I spent 30 minutes last week doing something uncomfortable. I sat down with a notebook and listed every task I do at work — then marked each one based on whether AI could already do it, could assist with it, or couldn’t touch it.

The results didn’t make me more concerned. But they made the exposure very, very concrete.

What I do

I’m in CX at a large insurance company. My job is to design and manage the experiences customers have when they interact with us — from filing a claim to updating a policy to calling in with a question. I’m an individual contributor on paper, but in practice I lead cross-functional teams and manage a matrixed direct report. The work spans research, strategy, design, storytelling, and a lot of organizational navigation.

It’s the kind of role that feels safe from AI. It’s creative. It’s strategic. It requires reading people and rooms, not just data.

I wanted to test that feeling.

How I did the audit

I broke my job into four buckets — Strategy & Planning, Research & Analysis, Design & Delivery, and Leadership & Collaboration — and listed every recurring task in each. Then I rated each one:

• ● AI could do this well right now. I could hand it off today and get a usable result.

• ◐ AI could assist, but needs me to drive. I set the direction; AI does the heavy lifting.

• ○ AI can’t really do this yet. The task fundamentally requires me — my judgment, my relationships, my presence in a room.

What I found

8 tasks AI could do now. 12 where AI assists. 6 it can’t touch.

That ratio — roughly 30% fully automatable, 45% AI-assisted, and only 25% still fully mine — is not what I expected walking in.

The pattern was clear: the further a task gets from paper and closer to people, the safer it is. My automatable tasks are all about documents and data. My AI-assisted tasks are about plans and strategy. My fully-human tasks are about being in a room with other humans. Paper → plans → people.

The ●’s: the tasks AI can already handle

The research and analysis work? Almost all automatable today. Analyzing customer feedback and survey data. Reviewing NPS and CSAT scores. Pulling insights from call center transcripts. Many companies already have internal AI tools that make this nearly effortless. You don’t paste in transcripts and wait. You just ask a question and get an answer. The barrier to automating these tasks isn’t technical anymore. It’s organizational.

I want to be clear: I’m talking about tasks, not people. Automating a task doesn’t mean the person who currently does it has less to offer — it means they could be doing higher-value work instead. The question is whether organizations redeploy that talent or just cut it. (More on that in a moment.)

Writing customer-facing communications — emails, letters, scripts? AI can draft these today. Documenting requirements for tech teams? Same. Competitive benchmarking? I suspect I could hand an AI research tool this task and get 80% of the way there in an hour.

The ◐’s: I’m still driving, but for how long?

Most of my strategy and planning work landed here. Identifying which journeys need redesign. Mapping current-state experiences. Defining the future state. Building the business case. In each case, I’m still the one providing the initial ideas, the context, the “this matters because…” framing. But AI can take that seed and - with a bit more back-and-forth - do an enormous amount of the work.

It reminds me of an example Aytekin Tank shared recently in Fast Company.¹ A journalist asked AI to generate interview questions for a political profile. AI produced 30 competent questions in under a minute. The editor rewrote most of them — because years of editorial judgment told her what was missing. AI didn’t know the senator’s backstory, the political context, the angle that would make the piece matter. That’s exactly what my ◐ tasks feel like: AI can do the work, but someone with contextual judgment still needs to know what to ask for and whether the output actually serves the goal.

The honest version is that some of these are trending toward ●. If AI has access to our data — and increasingly it does — it can draft a journey map or build a business case that I’d tweak, not rewrite. The line between “AI assists” and “AI does it” is blurrier than I’d like. And the question that haunts this category is: how long does the “driving” part stay mine? If AI gets better at understanding organizational context — and it will — my ◐’s become someone else’s ●’s.

The ○’s: the work that’s still mine

Leading cross-functional team meetings. Getting buy-in from stakeholders who have competing priorities. Mentoring others and managing my dotted-line report. Navigating internal politics to move work forward. Facilitating workshops. Communicating progress and blockers to leadership.

Every single one of these is fundamentally about being a human in a room (or on a call) with other humans, reading dynamics, building trust, and making judgment calls that require organizational context AI simply doesn’t have. AI can help me prepare for these moments — organize my thoughts, serve as a sounding board, draft an agenda. But I’m the agent. The human in the loop isn’t optional here; the human is the work.

A caveat: these are the safest tasks right now. But “right now” might have a shorter shelf life than I’d like. If organizations become mostly AI agents coordinating with other AI agents, the “human among humans” skill set becomes less central. And there’s an even subtler risk: what if the humans still in the room start preferring to work with AI over each other? As Ross Douthat recently pointed out, AI is unlike any prior technology because it simulates the human — and the real question isn’t just whether it can replace us, but whether we get so used to it that we want it to.² I don’t think we’re there yet — but even these tasks may not be safe forever.

What surprised me

Honestly? Not the overall number. I walked in feeling about a 4 out of 5 on how exposed I am, and I walked out feeling the same. What changed was the specificity. I can now point to exactly which tasks are at risk, which ones aren’t, and — most importantly — where I should be spending my energy.

The audit showed me three things clearly. First, for the ● tasks: I need to understand the tools that can do parts of my job — not to surrender that work, but to make informed choices about what I advocate for, what I adapt to, and where I push back. Pretending AI can’t do this stuff isn’t a strategy. Knowing what it can do is.

Second, for the ◐ tasks: the skill that keeps me in the driver’s seat isn’t the work itself — it’s the ability to direct the work. Knowing what to ask for, evaluating whether the output is good enough, and understanding the context that AI misses. As Ethan Mollick has argued, that’s essentially management — and it may be the defining professional skill of the next decade, whether you’re directing people or AI.³

Third, for the ○ tasks: I should spend way more time getting good at — and getting known for being good at — the relational and political work. The facilitating, the stakeholder navigation, the mentoring. That’s the most AI-resistant work I do. And honestly, I’m good at it — I’ve been called a “masterful facilitator” more than once, and I have a real knack for gaining alignment across groups that don’t naturally agree. But it’s not what lights me up most. I love the creative stuff — the visual problem-solving, stepping up to a whiteboard and reimagining what could be. That tension is worth sitting with: I’m excellent at the safest thing, and energized by a less-safe thing. AI is getting better at creative ideation and visual mockups faster than it’s getting better at reading a room full of people with competing agendas. My safest work isn’t my favorite work. It’s the work that requires me to be a human among humans.

The uncomfortable question

Here’s what keeps me up: the tasks AI can do or nearly do — the research, the analysis, the writing, the documentation — represent a significant part of my working hours. When those tasks get fully automated (not if — when), my employer is going to make a choice. Do they use AI to get more out of the people they already have? Or do they get the same amount of work done with fewer people?

I don’t get to choose which one they pick. Goldman Sachs research found that unemployment among 20- to 30-year-olds in tech-exposed occupations has already risen nearly 3 percentage points since early 2025.⁴ The World Economic Forum estimates that 59% of workers will need training by 2030 — and that roughly 1 in 10 workers is unlikely to get it.⁵ And those new jobs the optimists point to? They don’t require the same skills, aren’t in the same places, and won’t go to the same people.

That’s the real exposure. Not replacement — compression. My role might go from five days a week to three days’ worth of value. And whether that means I get repurposed, promoted, or let go depends on choices being made right now — by my employer and by me.

Should you bother?

Yes. This took me 30 minutes and it was the most useful career exercise I’ve done in a while. Not because it gave me a neat answer, but because it made everything concrete — where I’m exposed, where I’m safe, and where I need to double down.

Here’s what I’d tell you: do this audit for your own job. Break it into buckets. Rate every task. Be honest — not optimistic, not catastrophizing, just honest. Then look at the pattern. Where are your ●’s clustered? Where are your ○’s? Is the work that’s safest also the work you’re known for?

And here’s a tip: use AI for the process itself. Here’s how I did it. First, I asked AI to generate an audit template tailored to my specific role — it broke my job into the right buckets and pre-populated tasks I might have forgotten. Then I filled it out myself with honest gut ratings. Then I brought my completed audit back to AI and asked it to pressure-test my self-assessment — where was I right, where was I kidding myself, where was I too pessimistic? That three-step process (AI builds the scaffolding, you do the honest work, AI checks your homework) took 30 minutes total and gave me a sharper picture than I’d have gotten on my own.

This is the second field report from Prep for AI — a public experiment where I test one practical tactic at a time and report what happens. If you want to follow along, subscribe. Next up: I’m going AI-first for everything I can for a month. We’ll see what happens.

I use AI (Claude by Anthropic, at the moment) to help produce this newsletter. The audit, the feelings, and the uncomfortable questions are mine. More on how and why on the Framework & Transparency page.

Your turn: Have you audited your job? I’d love to hear what you found — reply to this email or leave a comment. What surprised you? And if you think I’ve got something wrong — about my own ratings, about what AI can or can’t do, about any of this — tell me. I’m an amateur running experiments, not an expert handing down wisdom. The whole point is to get smarter together.

The tactic, rated:

• ⏱️ Time required: 30 minutes

• 🔧 Difficulty: Easy (just be honest)

• 😬 Discomfort level: Medium-high

• 💪 Resilience payoff: High — you can’t prepare for what you won’t look at

Sources:

1. Aytekin Tank. “Here’s the Leadership Skill AI Can’t Replace.” Fast Company, 2026.

2. Ross Douthat. “How Fast Can A.I. Change the Workplace?.” New York Times, 2026.

3. Ethan Mollick. “Management as AI Superpower.” One Useful Thing, January 2026.

4. Goldman Sachs Research. “How Will AI Affect the Global Workforce?” August 2025.

5. World Economic Forum. “Future of Jobs Report 2025.”

Changelog:

• v1.0 — 04.04.2026 — Initial post.

I didn’t plan to become the guy fighting robots. I’m a Star Wars fan. When I first spotted a delivery bot rolling down my street, my honest reaction was: Finally, droids are here. My pre-schooler waved at it from our apartment window. We were charmed.

That changed on a walk a couple weeks later. A robot came toward my family on the sidewalk — bright headlights, cameras scanning, flag bobbing. We stepped aside to let it pass. That’s what you do when something with wheels and a little momentum is headed at you. But this was a sidewalk. And something about yielding to a machine in the pedestrian space felt wrong.

I started asking questions. What are our sidewalks going to be like if these programs scale the way the companies want? What will it be like to go for a walk? Who decided to put these here, and did anyone ask us?

I couldn’t find many others even asking questions, let alone any good answers. So I started a petition.

Why it matters

The delivery robots on my sidewalk are the advance guard. Serve Robotics grew its fleet twentyfold in 2025, deploying over 2,000 robots across US cities.¹ Coco Robotics operates another 1,000, with ambitions to scale to thousands more.² These are well-capitalized companies executing a land grab in public space — city by city, through pilot programs most residents don’t know exist until the robots show up.

This is the pattern we’re going to see again and again as automation moves into the physical world. A company deploys. A city permits it quietly. Residents notice. And the question becomes: do regular people have any say, or does this just happen to us?

Civic action — the boring, local, analog kind — is the primary mechanism ordinary people have to govern how this stuff enters our lives. That’s the tactic.

What I tried

Last summer, I launched NoSidewalkBots.org — a petition asking Chicago to pause its sidewalk robot pilot until the city releases safety and ADA compliance data, evaluates that data and local job impacts in a public hearing, and establishes clear rules. Not “ban all robots.” Not “technology is evil.” Not “not in my backyard.” Just: slow down, show us the data, and let the public weigh in.

I built the site. Printed t-shirts. Handed out flyers. Posted on Reddit and Nextdoor. Here’s a detail that matters to me: the campaign is now entirely AI-free. I used AI for some aspects early on, but as the stakes got real, that felt increasingly hollow. The medium is part of the message. I haven’t used it once in many months, though it’s been tempting.

What happened

I was hoping for 100 names. I thought 250 by Thanksgiving would be a stretch goal. We crossed 1,000 on Thanksgiving Day.

The media found us before that, around 700 or 800 names — and here’s a detail I still can’t quite believe: the flyer that caught CBS Chicago’s attention wasn’t mine. Someone I’ve never identified printed a flyer with a QR code to my petition and taped it to a pole on their own.

CBS broke the story on the evening news. Then it cascaded — Block Club Chicago, ABC7, NBC, WGN Radio, the Washington Times, Axios, Futurism. A second wave hit in February: the Chicago Tribune ran it on the front page, Fast Company published a feature that led to an NPR All Things Considered segment, and the Economist covered it.

As of this writing, more than 3,600 people across 55-plus Chicago zip codes have added their names. Over 900 have used the incident field. The stories are sobering: a man who needed stitches and a tetanus shot after colliding with a robot’s visibility flag while catching a bus. Children hit. Feet run over. A blind man with a seeing-eye dog unable to get past a robot on a narrow sidewalk. A Chicago Fire Department paramedic reporting standoffs with robots while driving to emergencies. Wheelchair users describing sidewalks made unnavigable. Robots repeatedly driving at groups of kids, stopping inches away. Near-constant beeping audible from third-floor apartments. A woman followed by a robot displaying heart eyes on its screen.

Elected officials took notice. Multiple alderpersons began surveying their own constituents after the petition gained attention. In December, the city added a robot-specific complaint category to 311. In February, Alderman La Spata held a community meeting and surveyed his ward — more than 83% strongly opposed the robots’ expansion — and moved to block further deployment. I don’t know exactly how much our petition caused versus surfaced, but I’ve been proactive: reaching out to alderpersons’ offices, sharing data, visiting my own alderman’s office for a sit-down with staff. This is what civic engagement actually looks like.

I thought people would divide along the usual lines — tech-optimists vs. Luddites, young vs. old. They didn’t. Parents, wheelchair users, dog walkers, bike couriers worried about their jobs, elderly residents. The unifying thread wasn’t anti-technology sentiment. It was a shared conviction that public space belongs to the public.

Should you bother?

If you don’t have delivery robots on your sidewalks yet, you probably will soon. And the pilot coming to your town might not be sidewalk bots — it might be AI-powered surveillance cameras, facial recognition at your kid’s school, or something nobody’s thought of yet. The pattern is the same.

The tactic isn’t “start a petition about robots.” It’s: pick one concrete issue where AI or automation is entering your community, your workplace, or your life in a way that feels questionable — and do something about it. The hardest part hasn’t been putting my name on something public. It’s been feeling my way through what has become organizing, having never done it before.

It was also the most energizing thing I’ve done in years. In a moment when most of us feel like AI is something happening to us, this was a reminder that we still get a vote. Not a metaphorical vote. An actual civic voice.

You have one too. Use it.

This is the first field report from Prep for AI — a public experiment where I test one practical tactic at a time and report what happens. If you want to follow along, subscribe. Next up: I’m auditing my own job for AI exposure. The results are uncomfortable.

I use AI (Claude by Anthropic, at the moment) to help produce this newsletter. The NoSidewalkBots campaign, notably, is entirely AI-free — by deliberate choice. More on how and why on the Framework & Transparency page.

Your turn: Has AI or automation shown up in your community — on your sidewalks, in your schools, in your workplace, in your local government? I’d love to hear about it. Reply to this email or leave a comment. And if you think I’ve framed something wrong here — about the robots, about civic action, about any of it — tell me. I’m an amateur running experiments, not an expert handing down wisdom. The whole point is to get smarter together.

The tactic, rated:

• ⏱️ Time required: A few hours to launch; ongoing (a couple hours a week to maintain)

• 🔧 Difficulty: Moderate — you need a clear ask, a simple website, and the willingness to put your name on it

• 😬 Discomfort level: High — this is public, in your real community, with real stakes

• 💪 Resilience payoff: Very high — civic muscle is the primary check on how AI enters your life

Sources:

1. Serve Robotics. “Serve Robotics Builds 2,000 Autonomous Delivery Robots, Creating Largest Sidewalk Delivery Fleet in the U.S.” Press release, December 12, 2025.

2. Grush, Bern. “From Cute to Contested: How Public Acceptance of Sidewalk Delivery Robots Has Changed.” Urban Robotics Foundation, March 11, 2026.

Changelog:

• v1.0 — 03.17.2026 — Initial post.

• v1.1 — 03.19.2026 — Clarified that NPR's All Things Considered interviewed the Fast Company author (not a syndication of the article).