AI can plan your trip, but it still can’t make it work

Generative AI has made travel planning look deceptively simple.

Type “7 days in Japan during cherry blossom season” into ChatGPT or Gemini, and you’ll get a neatly formatted, day-by-day itinerary, sightseeing, transfers, and even restaurant suggestions. The result feels magical. But beneath that polish lies a hard truth: most of these itineraries can’t actually run in real life.

The reason is simple. Today’s large language models are exceptional at assembling information, not at validating it. They can stitch together ideas from the internet, but they don’t know if the 7 am flight they recommend still operates, if the attraction they suggest is closed for maintenance, or if the drive time between two cities crosses a seasonal roadblock. They produce beautiful hallucinations, impressive, but often infeasible.

This is where the next phase of AI in travel begins, not in conversation, but in feasibility.

From chatbots to feasibility engines

The first generation of AI tools in travel focused on inspiration, helping people imagine trips. The next generation must focus on execution, ensuring those trips can actually happen.

That requires a different class of models entirely.

Feasibility engines have to process live supplier data, transport cut-offs, visa rules, weather advisories, and real-time availability. They must understand traveller context, families versus backpackers, elderly versus honeymooners, and filter itineraries accordingly. These aren’t challenges general-purpose models like GPT or Gemini were built to address; they need specialised, multi-model architectures trained on live operational data.

The human layer problem

Even when itinerary generation becomes reliable, the Indian traveller introduces another layer of complexity.

Unlike the western market, where travel planning is highly self-serve, Indian consumers expect assistance and accountability. They want the entire trip booked, verified, and managed through one interface, not an AI recommendation followed by a dozen manual bookings across platforms.

This is why the integration of global systems, like Expedia or Booking.com data pipes into OpenAI, while promising, still falls short for Indian users. These connections mainly serve affiliate purposes: the AI can display listings, but the actual booking experience, changes, and after-sales handling remain fragmented.

In India, AI systems need to extend beyond planning, into operations, hand-holding, and on-trip management. A model that can detect delayed flights, predict supplier defaults, or reroute a cab when weather changes, that is when AI begins replacing chaos with consistency.

Multi-model travel AI

To make AI useful across the traveller’s journey, multiple layers must coexist:

● Planning AI: to generate feasible, context-aware itineraries.

● Operations AI: to predict bottlenecks, coordinate suppliers, and automate exception-handling.

● Experience AI: to monitor on-trip sentiment, detect friction, and trigger proactive interventions.

Each of these layers needs to communicate in real time. For instance, when a planning model confirms an itinerary, the operations model must validate it against current capacity data. When a trip is live, the experience model must sense deviations, a late driver, a missed ferry, a weather alert, and act autonomously or escalate to humans.

This is where real innovation is happening quietly, not in chatbots, but in machine-to-machine coordination.

The cost of intelligence

The other conversation a few in the industry are having is the cost of AI.

Building and maintaining proprietary models can be expensive. APIs from global LLMs are not free; training and inference costs scale rapidly. If companies indiscriminately use off-the-shelf tools without optimising usage, the cost of “being AI-driven” can easily erode the very margins they’re trying to protect.

The smarter approach is hybrid: use large models for language understanding, but layer lightweight in-house micro-models for domain-specific prediction. This combination keeps accuracy high and costs under control, ensuring AI enhances operational efficiency, not inflates overheads.

The balance ahead

AI is undoubtedly reshaping travel, from dynamic pricing to itinerary generation, but the real winners will be those who build systems that think like travellers and operate like humans.

Machines can plan with precision, but only people can reassure when things go wrong. The most successful travel ecosystems of the next decade will not be purely algorithmic. They will be collaborative architectures, where AI anticipates and humans interpret.

ChatGPT and Gemini can suggest what to do.

But the future of travel will depend on who can make it actually happen.

— The writer is co-founder at Thrillophilia