Every product is "AI-powered" now. Every company has an "AI strategy." Every headline promises that AI will either save the world or end it. Now that you understand what AI actually does and what it is good and bad at, you have something surprisingly valuable: the ability to evaluate these claims for yourself.
This article gives you a practical framework for doing exactly that.
The Red Flags
These are the patterns that should make you skeptical. Not necessarily wrong, but worth questioning.
"Our AI does X" with no explanation of how. You now know that AI is pattern-matching software trained on data. If a company claims their product uses AI but cannot explain in basic terms what the AI component actually does, what data it was trained on, what patterns it finds, that is a red flag. "AI-powered" has become a marketing label that gets slapped on products the same way "all-natural" gets slapped on food packaging. Sometimes it means something. Sometimes it does not.
Claims of near-perfect accuracy. You learned in Article 2 that AI hallucinations are a real and persistent problem. Any company claiming their AI is 99% accurate, never makes mistakes, or eliminates the need for human review should be met with serious skepticism. The best AI systems in the world still make confident errors. If a vendor says theirs does not, they are either not measuring carefully or not being honest.
"Replaces the need for human oversight." This is a major red flag for anything consequential. If someone is selling you an AI tool for medical diagnosis, legal review, financial decisions, or hiring and they say it works without human supervision, walk the other way. The technology is not there, and the consequences of errors in these domains are too high to automate away the human check.
Buzzword stacking. "Our revolutionary AI-powered blockchain-enabled quantum-ready solution." The more buzzwords packed into a single sentence, the less likely any of them are being used in a meaningful way. Real products with real AI capabilities can explain what they do in plain language. If the explanation relies on stringing together impressive-sounding words, the substance is probably thin.
Before-and-after claims with no methodology. "Our AI increased productivity by 400%." How did they measure that? Over what time period? Compared to what? With how many users? Claims without context are not evidence. They are advertising.
The Green Flags
These are the patterns that suggest a company is using AI in a substantive, honest way.
They are specific about what the AI does and what it does not. A company that says "our AI handles initial customer inquiry routing based on topic classification, which reduces average response time by 30%, but complex issues are escalated to human agents" is telling you something real. They have defined the scope, named the specific function, and acknowledged the limits.
They are transparent about limitations. Any AI vendor who openly discusses where their system struggles, what types of errors it makes, and what safeguards they have in place is giving you a strong signal of credibility. Honesty about limitations is counterintuitive in marketing, which is exactly why it is trustworthy.
They explain what human oversight is involved. Good AI products are designed with human review built into the process, especially for high-stakes decisions. If a company can explain the specific points where a human checks the AI's work, that is a sign they understand the technology well enough to deploy it responsibly.
The product would make sense without the word "AI." Ask yourself: if I removed the phrase "AI-powered" from this product description, would it still sound like it does something useful? If the answer is yes, the AI is probably adding genuine value. If removing "AI" leaves you with nothing, the word was doing all the heavy lifting.
A Simple Framework for Any AI Claim
When you encounter an AI claim, whether it is a product you are evaluating, a news headline, or something your employer is introducing, run through these four questions.
1. What specifically does the AI do?
Not "it uses AI to improve outcomes." What does it actually do? Does it classify text? Summarize documents? Predict which customers are likely to cancel? Generate images? If you cannot get a clear answer to this question, the claim is vague enough to be meaningless.
2. What data was it trained on, and is that relevant to your situation?
AI systems learn from their training data. An AI trained on medical research papers may be useful for summarizing health studies. The same system would be unreliable for giving legal advice. Knowing what data sits behind an AI product tells you a lot about where it will work well and where it will not.
3. What happens when it is wrong?
Every AI system makes errors. The important question is: what are the consequences? For a tool that suggests subject lines for marketing emails, errors are low-stakes. Just pick a different one. For a tool that screens job applicants or flags potential fraud, errors can affect people's lives. The higher the stakes, the more you should demand human oversight and error handling.
4. Would this product be meaningfully different without the AI?
Some products genuinely use AI to do things that were previously impossible or impractical. Others have added a chatbot to an existing product and slapped "AI-powered" on the label. If the core value of the product existed before the AI was added, and the AI layer is thin or optional, you are probably paying a premium for a marketing term.
Putting This to Work
You will encounter AI claims in three main areas. Here is how to apply this framework in each.
Products and services you are considering buying. When a SaaS tool, app, or service claims to use AI, run through the four questions above. Check whether the AI component is core to the product or decorative. Look for independent reviews that test the AI features specifically, not just the product overall.
News about AI breakthroughs. When a headline says "AI achieves human-level performance at X," ask: what benchmark did they use? Who ran the test? Is the company that built the AI also the one reporting the result? Peer-reviewed research published in major journals is more credible than a company press release. And "human-level performance on a benchmark" is not the same as "ready to replace humans doing that job."
AI tools your employer introduces. When your company rolls out an AI system, the most productive thing you can do is ask good questions. What is this tool designed to do? What should I not use it for? Who reviews the output? How should I handle it when the tool gives me something that looks wrong? These questions are not signs of resistance. They are signs of competence.
The Bigger Picture
The goal of this entire series has been to move you from "AI is confusing and I do not know what to think" to "I understand this well enough to form my own opinions and make my own decisions."
If you have read all six articles, you now know what AI is at a fundamental level: pattern-matching software trained on data. You know what it does well and where it falls short. You have seen real examples of how people use it every day. You have tried it yourself. You understand what the evidence says about its impact on work. And now you have a practical framework for evaluating the AI claims that will keep coming your way.
You know more about AI than most people. Not because you learned to code or studied machine learning, but because you understand what is actually happening and you can think clearly about it.
That is AI literacy. It is the most useful skill for this particular moment, and you already have it.
You've Completed the Beginner's Path
You now understand AI well enough to form your own opinions and make your own decisions. That's AI literacy — and it's the most valuable skill in this particular moment.