The AI Tool Audit: How to Evaluate If a Tool Is Actually Worth Your Time
*Stick around to the end — I built an interactive scorecard so you can audit your own approach in about two minutes.
Unless you've been asleep for the last two years, you know what AI is. Everybody's using it. Or at least, they like to claim they are.
Companies are racing to market that their app/service/product has AI baked in, which sounds cool from a marketing perspective. But, what actual value is it adding?
Adopting AI without answering that question is doing it completely backwards.
What I keep seeing is people taking a technology they don't fully understand and trying to make it fit. Not because they've identified a problem AI can solve, but because they feel like they're supposed to be using it. They throw a training series at their team, call it a day, and add "AI-powered" to the marketing. No thought process behind how to actually incorporate it. No understanding of what the tool can and can't do. No plan for measuring whether it's working.
And then they wonder why nothing changed.
I recently completed Anthropic's AI Fluency course, built by the team behind Claude, and it validated something I've been doing instinctively for years. After 15 years of enterprise work at Canon, Scholastic, and Accenture, you learn one thing fast: define the problem first and work backwards from it. That's the foundation of how I evaluate any AI tool, and it's what most businesses skip entirely.
Here's the process I actually walk clients through.
Step 1: Define the Problem First
Before I look at any tool, I ask one question to my client: What problem are we solving?
That sounds obvious. It's not. Most business leaders come to me and say "I want to use AI for my business." Great, but for what? What's the actual challenge?
That answer tells me whether AI can even help here, or whether this is a human-level decision that needs to be thought through by a person, not a machine. Knowing that upfront saves you from buying a tool that was never going to fix the actual issue.
The problem is your north star. Without it, you get pulled in a million different directions. Every shiny tool looks like a solution. Every demo feels like a revelation. And six months later, you've spent money, spent time, and nothing has changed because you were solving the wrong problem, or no problem at all.
Step 2: Map the Existing Workflow
Once I understand the challenge, I want to hear about the workflow. Walk me through what you're doing right now, step by step.
Because here's what I usually find: there are gaps or inefficiencies in the existing process that are actually creating the problem. The workflow itself is the issue, not the absence of AI.
Sometimes we can fix those without AI at all.
And if AI does fit, it's because we've identified a specific place in a specific workflow where it can speed things up, check work, or handle the repetitive tasks that drain your team's energy. AI is strong at drafting, summarizing, brainstorming, pattern recognition. The stuff that eats hours before you get to the work that actually matters.
But the high-judgment calls? The decisions that require deep context that is unique to you, context that only you could possibly have from your experience and your perspective? Those stay with you. AI doesn't have your history with your customers. It doesn't know the politics of your team. It doesn't understand why you made the choices you made. That's yours.
That's not a limitation. That's the whole point. You're matching the right work to the right worker.
Step 3: Know What You're Actually Talking To
This is where most people trip up, and honestly, where the real damage happens.
The most common mistake I see is people thinking that AI is what the name implies: artificial intelligence. That's a loaded phrase because it gives the user a feeling that they're talking to a computer from Star Trek and it understands them.
It doesn't.
AI is a statistical model. It's a large language model. It predicts what text should come next based on patterns. It is not a repository of factual information. And because most people don't realize that, they take its output at face value. They treat it like a magic 8-ball and run with whatever it gives them.
I catch it because I'm skeptical by nature. When I get output from AI, I go over it with a fine-toothed comb. Not just "is this factually correct?" but, does that fact fit the context of the overall problem I'm solving?
Those are two very different questions, and most people only ask the first one. If they ask at all.
Anthropic's course calls this Discernment, and it gave structure to something I was already doing. But the bigger takeaway is this: if you don't understand that AI is a prediction engine, not an answer engine, you won't even know to check. And that's where things go sideways fast.
A Real Example: The Tool I Kept and the One I Didn't
I was an early ChatGPT adopter. As soon as they gave public access, I was on it. Loved it.
But I started noticing something a few months in. The answers I kept getting back just made me feel like a rock star. Like I was super smart. Everything I put in got validated, praised, built upon like it was genius.
And I knew it was disingenuous. Yeah, it's a good idea, but I don't know if it's that good of an idea. I could poke holes in what it was giving me. It was sycophantic. It told me what I wanted to hear instead of what I needed to hear.
So I went looking, and that's where Claude came in.
What impressed me about Anthropic's approach is the focus on doing fewer things well. OpenAI has been trying to cover enormous ground, trying to be the dominant force across every category, and I think that's actually worked against them. Anthropic came out with Claude Code, which developers adopted almost immediately, because they specialized. They built something excellent for a specific use case instead of something mediocre for every use case.
That's the evaluation process in action. I identified a problem (output I couldn't trust), evaluated the tool against that problem, found it lacking, tested an alternative, and stuck with the one that actually solved my specific problem. No hype. No "everybody's using it." Just: does this tool make my work better?
Common Pitfalls
Bolting AI onto a broken process. If your workflow is creating the problem, adding AI to that workflow just automates the problem faster.
Skipping the problem definition. Without a north star, you'll chase tools instead of solutions. We have a tendency to conflate things, to jumble multiple challenges into one messy request, when in reality it's more succinct than we think. Break it apart. Go piece by piece. Evaluate each problem on its own terms.
Trusting the output because it sounds polished. AI is excellent at sounding confident while being wrong. You have to be well-versed in the subject matter to begin with. Otherwise, you won't catch it when AI puts you on the wrong path, and you won't even know you're on the wrong path until it's too late. I wrote about what this looks like at scale - it’s called AI slop and it can creep in if you are not being diligent in reviewing the output.
Blaming the tool instead of the implementation. A lot of the zero ROI on AI comes from leadership not understanding the execution side. What are the actual deliverables people need? What should AI do versus what should the person do? The failure isn't the technology. It's the fact that nobody asked those questions before buying it.
How to Get Started Tomorrow Morning
Pick one workflow in your business that frustrates you. Just one.
Write down every step. Then ask yourself: What's the actual problem here? Not "I need AI." What's broken? What's slow? What's eating time it shouldn't be eating?
That's your audit. Start there. Before you Google a single AI tool.
Want Help Running This Audit?
This is exactly what I do with clients. We start with the problem, map the workflow, and figure out where AI actually fits before anyone touches a tool. If you want to run this process for your business, let's have a conversation.I wrote about what this looks like at scale — it's called AI slop, and it's everywhere
The AI Tool Audit
Most businesses adopt AI tools backwards — no strategy, no evaluation, just hype. This scorecard grades your approach across three dimensions so you can see where you stand before you waste time or money.
Takes about 2 minutes. Be honest — this isn't a validation machine.
Real talk: I use AI tools in my content workflow — research, drafting, refinement. The ideas and voice are mine. AI just helps me get them out of my head faster. It's the same process I use with my clients, and yeah, it works.
