Is AI-generated content bad for SEO?

Google rewards useful content and demotes unhelpful content regardless of how it was produced. The risk is publishing large volumes of content that says nothing, not the tool used to write it.

What is AI good at in marketing content?

First drafts of structured formats, generating variations for testing, summarising long inputs such as call transcripts, and editing your own writing for clarity.

How do I stop AI content sounding like AI?

Supply the real numbers and your opinion rather than asking it to invent them, cut the vocabulary that clusters in AI text, vary sentence length, and include a concrete situational detail.

What should never be AI-written?

Anything requiring a specific figure you have not supplied, and anything a client will read as your professional opinion without you editing it.

Using AI for Content, Without the Tells

AI can produce a competent blog post in thirty seconds. That is exactly the problem, because so can everyone else, and readers have learned the shape of it.

What AI is genuinely good at here

First drafts of things with a known structure: FAQs, product descriptions, meta descriptions.
Variations. Twenty ad hooks from one angle, so you can pick 3. Meta needs roughly 50 conversion events in 7 days to optimise, so having 3 to 4 creatives ready per ad set matters more than having 1 perfect one.
Summarising long inputs: call transcripts, competitor pages, survey responses.
Editing your own writing for length and clarity.

What it is bad at, and cannot be prompted out of

Anything requiring a real number you have not given it. AI fills gaps with plausible-sounding specifics, and in a market where your credibility is the product, a confidently invented statistic is expensive.

It is also bad at having an opinion. AI hedges by default, and hedged content is forgettable content. The stance has to come from you.

The tells readers notice

Certain words appear far more often in AI text than in human text, by margins of roughly 20 to 50 times in corpus comparisons, and Indian readers have seen enough of it now to recognise the pattern. Words like delve, tapestry, seamless, leverage, robust, elevate and unlock. Structures like “it is not X, it is Y”. Everything arriving in groups of three. Endless em dashes.

A practical threshold: 1 em dash in a piece is fine, 4 is a signature. Three consecutive sentences of near-identical length is another. None of these are errors, and all of them together are a fingerprint.

None of these are wrong individually. Clustered, they read as machine output regardless of how accurate the content is.

A working checklist

At least one specific number, name, price or date in the piece.
One clear opinion, not a balanced survey of options.
Vary sentence length. Three consecutive similar-length sentences is a tell.
One concrete situational detail: what a client actually said, what actually happened.
Cut every word from the list above.
Read it aloud. If you would not say it to a client on a call, rewrite it.

The workflow I would actually recommend

Give the AI your raw material: your numbers, your opinion, your example. Ask it to structure and tighten rather than to invent. Then edit the output as if a competitor were going to fact-check it, because eventually one will.

That produces content faster than writing from scratch and does not sound generated, because the substance was never generated in the first place.

On the fear that AI content gets penalised

Google has been consistent that it rewards useful content regardless of how it was produced, and demotes unhelpful content regardless of how it was produced. The risk is not the tool. It is publishing forty pages that say nothing, which was possible long before AI made it fast.

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