Guide · AI Adoption
Whether you're in tech, real estate, manufacturing, or any other industry, you may be asking yourself: how is AI actually useful to me, my job, my department, and my team?
This kind of question is extremely common, and it stems from a misunderstanding of AI and its capabilities.
At the risk of introducing an antithesis trope that AI so often reverts to:
AI adoption is not just about learning a new tool, it's an operational discipline.
So let's reset our expectations around AI, then learn how to identify areas where AI can be useful and how to build systems you can delegate to agents.
01
To anyone thinking AI will magically improve productivity and reduce your work burden: think again.
AI increases your production, but not necessarily your productivity. Out of the box AI gives you more: more drafts, more files, more options — more to review.
What you get out of the box
What you actually wanted
Better
Turning "more" into "better" is a different story.
An agent has all the knowledge in the world at its fingertips and no idea when or how to apply it.
Point it at a big, vague goal and you're using a hammer to implement a screw — it might technically work, but chances are you're going to break something and fail.
An increase in production, not productivity, also resets the headcount fantasy. Companies keep announcing AI-driven cuts as if AI deletes jobs. To be fair, AI does eliminate the need for some jobs, or the required quantity of those jobs, but the reality is it mostly just changes their roles.
Once you reset your AI expectations, you can then start identifying areas where AI can actually be beneficial to you and your team.
02
Most people get their intro to AI by asking it questions and getting answers — like a conversational search engine. While that's fine for personal use, it won't move the needle in a sustainable way professionally.
Here's the thing: AI is most useful when it can tackle challenges that require repeatable, step-by-step processes to reach a solution.
Humans have an amazing ability to perform multiple, cross-referencing processes at the same time. Have you ever been asked a question to a complex problem and you just "know" the answer? Or are you able to tell if something is "good" just by looking at it?
Our human processing isn't exactly linear. We run "quality checks" simultaneously and can reach accurate conclusions very quickly. AI is a computer — it essentially runs in a straight line with an order of operations.
For example, a customer email lands in your inbox and you answer it. That's one action with multiple quality checks happening inside of it: What is the customer actually asking? Are they satisfied? What can you actually do for them? Who else needs to be looped in? And so on.
Humans run quality checks and output processes simultaneously.
03
If you want to start delegating tasks and projects to AI, it begins with defining the process inputs and the output expectations before you build the agentic process.
That means successful AI adoption is front-loaded work. You define your standards, your context, and your planning before an agent even touches the project, so what comes back is worth keeping.
Two rules apply while you prepare to delegate to AI agents:
Rule 1
Whatever standards you'd apply at review time, encode them at the start in the form of guardrails and input expectations. Checking agentic work at the end of the process is the expensive way to find problems you could have tackled earlier. This will save you time, frustration, and ultimately money since you're paying for AI token usage.
Rule 2
AI is perfectly capable of handling big, complex projects — but don't start there. Adding complexity leads to weaker infrastructure and unclear success paths.
An author named Fred Brooks outlined this in The Mythical Man-Month, a book about software programming. Essentially, the more features and functions you add to a software program, the complexity multiplies exponentially — and thusly, so does the risk of failure.
Let's look at a physical example:
Level 1
This is a simple tool with a sharp edge that cuts things. Perhaps you made it yourself. It gets the job done for you and you alone. But it may not be suited for people with a different dominant hand or cutting style, or for people who cut different materials. It's not complex because it doesn't have to be.
Level 2
This may seem similar to the blade, but the distinction here is that this knife is a product usable by anyone for cutting most things. It's shaped different, made of different material, and requires a robust safety check process so people don't get mad and sue you if they cut themselves. This little cutting tool just got exponentially more complex!
Level 3
This product becomes a suite of tools inclusive of the knife, a saw, a screwdriver (flathead and Phillips), a can opener, and a nail cutter. All of these parts have to work with lots of different use cases, materials, and user preferences. Moreover, they must all fold safely into the same handle. And safety? Well, we now have five potentially dangerous parts to worry about! This product just got way more complex.
More features bring more complexity.
See how adding feature requirements complicates product builds, use cases, and break points.
A sharp edge that cuts.
You made it. It works for you.
✓ DONE
The same steel and the same edge on every unit
1 unit
It folds, so a stranger can carry it without getting cut
1 unit
A saw, a screwdriver, a can opener — five more jobs
1 unit
Every implement made identically, at volume
1 unit
Five sharp things now, so five ways to get hurt
1 unit
All of them folding into one handle
1 unit
Six parts on one pivot, and every unit closes the same
1 unit
Open one tool without opening the others — on every unit
1 unit
Finished. It cuts, it is yours, and it does not need to be anything more.
The point is, if you go straight to building a "multitool" of an AI workflow, you'll spend all your time trying to solve for unnecessary complexity without meaning to.
Start with simple, single-feature/outcome workflows.
04
Your first idea is fine.
As if teaching someone who's never done it.
Work with AI to offload one repeatable slice, with clear steps and a result you can check.
Run the process, then fix it where the output disappoints (the process is almost always what's wrong), and work with AI to update your inputs based on the feedback.
05
Eliyahu Goldratt's Theory of Constraints states that a system essentially has one binding constraint at a time — one place where the output is actually limited.
The moment you resolve that constraint, it moves somewhere else in your macro workflow and you start over.
Goldratt's Theory of Constraints
Try unblocking the bottleneck and see how the process changes.
Nothing improved yet. Improve the process to see what happens.
Efficiency & output are hindered by process blocks.
Expect the same from AI. You'll point it at a bottleneck and you will clear it. Then you'll find another one to clear.
All of this is to say, AI will help you improve efficiency, but it is not a limitless solution.
