A quick note before we dive in. AI is evolving faster than almost any technology we’ve seen. The tools, capabilities, and best practices in this space are constantly changing. And what’s true today may look different tomorrow. Use what you learn here as a foundation, and always verify the latest information directly with the platforms and resources you’re using. At Industrial Web Search, we’re committed to growing alongside this technology and bringing you the most relevant guidance we can. Now let’s get into it!
SECTION 1: WHAT TO CHECK BEFORE SENDING AI-GENERATED CONTENT
A good review isn’t just a read-through for typos. It’s a systematic check against the things AI is most likely to get wrong. There are five categories worth building into any review.
Factual accuracy is first. Every specific claim in the output — a number, a specification, a certification, a capability — needs to be verified against what’s actually true. AI will state things with confidence that it has no way of knowing. It doesn’t know your actual lead times, your current equipment capabilities, or which certifications you hold. Anything that looks like a fact needs to be treated as a draft until you’ve confirmed it.
Commitments and claims come second. AI tends to write aspirationally — it wants the content to sound compelling, which can lead to phrases like “we guarantee delivery within 48 hours” or “we can accommodate any specification.” Read for anything that makes a promise or sets an expectation, and make sure every one of those is something you can actually deliver.
Tone and voice matter more than people expect. AI has a default register that tends toward slightly formal and slightly generic. It may not sound like your company, or it may be more formal than the relationship warrants, or more casual than the situation calls for. Reading it aloud is a reliable way to catch this — if it doesn’t sound like something a person would actually say, it needs adjustment.
Confidential information is the fourth check. Even when you’ve been careful about what you put in, AI can sometimes surface or infer details in its output that weren’t intended. Review for anything that reveals pricing, customer-specific details, proprietary processes, or other information that shouldn’t be in an external document.
And completeness rounds out the checklist. Did the output actually address everything it was supposed to? AI sometimes drops requirements mid-document, especially in longer outputs. Cross-reference the final draft against the original request or the buyer’s RFQ to make sure nothing was missed.
SECTION 2: RECOGNIZING COMMON AI MISTAKES IN TECHNICAL WRITING
Technical writing is where AI errors are most consequential and also where they’re easiest to miss if you’re reading quickly. Understanding the patterns helps you know where to slow down.
Plausible but wrong specifications are the most common issue. AI has been trained on vast amounts of technical content, which means it can generate tolerances, material properties, and process parameters that sound entirely reasonable but may be inaccurate for your application, your equipment, or your industry. A tolerance of ±0.005 inches might be standard in one context and completely unachievable in another. AI doesn’t know which context you’re in.
Outdated standards and references appear frequently. AI training data has a cutoff, and industry standards get revised. If AI references a specific standard by name and revision level — say AS9100 Rev D or ASTM A36 — verify that it’s citing the current version and that the reference is actually applicable to what you’re writing about.
Overconfident language around capabilities is another pattern. When writing about what your business can do, AI tends to generalize in ways that may not reflect your actual scope. Phrases like “our team has extensive experience with” or “we routinely work with” may overstate what’s accurate. If a customer makes a purchasing decision based on a capability claim that turns out to be aspirational rather than real, that’s a serious credibility problem.
And in longer documents, internal consistency is worth checking. AI can contradict itself between sections — stating one lead time in the proposal narrative and a different one in the timeline section, for example. Read the document as a whole, not just section by section.
SECTION 3: BUILDING REVIEW PROCESSES INTO YOUR WORKFLOW
A review process that depends on people remembering to do it isn’t reliable. The goal is making review a natural part of how AI-assisted work gets done, not an extra step that gets skipped under deadline pressure.
The most practical approach is a tiered system based on what the output will be used for. Routine internal communications — meeting summaries, internal updates, draft documents for team review — need a light check: a quick read for obvious errors and tone. Customer-facing content — proposals, emails, capability statements — needs the full five-point review from Section 1. Technical documents and anything going into a quality management system needs expert review against the actual standard or specification, not just a general read.
Make the checklist physical or visible. A review checklist that lives in someone’s head gets abbreviated under pressure. A short printed checklist next to a workstation, a pinned document in a shared drive, or a required field in your workflow tool gets used consistently. It doesn’t need to be long — the five items from Section 1 fit on a half-sheet of paper.
Treat mistakes as information. When an error in AI-generated content makes it through review and causes a problem, that’s valuable data. What was missed? At what point in the review did it slip through? Was it a gap in the checklist, a gap in the reviewer’s expertise, or a gap in the process itself? A brief debrief after a significant error is worth far more than a reminder to be more careful. It tells you specifically what to fix.
CONCLUSION
AI accelerates the work. Human review is what makes the output trustworthy. The goal isn’t to slow down the process — it’s to make the review step fast, consistent, and targeted at the things that actually matter. A good checklist and a tiered process get you there.
That wraps up Course 5 and the full AI for Industrial Businesses module. You now have the tools to use AI effectively across sales, marketing, content creation, internal operations, and governance — and the framework to do it responsibly. The next step is simply to start. Pick one area, apply what you’ve learned, and build from there.
