When we work with customers, we spend a lot of time uncovering problems. Usually, that’s why they call us in the first place — something isn’t working. And often, it’s not just one problem. It’s a mix of issues tangled together.
One of the simplest, and most powerful, ways to make sense of those issues is to split them into two buckets:
- Content problems
- Process and content management problems
AI is shaking things up in both categories. But here’s the catch: if you don’t know which kind of problem you’re dealing with, AI can become more of a distraction than a solution.
So, let’s walk through what each category means, where AI can (and can’t) help, and then you can test yourself with a quick quiz at the end.
Content problems
Content problems live in the content itself. These are the things readers see, and AI can often lend a hand here. Think about it:
- Incorrect information. AI can draft content at lightning speed, but it also “hallucinates.” Human fact-checking is still non-negotiable.
- Spelling and grammar. Tools like Grammarly or Microsoft Editor are AI-powered assistants that catch most slipups before your readers do.
- Inconsistent style. Maybe one page says “customer” while another says “client.” AI can help flag these inconsistencies and nudge content toward your style guide.
- Messy visuals. Poor-quality images or inconsistent graphics? AI tools can now resize, compress, and even generate visuals. But if they’re used carelessly, you’ll just trade one style problem for another.
Content problems are addressed by fixing the content itself. At a minimum, you need a style guide and writer training. Optimally, you’ll adopt good editorial practices and implement technology to enable content creators to follow the organization’s standards for structure, style, and terminology.
The bottom line: AI can fix and polish, but it doesn’t replace the need for a style guide, trained writers, or an editorial process.
Process and content management problems
Process problems are sneakier. They show up in the way content is created, stored, and managed — not in the words or images themselves. Here’s what they look like:
- Outdated content. AI might spot old content or even suggest an update, but the real fix is a repeatable process that reviews content regularly.
- Content that can’t be found. AI-powered search is getting smarter, but without good tagging and structure, it’s still a haystack problem.
- Missing content. Sure, AI can generate drafts to fill gaps. But if your process doesn’t ensure the right content gets created at the right time, those gaps keep reappearing.
- Too much content. AI can summarize or classify mountains of text, but deciding what to retire or archive? That’s a process call, not an algorithm call.
So, while AI can help you manage scale, it won’t give you governance, workflows, or accountability. Those are human jobs.
What’s the difference still matters
Here’s why this distinction is more important than ever:
- AI works best on content problems — catching errors, enforcing style, polishing text.
- AI can help with process problems — making search smarter, tagging easier, content cleanup faster — but it won’t fix the root cause.
If you misclassify the problem, you’ll end up solving the wrong thing. That’s true with or without AI.
Quiz time!
Think you can spot the difference? Categorize the problems below — and consider how AI might (or might not) help.
Final thought:
AI is a powerful ally, but it’s not a magic wand. The real trick is still knowing whether you’re dealing with a content problem or a process problem, then letting AI play the right supporting role.
