Artificially Designed

Another satisfying year of talking about AI while quietly drowning in AI.

You adopted the tools. You watched the demos. You said yes to pilots and playgrounds and "just trying this one thing." And now your browser has seventeen tabs open, your brain has half-finished thoughts scattered across three platforms, and your calendar is somehow fuller than it was before you automated anything.

This month's focus is the uncomfortable audit. Not predictions. Not trends. A reckoning with how we actually worked this year and whether any of it made us sharper, calmer, or more capable of the thinking that matters.

AI promised presence. Most of us got more noise.

The people who will thrive in 2026 aren't the ones who touched every tool. They're the ones who got ruthlessly honest about what served them and had the discipline to let go of what didn't.

This is what working smarter and living slower actually requires: fewer subscriptions, clearer boundaries, and the humility to admit that more capability doesn't automatically mean more clarity.

You adopted the tools. You watched the demos. You said yes to pilots and playgrounds and "just trying this one thing."

Now your browser has seventeen tabs open, your brain has half-finished thoughts scattered across three platforms, and your calendar is somehow fuller than it was before you automated anything.

This issue is the uncomfortable audit. Not predictions. Not trends. A reckoning with how we actually worked this year-and whether any of it made us sharper.

What I reject: The idea that more AI capability automatically produces more clarity. It doesn't. Capability without intention just accelerates drift.

Not because the models got smarter. Because the humans around them stopped pretending pilots were strategy.

The numbers: 88 percent of organizations now use AI in at least one function. Only a third have figured out how to scale it.

That gap isn't a technology problem. It's a leadership gap dressed up as an integration challenge.

The companies that made progress did something unfashionable. They slowed down. They built governance before features. They invited skeptics into demos. They equipped managers with language for hard conversations.

The ones still struggling? They confused proof of concept with proof of value.

The question for 2026: Where does AI create measurable impact-and can your team verify that impact without hand-waving?

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You know that moment-fifteen tabs open, three AI models running, notifications drifting in-and your brain feels like it needs a reboot?

That isn't a lack of discipline. It's cognitive overload disguised as productivity.

Research shows we switch screens every 47 seconds. Context switching costs hours each week. Tool hopping can erode 40 percent of productive time.

Here's the plot twist: the most AI-fluent professionals I work with don't use every tool. They build depth with a select few.

Would you rather be average at twenty tools or exceptional with three?

The fear isn't that AI will replace us. The fear is that chaotic adoption will drain us before we participate in the future we're building.

Your move: Count your AI-related tabs. Close all but three. Feel that drop in your shoulders? That's cognitive load recalibrating.

Read the full piece →

Your relationship with AI probably feels familiar. Open a tool. Write a prompt. Tweak it. Try again.

The output is decent. The experience feels scattered. You move faster, but your thinking doesn't feel steadier.

That friction isn't personal failure. It's structural.

Prompting was built for interaction, not sustained collaboration. Context evaporates. Momentum leaks between sessions.

In my work, I use the AI Collaboration Pyramid to map how humans and AI develop working relationships. At the base: basic prompting with no memory of goals. At the top: stable collaboration where humans bring judgment and AI supports reasoning without overpowering it.

Many teams believe they're at the top. Few are.

As models improve, output quality will level out. Judgment will remain uneven. Knowing when to involve AI, how much influence it should have, and where it should stop-that's the differentiator.

Read the full piece →

December tempts us into aggressive future-casting. New roadmaps. New tools. New "this time for real" goals.

There's a quieter move that sharpens judgment more than any planning sprint.

Thank the systems that got you here. Not as a ritual. As an audit.

The prompt templates no one talks about anymore. The risk reviews that slowed things down but prevented harm. The human-in-the-loop checkpoints that felt tedious until they saved a launch.

If you shipped anything responsibly this year, it wasn't because of velocity. It was because of structure.

Gratitude, used correctly, is diagnostic. When you name what supported you, you surface which processes reduced cognitive load and which constraints improved decision quality.

Before you ask what to adopt in 2026, ask: What quietly prevented failure in 2025?

The answer is rarely a tool. Almost always a system.

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Use this prompt to map where your attention is leaking and where AI can create breathing room without removing you from the work that matters.

Something I've been sitting with from recent workshops:

The people who struggle most with AI adoption aren't the skeptics. They're the enthusiasts who said yes to everything and now can't find their own thinking underneath the stack.

The hardest conversation isn't "how do I start using AI?" It's "how do I stop using the parts that aren't serving me?"

That's the real work heading into 2026. Not more. Less, but deliberate.

What would you stop using tomorrow if no one was watching-and what does that answer tell you about why you're still using it?

If you're feeling friction between AI adoption and your own judgment-if the tools are multiplying but the clarity isn't-that's the work I'm in.

Reply to this email. Tell me what's not working.

The door to 2025 has closed. What you carry through it is a choice.