AI Overviews & Health Advice: What SMBs Must Fix

A single AI-generated summary can outrank every carefully written page on the internet—and that’s exactly why Google’s AI Overviews have become a flashpoint for trust.

Earlier this month, The Guardian reported that Google’s AI Overviews produced misleading health guidance for some medical searches. Google pushed back, arguing the examples were taken from “incomplete screenshots” and that most results are accurate and link to reputable sources. Regardless of who “wins” the debate, one thing is already clear for small and midsize businesses: AI can amplify mistakes faster than you can correct them.

This post is part of our series, “How AI Is Powering Technology and Digital Services in the United States.” Most SMBs I talk to want the upside of AI—faster content, lower costs, more leads—without taking on brand risk. The reality? You can have both, but only if you stop treating AI outputs as publish-ready.

What The Guardian report signals for every SMB

The signal is simple: when AI summaries are wrong, the harm isn’t evenly distributed. In health topics, bad advice can be dangerous. In business topics, bad advice can be expensive. Either way, the trust damage is real.

The Search Engine Journal write-up highlighted several examples from The Guardian’s investigation:

• A pancreatic cancer nutrition example that a major UK charity said was “completely incorrect,” with potential to affect treatment readiness.
• Mental health queries where reviewers warned the advice was “dangerous” and could discourage people from seeking help.
• A cancer screening example where a pap test was reportedly miscategorized.

Google’s position: the “vast majority” of AI Overviews are factual and helpful, and the feature typically points to reputable sources.

Here’s my take: both things can be true—AI Overviews can be broadly helpful and still produce high-stakes errors. And for SMB marketing, “mostly accurate” isn’t a comfort when the wrong answer is the one your customers remember.

Why this is bigger than a Google story

AI Overviews sit above the traditional search results. That placement changes user behavior.

When a summary appears at the top, many people don’t click through. They screenshot. They forward it to a spouse, a coworker, a group chat. The summary becomes “the answer,” even if it’s missing context.

The Guardian also noted that repeating the same query can produce different summaries over time. That variability matters for businesses because it creates a verification problem:

• Your team might try to reproduce what a prospect saw and fail.
• A support rep might answer based on yesterday’s summary, not today’s.
• A customer might cite an AI answer that no longer exists.

If your business publishes guidance—especially around regulated or safety-sensitive topics—you need a plan for an internet where the “top result” is a moving target.

The uncomfortable truth: SMBs are using AI like an intern with admin access

Most companies get this wrong: they use AI to “save time,” then they publish without a serious review process.

That’s understandable. Budgets are tight. Teams are small. The demand for content is constant.

But the moment you publish incorrect advice (or even advice that reads as overconfident), you create three problems at once:

1. Brand risk: Your credibility drops, and it’s hard to rebuild.
2. Legal/compliance risk: This is especially true in healthcare, supplements, fitness, finance, and insurance.
3. Lead quality risk: You attract the wrong customers with the wrong expectations.

The Guardian/Google dispute is a perfect cautionary tale because it shows how easily a plausible-sounding summary can pass a casual “looks fine” test.

Why “AI cited sources” doesn’t guarantee truth

A common misconception is: “If the AI links to reputable sources, it must be accurate.”

The SEJ piece referenced research pointing to citation-support gaps—cases where an AI answer cites a source, but the source doesn’t fully support the claim.

For SMB content, that means you can’t just check whether citations exist. You have to check:

• Did the source actually say what the summary claims?
• Was the claim stripped of context?
• Is the advice appropriate for the reader’s situation?

In other words: citations are not verification. They’re a starting point.

A practical, budget-friendly “Trust Stack” for AI-assisted content

The best SMB strategy is to use AI for speed, then use humans for responsibility. You don’t need a 20-person editorial team to do this. You need a simple system.

Here’s a workflow I’ve found works for lean teams creating AI-assisted content marketing in the United States.

Step 1: Decide what AI is allowed to write

Start with boundaries. Not every topic should be drafted by AI, and not every claim should be made at all.

Create a “red list” of content types that require expert review or should be avoided:

• Medical, mental health, supplement, or treatment advice
• Financial/tax/legal guidance (beyond general education)
• Safety instructions (equipment, construction, chemicals)
• Anything that implies guarantees (“will cure,” “will prevent,” “will save”)

If you must cover these topics (common in wellness clinics, med spas, nutrition brands, insurance agencies), then AI can help outline and organize—but a qualified reviewer owns the facts.

Step 2: Write in claims, not paragraphs

AI is good at prose. It’s worse at precision.

So flip your drafting process: have AI produce a list of atomic claims first, then build the article.

Example:

• Claim: “AI Overviews can change across searches.”
• Claim: “Health YMYL queries trigger AI Overviews at a higher rate.”

Then assign each claim a status:

• Verified (with internal notes and source confirmation)
• Needs review
• Remove

This turns editing from “vibes” into quality control.

Step 3: Add “evidence blocks” that AI search engines can quote

If you want visibility in AI-powered search (Google AI Overviews, ChatGPT-style answers, Perplexity), you need content that’s easy to extract and cite.

That doesn’t mean fluff. It means clear, structured statements like:

Rule: If an AI-generated summary could cause harm when wrong, it requires human review before publishing.

And:

Process: Treat AI citations as leads to investigate, not proof.

These are snippet-friendly, and they also keep your team aligned.

Step 4: Use disclaimers that don’t sound like legal panic

Disclaimers matter, but most SMBs do them badly. The goal is to set expectations, not scare readers away.

A practical approach:

• Put a short disclaimer near the top for sensitive topics.
• Repeat it near CTAs (booking, consultation, purchase).
• Use plain language.

Example:

• “This article is for general education and doesn’t replace advice from a licensed professional.”

Step 5: Build a lightweight review loop (48-hour rule)

If AI Overviews can change quickly, your content needs a quick refresh rhythm.

For high-intent pages (services, pricing, “how it works,” FAQs), set a 48-hour post-publish check:

• Re-read the page for claims that sound absolute
• Confirm any stats or medical/financial statements
• Check internal links and references

Then move to a monthly cadence.

What to do if AI (or Google) misrepresents your business

You can’t control AI Overviews, but you can reduce the chance they misunderstand you.

If your content is vague, AI fills in the gaps. If your content is precise, AI has less room to improvise.

Improve “AI readability” on your key pages

On service pages and FAQs, add:

• Short definitions (“A pap test screens for cervical cancer, not vaginal cancer.”)
• Clear eligibility statements (“This service isn’t appropriate for…”)
• Step-by-step processes
• “When to call a professional” guidance

This isn’t just good for AI search optimization. It’s good for conversions.

Create a single source of truth (SSOT) page

If you operate in a sensitive niche (health, wellness, finance), publish a “How we use information” or “Clinical/Professional standards” page that explains:

• Who reviews your content
• How often you update it
• What you don’t do (diagnose, prescribe, guarantee outcomes)

It’s a trust asset for humans and a context asset for machines.

The January 2026 reality: trust is now a ranking strategy

The SEJ article referenced Ahrefs research analyzing 146 million SERPs, reporting that 44.1% of medical YMYL queries triggered an AI Overview—more than double the baseline rate in that dataset.

That stat matters for SMBs because it points to a broader trend: AI search surfaces are becoming the default interface for high-stakes questions.

If your business depends on search—local SEO, blogging, YouTube explainers, service pages—your content strategy has to assume:

• Many prospects will see a summary before they see your site
• The summary may be incomplete
• The summary may change

So your job is to be the clearest, most verifiable source in your category. Not the loudest.

A better way to use AI content tools without risking your brand

AI is absolutely powering technology and digital services across the United States. It’s speeding up support, content production, ad testing, and customer communication. I’m not anti-AI.

I am anti-unreviewed AI—especially when the content affects health, money, safety, or major life decisions.

If you want to keep costs reasonable and still publish content that earns trust (and leads), start here:

1. Use AI to outline, structure, and generate first drafts.
2. Convert drafts into claim lists and verify them.
3. Add human-reviewed context, boundaries, and “when to get help” guidance.
4. Refresh your highest-intent pages on a schedule.

The question worth asking now isn’t whether AI Overviews will get better. They will.

The real question: when your customer reads an AI summary about your business or your advice, will the internet have enough accurate material to represent you correctly?