The AI Integration Audit
You’re already using AI. The question is whether you’re using it well.
If you’re like most professionals right now, AI lives in a browser tab. You copy text in, get something back, paste it somewhere else. Maybe you’ve tried a few tools. Maybe you’ve built a habit around one or two tasks. But there’s a gap between “I use AI sometimes” and “AI is part of how I work.”
This article is about seeing that gap clearly.
The Integration Spectrum
Think of AI integration as a spectrum with three rough zones:
Separate. AI is a tool you visit. You go to ChatGPT or Claude, do a thing, come back. There’s friction every time: context-switching, copy-pasting, re-explaining what you need. Most people are here.
Assisted. AI is wired into some of your workflows. It sees relevant context automatically. It prepares things for your review. The friction is lower, but it’s still selective; most of your work happens without AI involvement.
Integrated. AI is a layer across your decision-making. Not making decisions for you, but feeding you better information, faster synthesis, and more consistent preparation. You’re still the one deciding. You’re just deciding with better inputs.
The goal isn’t to reach “integrated” everywhere. It’s to be intentional about where you sit on this spectrum for different parts of your work.
Mapping Your Information Flows
Here’s a practical exercise. Pick a typical week and ask yourself: where does information flow into decisions?
You’re looking for moments where you gather, synthesize, or prepare before you act. Some examples:
- Reading emails to figure out what needs attention
- Scanning reports to find the relevant numbers
- Researching a topic before a meeting
- Drafting a first version of something
- Reviewing work from your team
- Comparing options before making a choice
For each of these, ask two questions:
Where is AI already helping? Maybe you’re using it to draft emails, summarize documents, or explain unfamiliar concepts. Note what’s working.
Where are you still doing the synthesis manually? This is where the opportunities hide. You’re spending time gathering and processing information that AI could gather and process; then you’d spend your time reviewing and deciding instead.
The point isn’t that AI should do everything. It’s that you should see clearly where you’re doing work that AI could do, so you can make a deliberate choice about it.
The Three Questions
For any workflow you’re examining, three questions help clarify whether AI integration makes sense:
1. Is this information-heavy?
AI shines where there’s lots of input to process: long documents, multiple sources, extensive back-and-forth. If a task involves reading, summarizing, comparing, or synthesizing, AI can probably help. If it’s a quick judgment call based on experience, maybe not.
2. Is the output a starting point or a final product?
AI is better at preparation than completion. First drafts, initial research, structured summaries, option lists: these are things AI can do well, and you can refine. Final decisions, nuanced communication, anything requiring your specific judgment: these stay with you. The best integration points are where AI prepares and you finish.
3. What context would AI need?
This is where many integration dreams die. AI can only work with what it can see. If a task requires context that lives in your head, in private conversations, or across systems that don’t talk to each other, integration gets complicated. That doesn’t mean impossible; it means you need to think about what access you’re willing to grant and what context you’re willing to provide.
A Simple Audit Framework
Here’s a lightweight way to map your current state. Grab a piece of paper or open a doc, and create three columns:
Column 1: The workflow. What’s the task or decision? Be specific. Not “email” but “triaging my inbox each morning” or “responding to client questions.”
Column 2: Current state. Where does this sit on the spectrum? Separate, assisted, or integrated? If you’re using AI, how?
Column 3: Opportunity. Could AI help more here? What would it need to be useful? What’s blocking that?
Run through five to ten of your regular workflows. You don’t need to be exhaustive. You’re looking for patterns.
Some things you might notice:
- Tasks where you’re doing manual synthesis that AI could handle
- Places where you’ve tried AI but the friction was too high
- Workflows where AI is already helping more than you realized
- Gaps where context or access is the limiting factor
What You’re Not Trying to Do
A quick note on what this audit isn’t.
You’re not trying to automate yourself out of your job. The goal is better inputs for your decisions, not fewer decisions. AI handles the gathering and processing; you handle the judgment and action.
You’re not trying to integrate everything. Some workflows don’t need AI. Some aren’t worth the setup cost. Some require human nuance from start to finish. The audit helps you see where integration makes sense, which also means seeing where it doesn’t.
You’re not trying to do this all at once. This is reconnaissance, not a battle plan. You’re mapping the terrain so you can pick your entry points wisely. (That’s the next article.)
The Takeaway
Most people underestimate how much time they spend on information gathering and synthesis. They think of themselves as decision-makers, but a huge portion of their week is actually preparation: reading, summarizing, comparing, drafting.
That preparation is where AI integration pays off. Not by making decisions for you, but by making your decision-making faster and better-informed.
The audit is simple: look at where information flows into your decisions, see where you’re doing the synthesis manually, and ask whether AI could do that preparation instead.
Once you see the gaps clearly, you can start closing them. One workflow at a time.
This is the first article in the series “Building Your AI Decision Infrastructure.” Next up: how to choose which integration points are actually worth pursuing.
