Spot AI Writing Fast: Wikipedia’s Practical Checklist

Most teams don’t have an “AI writing problem.” They have a trust problem—and AI-generated text is just one way it shows up.

If you publish scripts, product copy, press releases, training content, supplier updates, or internal memos, you’re now living in a world where convincing prose is cheap. That’s great for speed. It’s also risky: the wrong paragraph in the wrong place can trigger compliance issues, brand damage, or plain old confusion.

Russell Brandom recently pointed out something refreshing: one of the most useful guides to spotting AI-generated prose isn’t a pricey SaaS tool—it’s Wikipedia’s guide to “Signs of AI writing.” Wikipedia has been fighting low-quality and synthetic-sounding text for years, and their detection instincts are practical, not mystical.

This post uses that Wikipedia checklist as a springboard, then brings it into two places where authenticity is now non-negotiable: media & entertainment (audience trust) and—because this is part of our AI in Supply Chain & Procurement series—supply chain communication (operational trust). Same core issue, different stakes.

Why Wikipedia’s “Signs of AI writing” matters

Wikipedia’s value is simple: it’s a living encyclopedia maintained by humans who care about verifiability, clarity, and neutrality. When AI text shows up, editors don’t just ask “Was this written by a bot?” They ask a more useful question: “Does this read like content that was responsibly produced?”

That framing is gold for organizations. Whether you’re commissioning a trailer synopsis or issuing a supplier notice, the standard shouldn’t be “no AI.” The standard should be:

• Accurate and attributable (even when no citations are shown, the underlying facts should be checkable)
• Consistent with house style (tone, terminology, and formatting)
• Specific where it counts (dates, names, parameters, constraints)
• Transparent about uncertainty (not falsely confident)

The reality? A lot of AI-written text fails these tests in repeatable ways. And that’s what Wikipedia’s checklist trains you to notice.

The bigger shift: detection is now basic digital literacy

A few years ago, “AI writing detection” sounded niche. In late 2025, it’s closer to spellcheck: not perfect, not final, but part of everyday editorial hygiene.

This is especially true for:

• Entertainment brands protecting audience trust (and avoiding PR blowups)
• Procurement teams managing vendor communications, RFP responses, and contract language
• Operations leaders relying on incident reports and shipping updates that must be precise

If your process assumes every paragraph was written with human intent and accountability, you’re behind.

Wikipedia’s telltale signs of AI writing (and what they look like at work)

Here’s the practical part. Wikipedia’s guidance boils down to patterns—not “gotcha” phrases. Use these as a quick screen before you approve, publish, or route content.

1) Overly smooth prose that says very little

AI can produce fluent text that feels “complete” while avoiding real commitments. You’ll see:

• Lots of general statements
• Few numbers, names, constraints, or dates
• A suspicious lack of “messy” detail (tradeoffs, edge cases, exceptions)

Media & entertainment example: A show description that’s all mood and zero specifics—no setting, no time period, no character motivations beyond generic archetypes.

Supply chain example: A supplier update that says “we’re optimizing our operations to ensure timely delivery,” but never states which SKUs, which lanes, which timeline, and what mitigation plan.

Fix: Require a “specificity pass.” Ask the writer (human or AI-assisted) to add:

• concrete parameters (timeline, quantities, impacted regions)
• clear ownership (“Operations will…”)
• verifiable claims (what changed, when, and why)

2) Weirdly formal tone or “corporate fog”

Wikipedia editors often flag text that feels like it’s written by someone trying to sound encyclopedic—without understanding the topic.

AI also tends to default into:

• inflated formality
• abstract nouns (“utilization,” “enablement,” “enhancement”)
• safe, bland phrasing

Why it matters: Corporate fog isn’t just annoying. In procurement and operations, it can hide risk. In entertainment marketing, it reads like a press kit no one wants to share.

Fix: Force sentences to carry a concrete subject and verb.

• Bad: “A review of processes was conducted to facilitate improvements.”
• Better: “We audited inbound QA at Plant B and changed the inspection threshold from AQL 1.0 to 0.65.”

3) Repetition without purpose

AI will restate the same idea in slightly different words—especially in intros and conclusions.

Look for:

• paragraphs that mirror each other
• repeated claims of importance without new evidence
• multiple synonyms circling one point

Media & entertainment: Press releases that repeat “immersive,” “unforgettable,” “bold new chapter” while giving no production details.

Supply chain: RFP responses that repeat “we prioritize quality and on-time delivery” instead of explaining the actual OTIF process, penalties, or escalation path.

Fix: Enforce a rule: every paragraph must add one new fact, example, or decision. If it can’t, cut it.

4) Confident errors and invented specifics

This is the scary one. AI can fabricate names, dates, standards, and references. Wikipedia’s editorial culture is built around catching exactly this.

In business contexts, the “hallucination” pattern shows up as:

• a cited standard that doesn’t apply (or doesn’t exist)
• incorrect product capabilities
• invented customer examples
• made-up regulatory language

Supply chain reality check: A single fabricated compliance claim (say, about chain-of-custody or labor documentation) can ripple into audits and legal exposure.

Fix: Add a verification step that doesn’t rely on vibe:

1. Highlight every number, date, named entity, and policy claim.
2. Confirm each against a source of truth (contract, ERP, QMS, legal template).
3. If it can’t be verified quickly, it doesn’t ship.

5) Generic “balanced” framing that avoids taking a stance

AI text often tries to be polite and even-handed, even when a real human would take a position.

Wikipedia is neutral by design, but even neutral writing has structure and evidence. AI neutrality tends to be empty.

Media & entertainment: A review or recap that refuses to say anything sharp—no criticism, no insight, no point of view.

Procurement: A supplier scorecard narrative that won’t call out repeated late deliveries, because the language stays “constructive” to the point of uselessness.

Fix: Require a decision and the rationale.

• “Approve this vendor because… despite… and we’ll mitigate by…”
• “This content angle works because… and we’re not doing X because…”

Where AI writing detection pays off: media authenticity and supply chain risk

AI writing detection isn’t about “catching cheaters.” It’s about protecting downstream outcomes.

Audience trust in media & entertainment

Entertainment runs on emotion and credibility. If audiences feel like they’re being fed synthetic filler—synopses, actor “quotes,” behind-the-scenes stories—they disengage fast.

Two practical reasons teams are building AI-detection workflows in media:

• Brand consistency: AI tends to average out voice. That’s deadly for distinctive brands.
• Reputation risk: If a studio is perceived as faking authenticity (even in small ways), that story spreads faster than the content it was promoting.

A simple Wikipedia-style checklist helps editors flag low-effort synthetic copy before it hits a newsletter, app, or press desk.

Operational trust in supply chain & procurement

Here’s the bridge most people miss: supply chain content is also audience content.

Your audiences include:

• suppliers responding to RFPs
• internal stakeholders approving spend
• customers reading delivery notices
• auditors reviewing documentation

If AI-generated text sneaks into these artifacts, the risk isn’t “someone notices.” The risk is someone acts on it.

Examples:

• A contract clause summary that subtly changes meaning
• A supplier capability statement that overpromises (and procurement buys based on it)
• A logistics update that sounds reassuring but omits the actual constraint (port delay, capacity cap, embargo, QA hold)

This is why AI governance in procurement isn’t just model choice. It’s document quality control.

A lightweight workflow: human review that actually scales

If you’re thinking, “We don’t have time to manually review everything,” I agree. But you don’t need to.

You need a workflow that treats high-risk text as high-risk—like you already do with payments and approvals.

Step 1: Classify content by risk (not by channel)

Use three tiers:

• Tier 1 (High risk): contracts, compliance statements, incident reports, earnings/PR claims, safety notices
• Tier 2 (Medium risk): RFP responses, supplier communications, product/service descriptions
• Tier 3 (Low risk): internal brainstorming, rough drafts, ideation notes

Tier 1 content should never be “AI-generated and published.” It can be AI-assisted, but must be human-owned and verified.

Step 2: Add a “Wikipedia pass” to your review checklist

Make reviewers look for:

• missing specifics
• repetition
• inflated tone
• unverifiable claims
• odd structure (intro/outro padding)

This takes 3–5 minutes for most documents once people learn the patterns.

Step 3: Require provenance for claims

Provenance can be simple:

• “This number comes from ERP report X.”
• “This policy language is from Legal template Y.”
• “This quote is approved by Comms ticket Z.”

When writers know they’ll be asked, they write differently. AI prompts become better too.

Step 4: Use tools, but don’t worship detectors

Automated AI writing detectors are inconsistent—especially as models improve and humans edit drafts.

Here’s what works better:

• tool signals as triage, not verdict
• human review for high-risk tiers
• structured templates that force specificity

A strong template beats a weak detector almost every time.

“People also ask” (the practical questions teams are dealing with)

Is AI writing detection even possible if someone edits the text?

Yes, but it becomes less about “detecting AI” and more about detecting low-accountability writing. Wikipedia’s signs still show up: vagueness, repetition, fabricated specifics, and weird tone.

What’s the fastest way to reduce AI-related content risk?

Implement a two-part rule: (1) verify every named fact, (2) require concrete specifics (numbers, dates, owners, constraints). This catches the failures that cause real damage.

Should we ban AI writing in procurement documents?

I don’t think bans hold up in practice. The workable approach is tiered governance: allow AI assistance, require provenance, and enforce human accountability where the risk is highest.

The stance I’ll take: authenticity is a process, not a vibe

Wikipedia’s guide is useful because it treats AI writing as a quality problem you can train for—not a moral panic and not a magic trick.

For media and entertainment, that process protects the thing you can’t buy back easily: audience trust.

For supply chain and procurement, it protects something just as fragile: operational trust—the confidence that when a document says “10 days,” it’s actually 10 days, and someone is on the hook for it.

If you want one next step that fits into next week’s workflow: add a “Signs of AI writing” section to your editorial or procurement review checklist, and make the reviewer flag any sentence that lacks a verifiable anchor. After a month, you’ll notice something funny—your human writing will get better too.

So here’s the forward-looking question worth sitting with: as AI makes text cheap, what will your organization do to make trust expensive again—in the right way?