An imagined canyon in the Peruvian Andes, a phantom Eiffel Tower in Beijing and a stranded couple waiting for a ropeway that never ran: recent reporting shows that letting generative AI plan a trip can produce more than awkward suggestions — it can be actively dangerous, confusing and expensive for travellers who take its prose at face value.
AI travel planning — using large language models (LLMs) such as ChatGPT, Microsoft Copilot or Google Gemini and dedicated travel AIs like Layla or Wonderplan to generate itineraries, route advice and local tips — has become mainstream fast. Surveys and industry data show that roughly a third of travellers now rely on generative AI tools for trip ideas or planning, and adoption is especially strong among younger cohorts.
That rapid uptake is producing real-world failure modes. Reported incidents include:
The sensible approach is hybrid: use AI to brainstorm and draft, then apply traditional verification with official sources, local operators and human experts before committing to bookings or routes. Travel companies building AI products must invest in grounding, verification and transparent uncertainty indicators; travellers must treat AI outputs as assistance, not authority. With those guardrails in place, AI can enhance travel without turning our itineraries into fiction.
Source: Kenya Association of Travel Agents The perils of letting AI plan your next trip – Kenya Association of Travel Agents
Background / Overview
AI travel planning — using large language models (LLMs) such as ChatGPT, Microsoft Copilot or Google Gemini and dedicated travel AIs like Layla or Wonderplan to generate itineraries, route advice and local tips — has become mainstream fast. Surveys and industry data show that roughly a third of travellers now rely on generative AI tools for trip ideas or planning, and adoption is especially strong among younger cohorts. That rapid uptake is producing real-world failure modes. Reported incidents include:
- Tourists guided by AI to a non‑existent “Sacred Canyon of Humantay” in Peru and left on a rural road far from safety.
- A couple who followed ChatGPT’s timetable for Miyajima’s Mount Misen and found the ropeway already closed, leaving them stranded at the summit.
- Cases where travel AIs suggested impossible itineraries (for example, an “Eiffel Tower in Beijing” error flagged for a tool called Layla), or produced marathon routes that were logistically absurd.
How travel AI breaks: mechanics and common failure modes
Why LLMs invent things
LLMs are trained to predict the next word in a sequence across vast corpora of text. That makes them extremely good at sounding knowledgeable, but it does not give them a grounded model of the physical world, real‑time schedules, or local accessibility. When the training signal encourages giving an answer rather than admitting uncertainty, the model often fills gaps with invented facts that fit the pattern — i.e., hallucinations. This is not a bug unique to one vendor; it is an architectural and training‑policy issue across contemporary LLMs.Real‑world travel failure modes
- Invented landmarks and coordinates. LLMs can stitch together plausible place names, images and coordinates into a convincing but non‑existent attraction. In remote or high‑altitude regions, following such a suggestion without local verification can be dangerous.
- Out‑of‑date or wrong schedules. Many AIs lack guaranteed, up‑to‑the‑minute schedule access; they may cite outdated ropeway or transit hours, or confidently supply a last‑departure time that is incorrect. The Mount Misen case is illustrative: ropeway schedules show last returns around mid‑ to late‑afternoon (often 16:30), so a ChatGPT claim of a 17:30 final descent was unsafe for hikers relying on it.
- Logistically implausible itineraries. Some AI-generated routes chain long-distance segments without realistic travel times, producing plans that would spend most of a traveller’s day on transport rather than sightseeing. Reports from users and journalists document itineraries skewed toward improbable travel legs.
- Visual and media hallucinations. AI imagery and video tools can create convincing but fabricated clips and photos — a risk when social proof (an “Instagram reel”) is used to confirm a destination’s existence. Viral AI‑generated content has already sent travellers hunting for attractions that were never real.
The costs: more than inconvenience
Safety
High-altitude treks, remote roads and mountain weather require specialist knowledge. A fabricated destination that places someone at 4,000 metres without oxygen or phone signal is not merely a disappointment — it is a life‑threatening risk in some regions. Local guides and operators warn that elevation, access and rescue logistics must be planned and verified by humans.Time and money
False itineraries can waste money (transport to nowhere, guided fees) and time: travellers often pay for taxis or transfers based on AI directions, only to discover no attraction exists or that the route is impossible. The Peru example involved a near‑US$160 expense for a rural drop‑off with no destination reached.Privacy and fraud
Using public chat tools to plan trips sometimes encourages users to paste booking references, itineraries, or personal data into services that may log or reuse that content. Travellers should avoid dumping sensitive PII into consumer chatbots when planning. Additionally, malicious actors can weaponize AI‑generated media to create scams (false booking confirmations, fabricated reviews) that appear authentic.Erosion of trust and place narratives
When AI fabricates or sanitises cultural detail, it can propagate inaccurate narratives about destinations, reducing the opportunity for travellers to form first‑hand impressions and genuine empathy — an intangible but important cost for cultural exchange. Psychotherapists and travel advocates warn that prepackaged, AI‑driven myths about a place risk supplanting real human encounters.What the data say about uptake and reliability
- Multiple surveys place adoption of AI in travel planning roughly in the 20–35% range, depending on geography, framing and sample. A Matador Network‑linked survey reported ~30% of travellers likely to use AI for holiday travel; Adobe and other industry metrics put AI usage in travel in a similar band. These figures emphasize rapid adoption but not universal trust.
- Separately, consumer polling and smaller surveys show notable reliability concerns: in one representative sample, about 37% of AI‑assisted users said the tool did not provide enough information, and roughly a third reported encountering false information in AI recommendations. Those numbers explain why travel pros urge caution and human verification.
- Industry reaction is mixed: travel technology vendors call AI transformative, while agents and guides flag hallucination and governance risks. The travel sector is simultaneously experimenting with generative AI for personalization and grappling with operational hazards.
Critical analysis: strengths, weaknesses and where AI adds value
Strengths — where travel AI helps
- Idea generation and discovery. AI is excellent at surfacing novel ideas, lesser-known attractions and theme‑based lists that can inspire travel planning faster than manual searching. It shines at high‑level suggestions and creative prompts.
- Packing lists, checklists and first drafts. Travelers can save time generating packing lists, day‑by‑day outlines and route sketches that a human then refines. AI reduces friction for repetitive tasks.
- Language assistance and local tips. When integrated with verified local data, AI can help translate, suggest local customs and give quick directional advice — if the underlying data stream is live and authoritative.
Weaknesses — why problems persist
- Hallucinations are systemic. Because LLMs are optimized as next‑word predictors and often rewarded for producing an answer, they sometimes invent plausible but false facts. This is a fundamental shortcoming in current training regimes that industry research acknowledges and is actively trying to mitigate.
- Lack of real‑time grounding. Without guaranteed, authoritative connectors to transport providers, official schedules or municipal sources, an AI’s timetable or operational advice can be out of date or plain wrong.
- Presentation parity. A core usability hazard is that AI presents hallucinations and facts with the same confident tone — users can’t tell which outputs are verified without extra steps.
Recommendations — how travellers should use AI safely
Follow a simple, practical checklist when using AI for travel planning. Treat AI outputs as a research assistant, not a travel agent.- Be explicit in prompts. Ask for sources, update timestamps and the data provenance for any schedule or contact information.
- Always verify operational details (transfer times, last ropeway departures, ticket office hours, emergency contacts) on an official site or directly with a local operator before you act. Example: Miyajima ropeway hours are published by the operator; confirm them rather than rely on a generated itinerary.
- Cross‑check locations with at least two independent sources (official tourism board, verified local guide or reputable aggregator) before booking or paying. If the attraction appears in only AI outputs and nowhere else, treat it as suspect.
- Avoid pasting personal data (passport numbers, booking references, payment details) into consumer chatbots. Use enterprise tools with contractual non‑training clauses when dealing with sensitive information.
- Prefer AI tools that show provenance (citations, links to official pages) and that explicitly note uncertainty or offer “I don’t know” as an outcome. If the tool refuses to show sources, use competitors that do.
- Use human expertise for high‑risk activities: mountain treks, remote travel, medical restrictions and parenting/travel with vulnerable people. Local guides, licensed operators and official tourist offices are still the definitive authority.
What the industry and regulators are doing (and where they fall short)
Technology companies and policymakers are increasingly aware of the problem. There are efforts to:- Add provenance and watermarking to AI‑generated media (to label images or videos that are synthetic), which helps with fake viral posts but does not prevent conversational hallucinations.
- Build retrieval‑augmented systems that ground answers in curated, up‑to‑date databases (reducing hallucinations when properly implemented). These systems help, but depend heavily on the quality and freshness of the retrieval sources.
- Propose transparency and consumer protection rules in multiple jurisdictions; however, conversational hallucinations remain technically challenging to police — a watermark won’t stop a bot from inventing a nonexistent canyon in dialog.
Practical guidance for travel professionals and product builders
For travel companies, OTAs and destination management organisations, the path to safer AI travel products requires three pillars:- Grounding and retrieval. Architect assistants to use retrieval‑augmented generation (RAG) so answers cite fresh, authoritative databases (transit APIs, official timetables, local operator feeds). That reduces the model’s tendency to invent.
- Human‑in‑the‑loop verification. Critical outputs (booking instructions, route plans for remote hikes) should generate a human‑review flag before being presented as definitive to customers. Enterprises must instrument audit trails.
- Clear UI signals and uncertainty indicators. Present confidence levels, source links and “last verified” timestamps; make it easy for users to escalate to a vetted human agent or local operator. Do not present AI suggestions as immutable itineraries.
Case study summary: three short lessons from the reports
- Peru (Sacred Canyon of Humantay): Hallucinations create invented places that sound credible. The remedy: cross‑check local names against official maps and local guides before moving people off established tracks.
- Japan (Mount Misen ropeway): Operational hours matter. Always verify transport cutoff times with the operator (ropeway pages and official guides publish last‑return times). Don’t rely solely on a generic itinerary produced by chat models.
- Layla/Eiffel Tower example: Even travel‑focused AIs can misclassify or mislabel attractions. Vendors should build layered verification and make known limitations explicit to users.
Unverifiable claims and cautionary flags
Some numbers quoted in social summaries and secondary write‑ups vary by methodology and time. For instance, headline adoption percentages fluctuate by sample (Matador, Adobe, YouGov and Global Rescue all report different but broadly consistent signals). Where a precise percentage is material to a decision, check the original survey methodology and sample frame before relying on the figure. In several reporting threads, secondary outlets paraphrase the same BBC story; that replication confirms the anecdotes but does not substitute for primary local reporting or operator confirmation.Conclusion — an operational credo for AI travel planning
Generative AI is already a useful tool for travel inspiration and low‑risk tasks; it speeds idea generation, helps create checklists, and can surface creative itineraries. However, its current tendency to hallucinate — to invent plausible‑sounding but false facts — makes it unsuitable as the single source of truth for operational details, safety‑critical decisions and remote travel planning.The sensible approach is hybrid: use AI to brainstorm and draft, then apply traditional verification with official sources, local operators and human experts before committing to bookings or routes. Travel companies building AI products must invest in grounding, verification and transparent uncertainty indicators; travellers must treat AI outputs as assistance, not authority. With those guardrails in place, AI can enhance travel without turning our itineraries into fiction.
Source: Kenya Association of Travel Agents The perils of letting AI plan your next trip – Kenya Association of Travel Agents
