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As of 13 August 2026, AI can only partly decide which stock to discount.
This still needs a person who signs their name to it.
Can you do it?
15 minutesto a draft.
30 minutesto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ityou
What the alternative costsThe supplied tool data does not give a price for a human alternative.
If this goes wrong: you discount products that would have sold at full price, miss genuinely slow stock or breach a pricing or supplier constraint.
What to actually do
Hand it to a person
The route this page recommends
A person who owns the outcome does this end to end, worth it when the failure is dear.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.
How to actually do it
- Open your stock system and export the current product, quantity, unit cost, current price, stock age and supplier restriction fields.
- Export recent sales history for the same product identifiers, including units sold and dates, then combine it with the stock export without changing the source values.
- Write down the commercial rules that AI must follow, including minimum gross margin, products that must not be discounted, campaign dates, channel restrictions and any expiry or condition limits.
- Paste the combined table, rules and the copyable prompt into a chatbot, asking it to identify missing or inconsistent fields before ranking products.
- Check each calculated price and margin against your own spreadsheet, stock system and current price list, and correct any mismatched product identifiers or costs.
- Ask the chatbot to rerun the recommendations after corrections, then compare the proposed discounts with supplier agreements, live availability and your planned promotion.
- Approve the final list yourself, publish only the selected prices through your normal sales system, and record the original price, discount, date and reason for each change.
Prompt
Act as an inventory analyst, not the final decision-maker. Using the stock and sales data below, rank the products that are strongest candidates for a discount and suggest a discount range for each. Use only the facts supplied and show the calculation or reasoning for every recommendation. Prioritise aged or slow-moving stock while protecting minimum gross margin, current availability and any stated supplier, channel or brand restrictions. Separate observed facts from assumptions. Do not invent demand, costs, competitor prices, expiry dates or legal requirements. Flag missing data that could change the ranking. Give me a table with: product, stock quantity, sales evidence, current price, unit cost, current gross margin, proposed discount, estimated resulting price, estimated resulting gross margin, reason, confidence, and information still needed. If a discount cannot be calculated safely, say so. Data and rules: [paste stock file or table, sales history, current prices, unit costs, minimum margin, stock-age information, supplier restrictions, channel rules, campaign dates and any other constraints].
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot know whether a temporary sales dip reflects poor demand, a stockout, a local event or a product problem unless you provide that context.
- AI cannot take responsibility for choosing between protecting margin and clearing stock.
- AI cannot verify that your stock, cost and sales exports are complete or correctly matched.
- AI cannot see every supplier, brand, channel or customer-pricing restriction unless you provide the current rules.
- AI cannot guarantee that a proposed discount will create enough additional sales to justify the lost margin.
What caps this at PARTLY: judgement under ambiguity, verification cost and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 1 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 2 |
| Total | 6 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT tell me which products to discount?
- Partly. It can rank products from stock, sales, price and margin data, but you must supply the relevant rules and approve the recommendation. It cannot reliably account for missing context such as a local promotion, a supplier restriction or a temporary stockout.
- What data does AI need to choose stock for discounting?
- Give it product identifiers, stock quantities, stock age, sales history, current prices, unit costs and minimum margin rules. Also include supplier restrictions, channel rules, expiry or condition limits and the dates of any planned promotion.
- Can AI calculate the right discount without losing money?
- It can calculate the resulting price and stated gross margin when the price and cost data are accurate. It cannot guarantee the sales uplift, so a mathematically valid discount can still reduce your total profit.
- Is it safe to let AI automatically change my prices?
- Not without a controlled approval step. You remain responsible for incorrect prices, missed restrictions, misleading promotions and unnecessary margin loss, so compare the recommendation with your live systems and approve each change.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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