AI for Wine Marketing: What It Can and Can't Write

AI-generated copy has made its way into wine marketing whether the industry is ready for it or not. Importers, distributors, and retailers are using ChatGPT, Claude, and other tools to draft tasting notes, shelf talkers, and trade descriptions at speed.

Some of it is good. Most of it is identifiably mediocre. A small amount is actively harmful to a brand.

This article covers what AI can genuinely do well for wine marketing copy, where it reliably falls short, and how to use it without producing output that a sommelier, buyer, or consumer would immediately recognise as machine-generated.

What AI does well

Structural consistency

AI is excellent at applying a format consistently across a large number of inputs. If you need 150 tasting notes that all follow the same structure — appearance, nose, palate, finish, within a word limit — AI will apply that structure reliably every time.

A human writer doing the same task at volume will inevitably vary the structure, miss elements, or collapse under deadline pressure. AI doesn't get tired.

Channel adaptation

Given clear instructions about audience and format, AI can shift register effectively. The same Grenache can become a 120-word sommelier note, a 50-word shelf talker, and a 40-word wholesale one-liner from the same input in seconds. A human writer can do this too — but not 200 times before catalogue print.

Vocabulary range

Trained on a large corpus of wine writing, AI has access to a wide descriptive vocabulary — including regional terminology, aromatic descriptors, structural language, and food pairing conventions. It knows what garrigue smells like (in the descriptive sense), what Mourvèdre contributes to a GSM blend, and what "by-the-glass programme" means in a trade context.

First drafts

AI is efficient at producing a first draft that's 70–80% there. For most wine copy use cases, a fast first draft that needs light editing is dramatically more useful than a blank page and a deadline.

Where AI reliably fails

Accuracy without input

AI doesn't taste wine. It generates copy based on what it knows about the inputs you provide — grape variety, region, vintage, production method. If your inputs are good, the output is good. If your inputs are vague, the output is generic.

"Write a tasting note for a Burgundy Pinot Noir" will produce a competent-sounding but entirely invented note that describes the archetype of Burgundy Pinot Noir, not the actual wine. It may sound professional. It will not be accurate.

The rule: AI output is only as specific as your input. Garbage in, garbage out — in this case, polished-sounding garbage.

Sensory experience

AI has never smelled brett, tasted a wine with aggressive volatile acidity, or experienced the iron-and-blood mineral quality of old-vine Grenache from a specific parcel. It has read descriptions of these things and can reproduce the vocabulary accurately — but it cannot describe what's actually in a glass.

For wines with unusual or distinctive sensory characteristics, AI copy will tend toward the typical rather than the specific. This is the most common failure mode in AI-generated tasting notes.

Distinguishing quality levels

AI struggles to calibrate quality signals accurately. A $9 Côtes du Rhône and a $65 Crozes-Hermitage may receive similarly enthusiastic notes because AI doesn't have the commercial context to modulate appropriately. You need a human reviewer — or very detailed prompting — to get the calibration right.

Knowing when not to say something

A good wine writer knows that some characteristics are better left unmentioned. A wine with a slight VA (volatile acidity) doesn't need the VA called out in retail copy — describe the aromas it contributes without flagging the flaw. AI doesn't always have this editorial instinct.

Wine-trade-specific commercial judgment

"Will this sell at £12 RSP in an independent wine shop?" is a question AI cannot answer. It can tell you about the wine's stylistic characteristics. It has no idea about your specific accounts, your competitors' pricing, or the commercial context you're operating in.

The quality spectrum in practice

Here's what the same Grenache note looks like across three quality levels of AI use:

Low-quality use: no structure, vague input

Input: "Write a tasting note for a French red wine"

Output:

This elegant French red wine offers a rich bouquet of red berries, spice, and subtle earthy notes. On the palate, it's well-balanced with smooth tannins and a lingering finish. Perfect for pairing with a variety of dishes, from grilled meats to hearty stews.

This is useless. Every word applies to every red wine. No one should publish this.

Medium-quality use: structure provided, some input

Input: "Write a 80-word retail shelf talker for a Grenache from Côtes du Rhône, 13.5% ABV, with notes of red cherry and garrigue"

Output:

A classic expression of the southern Rhône, this Grenache offers vibrant aromas of red cherry and garrigue, complemented by a smooth, medium-bodied palate. With its generous fruit character and soft tannins, it's an approachable wine that pairs beautifully with roasted meats, Mediterranean cuisine, or simply enjoyed on its own. A reliable choice for everyday enjoyment.

Better. Recognisably structured as retail copy. But "pairs beautifully," "approachable wine," "everyday enjoyment" are all filler phrases. Needs editing.

High-quality use: structured input, specific prompting

Input: Grenache 80%, Syrah 20%. Côtes du Rhône, sourced from 35-year-old vines at 300m elevation. Stainless steel fermentation. Winemaker notes: "garrigue, iron mineral, red cherry." Retail audience, 50 words, confident and specific tone.

Output:

Old vine Grenache from elevated southern Rhône. Red cherry and garrigue on the nose — that wild herb character that makes you think of Provence in August. Silky on the palate, bright acidity, barely a tannin. Drinks well now with grilled lamb or a simple roast. This is what Côtes du Rhône should taste like.

This is publishable with minimal editing. The quality of the output came from the quality of the input.

A practical workflow

The most effective use of AI in wine copy production is as a first-draft generator, not a finished copy machine:

1. Capture structured input for every SKU — grape, region, vintage, ABV, production method, winemaker notes
2. Generate first drafts by channel and audience
3. Review against the actual wine — someone who has tasted it checks for accuracy
4. Light edit — adjust register, cut filler phrases, tighten where needed
5. Approve and publish

The review step is non-negotiable. AI doesn't taste. You do.

What this means for the wine trade

The importers and distributors who will use AI copy best are those who understand its limitations as well as its capabilities. They'll use it to eliminate the blank-page problem, compress the time from sample to published description, and maintain consistency across large portfolios.

The ones who'll use it badly will paste AI output directly to print without review, produce tasting notes that describe a grape archetype rather than a specific wine, and lose the trust of the sommeliers and buyers who notice.

The technology is neutral. The craft is still human.

GlassNotes is built for the high-quality end of this workflow: structured SKU input, wine-specific AI prompts tuned to channel and audience, and a library that keeps your portfolio copy organised and editable. Free trial covers 5 wines, no credit card required.

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