Spot AI Writing: A Practical Guide for Procurement Teams

A procurement analyst skims a “supplier update” that landed in the shared inbox overnight. It’s polished, confident, and full of reassuring phrases about “ongoing improvements” and “commitment to quality.” Nothing jumps out—until you realize it says almost nothing.

That’s the new operational risk: AI-generated text is cheap, fast, and increasingly convincing, and it’s now showing up in vendor communications, RFP responses, compliance attestations, customer-facing statements, and even internal reporting. The punchline is that one of the most useful guides for spotting it isn’t a fancy enterprise tool—it’s Wikipedia’s community-built checklist for “Signs of AI writing.”

This matters for the AI in Supply Chain & Procurement series because supply chains run on trust and documentation. If you can’t tell whether a paragraph was written by a subject-matter expert or synthesized from generic patterns, you’re exposed—commercially, legally, and reputationally.

Wikipedia’s “Signs of AI Writing” is a surprisingly sharp playbook

Answer first: Wikipedia’s guidance is valuable because it focuses on observable writing behaviors—not magical “AI detectors”—and those behaviors map directly to procurement risk.

Wikipedia has been under constant pressure from low-quality, auto-generated contributions. Their response is practical: rather than claiming a tool can “detect AI” with certainty, the guide concentrates on patterns humans can verify. That’s exactly how procurement teams should think: you’re not trying to “catch” AI; you’re trying to validate reliability.

Here’s the mindset shift I recommend:

Treat AI-written text as a signal to request evidence, not as proof of wrongdoing.

In supply chain and procurement, the goal isn’t to police writing style. The goal is to reduce downstream risk—bad suppliers, fake certifications, inaccurate lead times, and misaligned expectations.

Why this is a procurement issue (not just a media issue)

Wikipedia’s focus is writing quality and verifiability. Procurement’s focus is decision quality. But the overlap is huge.

• A vague, generic sustainability claim can lead to ESG audit failures.
• A boilerplate “security posture” statement can conceal missing controls.
• A polished but empty incident report can delay root-cause containment.

If your process relies on text-only artifacts, AI-generated prose increases the volume of documents that look finished while containing fewer checkable facts.

The most common “AI writing” signals (and what they mean for supplier risk)

Answer first: The most reliable indicators aren’t weird word choices—they’re generic specificity, weak sourcing, and overconfident structure.

Wikipedia’s community has identified recurring traits that show up when large language models produce prose. Below are the ones that matter most in procurement, plus how I’d translate each into a risk check.

1) High polish, low information density

AI text often reads like a press release: smooth sentences, minimal concrete detail.

Procurement translation: If a supplier response is heavy on values (“commitment,” “robust,” “industry-leading”) and light on numbers, treat it as incomplete.

What to request next:

• Specific KPIs (OTIF %, defect rate PPM, return rate, on-time shipment %) over the last 4 quarters
• A sample of anonymized shipment records or scorecards
• Names/titles for accountable owners (quality lead, security lead)

2) Overly balanced language that avoids tradeoffs

AI tends to present everything as reasonable and harmonious. Real operations involve constraints.

Procurement translation: A mature supplier will admit where they’re tight (capacity, sub-tier dependency, seasonality). A too-perfect narrative can hide fragility.

What to request next:

• Capacity by line/plant and utilization bands
• Known bottlenecks and mitigation plans
• Sub-tier map for critical components

3) Repetition with mild rephrasing

LLM prose often circles the same point using synonyms, especially in long responses.

Procurement translation: Repetition is a clue the writer didn’t have underlying data.

What to request next:

• “Show your work” attachments: audit reports, test results, certifications with issuing body, and effective dates
• A short call with an SME to walk through one claim end-to-end

4) Confident claims without verifiable anchors

Wikipedia’s culture is built around citations. AI text frequently asserts “facts” without leaving a trail.

Procurement translation: Your team needs verifiability, not eloquence.

What to request next:

• Evidence hierarchy (strongest to weakest): third-party audit → signed customer reference → system screenshot/export → internal policy PDF → marketing deck
• Document control: version, owner, approval date, review cadence

5) “Template-y” structure that fits any company

AI content can sound like it was written for every supplier at once.

Procurement translation: If you could swap the supplier’s name out and nothing changes, the response doesn’t reduce uncertainty.

What to request next:

• Facility-specific details (locations, certifications by site)
• Process-specific details (incoming inspection sampling plan, CAPA cycle time)
• Product-specific constraints (shelf life, MOQ, change control)

Don’t rely on AI detectors—build a verification workflow

Answer first: AI detection tools are inconsistent; a repeatable verification workflow is faster, fairer, and more defensible.

A lot of organizations respond to AI-written content by shopping for a detector. That’s understandable—and usually disappointing. Detection is probabilistic, models evolve, and false positives create unnecessary friction.

Here’s a workflow that scales better in supply chain management and procurement.

A simple 4-step “text-to-evidence” triage

1. Classify the document’s decision impact

   • Low impact: routine update, marketing brochure
   • Medium impact: RFP narrative sections, policy attestations
   • High impact: security claims, compliance statements, incident reports, audit responses

2. Score information density (fast read) Look for:

   • Numbers with time windows
   • Named standards (and scope)
   • Site/product boundaries
   • Owners and dates

3. Demand evidence proportional to risk For high-impact claims, require at least two independent artifacts (e.g., audit report + system export).

4. Capture the trace Store claims and evidence in your supplier record (SRM). If the claim changes later, you’ll see it.

A procurement-ready checklist you can paste into your SOP

• Does the response include metrics with time ranges?
• Are sites and scopes clearly defined?
• Are there named owners and review dates?
• Can at least 3 key claims be verified with attachments or external audits?
• Does the supplier acknowledge constraints and tradeoffs?

If the answer is “no” too often, it’s not automatically AI—it's worse: it’s unusable for decision-making.

Media & entertainment already learned this lesson—procurement should borrow it

Answer first: Media organizations treat AI text as a credibility risk; procurement should treat it as a supplier risk signal.

In the AI in Media & Entertainment world, AI-written content creates problems like fake interviews, SEO spam, and credibility erosion. The best editorial teams respond with process: verification, sourcing, and accountability.

Procurement can borrow the same approach:

• Editorial standard → Supplier standard: “Can this be substantiated?” becomes “Can this be audited?”
• Attribution → Accountability: “Who wrote this?” becomes “Who owns this control?”
• Corrections → Change control: “Update the story” becomes “Update the spec and notify impacted parties.”

And there’s a second bridge point: the same AI that generates text can help you analyze it.

Use AI for review, not for trust

I’m firmly pro-AI for procurement ops—just not as the final judge of truth. The best pattern I’ve seen is:

• Use AI to summarize long RFP answers
• Use AI to extract claims into a structured list (metrics, dates, standards)
• Use AI to spot missing fields (scope, site, evidence)
• Then have a human reviewer verify the highest-risk claims

That’s how you get speed without pretending the model is a compliance officer.

Practical examples: what “AI-ish” writing looks like in procurement documents

Answer first: The red flag isn’t “sounds like a robot”—it’s sounds finished while avoiding specifics.

Example A: Sustainability statement

Risky (low verifiability):

• “We prioritize sustainable sourcing and continuously improve our environmental footprint across operations.”

Stronger (auditable):

• “For 2024, Scope 2 emissions were 1,240 tCO₂e for Site A (market-based), verified in our annual inventory approved on 2025-02-10. Top 3 reduction projects: HVAC retrofit (completed), LED conversion (in progress), supplier packaging redesign (planned Q2 2025).”

Example B: Cybersecurity posture

Risky:

• “We maintain robust security controls aligned to industry standards.”

Stronger:

• “We completed a SOC 2 Type II audit covering production systems from 2024-04-01 to 2025-03-31. MFA is enforced for all privileged access. Critical vulnerabilities are remediated within 15 days; 2025 YTD compliance is 92%.”

Example C: Lead time and capacity

Risky:

• “We can meet your timeline and scale as demand grows.”

Stronger:

• “Standard lead time is 6 weeks for SKU family X at Site B, assuming MOQ 5,000 units. We can flex +20% output for 8 weeks with overtime; beyond that requires a second shift (6-week ramp).”

Notice what changes: not the tone. The testable claims change.

People also ask: “Is AI-written supplier content automatically bad?”

Answer first: No—AI-written content is fine when it’s used for formatting or drafting, and dangerous when it replaces evidence.

If a supplier uses AI to clean up grammar, that’s a non-issue. If they use AI to invent a compliance narrative or pad an RFP with generic assurances, you have a problem.

A practical policy is simple:

• Allow AI for drafting
• Require evidence for claims
• Require accountable owners for high-risk areas (quality, security, regulatory)

This keeps relationships healthy while protecting your sourcing decisions.

Where this fits in the AI in Supply Chain & Procurement series

Answer first: Detection is part of a bigger shift: AI increases the volume of content, so procurement must upgrade how it validates information.

Across this series, we’ve talked about AI forecasts demand, optimizes supplier selection, and improves risk monitoring. Those gains depend on inputs you can trust. If your supplier master data is polluted by unverifiable narratives, your shiny AI models will produce confident outputs built on sand.

The practical next step is to operationalize Wikipedia’s instinct—pattern recognition plus verification—inside your procurement workflow:

• Add an “evidence required” rule set to your RFP templates
• Train reviewers on 5–7 common AI writing signals
• Store claims and proof in SRM so you don’t re-litigate every renewal
• Use AI internally to extract and structure information, then audit the high-risk pieces

The open question for 2026: as AI-generated text becomes the default, will your procurement function be the one that still demands receipts—or the one that signs off on nice-sounding paragraphs?