Editor's note: This interview is part of a series with executives leading GenAI adoption. Read the first and third installments here.
With extensive expertise in transforming consumer-facing companies through digital innovation, Rajat Dhawan, AI & Digital Leader and Group CTO at Soho House, shares how AI is reshaping customer experience and driving commercial growth. By using GenAI tools and through refined data analysis capabilities, the organization is creating even more personalized experiences—from custom event suggestions to adaptive pricing creating relevant digital products to real life experiences—to make members feel truly at home.
ChatGPT and other GenAI tools have spread significantly over the last two years. Could you share how you've implemented them with your team at Soho House?
We use ChatGPT in three main ways at Soho House:
- First, we’ve built a recommendation model to personalize event suggestions for members, similar to Netflix’s approach to recommending movies or series. Up until about 12 months ago, every member within a city would see the same type of events on the app. Now, by analyzing stated preferences, browsing and booking history, and local likes, we recommend events specific to each member’s interests. ChatGPT also helps create detailed tags for events, enabling better matches and boosting relevance. This personalization has increased attendance by more than 20%, improved profitability, and enriched member experiences.
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Second, we created a prompt model to estimate members’ incomes, which helps us tailor products and pricing—for us, that was extremely valuable because then we could look at creating products that are relevant to them. Combined with other information, it could be things on the menu, it could be the nature of events, it could be the pricing of a bedroom. We started testing this model in London with 1,000 members, then expanded to New York, using data like age, location, industry, and tenure as income indicators. -
Lastly, we use ChatGPT in our contact center to respond to high-volume, low-stakes tactical emails—such as simple requests about their membership. for invoices or child policy questions. Responses are generated, reviewed by humans, and then sent, maintaining a human-in-the-loop approach. Once fully scaled, this would allow us to respond to our members quicker.
Ultimately, what AI and data analysis have done for us is, one, helping us become more commercial, and second, enabled us to improve member experience because now members are seeing things that they want to and therefore are attending, and we are making investments based on their feedback hosting events that are making us more money. So, it's a “win-win.”
How did you introduce GenAI to the organization? Was it through building new capabilities, additional investment, or something else?
Rather than following a single plan, I developed our data and AI capabilities based on Soho House’s current appetite, commercial priorities, and budget. Quick wins were essential because, despite the excitement around GenAI, implementing it internally often raises concerns—people worry about their roles and responsibilities and whether this will truly enhance member experience. Managing this change was critical, especially for a company moving from traditional practices to a more advanced approach. Our success came from having the right talent, executive CEO support, alignment with business needs, and delivering practical, effective results. Today, our AI and customer data maturity surpasses most retailers, and even more so within hospitality and luxury brands.
What advice would you give others leading this type of transformation?
To tell a story effectively, it’s a blend of understanding the company’s growth direction, the revenue streams, and the customer behaviors through data. It’s not enough to just throw data at people, especially when not everyone is a natural user of data data-literate; they can quickly tune out. But if you frame insights within a context that relates to what the business cares about, that’s when it resonates. For example, changing menu prices or event or communication strategies works only to a certain point.
Broad solutions only go so far—you can’t just apply one approach across all markets or customer groups. Knowing what drives average order value, and tying customer insights to actual business results, takes the conversation beyond high-level metrics. It shows what’s really happening, and when people see these connections, they often respond with, “I had no idea.” That “wow” factor, combined with commercial context, enables you not only to provide insights but to make real recommendations. Once you show that kind of value, it sets a new standard for analysis going forward, and your investments become very targeted.
How do you operationalize this?
There are two approaches: you can create a strategy, roadmap, and processes and start implementing, or tackle key projects first. At Soho House, I found it more effective to solve immediate problems first rather than spending too much time explaining strategic frameworks. People often want immediate answers, so I initially focused on actions, sometimes keeping the strategy in my head, sometimes written. Then, after we made progress, I’d explain the strategy – which is easier to convey then – clarify, “This was our strategy,” and reinforce how we operate. It’s about balancing articulation with action and letting results show the strategy organically.
Are there particular partners you’ve worked with, and how has your team evolved over the years?
When I started five years ago, we had no data team—just one person doing basic data engineering. I grew the team gradually, focusing first on digital areas like the app and website, which needed immediate attention. At the time, the company wasn’t receptive to data, so I kept a low profile until we had reliable data. Once we reached that point, I began expanding the team’s role into operations, membership, finance, HR, and other areas. Now, we’re a team of 20–30, covering core data areas from architecture to data science to engineering, and the associated digital engineering support, with high engagement and demand for our work.
Early on, we focused on data engineering, pulling data from 20 different sources, building our platform on Google Cloud, and are now using Snowflake. With those foundational systems in place, we shifted more towards data science and analytics, like recommendation systems and churn prediction, as well as data analysts who create reports for business needs.
In terms of partnerships, I work with “best of breed” vendors across various areas, including larger global consulting firms Accenture for enterprise automation and partners in India and Eastern Europe. We need flexible partners who can adapt to our unique needs, so we often prefer smaller or boutique firms, where we have direct relationships with leadership. Larger firms sometimes don’t work for us, as we’re not a massive account and don’t get their A-team. This flexibility and partnership is critical for us.
Which area of Soho House has benefited the most from Gen AI, and where’s the biggest potential?
Membership, our core business, has seen the greatest benefit. Gen AI helps us understand our members in depth—how they engage with us across many lifestyle areas like dining, travel, wellness, and workspaces. Unlike traditional retail, we get to know our members’ preferences and experiences in detail. Our high member retention (over 90% for 30 years) shows the level of trust in our brand. Members expect us to use their data to personalize their experience rather than feeling concerned about privacy. They trust us and want a tailored experience. We’re also lucky fortunate to have invested in to have a modern data infrastructure and built a skilled team to support it. The cultural shift in the company over the last five years has made this transformation possible, bringing us to where we are today.
How has AI impacted your team’s productivity and the broader tech team?
I could double my data team and still not keep up with demand—there’s that much need. We’re regularly conducting advanced AI-driven analyses, which spark new initiatives that the leadership team, including the CEO and board, fully support. The demand is enormous, and AI has provided productivity and intelligence across the business, giving insights we never had before. It’s had a huge impact not just on daily operations but also on making our team a highly attractive place to work. Many people join us because we’re not as large as a FTSE 100, which can be slow-moving, nor are we a startup struggling with funding. As a mid-sized, publicly traded company, we can make fast, meaningful changes. The work here is fast-paced; people get to see the results of their contributions almost immediately. It’s very motivating, and they’re proud to see their impact.
What about potential concerns or challenges around AI use? Do you see many within the company?
Yes, there will likely be legal challenges in the future, given the rising awareness and rapid growth of AI. AI is everywhere—from banking to retail to social media—so concerns will continue to grow, especially as misuse occurs. Right now, we have strong policies in place, overseen by my team, covering data privacy, ethics, and we partner with our Legal team on key areas. legal considerations. Even if I wanted to push boundaries, everything goes through a stringent review process.
We’re using AI to do things that are well-established, like recommendation engines. Companies like Netflix and Amazon have done this for years, taking past behavior and preferences to make recommendations. Our members trust us, and everything is documented and transparent. We’re not doing anything unusual or invasive—members come to us for dining, socializing, and experiences, so our data use is focused on enhancing those interactions. For example, recognizing a member by name when they visit creates a welcoming experience. Even if a restaurant has thousands of guests, data and technology can help recreate that level of personalization, making people feel valued and connected. It’s simple but offers a meaningful experience for the customer. It makes you feel that you are part of that place.
