Summary Careem’s head of data and AI talks about how the Middle Eastern ride-hailing pioneer is using AI technology to simplify people’s lives and build a tech institution that inspires. This article has also been published in QuantumBlack, AI by McKinsey.
Team Data & AI
Author(s) Selim Turki, Vinay Chandran, Karan Soni
About the Author(s) Selim Turki is the head of data and AI at Careem. This interview was conducted by Vinay Chandran, a partner in McKinsey’s Dubai office, and Karan Soni, a consultant in the Dubai office.

 

Careem’s head of data and AI talks about how the Middle Eastern ride-hailing pioneer is using AI technology to simplify people’s lives.

QuantumBlack, AI by McKinsey recently sat down with Selim Turki, head of data and AI at Uber-owned mobility company Careem, to discuss the latest trends in advanced analytics and artificial intelligence. Far from a dry discussion of theory, the conversation coalesced around several fascinating use cases in which Careem is using AI to make a difference in people’s lives. We discussed how AI is being leveraged to improve customer and driver security through targeted facial-recognition checks to ensure drivers (captains) are who they say they are. We also discussed how AI is being used to provide customers with the most accurate and up-to-date estimated times of arrival (ETAs) by factoring in a host of conditions, including local weather conditions, prayer times, and even iftar times during Ramadan. Along the way, we discussed what it means to be an “AI first” company and the outlook for AI tech—and talent—in the region.

QuantumBlack: Was AI always an important part of Careem’s growth journey? How has AI’s role evolved since Careem’s inception?

Selim Turki: We started our journey as a ride-hailing company booking journeys for corporate clients. We were initially booking cars manually, without a data server, before introducing more advanced systems to deliver more efficient, personalized experiences. Since day one, our mission has been to simplify and improve the lives of people—particularly our customers and captains. We quickly understood that maintaining high reliability for our dynamic marketplace 24/7 was a complex process that needed to be driven by instant decision making through continuous automation at scale.

We began processing real-time data, using algorithms and machine learning [ML] models to solve some of the core issues for our ride-hailing marketplace, including matching customers and captains efficiently, shaping our demand and supply via surge pricing, calculating accurate ETAs for our captains, and improving our maps and location search functionality.

Today, we are scaling the Middle East, North Africa, and Pakistan [MENAP] region’s first super app. AI is in our DNA as we invest more in platform capabilities and team skill sets. Our hiring strategy is focused on growing a diverse team of data and machine learning scientists to build out our in-house experimentation and machine learning platforms.

QuantumBlack: Has the adoption of these new AI techniques changed the way Careem works to serve its customers? How has this affected business teams within the organization?

Selim Turki: We use several AI techniques depending on the type of service we offer in our super app. All of these techniques are directed at three particular needs:

  • Personalization. Customers have a unique experience with our super app based on their preferences and historical behavior. For instance, we use our understanding of customer ride-hailing patterns to facilitate a one-click widget for them to choose to ride to their most frequent destinations, such as their homes, workplaces, or gyms. On the food side, which is usually a more personal experience, the personalization includes the cuisines users like, the specific dishes they may be craving or are interested in exploring, and what contextually relevant search content to show. The more our users engage and transact with us, the better the experience we can dynamically hone and craft for them.
  • AI used for a local experience. We serve our customers, captains, and partners better by being local and closer to them. We use AI to factor in prayer times, iftar time during Ramadan, and weather conditions to better predict the ETA accuracy of when the food will be delivered to our customers.
  • AI used for a safer experience. We use AI to conduct targeted facial-recognition checks for our captains to detect potential imposters and ensure that the captain driving is the same one who registered and passed regulatory and clearance checks. On the customer side, we use AI to detect genuine or fraudulent sign-ups or log-ins and transaction integrity checks to allow or block super-app transactions.

“We use AI to factor in prayer times, iftar time during Ramadan, and weather conditions to better predict the ETA accuracy of when the food will be delivered to our customers.”

QuantumBlack: How many AI practitioners work with Careem today?

Selim Turki: We have dozens of AI and machine learning experts who are driving forward our strategy of being an AI-first company. Part of our plan is to educate the entire organization on the topic, inviting our engineers and business counterparts to use AI to solve some of their challenges. We have also designed a program dedicated to new college graduates to ensure future talent is up to date with the latest AI techniques and to encourage them to further develop their skills.

QuantumBlack: How do you integrate AI into your decision making now? How do you stay ahead of competition in the market?

Selim Turki: AI is part of Careem’s decision-making framework. We set quarterly goals to measure and assess the usage and impact of our ML models on the different business streams.

We use rigorous statistical methodologies, taking confounding effects into account, to accurately estimate the model’s impact on different areas of the business.

To help our data and AI teams stay on top of the changes happening in the industry, we have started collaborating with regional academic institutions to solve some of the most significant super-app challenges and to identify exciting new opportunities for AI innovation.

We publish our progress on the Careem engineering blog and invite third parties to collaborate with us on specific areas related to AI.

We also contribute to open-source data communities and offer our work to other AI and ML professionals.

QuantumBlack: Can you share a recent instance of how AI fundamentally changed the way Careem does business with its customers or captains?

Selim Turki: With any digital platform, fraudsters will look for loopholes to exploit, whether through creating fake-identity accounts or exploring ways to hijack open accounts. Our team uses advanced AI techniques focusing on the identity of users to detect and prevent losses stemming from fraud. One system we use, called Crazy Wall, uses a relational graph convolutional network to map different data points of a customer’s identity. It also identifies characteristics shared across different identities to detect and mass-block fraudulent patterns across customer or captain activities.

QuantumBlack: AI talent has been a key challenge for companies in the region. How have you dealt with the region’s structural talent issues?

Selim Turki: The region’s tech talent is growing rapidly, and it’s exciting to see more specialists choosing to come to the region to make an impact in some of the fastest-growing countries in the world. It’s also exciting to see a growing number of local university graduates specializing in AI. We’re fortunate to have attracted a strong community of AI talent both locally and from surrounding markets to Careem. Our teams are building tech across various areas, including e-commerce, technology-enabled logistics, maps, identity, and fintech. They can solve complex and meaningful challenges at scale thanks to Careem’s deep tech expertise, strong regulatory relationships, local presence, and increasingly specialized global teams that are structured to operate as autonomous start-ups. Our team of more than 400 engineers and developers are empowered to develop cutting-edge technology every day. Being a remote-first company allows us to attract talent from across the world who want to have an impact on the MENAP region. This means that the opportunities to gain new perspectives and solve complex, real-world challenges alongside talented peers are endless.

QuantumBlack: Do you think the talent-supply challenges are here to stay? What is your ambition for attracting cutting-edge AI practitioners to Careem in the next three to five years?

Selim Turki: As AI becomes more widely used across industries, the demand for specialists will continue to rise. We need to inspire the next generation of data and AI specialists to be curious and gain exposure to the workplace at an earlier age.

At Careem, we are focused on building an AI culture where opportunities to learn and thrive are fostered by adapting, mentoring, and sharing within our AI communities and beyond. We are also hoping to make AI more accessible to stakeholders across Careem with initiatives like “no-code AI,” where AI is accessible without existing coding skill sets, as well as partnerships with AI labs to democratize AI usage across the company.

QuantumBlack: How will AI specifically change the mobility space in MENAP? Are there any white spaces where MENAP companies could be global first movers?

Selim Turki: The global mobility space is at a very nascent phase, with considerable opportunities to solve using AI techniques. At Careem, we have the vision of creating an internet-like network to transport packages of atoms, like how the internet transports packets of bits, called the “AtomNet.”

The AtomNet provides an open-network platform that connects, manages, and routes multimode autonomous vehicles [AVs] to make transport ubiquitous. Similar to how packets can travel across multiple modalities of transport (Wi-Fi, DSL, cable, and fiber), packages on the AtomNet can travel in autonomous motorcycles, cars, vans, trucks, ships, drones, and airplanes. We foresee an AtomNet industry ecosystem with open package headers and protocols to allow package switching and efficient package mobility. With open protocols, coordination costs will drop significantly, and local, national, and international transport gaps will narrow over the years.

AtomNet will support Careem’s quick commerce, fulfillment centers, restaurants, groceries, dark stores, transportation, and cross-border commerce. We see the epicenter of AtomNet starting in the UAE due to its progressive regulation and culture of innovation.

QuantumBlack: AI is still in its nascency in the broader context of this region. How do you think this will change in the next five to ten years?

Selim Turki: A long and exciting journey is ahead of us in the wider Middle East. With the growing pace of technology, more and more regional corporations will use AI to enhance their products and offer a better experience to customers.

At Careem, our primary focus will continue to be building the internet platform of the Middle East to provide access to our services—using data and AI as a core to simplify and improve customers’ lives. The meta goal is to delight all our users and personalize their experience through data and AI in every service offered through our super app.

The current trend of making trade-offs by improving AI prediction will be strengthened at the cost of short-term factors such as ingestion costs, customer experience, and operational excellence. We will continue investing in our data streams to help our models learn, build, and manage algorithms at scale. Moreover, real-time feedback loops will continue to decipher customer behavior and how it evolves by using our services through leveraging more intelligent software and hardware. Some of the emerging machine learning models will be tailored more to our region, considering language, customer behavior, and product relevance.

Our goal is to provide the simplest and best possible customer experience. To make things simple, you have to make them intuitive. To make things intuitively simple, we need to:

  • Know the intention.
  • Understand the user’s context, pains, gains, needs, and delights.
  • Create and implement the right data infrastructure with the right attribution, data provenance, and governance.
  • Build ML models to classify, personalize, contextualize, anticipate, recommend, and adaptively learn.
  • Enable parallel and faster AI experimentation and use our large-scale data as a competitive advantage and an asset.
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