How To Use A Tool Nobody Can Fully Explain
The people who build these systems can describe the architecture and the training. What they cannot reliably do is tell you why one specific answer appeared for one specific input.
That bothers people, and I understand why. It also is not the obstacle it appears to be, and I want to lay out the practical discipline, because a lot of Santa Clarita business owners are stuck between using something they do not understand and refusing on principle.
You already run on things you cannot open
Almost nothing you depend on is transparent to you.
You do not know how your payment processor detects fraud. You cannot inspect the algorithm that decides where your business appears in a search result, and that one determines a real share of your revenue. You do not know the internals of the truck you drive.
What you do instead, in every one of those cases, is verify by behavior. Does the money arrive. Do the calls come. Does it start. That is not a lesser form of knowledge, it is the normal engineering answer to opacity, and it works.
The difference that actually matters
AI is opaque in a specific way that requires a specific adjustment: it fails without signaling.
Your truck makes a noise. Your payment processor throws an error. A language model produces a fluent, confident, correctly formatted answer whether it is right or wrong. There is no noise. That is the entire practical problem, and every discipline below exists to compensate for exactly that.
The discipline
Test against known cases. Keep five to ten inputs where you already know the correct answer, drawn from your real work. Run them periodically. You are not grading the model, you are watching for drift, and drift is invisible in daily use and obvious in a side by side comparison.
This matters more than it sounds. In July 2026 a major lab was found routing paying subscribers to a weaker model. The users who noticed were the ones with a baseline. Everyone else just felt vaguely like it had gotten worse and assumed they were imagining it.
Verify against the system of record, never against the machine's own report. If it says the appointment is booked, the calendar is the truth. If it says the refund went through, the processor is the truth. Never let the same system be both the actor and the auditor. A customer service AI told a customer a refund had cleared when it had not, and there was nothing in that setup positioned to catch it.
Put a check where being wrong is expensive. Not everywhere, which is exhausting and unnecessary. Where the cost of a confident wrong answer is high: money, commitments, anything a customer will rely on.
Advising versus deciding
This is the cleanest line I know and it resolves most of the anxiety.
An opaque system is entirely appropriate as an input to a decision. It surfaces things you missed, drafts what you would have written slower, and organizes what you already knew. You remain the one who decides, which means you remain the one who can be wrong on purpose and answer for it.
An opaque system is not appropriate as the decision itself in anything consequential, not because it is worse than you at deciding, but because nobody can explain the reasoning afterward, and being unable to explain a consequential decision is a problem regardless of whether the decision was correct.
Why I am not troubled by the deeper version
There is a larger conversation happening about what these systems might be, whether the opacity points at something more than complexity.
I find it genuinely interesting and it does not change anything about how you should run your business this quarter. Whatever these systems turn out to be, the operational answer is identical: verify by behavior, keep a baseline, check where it counts, decide the things that need a human name attached.
That discipline holds if the answer is complicated math. It holds if it is something stranger. It is the part you control either way, and it is available to you today without resolving a question that may not resolve for a very long time.
Common questions
Is it true that AI builders cannot explain how their systems work?
They can explain the architecture and the training process. What they cannot reliably do is account for why a specific output appeared for a specific input, which is a different and more practical question.
Does that make AI unsafe to use in business?
No. We use opaque systems constantly. It means you verify by output rather than by inspecting the process, which is a normal engineering discipline.
How do you verify something you cannot inspect?
By testing behavior against known cases, watching for drift over time, and building a check into any process where being wrong is expensive.
Should I avoid AI for important decisions?
Use it as an input to important decisions, not as the decision. The distinction between advising and deciding is the whole safeguard.
What is the one habit that matters most?
Keep a small set of test cases with known correct answers and run them periodically. It is the cheapest early warning available.
This is part of AI For Santa Clarita Businesses: A 2026 Field Guide, the working guide to what AI is actually worth to a business in Santa Clarita.
Connor T. MacIvor · CalDRE #01238257 · Sync Brokerage, Inc. · DRE #02031490