Choosing Your Entry Points

You’ve mapped your workflows. You can see where AI might help. Now comes the harder question: where should you actually start?

The temptation is to go after the biggest opportunity, or the most painful problem, or whatever seems most impressive. But the best entry point isn’t always the most obvious one. It’s the one that actually sticks.

This article is about choosing wisely.

The Integration Trap

Here’s a pattern I see constantly: someone identifies a workflow that’s clearly inefficient, spends time setting up an AI-assisted version, uses it twice, and then quietly goes back to the old way.

Why? Usually one of three reasons:

The friction was too high. The new workflow required too many steps, too much context-switching, or too much babysitting. It was theoretically better but practically annoying.

The stakes were too high. They picked something mission-critical, got nervous about AI mistakes, and ended up double-checking everything so thoroughly that they saved no time at all.

The feedback was too slow. They automated something that happens monthly, which meant they only got to practice the new workflow twelve times a year. Not enough repetition to build a habit.

The best entry points avoid all three traps. Low friction, moderate stakes, fast feedback.

Four Criteria for Good Entry Points

When you’re evaluating where to start, run each candidate through these filters:

1. Frequency

How often does this workflow happen? Daily is better than weekly. Weekly is better than monthly.

Frequency matters because you’re building a habit, not just solving a problem. The more often you run the workflow, the faster you learn what works, the quicker you refine your approach, and the sooner it becomes automatic.

That quarterly board report might be a huge time sink, but it’s a terrible entry point. You won’t remember what you learned by the time the next one comes around.

2. Forgiveness

What happens if AI gets it wrong?

You want workflows where mistakes are catchable and correctable. First drafts, not final products. Internal documents, not client-facing ones. Research and preparation, not execution.

The goal is to build confidence through experience. That means starting where errors are learning opportunities, not disasters. Once you’ve developed judgment about when to trust AI output (and when to verify more carefully), you can move to higher-stakes applications.

3. Clarity

Can you clearly define what good looks like?

AI works best when you can evaluate its output against a standard. “Summarize this document” is clear. “Make this better” is not. “Draft a response to this email” is clear. “Handle my communications” is not.

Vague workflows are hard to integrate because you can’t tell whether AI is helping. You end up with a fuzzy sense that “it’s okay, I guess” rather than a clear win. Start with workflows where you’ll know immediately if the output is useful.

4. Containment

How many systems and contexts are involved?

The simplest integrations are self-contained: one input, one output, minimal dependencies. The hardest require pulling from multiple sources, pushing to multiple destinations, or coordinating across tools that don’t talk to each other.

You can tackle complex integrations later. For your first entry points, look for workflows that live mostly in one place. Email is often good. So is document review. Anything that starts and ends in the same tool.

A Quick Scoring Exercise

Take the opportunities you identified in your audit and score each one:

• Frequency: Daily (3), Weekly (2), Monthly or less (1)
• Forgiveness: Low stakes, easily caught (3), Moderate stakes (2), High stakes or hard to verify (1)
• Clarity: Clear success criteria (3), Somewhat clear (2), Vague or subjective (1)
• Containment: Self-contained (3), Two or three systems (2), Complex dependencies (1)

Add up the scores. Anything above 10 is a strong candidate. Below 8 and you’re probably setting yourself up for friction.

This isn’t a perfect formula. A score of 9 might still be worth pursuing if it’s a major pain point. But the exercise forces you to think through the factors that predict success.

What About the Big Opportunities?

Maybe your audit revealed something important: a workflow that eats hours every week, that’s clearly inefficient, that AI could obviously improve. But it scores poorly on these criteria because it’s high-stakes, complex, or infrequent.

Don’t abandon it. Just don’t start there.

Use your early entry points to build skill and confidence. Learn how AI behaves, how to prompt effectively, how to verify output, how to spot mistakes. Then bring those skills to the bigger opportunities.

Think of it like weight training. You don’t start with your max. You build up to it.

The One-Win Principle

Here’s my advice: pick one entry point and make it work.

Not three. Not five. One.

Get that workflow running smoothly. Build the habit. Refine your approach. Experience what good integration actually feels like.

Then pick the next one.

The goal right now isn’t to transform how you work. It’s to prove that transformation is possible, one workflow at a time. Small wins compound. A single successful integration teaches you more than a dozen half-finished experiments.

The Takeaway

Not every AI opportunity is a good entry point. The best ones are frequent (so you build habits), forgiving (so you learn safely), clear (so you know what success looks like), and contained (so the setup stays simple).

Use the scoring exercise to evaluate your candidates. Pick one. Make it work. Then expand from there.

The goal isn’t to integrate everything at once. It’s to start somewhere that sets you up for success.

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This is the second article in the series “Building Your AI Decision Infrastructure.” Next up: a practical walkthrough of building your first AI-assisted workflow.