder, Remote Work Prep​ P.S. Did you enjoy this issue? Share the article with a friend. Struggling with a remote work problem? Book a coaching call. If you liked this, consider supporting this free newsletter by leaving a testimonial. ​

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