Last month, Nasdaq-listed Indian travel tech giant MakeMyTrip launched a GenAI-powered feature, Collections, on its platform to make hotel and homestay discovery smarter, easier, and more personalised. The feature leverages AI and deep traveller insights to intelligently find accommodations to match customer needs and preferences.

Notably, MakeMyTrip is doubling down on the personalisation of user experience at a time when the GenAI wave has taken the industry by storm, and hyper-personalisation is the new norm.

However, what could surprise you is that it is not the travel tech platform’s first tryst with artificial intelligence (AI). In fact, the company’s rendezvous with AI is now nearly a decade long.

Over the years, the company has remained steadfast in its quest to maintain quality data to train and fine-tune its models, giving its customers features like zero cancellations and price lock ahead of time. However, with the advent of GenAI two years ago, it is now forced to relearn the rules of the game.

This is where the company’s Group CTO, Sanjay Mohan, sees a dearth of AI talent as a major hurdle, even as the company has largely kept itself ahead of the curve by constantly training its team on the subject.

Having jumped on the GenAI bandwagon just two years ago, the company has been successful in integrating the tech into some of its operations such as email communication sent by executives to customers and streamlining content on its description pages, much is still left to explore.

What’s on the cards? Let’s understand as Inc42 speaks with CTO Mohan in detail about the company’s AI adoption journey, especially the prerequisites of embarking on this quest.

For more on the $11 Bn company’s AI journey, data collection and security, and GenAI adoption, here are the edited excerpts…

Inc42: The rise of GenAI has put the spotlight on the adoption of AI in companies. How did MakeMyTrip start its AI journey?

Sanjay Mohan: We started building our first data platform nine years ago. It took us several months to begin data collection and enter the production phase. This required building data science models, fine-tuning them, and testing and tweaking them as needed.

We built a robust data pipeline in the company for almost two to three years before launching rich AI-enabled features for our customers about six years ago.

The first use case of AI in our company was in personalisation. We started with personalising the look and feel of the home page for every user or cohort of users.

Features such as........

© Inc42