AI is getting better — a lot better.

A year ago, AI tools felt like a roulette wheel. Some answers were great. Most were generic. Plenty were just chat-and-transfer with extra steps.

That has changed.

I’ll be honest: I was behind going into 2026. I’d spent the prior two years too heads-down on client projects to go deeper than what the big names were showing on the surface. If you know me, you know I’m naturally skeptical and grounded in results, not hype. So when I tell you it’s time to pay attention, it’s not because I got swept up in it — it’s because I finally sat down, did the work, and saw it for myself.

Here’s the honest version of how I got there, and the five things I’d tell anyone starting now.

The false starts

I jumped in early. When ChatGPT took off in early 2023, I was one of the first adopters — downloaded it, paid for Pro, used it for the better part of a year. It was genuinely impressive; there was nothing else like it.

Then in 2024 I tried Microsoft Copilot. I want to be fair here: I’m a Microsoft shop, I build on their stack every day, and I respect what they’re doing — but not every product lands the same, and at that point Copilot wasn’t ready for the way I work. The pitch was perfect: connect into my Microsoft ecosystem and take on the admin side of consulting — intake, contracts, meetings, design docs, training. I bought licenses mid-project hoping for help in the thick of it. What I got was connection issues, formatting headaches, the occasional hallucination, and enough friction that I lost time I didn’t have. I set it down and went back to ChatGPT — it did what I needed, when I needed it.

A year later Microsoft came back around with an improved Copilot and a discount, so I gave it another honest shot. Better — but still not enough to change how I worked. So I stuck with ChatGPT and ate the subscription.

Here’s the thing, though: even with ChatGPT, I was always the middleman. It could tell me how to do something, or draft something I’d then have to fix — but it couldn’t actually do the work. I kept hearing acquaintances and podcasters rave about Codex and Claude Code, so I knew something was shifting. I just hadn’t made the time to find out. I was working in my business, not on it — and with three young boys and everything that comes with that, “later” kept winning.

The decision

In early 2026 I changed that. I made a deliberate call to carve out at least 20 hours a week to work on the business — and AI was the centerpiece.

I started where a skeptic should: researching the frontier labs and what actually separated them. What I kept coming back to was a simple shift in what I needed. I didn’t need an AI that could tell me how — I needed one that could produce the deliverable. Do the work, not narrate it. From what I was reading, Anthropic’s Claude was built to do exactly that, so I signed up.

(Honesty tax: at this point I was paying for ChatGPT, Copilot, and Claude. I’d signed up partly for a new tool called Cowork — then found out it was Mac-only at launch and I’m on a PC. Mild letdown. I kept testing anyway.)

A bit later Cowork came to PC, and I started there — it was closer to what I needed. But it nagged at me that Claude came in three flavors — Chat, Cowork, and Code — and I didn’t really understand the difference between them. So I tried each. Long story short: Claude Code was the one. That’s where I found the real powerhouse.

The lightbulb

I read through Anthropic’s documentation and found a few sharp writers explaining how Claude gets context — and why context is the whole game. The building blocks turned out to be surprisingly simple:

• Markdown files — a plain-English file that tells the model who you are and how you work.
• Skills — written standards for how to do a specific task or process.

None of it is code. It’s clear instructions written in plain English. What surprised me was how powerful that was. The real lightbulb went off when I connected Claude to my computer and watched it read and edit my own files. That’s the moment it stopped being a chatbot and started being a coworker.

The bet: learn AI by rebuilding my own business

I learn by building. I can sit with theory, but it doesn’t lock in until I make something real. So I asked: what if I learn this tool by using it to improve my own consulting business?

My engineering background kicked in. Years of process work at large companies like Boeing and UTC taught me to standardize the documenting process before documenting anything else. So that’s where I started. With Claude, I spent about 20 hours putting my entire business on paper — six years of consulting and 25 years of experience, mapped out. That alone was worth it. For the first time, I could see the whole thing clearly, spot the contradictions, and start improving it. And honestly, doing that simply wasn’t practical without AI help.

That’s when the bigger opportunity hit me. My processes and tech stack had grown organically — human-first. Luckily, I didn’t have years of tech debt, and I was nimble enough to change. I had the opportunity to become an AI-first business. So I re-evaluated my whole stack through one lens — what plays well with AI and what doesn’t — and moved my core systems (project management, notebooks, deliverable docs) to tools that do. Then I found or built the connections so Claude could actually work inside them.

Guardrails first

Here’s where I slowed down on purpose. The moment an AI can read and write to your real systems, it’s powerful — and that’s exactly where you have to be careful. I treated client safety as a hard constraint, not an afterthought, and I built to it before doing anything else.

The line I drew: Claude helps run BI Visualized’s own business — my admin, scheduling, and documentation — inside my own environment. It never connects to a client’s database or their systems. And the enterprise AI I use operates under terms that bar it from training on my inputs or using them for its own purposes. Client systems stay where they belong: with the client. Guardrails first, capability second.

Building the machinery

With the foundation set, I went after the time sinks. I ran the equivalent of a Pareto analysis on my own processes — which steps eat the most hours? — and started there. Then, like a good engineer, the first thing I built was a tool to build the other tools: a standard way to create new “skills,” with validation, feedback loops, and version control baked in so they’d last.

Then I built. It took about three weeks to create and validate the skills and rules for my most important processes. This is where it gets real: those skills and rules guide Claude to take actions inside my systems, to my standards, every time. That was version one — and I was already seeing big gains. But I needed to test it on real work, carefully, because the blast radius of a mistake is real.

So I moved out of the sandbox onto a live client lifecycle — Intake → Discovery → Development — validating process by process, skill by skill, making sure each one did exactly what I wanted.

What it actually did

My conclusion: this is a force multiplier in three distinct ways.

1. More capable. I can do a wider range of things and do them well.
2. Higher quality. I’m human — I don’t have the bandwidth or memory to be as consistent and detailed as world-class work demands. AI does, and it keeps me honest.
3. More productive. For the same input (plus some tokens), it multiplies my output 2-10x depending on the task. A concrete one: a client proposal and contract that used to take me half a day or more now takes about an hour — and the result is higher quality and more consistent.

That third point is what most people are still underestimating. It’s why a small professional services firm can now operate at the throughput of a much larger team — a near-solo owner doing the work that used to take three people.

Five things I learned

Context is king. It’s what takes an AI tool from smart to truly useful. The same model with the right context around it is a different tool entirely.

There are levels to this. If you skip levels, you won’t get the results you want. You have to build the foundation first.

Simplify aggressively. Simple, well-designed architecture almost always beats unnecessary complexity. Less is more — every time.

Teach the AI to help you build the system early. This is where the real power shows up. Do this, and the system starts building and improving itself.

Don’t trust blindly. We’re still early. There’s no “easy button” where AI just does everything without your input or oversight. Think of it as a very smart, very fast new employee you have to guide and train. Done right, it moves quickly from newbie to capable, to better than you in certain areas — exactly how top talent does.

The pattern

Here’s the part everyone wants to skip straight to. Once the foundation is in place — the AI-first stack, the connections into your tools, the guardrails, and a repeatable way to build skills — the pattern is the same every time: take a repeatable cognitive task, codify the standard, wire the AI into the actual tools, and iterate. The first version is rough; the fifth is faster than I am, and more consistent.

But that repeatability is earned. The pattern only runs this cleanly because the foundation underneath it carries the weight. Skip the foundation and every one of these steps gets harder and more fragile — which is exactly why the levels matter.

What’s next

Since then, I’ve gone deeper — fine-tuning what I built and moving into agents: AI that can take on more responsibility and run more independently. The further I get, the more thankful I am that I set the foundation first, because agents carry a much larger blast radius. And the more I learn, the more I see how much is still left to learn. We’re early — which is exactly why now is the time to start.

Your turn

Where in your work is there a repeatable cognitive task that costs you 30+ minutes today — and what’s stopping you from codifying it?

If you’re trying to figure out how to get started on your AI journey, I’m happy to share my thoughts in more detail — book a 30-minute discovery call.

— Jake Prevost, BI Visualized | Microsoft Fabric & Power BI Consulting