AI-generated hotel responses are here: how planners detect them

Hotel sales managers have always written RFP responses under time pressure. Now they have help. In late 2024 the first quietly-AI-drafted responses started showing up in the European MICE corpus we work with, and through 2025 the proportion crossed from anecdote into a recurring pattern. This post is not anti-AI. It is a planner-facing field guide to a question the industry has not yet answered: when a response arrives, how do you know who — or what — wrote it, and does it matter?

Our position throughout is pro-transparency. Generative AI as a drafting aid is, in our view, a reasonable productivity move for a hotel sales desk. AI as a way to fabricate availability or hide the absence of a human review is a different problem. The seven signals below are designed to surface responses worth a clarification call, not to ban a tool that — used well — makes responses faster, clearer, and more complete.

The 2025 inflection: when LLM-drafted responses crossed the noise floor

Through 2024, automated text in hotel RFP responses was rare and easy to spot — early ChatGPT outputs were so distinctively stilted that one paragraph gave the game away. By mid-2025, two things changed. First, GPT-4-class models and Claude 3.5 Sonnet became markedly less detectable on a single-paragraph basis. Second, hotel sales operations began standardising on AI-augmented drafting workflows, often without explicit policy approval from above. The result: the share of responses showing at least three of the seven tells described below has risen from a rounding error into something procurement teams notice. We are intentionally not publishing a precise percentage. Without an audited, public sampling methodology, any specific number we put on the page would be a guess dressed up as data — and the most common mistake in writing about this trend is exactly that kind of false precision.

The academic literature has been clear since the GPTZero white paper in early 2023 (arXiv:2306.04723) and the Ghostbuster classifier from Verma et al. (arXiv:2305.15047): single-signal detection of LLM-generated text is fundamentally limited and degrades rapidly once the text is paraphrased, mixed with human edits, or run through a humaniser. For the kind of short, partly-templated business writing an RFP response contains, no off-the-shelf detector should be trusted as a yes/no judgement. What does work, in our experience, is multi-signal audit: combining linguistic, structural, pricing, and metadata tells and flagging responses where three or more cluster.

The industry context here is Gartner's Hype Cycle methodology: generative AI moved from the Peak of Inflated Expectations in 2023 into the Trough of Disillusionment in 2024–2025 for many enterprise use cases. Hotel RFP drafting is one of those cases where the technology is genuinely productive but the operational norms — disclosure, review, accountability — are still being written. Our take is that hotels and planners are better served by writing those norms together than by either side pretending the technology is not in the room.

Signal 1 · Linguistic register

The most-discussed tell is also the weakest in 2026. Early LLM-generated business writing had a recognisable cadence: a polite opener ("I hope this email finds you well"), a tendency to restate the planner's question before answering, and a stiff handover phrase ("Please let me know if you require further information"). Modern models, properly prompted, no longer always produce this register — but a sales manager who pastes a brief into ChatGPT without re-prompting will often get exactly this output and forward it as-is.

What to look for: the response reads less like sales-desk pragmatism and more like a contestant in a corporate-writing competition. Specific tells include "I am delighted to share", "Should you have any further questions", "Please find attached", and a tendency to use semicolons in sales correspondence. None of these prove AI authorship — many human sales managers write this way too — which is precisely why this signal scores low in isolation. It earns its keep as a tie-breaker.

Signal 2 · Round-number pricing

This is the strongest single signal we see. A human revenue manager working from a rate sheet quotes specific numbers — €184 single room rate, €27 per delegate coffee break — because those numbers come from a real internal cost model. An LLM that has not been given the rate sheet (or that has been given it and then asked to "make the proposal compelling") tends to round: €185, €25, €1,500 day-delegate package. Round numbers in a multi-item quote, especially when they cluster (€25 coffee break, €50 lunch, €150 dinner, €300 day delegate rate) are a strong tell.

The signal degrades when a hotel's actual rate sheet uses round numbers — chain properties with corporate rate cards often do. But for independent and boutique properties, where every quote is a small revenue calculation, round numbers across four or more line items in the same response warrant a clarification call: "Can you confirm these are the rates from your current sheet, or are they indicative?"

Signal 3 · Generic policy language copy-pasted from the website

Ask an LLM to write a hotel RFP response without giving it the hotel's actual cancellation and attrition policies, and it will confidently produce something. Often, that something is lifted verbatim — or near-verbatim — from the hotel's public website terms-and-conditions page. The signal: the cancellation clause in the RFP response is identical, sentence for sentence, to the version on the hotel's public site, even when the planner's brief asked for an event-specific policy.

This matters because the public website terms are usually for transient leisure guests, not group business. A planner who accepts the response as-is may find at contract stage that the hotel's group policy is quite different. The fix is procedural: cross-check the response against the website with a thirty-second grep, and if you find a verbatim match on policy language, request a group-specific clause in writing. This is one of the items in our common-mistakes guide that consistently bites planners later.

Signal 4 · Inconsistent F&B specificity

Real F&B quotes from a hotel that has read the brief contain specific menu logic: "Welcome reception canapé selection of six items, hot and cold, with a vegan and a gluten-free option in each round." LLM-generated F&B quotes oscillate between vague ("a selection of international canapés") and over-specific in ways that betray the model's training data ("artisanal sourdough crostini with smoked salmon roulade, micro-herb garnish, citrus crème fraîche").

The tell is the inconsistency: a response that is vague on the substantive question ("can you accommodate 12 percent vegan, 4 percent gluten-free, 2 percent kosher?") but suspiciously novelistic on the canapé description. Real revenue and F&B managers usually invert this: brief on the menu poetry, specific on the operational constraint. When you see it the other way around, ask the hotel for a sample BEO (banquet event order) for a comparable group — a request a human-drafted response can answer in a day and an AI-hallucinated one cannot.

Signal 5 · Response length distribution

Across a normal multi-hotel sourcing, response length follows a long-tailed distribution: a few extremely thorough responses, a fat middle, and a few short ones from properties that did not really compete. Sourcings where AI drafting is widespread show a flattened distribution: most responses cluster around the same length (typically 800–1,200 words) because LLMs default to similar verbosity given similar prompts. The shape of the distribution is itself diagnostic — if you have nine responses and seven are within a hundred words of each other, the prompting (not the hotels) may be the common factor.

This signal is most useful at the sourcing level, not the individual-response level. It tells you that something systematic is happening across the cohort, which is the cue to apply the other six signals to each response in turn.

Signal 6 · Time-of-day cluster (the 3 a.m. responses)

Metadata is often more honest than text. We routinely see clusters of RFP responses submitted between 2 a.m. and 4 a.m. local hotel time — far outside any normal sales-desk working pattern. These cluster because the workflow is automated: a script reads incoming RFPs, drafts responses with an LLM, and sends them when the queue runs. Not all 3 a.m. responses are AI-drafted, and not all AI-drafted responses arrive at 3 a.m. But the timestamp is a free signal: combine it with one or two of the other tells and the picture sharpens quickly.

For planners who want to operationalise this, the fix is to log inbound response timestamps in the sourcing tracker. If you use a structured response template (or a platform like Easy RFP that timestamps responses automatically), the data is already there.

Signal 7 · Identical sentence structure across competing hotels

The most uncanny signal, and the one that has converted the most procurement skeptics. When three hotels — separately owned, in different cities, with different sales teams — return responses that share the same paragraph order, the same transition phrases, and the same "Why choose us" three-bullet rhythm, the common factor is not coincidence. It is a shared prompt template or a shared agency that uses one. We have seen responses from four properties in a single sourcing that opened with the literal sentence "Thank you for considering our property for your upcoming event."

To audit for this, lay the responses side-by-side and read the first 200 words of each. Human-written responses diverge immediately on tone and structure. AI-drafted responses with similar prompts converge. If three or more responses converge, the sourcing has a provenance question worth raising before BAFO.

Try it: a live AI-tell detector for a single response

The tool below applies a simplified version of the seven-signal audit to text you paste. It uses transparent regex and structural heuristics — no model call, no data leaves your browser — and is intended as a starting point for the conversation with the hotel, not a verdict. Pasting your own draft is a useful test of how your writing would score.

What the industry standard should be: disclose, do not ban

Two camps are forming. The first wants to ban AI-drafted responses outright; the second treats AI as a private productivity choice that no planner needs to know about. We think both miss the point. The useful norm is disclosure — a one-sentence clause in the RFP brief that asks hotels to indicate whether any portion of the response was AI-drafted, which sections, and which model. Hotels using AI responsibly will be relieved to disclose. Hotels hiding it create their own evidence. Procurement teams get the provenance information they need to weight responses appropriately, without sliding into a brittle anti-tool posture that will look quaint by 2028.

The closer analogy is academic publishing, where major journals now require authors to disclose AI assistance without prohibiting it. That norm took eighteen months to settle and is broadly working. There is no reason the MICE industry cannot reach a comparable equilibrium in a similar timeframe — if the conversation moves from detection theatre to disclosure standards. The first step is for planners to start asking; the second is for hotels to start volunteering. Tools like the seven-signal audit above exist to bridge the gap during the asking phase.

For planners who want to operationalise this now, three concrete moves: (1) add a disclosure clause to the brief template; (2) log response timestamps and length so you can see signals 5 and 6 across a cohort; (3) for any response showing three or more tells, schedule a fifteen-minute clarification call before shortlist. The call itself is the most reliable detector — a human sales manager who knows their property can answer follow-up questions in a way no current model can fake. The point of the seven signals is to tell you which calls are worth making. For the underlying writing craft, our RFP-response playbook is a useful counterpart for hotels who want to draft responses that score zero tells without needing to hide their workflow.

Frequently asked questions

Is it ethical for hotels to use AI to draft RFP responses?
Drafting with AI is not inherently unethical — using it to fabricate availability, fake capacity, or commit to numbers a human did not verify is. The line most procurement teams draw is disclosure: AI as a writing aid is fine if the sales manager has read every number, signed off, and disclosed the tooling on request.

Can detection tools reliably identify AI text in 2026?
Standalone detectors like GPTZero or Ghostbuster reach roughly 70–95 percent accuracy on long, untouched LLM output but drop sharply once text is paraphrased or run through a humaniser. For RFP responses — short, structured, partly templated — single-signal detection is unreliable. A multi-signal audit is far more robust.

Should planners require hotels to disclose AI use?
Increasingly yes. A short clause in the RFP brief — "If any portion of this response was drafted with generative AI, indicate which sections and which model" — costs nothing to add and signals that the planner cares about provenance.

Does Easy RFP have an AI-detection feature?
Easy RFP flags responses scoring high on the seven signals, with a confidence band and the specific tells that triggered the flag. The signal is advisory — it surfaces responses worth a call, not an automatic rejection.

Are AI-generated responses lower quality?
Not automatically. A well-prompted LLM response with verified numbers can be clearer than a rushed human one. The quality problem is upstream: when sales staff forward LLM output without checking, the response inherits the model's hallucinations.

Will detection still work in 2027?
The linguistic tells will weaken; the structural tells (round-number pricing, time clusters, identical frames) will persist. By 2027 the question moves from detection to disclosure norms.

What share of responses are AI-generated in 2026?
There is no public, audited benchmark. Internal observations suggest the share showing at least three tells crossed a low-double-digit percentage during 2025 and is rising. We treat any specific figure as illustrative — anyone publishing a precise percentage without an audited methodology is guessing.

Related reading

• RFP Response Template — for hotel sales teams
• How to write a hotel RFP that gets quality responses
• Hotel RFP common mistakes (2026)
• Easy RFP blog — hub
• Free hotel RFP template
• 9-dimension hotel scoring framework

Author: Easy RFP Editorial. The seven-signal framework is the work of the Easy RFP product and editorial teams, refined against the platform's anonymised response corpus through 2025–2026. Contact the team.

Sources: Tian, E. et al. (2023), GPTZero classifier white paper — arXiv:2306.04723. Verma, V. et al. (2023), "Ghostbuster: Detecting Text Ghostwritten by Large Language Models" — arXiv:2305.15047. Gartner, Hype Cycle for Artificial Intelligence (2024–2025) — methodology overview. Internal Easy RFP corpus observations are directional and not audited; we do not publish precise share figures without verifiable methodology.

Disclosure: Easy RFP is a commercial MICE sourcing platform. We have a product interest in clear response provenance. We have made the seven-signal framework public and free of proprietary gating to encourage adoption beyond our platform.