A Deep Dive Into Crafting a Data and AI Playbook in Travel

Like any emerging technology, AI is not a foregone conclusion. Realizing its full potential in travel will require bold action within companies and collaboration between them.

Following the inaugural Skift Data and AI Summit in New York City earlier this month, I am hopeful about how the whole travel industry can embrace AI. It was great to see common themes emerge throughout the day, both on stage but also in the conversations among the attendees.

“AI is only as good as the data that feeds it” was a common theme at the summit. It is clear to me that this statement needs one important append: AI is only as good as the data that feeds it and the human ingenuity that drives it.

We have to make the effort to engage, learn and unlearn with AI. It is our individual and collective conviction that will shape how travel embraces and unlocks value from AI.

I outline below five questions to consider as we pursue Data and AI initiatives in travel. The goal of these questions and their underlying recommendations is not to predict the future of AI. Instead, it is to frame the Data and AI opportunities in travel realistically.

1. What Is the Right Approach to Get Started?

Commit to learning along the way

Despite all of the AI developments announced almost daily, in reality, we are at the very beginning of the AI era.

We don’t know what we don’t know. We have to learn, experiment, and accept that there will be setbacks, missteps, and failures. Our practices and assumptions will evolve. This is especially true in how we evaluate and allocate resources to AI initiatives.

Plan ahead and be flexible for multimodal and multi-model AI

It is already clear that AI models are proficient in changing between text, speech, and vision as needed to be effective and relevant — hence, they are multimodal. It is also possible that different AI models will excel at certain tasks better than others.

The projects we develop need to recognize this multimodal and multi-model approach. We have to be flexible in how users might want to interact because their behaviors might change over time. As AI initiatives evolve, we may have to switch the underlying AI models and technology to improve results and relevance.

Source: Skift/Vivek Bhogaraju

Evaluate competing priorities and investments

Competing initiatives are often evaluated based on the value they create for the business. With an emerging technology like AI, two additional factors play a critical role: the ease of execution, and the acceptance by the end user.

Depending on the AI use cases, we will need to expand the evaluation criteria.

Here are some examples: What are the ethical considerations around the use of this technology? If we build it, how do we ensure that the current biases, prejudices, and challenges do not color the new tech? Once we have built it, how do we monitor to ensure it’s serving its designed purpose?

Accept that evolving goalposts are part of the journey

As the tech evolves, we have to acknowledge that the original premise will change. The prescribed guidelines will expand. New regulations will be introduced. Even the desired outcomes will change. How do we keep up with the shifting benchmarks? An evolutionary approach that adopts a test-and-learn mindset may be best suited as teams and organizations navigate the maturing of AI over the coming years.

2. Are We Looking at Data in the Right Way?

Revisit data sources, ethics and data acquisition practices

Data is the prerequisite for AI. It is essential. This is especially true for travel. There’s a lot of focus now on the availability and abundance of data for AI, and we are already seeing data licensing agreements from the AI companies that are building the foundational models.

Travel is awash in practices that involve pulling publicly available data from the internet. These are tedious practices with results that are often inaccurate, inconsistent, and unreliable. Most importantly, these practices are not ethical unless the data providers have opted to be a part of the process. It’s time we change these practices.

Source: Skift/Vivek Bhogaraju

Make data sourcing sustainable and reliable

This is the time to revisit the data sources and insist on data that is acquired by developing integrations and using open APIs. This will ensure reliable and sustainable data streams. As we rethink data practices, let’s create commercial incentives for data providers to create a robust infrastructure for data sharing.

Stitch the data across all travel sub-sectors

Revisiting the infrastructure is also a unique opportunity to address the lack of data availability between various travel sub-sectors: activities, air, car, cruise, hotels, rail and short-term rentals.

Data today is siloed in legacy platforms, and travelers often have to complete bookings in multiple places to plan a trip. But it doesn’t have to be this way. It’s time we rethink how data is shared between these sub-sectors and build something better that simplifies the experience for travelers.

Balance innovation and commercial interests

There is always going to be a healthy debate about the clash between innovation and commercial interests when it comes to making data available for emerging technologies.

Innovation thrives on free flow of information, data, and open-source technology. Innovation also needs to be funded and backed by investors, but the same investors are often motivated by returns, and proprietary tech might best serve that purpose.

Once we break down current data silos, we have to be careful not to replace them with new ones that are behind paywalls.

3. What is the Right Organization for AI?

Find the right leaders and give them the space to execute

Chief data officers have notoriously short tenures — an average of 30 months, according to HBR. They often have extensive technical knowledge but lack background in the travel industry. They need both to be successful.

Once hired, they are on the clock to deliver results. And when those results are not immediately obvious or require more restructuring than originally thought, organizations cut their losses. Data and AI initiatives and investments are not a short-term commitment.

Define the right organizational structure for data and AI

Everyone wants to have an AI initiative, but not many people are thinking about the org design.

Organizations need to find a home for data and AI teams that gives them a holistic view and influence — without getting bogged down by turf wars.

We have to think through how these teams collaborate and how we ultimately hold these teams accountable.

Choose between a standalone versus an embedded team

Essentially, organizations need to decide whether to establish standalone data and AI teams that interact with other departments, or embed team members throughout the organization to allow the infusion of data and AI into each and every function.

If the latter sounds better than the former, that’s because it is. It’s also much harder to execute.

There is no perfect home for the AI team today. Each organization will navigate this based on their own unique characteristics.

Set expectations and establish handoffs

Irrespective of how organizations define the right home for data and AI, they’ll need to recognize where one function ends and the other begins. Handoffs between teams and clear expectations will be key.

Many teams — analytics, revenue management, pricing, sales, loyalty, distribution, and more — will benefit from the success of data and AI across the organization; however, they have to commit their own resources and balance priorities for this to happen.

4. What Is the Right Mindset in Travel for AI’s Full Potential?

Travel is cyclical and seasonal

Travel businesses are subject to the whims of weather, viruses, and war. This is just the nature of the business.

That means successful data and AI execution requires urgency and efficiency. It’s often difficult for travel businesses to commit to and resource long-term strategic initiatives while also responding to changing market conditions.

Play the long game

Successful data and AI projects take time, iteration, and — most importantly — persistence. And we’ll have to evolve data and AI initiatives as the underlying technology matures.

The winners will not necessarily be those that start early or jump on the bandwagon later.

The winners in data and AI will be those who are bold and passionate about their customers, open to learning and unlearning, willing to be agile in their tactics — and downright persistent. If you are looking for anything more substantial than a chatbot in the long term, temper your expectations and dig in.

5. Why is This the Right Time to Invest in AI

Don’t let the skeptics shape the narrative

There are always naysayers in travel that point out the industry’s slow tech adoption, let alone adoption of emerging tech like AI.AI readiness and maturity in travel is among the worst as compared to other industries, yet travel represents the biggest opportunity for AI. Our history does not have to define the future.

Overcome industry-specific challenges

Travel actually has come a long way to adopt new technology. Think about the rate of change in consumer preferences, search, and ecommerce.

It is hard to scale tech in travel and make it pervasive for a number of reasons: the industry’s global nature, the need to be hyperlocal and relevant and useful to the traveler, subscale technology providers, fragmented ownership structures, and a long tail of customers in each sub-sector.

Rise to this unique moment in time

AI presents an opportunity to rebuild how travel drives insights from data, curates recommendations, and automates actions. This only happens once at the beginning of a new tech wave. We have to grasp this opportunity to rewrite the rules and help usher in a new era of innovation and value for all stakeholders involved in travel. Let’s go!

Vivek Bhogaraju is the advisory partner, data & AI at Skift. Vivek partnered with the Skift team to host the inaugural Skift Data + AI Summit earlier this month. He has built his career at the intersection of travel, data and technology with recent stints at Expedia Group, IDeaS – A SAS Company, Lighthouse and Oberoi Hotels & Resorts.