AI why It Is Only a Drafting Tool in Retail.

Recently, I was reading a fascinating book about AI in modern legal practice and realised much of it would apply to retail too. The gap isn't whether they use artificial intelligence; it is understanding where it fails and where human control remains essential. What we need to do is safely use AI as an assistant while protecting our stores from its blind spots.
Key Takeaways
- AI drafting tools can only generate rough, initial versions of marketing content and product descriptions.
- Human reviewers must rigorously check all AI-generated text because the software frequently invents incorrect facts, prices, and legal statements.
- Retailers remain legally and commercially responsible for all their published errors.
- Sensitive customer data and wholesale pricing must never be pasted into public AI platforms without a thorough understanding of the provider's privacy rules.
- Sharing confidential material with an outside AI provider may create contractual, privacy, or legal issues for your business.
- Privacy Act obligations apply strictly to customer information even when you use AI to draft emails or handle complaints.
What Is an AI Drafting Tool?
An AI drafting tool is software that quickly creates a first draft of text for review, correction, and finalisation. Our survey shows our retail clients mainly use AI for advertising, such as drafting Father's Day promotional emails. Here, the AI is not an expert. It is not a decision-maker; you are. It must function solely as a brainstorming helper, aiding in organising ideas and overcoming writer's block, thereby speeding up your workflow.
Why Does Human Oversight in Retail AI Matter?
Crucially, human oversight matters partly because artificial intelligence lacks your real-world commercial context and because it is not factually reliable. You cannot simply trust software to understand the nuanced tone of your local business or the specific legalities of your supplier contracts.
Why Does AI Sound Confident Even When It Is Wrong?
An AI is trained to sound confident. They train it to give you what it thinks you want. If you ask it to describe a new candle brand, it might say it has a 100-hour burn time simply because that phrasing sounds good to it. This is what one of my clients got. The problem was that the candle certainly didn't have a 100-hour burn time, not even close.
I had a client disputing his claim for money owed by a private superannuation fund. The AI prepared a highly formal, legally sound letter threatening to escalate the matter to a small-claims tribunal, if it was not paid. He sent it and was ignored. In frustration as time was running out, he lodged his complaint with the small claims tribunal. The tribunal rejected the action because the AI had completely misunderstood the legal structure of the superannuation fund involved. What the AI had created was polished legal rubbish. When queried later, the AI system admitted it had mucked up the legal positioning. It did not help my client by then. His legal position was worse too as he blamed the wrong person. Just because the AI was smooth and fluent did not prove it was accurate.
Checking the Crucial Details
Inevitably, you must review specific elements whenever AI drafts your seasonal promotional copy or event notices. Specifically, you need to verify rigorously:
- Product names and brand descriptions
- Prices and sale conditions
- Dates, times, and store locations
- Stock availability
- Supplier details
- Store contact information and correct spelling
- Whether the tone sounds like your business
I often ask a second AI system to review the draft for possible errors. This can sometimes identify problems. It's not foolproof, but it often works. Here is the prompt I use, feel free to use it or modify it. I think you will find it useful too.
Act as an expert-level research assistant and meticulous fact-checker. Your task is to verify the factual accuracy of the text provided below.
=== PROCESS ===
- Decompose the text into individual, atomic, verifiable claims (statistics, dates, names, events, technical specifications, causal assertions, comparative claims, and logical inferences). Number them for traceability.
- For each claim, research against the best available sources, prioritising:
- Primary sources (official data, original research, legal documents)
- Authoritative secondary sources (peer-reviewed journals, established reference works)
- Reputable journalism (major outlets with editorial standards)
Note the publication date or publication year of the source used. If sources conflict, explicitly note the disagreement.
- Classify each claim using this taxonomy:
- True — Fully supported by credible, current evidence.
- Mostly True — Essentially correct but missing nuance or minor caveats.
- Misleading — Technically true but framed to imply something false, or cherry-picked out of context.
- False — Contradicted by credible evidence.
- Unverifiable — No reliable source exists to confirm or refute (note whether this is because the claim is inherently subjective, the data is private, or you simply couldn't find a source within reasonable effort).
- Outdated — Was true at one point but no longer reflects current reality.
- For each claim, assign a confidence level: High / Medium / Low.
=== OUTPUT FORMAT ===
TL;DR Verdict
[A 2-3 sentence summary: overall accuracy, most serious errors found, and whether the text's central argument holds up despite any factual issues.]
1. Source Baseline
[Briefly note: what date range did you search? What kinds of sources were available? Did you hit any access limitations? This establishes transparency about your research process.]
2. Claim-by-Claim Analysis
For each numbered claim, provide the following breakdown:
- Claim #[Number]: [verbatim text from note]
- Verdict: [True / Mostly True / Misleading / False / Unverifiable / Outdated]
- Confidence: [High / Medium / Low]
- Source(s): [Specific name of the organisation, study, or publication, including the year published]
- Analysis: [1-3 sentences explaining the reasoning and evidence]
3. Internal Contradictions
[Flag any claims within the text that contradict each other. If a contradiction exists, suggest which claim appears more credible and why.]
4. Missing Context / Omissions
[Identify important facts or context that, while not directly contradicted, would materially change a reader's understanding if included. This is distinct from factual errors — it's about what's absent.]
5. Revised Text
[Provide a corrected version that:
- Fixes all identified factual errors
- Adds caveats to Mostly True and Outdated claims
- Removes or clearly hedges Unverifiable claims
- Flags via [bracketed note] where you've changed the original
- Preserves the original author's voice, tone, and rhetorical goals as much as possible]
Here is the text to fact-check:
[INSERT TEXT HERE]
What Sensitive Information Should Retailers Hide from AI?
Legally, you should be careful about putting sensitive information into a consumer AI tool.
Specifically, sensitive information could include:
- Customer names, email addresses and phone numbers
- Employee details, payroll information or performance issues
- Supplier contracts, wholesale prices and confidential terms
- Financial figures, margins, forecasts and banking information
- Unpublished business plans and new product strategies
- Legal disputes, personal matters or documents that may be legally sensitive
If a customer emails you a complaint, if you use their name, contact details, and order number to ask an AI tool to draft a polite response, your discussions with the AI may now be public to the police or courts.
Privacy and Confidentiality
Sharing confidential material with an AI provider may cause privacy breaches and may also violate agreements. Simply entering it into an AI chat might make it public. Supplier agreements often contain confidentiality clauses. Customer information is covered by the Australian Privacy Act. You carry its strict obligations. The legal consequences will depend on the facts. However, a business should not take unnecessary risks with information that could harm customers, employees, suppliers, or the business itself if disclosed.
Consider anonymising your prompts when seeking AI help. Alternatively, you might explore secure AI platforms if you frequently handle sensitive data. They are more expensive. They do offer stronger controls.
Consider Local AI, which I previously looked into for AI for Australian retailers. I will need to do an update soon, as so much of the technology has changed, but the central issue here of privacy remains important.
What Are the Best Next Steps for Retailers Using AI?
Set a clear, simple rule for your entire team that a human approves the final version.
Review the privacy settings of your AI tools.
Opt out of data-sharing features if you are worried.
Do not use public AI systems with your confidential information.
What Is the Final Word on Keeping AI in Its Lane?
In conclusion, AI serves as an incredibly helpful drafting tool that generates fresh ideas and improves your wording quickly. Modern retailers now use it continuously.
Finally, if you would like to read the legal book that originally prompted these thoughts about AI, please let me know. I can easily get you a copy, and it is not expensive at all!
Written by:

Bernard Zimmermann is the founding director of POS Solutions, a leading point-of-sale system company with 45 years of industry experience, now retired and seeking new opportunities. He consults with various organisations, from small businesses to large retailers and government institutions. Bernard is passionate about helping companies optimise their operations through innovative POS technology and enabling seamless customer experiences through effective software solutions.









