The AI Preparedness Hotline for Civilians

Before We Begin: A Word About Time

That line comes from the sci-fi film In Time, where the character is literally racing against a clock embedded in his arm. I don't think about that quote often — but lately, it's been surfacing.

Here's why. As someone who has spent the better part of three decades working across multiple programming languages, platforms, and technologies, there has always been a gap between what I know and what any given project needs from me. Not a gap from lack of effort. A gap from lack of time. My learning style has always been hands-on first, then reinforcement by seeing how someone else solved the same problem. That works well — but it requires time I rarely have in abundance.

Then AI came along, and something shifted.

For me personally, working heavily with AI in my own technical lab has been less like adopting a new tool and more like discovering a wormhole. A portal. A time machine. It compresses the distance between where I am and where I need to be. It takes the journey from A to B and collapses it. The things I never had time to learn well enough — the stitching-together tasks, the connective tissue of a complex project — AI handles them while I focus on the work that requires my judgment, my experience, and my domain knowledge.

And that brings me to waste. Specifically, wasted time.

Time is the single most expensive resource in any organization. We talk constantly about not wasting the time of executives, directors, and managers — yet time bleeds out in a hundred invisible ways every day. Waiting on answers. Chasing down data across systems. Discovering too late that the right action wasn't taken upfront, and now multiple people are spending hours untangling what should have been minutes. AI doesn't generate more output on its own. But it aggressively attacks waste — and that matters enormously. Watch this space. There's more to say on that topic. But now, the main event.

The Civilian's AI Preparedness Checklist

The echo chamber is loud right now. AI thought leaders, technology influencers, and press hungry for clicks are all sounding the same alarm: AI is going to take your job. Maybe we should look at it differently.

Because the people I'm thinking about right now aren't enterprise architects or venture capitalists. They're regular people — people who just want to work, generate income, be productive, provide for their families, and achieve something meaningful. Civilians, in the best sense of the word.

The education system, as it always has, will lag behind the pace of change. By the time curricula catch up to where the working world has moved, another wave will have arrived. That's not pessimism — it's history. Which means the most important preparation happens outside institutions: self-directed, community-driven, and started today, not next semester.

To that end, here is a preparedness checklist — not for technologists, not for executives — but for anyone who wants to stay employable, useful, and ahead of the curve as AI becomes woven into everyday work.

Item 1 of 6

Technical Fluency

What is your level of technical fluency — and what are you doing about it?

In many organizations, there's an umbrella concept called digital transformation — the process of moving from paper, from manual steps, from post-it notes and smoke signals, to integrated computer systems that talk to one another, automate the repetitive, and support smarter decisions across the enterprise. But digital transformation isn't just an organizational journey. It's a personal one too.

Think about the people you know who seem to just operate in the digital world. They get a new device, download the app, and they're productive within the hour. Something breaks — they diagnose it and move on. They don't submit a support ticket for every friction point; they develop intuition over time and apply it. That's technical fluency. It's not about knowing everything. It's about having gained enough familiarity with digital systems, interfaces, and tools that you can move through them without constant friction — without constantly wasting time.

Now think about the opposite. Every technical challenge is a stressor. Passwords are a recurring crisis. Multi-factor authentication feels like a puzzle designed specifically to slow you down. Copy and paste requires a detour through the Edit menu. These aren't character flaws — they're skill gaps. And in a world where AI is accelerating everything around you, unclosed skill gaps become exponentially more costly.

Here's a concrete example. Copy and paste — Ctrl+C / Ctrl+V, or Command+C / Command+V on a Mac — is one of the most powerful micro-efficiencies ever invented. I use it constantly, because even though I type well, I am a lazy typer in the best possible sense. If I already have that information somewhere, why retype it? But I've watched technically capable IT professionals navigate up to the Edit menu, select Copy, reposition the cursor, go back to Edit, and select Paste. That's six steps for a two-keystroke operation. Multiplied across a workday. Multiplied across a career. That's an enormous amount of time returned to waste.

Technical fluency also means understanding that in nearly every computing environment, there are at least three ways to accomplish any given task. The person with high fluency finds the most efficient path — not because they've memorized every shortcut, but because they've built the habit of looking for better ways. That compound learning builds on itself. It becomes a professional differentiator.

Why does this belong on an AI preparedness checklist? Because AI is going to expose technical fluency gaps at an accelerated rate. The tools are getting more powerful. The interfaces are getting more capable. The professionals who thrive will be the ones who can keep pace with those interfaces — who have built enough digital intuition to work alongside AI rather than simply in front of it.

Assessment

Where is your technical fluency today, and what's one thing you could practice or learn this week to raise it?

Item 2 of 6

Generalist Agility Across Multiple Lanes

In the age of AI, the specialist who knows nothing else may be the most vulnerable person in the room.

Let me be direct about something first: I have deep respect for specialists. Over the course of my career, I developed real expertise in EDI — electronic data interchange — which is the backbone of how companies exchange business documents across their supply chains. I went deep. I traveled for training, learned X12 standards, worked with multiple EDI translators and platforms, and that specialized knowledge gave me a competitive edge I've drawn on for years. Specialists matter enormously. This is not an argument against depth.

It's an argument for breadth alongside depth.

Here's what I've observed working across manufacturing ERP, distribution platforms, accounting systems, supply chain, human capital management, and software development over many years: the people who are most effective in an AI-enhanced environment are not the ones who know one thing deeply. They're the ones who know enough about many things to ask the right questions — to know when something isn't right, when a process needs a closer look, when the output doesn't line up with what they understand the input to be.

The specialist with no surrounding context will struggle to provide direction. The generalist with genuine, hard-won knowledge across multiple domains is positioned to guide AI effectively — and that changes everything.

The industrial revolution analogy is useful here. When the machines arrived and began replacing manual labor, workers who understood only the specific motion they'd been assigned — and nothing about the surrounding process — found themselves without transferable footing. But those who understood the whole process, even if not at expert depth in every step, could adapt. They could set up the machines. They could identify when variables — materials, temperature, tooling — were off. They could troubleshoot because they understood what the result was supposed to look like.

AI is doing something similar. It can automate many specific tasks. But it needs direction. It needs someone who understands the domain well enough to recognize when a response is wrong, when an assumption needs to be challenged, when a result needs to be sent back for revision.

I'll speak from personal experience. I came into IT later than many of my peers, and I benefited enormously from the fact that my early assignments involved talking to people — learning what accountants needed, what the shop floor looked like, what accounts payable and receivable actually meant in practice. That breadth of domain knowledge became the thing that made my technical work meaningful. I wasn't just writing code. I was writing code for people I understood, solving problems in contexts I had walked through firsthand.

Generalist agility is not being a jack of all trades and a master of none. It's being someone who can think across boundaries, understand what matters in multiple contexts, and bring that understanding to bear when working with tools — including AI — that are powerful but not automatically wise.

Homework

Think about the domain around you. What departments, processes, or disciplines do you touch regularly but don't fully understand? Pick one. Go learn something real about it.

Item 3 of 6

Automation Mindset Over Methodical Effort Mindset

The most dangerous habit in a world of AI is doing manually what could be done automatically.

Consider the modern automobile for a moment. Not just to drive it, but to think about everything happening inside it while you're on the road. The engine is monitoring and adjusting. Sensors are measuring distances. Lane assist is active. Tire pressure is being tracked. Entertainment systems are running. In newer vehicles, the car is navigating, braking, and correcting with minimal input from the driver. None of this requires conscious thought from you. It all happens in layers of automation, cycling constantly, working together — and you arrive at your destination having spent your attention on the things that required your judgment, not the things the machine can handle.

Software works the same way. The apps you use every day are loaded with automation — when you click this, the system knows to do that. You've internalized that behavior without realizing it. You've built a working model of the software's automation, and you leverage it naturally.

Now apply that thinking to your work. Every job has a series of steps. Some of those steps require your intelligence, your judgment, your institutional knowledge, and your human understanding. Others are purely mechanical — move this number here, enter this value there, wait for this process, trigger the next one. Those mechanical steps are automation candidates. An automation mindset is the discipline of being able to see them clearly.

This is not just a philosophy for software developers. An automation mindset asks:

• What are the individual tasks in this process, from the first micro-step to the last?

• Which of those tasks are repeatable and rule-based — things a system could execute reliably without human judgment?

• How do I know when the automation succeeded? How do I detect when something went wrong?

• Have I automated too much — where the guardrails of human review are now missing?

That last question matters. Automation is not fire-and-forget. It requires audit. It requires checkpoints. It requires someone with enough understanding of the process to know when the output isn't right — even if everything appeared to run correctly. Over-automation without oversight produces errors at machine speed.

AI tools — including coding assistants, document processors, and data analyzers — are extending automation into territory that previously required significant technical expertise to access. That means the automation mindset is no longer optional for technical professionals only. It's becoming a baseline expectation across many kinds of work.

The professionals who can look at a workflow and immediately begin identifying which pieces could be systematized, monitored, and made more efficient — those are the people who will multiply their effectiveness with AI rather than just using it as a faster typewriter.

Developing this mindset starts with observation. Start watching your own work as if you're an efficiency auditor. Every time you do a repetitive manual step, ask: does this have to be me, or is this a pattern that a system could handle?

Watch This Space

The next wave of software tools is going to reward people who bring this mindset to the table. Those who don't will spend their time feeding machines. Those who do will spend their time directing them.

Item 4 of 6

Entrepreneurial Learning Mindset

Your company will invest in you to the degree it benefits the company. The rest is on you.

Let me ask you a direct question: how much employer-sponsored training have you received in the last five years? For most professionals I talk to, the honest answer is: not much, and mostly chosen by someone else. In my own experience, the training that's truly mattered — the knowledge that's differentiated me, opened doors, and compounded over time — was the training I went out and found myself. Most corporate training is designed to close the gap between where you are and where your current job needs you to be. It is, by design, calibrated not to make you too marketable.

That model worked in a slower world. In a world where AI is shifting the skill baseline every twelve to eighteen months, it is no longer sufficient. The skills that made someone hireable three years ago are already being renegotiated. In my early years in IT, interview questions were about Word, Excel, and Outlook — can you make a formula, can you book a meeting? Those same organizations today expect you to navigate ERP platforms, CRM systems, Power BI dashboards, and now AI-assisted tools without a second thought. The bar doesn't sit still. It climbs.

Almost twenty years ago, I wrote a blog post called "The Old Work Is Dead." I believed it then. I believe it even more now. The old work assumed that if you showed up, did your job well, and kept your head down, you'd be fine. The new work requires continuous, self-directed improvement — not as a nice-to-have, but as a survival strategy.

What I mean by an entrepreneurial learning mindset is this: treat your own professional development the way a serious entrepreneur treats their business. A good entrepreneur doesn't wait for the market to tell them they're obsolete — they study trends, measure where they are, set growth targets, and deliberately invest in what they need to be more capable next quarter than they are this one. Your bottom line is you. Your market share is your employability. Your new customers are the opportunities that come to people who are growing.

I've had conversations with very capable technical professionals who, when they leave work, completely disconnect from the craft. No articles, no videos, no experimentation. "I don't have time for that." And I understand the sentiment. Life is full. But the consequence of that decision compounds quietly over time, until one day you find yourself behind a curve that moved while you weren't watching. The AI age is not forgiving of that kind of drift.

The good news is that the barrier to self-directed learning has never been lower. AI itself is one of the best learning partners available. You can ask it to explain trends, build you a learning plan, point you toward resources, quiz you on concepts, and help you practice applying new ideas in your actual domain. The tools are there. The question is whether you're going to use them.

By 2030, 59 percent of the global workforce will require significant retraining due to technological change. That figure isn't an argument for panic — it's an argument for starting now, on your own terms, rather than waiting until your employer or the market forces the issue.

Source: McKinsey & Company, "Reimagine Learning and Development for the AI Age," March 2026 — mckinsey.com

Challenge

Treat this year like a business year. What three things are you going to learn, and what does growth look like for you by December?

Item 5 of 6

Level / Component Thinking Proficiency

If you can't think in layers, you'll always be stuck at the surface.

Early in my IT career, I sat in a meeting with my manager and two other programmers. We were working on a bill of materials process for a manufacturing client — developing software to manage the components, routings, and relationships that define how a product gets built. My manager had a gift for explaining concepts that I've since learned is its own rare skill: he could take a complex system and show you exactly how it was structured, from the top level down to the smallest working part. That ability — to see systems in layers and understand where each component lives within the whole — is what I now call level/component thinking.

Here's the simplest version of the idea. Think about a tumbler — the kind people carry around for coffee or water. You experience it as one thing: a tumbler. But take it apart and you have a body, a handle, a lid, a locking mechanism, a slot, a straw, and a cap. Each of those is a component. The tumbler is the level. Understanding that relationship — and being able to move fluidly between them — is the foundation of this skill.

Now apply it to technology, which is where it becomes genuinely powerful. Your phone is a perfect example. Inside settings, you find top-level categories. Inside each category, more options. Inside each option, more controls. Every modern application works this way — menus, sub-menus, panels, tabs, right-click options, settings that are three levels deep and completely invisible until you know to look for them. The person who understands this hierarchy can navigate any application with confidence. They know where they are. They know how to go deeper, and they know how to come back up.

I've seen a young child — my niece, actually — calmly navigate multiple levels of settings on a phone, change a wallpaper, and back out cleanly to where she started. She didn't know the application. She knew the pattern. That's the skill.

One of the places I see this most clearly is in document creation. I've used Microsoft Word at a deep level for years — styles, navigation panes, section breaks, heading hierarchies, the works — and that proficiency has been a genuine professional differentiator. A document that is well-structured and professionally formatted communicates something about the person who made it before a single word is read.

In the age of AI, this matters even more. Working with AI tools — whether you're building prompts, navigating model interfaces, structuring workflows, or integrating AI into existing systems — requires you to think in layers. You need to understand which level of the system you're operating at, what's happening above and below you, and where to go when something isn't working. Flat, single-level thinking will leave you stranded.

The World Economic Forum's Future of Jobs Report 2025 identifies analytical thinking and technological literacy as the top core skills for the workforce going forward — and both of them, at their core, require the ability to work across multiple levels of complexity rather than treating everything as a single undifferentiated surface.

Source: World Economic Forum, "Future of Jobs Report 2025," Skills Outlook — weforum.org

Exercise

Open an application you use every day and explore three levels deeper than you normally go. Map what you find. Build the habit of knowing where you are within a system.

Item 6 of 6

The Multitool SME Skill Set

There is no all-in-one. There never was. The craftspeople who thrive are the ones who know their tools.

Let's close this list with something that pulls everything together — because this last point is really where all six of these skills meet in practice.

Start with a picture. Think about your phone. Twenty years ago, what that device can now do in your pocket would have required a powerful, expensive computer, professional software, and significant dedicated training. Today you carry a camera, a video editor, a design studio, a communication hub, a calendar, a research assistant, a music library, a navigation system, and increasingly, an AI collaborator — all in your pocket, all working in combination, all available instantly. You're already a multitool operator. The question is how proficient you are with the full set.

In the traditional software world, we used to think in terms of the big, monolithic application — the one heavy platform that does everything. Microsoft Office. Photoshop. The enterprise ERP system. And those tools still matter. But the modern working environment looks more like a craftsperson's workshop than a single machine shop. You have a set of specialized tools, each excellent at its specific job, and your value comes from knowing which tool to pick up, when to use it, how to move work between them, and how to bring it all together into a finished product.

Canva has made this concrete for a lot of people. Where Photoshop required deep professional training to use at any real level, Canva brought high-quality visual design within reach of someone willing to invest a few hours in learning the tool. The ceiling isn't as high — but the floor is dramatically lower, and for the vast majority of professional work, what Canva provides is entirely sufficient. The person who becomes an SME in Canva — who knows it deeply, who can produce polished, consistent output efficiently — is more valuable than the person who technically owns a Photoshop license but uses five percent of its capability.

SME, for those unfamiliar with the term: Subject Matter Expert. It's business shorthand for the person who really knows a thing — not the one who's been shown around, but the one others come to when something needs to work correctly. In the context of tools, becoming an SME means going beyond the surface features into the deeper capabilities. It means knowing the keyboard shortcuts, the settings, the edge cases, the integrations. It means being the person on the team who can make the tool perform.

Here's what genuinely fascinates me about AI: it works a lot like a strong multitool SME. It assesses the task. It identifies which capability applies. It builds from there, loading the right skill for the right step, moving the work forward, asking when it needs input, and delivering a result. The best human professionals work the same way.

Research from Workera, drawing on work done at Stanford, frames this well with what they call the "AI+X" concept — the idea that domain expertise combined with AI proficiency will define the most valuable professionals of the coming decade. The X is your specialized knowledge, your SME depth. AI is the amplifier. Without the X, the AI has nothing meaningful to work with. Without the AI fluency, the X operates at reduced capacity.

Source: Workera / Stanford, "AI+X: Combining Subject Matter Expertise with AI Proficiency" — workera.ai

What this means practically: build your toolkit deliberately. Know what each tool does well and where it hands off to the next one. Get deep enough in your most-used tools that you're not just operating them — you're commanding them. Think of your workflow as a production line, not a single machine. Where does the work start? Where does it move? What gets done where? How does the final product come together?

And they will change. That's the one constant in this field. The specific tools will evolve, new ones will emerge, and some will go away. But the skill of becoming an SME in whatever tools you're working with — that compounds. The person who has learned how to learn a tool deeply will always be able to do it again with the next one.

Inventory

List the five tools you use most in your work. For each one, honestly rate your proficiency on a scale of 1 to 10. Then pick the lowest score and go deeper this month.

Bringing It All Together

Six items on the checklist. Let's look at what they share.

Every one of these skills is about agency. Technical fluency, generalist agility, automation mindset, entrepreneurial learning, level/component thinking, multitool SME depth — none of these are things that will happen to you. They are things you build. They are choices you make about how seriously you take your own professional growth, and how deliberately you invest in becoming the kind of person who can work effectively with the tools and systems of this new era.

The AI alarm is real. Not because AI is coming to eliminate human work, but because AI is raising the floor. The baseline of what's expected is moving. The skills that were sufficient last year are being reevaluated. That's not a threat — it's an invitation. The people who treat this moment as an invitation to grow will find themselves in extraordinary positions. The people who wait for the wave to pass will find themselves significantly behind it.

I've worked alongside brilliant people who knew their craft deeply but resisted the tools around them. And I've worked alongside people with half the raw ability who were so curious, so willing to learn, and so relentless about getting better that they outpaced everyone around them. In the age of AI, that second type of person wins. Not because they're smarter. Because they're ready.

Use AI to guide you toward resources. Use it to help build your learning plans. Use it to practice new concepts and explore new domains. Use it to eliminate the waste in your day so that the time you reclaim can go toward growth. It is, in every meaningful sense, a time machine. Use it to get to where you need to be.

Closing Thought

Keep learning. Keep trying. Keep searching. And never give up.