Super Apps in the East: How Grab is Using GenAI to Innovate Beyond Contact Centers

Super apps in Asia, particularly in Southeast Asia, are rapidly adopting cutting-edge technologies such as Generative AI (GenAI) to transform their operations and services. Generative AI is playing an increasingly critical role in shaping the future of these platforms, moving beyond traditional applications like customer service and AI agents at contact centers.

Grab had begun investing in AI as early as 2019 when it announced a US$150 million budget for AI research. Grab, in collaboration with OpenAI, has already begun leveraging GenAI not just to improve customer service but to drive innovation across multiple touchpoints within its platform. This interconnected GenAI deployment is helping improve the experiences of employees, driver-partners, merchant partners, and customers while enhancing overall operational efficiency. It is also a significant enabler in Grab`s expansion across the different countries in South East Asia. The region not only includes languages with different alphabets but also with a large part of the population using lower-end phones making the design of the SuperApp local interface an additional challenge.

Generative AI in Grab’s Ecosystem

The company is employing GenAI across multiple operational areas, benefiting employees, customers, drivers, and merchants in ways that extend far beyond customer support chatbots. Some of these use cases include enhancing mapping accuracy, allowing natural language queries of internal data, improving route optimization, and efficient customization of the design of the SuperApp interface.

1. Improved Map-Making with GenAI: Enhancing Driver and Customer Experience

One of the most significant innovations comes from using GenAI to enhance Grab’s mapping systems. Accurate mapping is essential for Grab’s core services, particularly ride-hailing and delivery. Southeast Asia’s complex geography, with its dense urban environments and varied infrastructure, poses unique challenges for traditional mapping systems. Grab has overcome these challenges by leveraging GenAI to continuously improve its maps.

Grab reports that 84% of deliveries have been accompanied by notes from consumers. GenAI is now parsing through these consumer-generated notes which were left unused before.

Here’s how GenAI now unlocks value: Grab uses GenAI to analyze and integrate customer notes and driver feedback into its mapping system. For instance, customers frequently leave instructions like “Use the back gate” or “Entrance is on the side of the building.” Previously, this feedback was limited to individual deliveries or rides. With GenAI, Grab can now automatically parse and incorporate this data into its map updates, improving the accuracy and precision of future routes.

Additionally, GenAI helps Grab dynamically optimize routes by analyzing real-time traffic data and adjusting navigation suggestions based on live conditions. The system leverages historical traffic patterns and live data to identify the best possible routes, reducing delays and improving driver efficiency. This not only enhances the customer experience by ensuring timely deliveries and rides but also improves fuel efficiency for drivers and minimizes operational costs.

Now, when a delivery partner is approaching a destination, an expanded pin within the Grab Navigation app displays additional information that will help them locate their drop-off point. This is accompanied by images from Grab`s crowdsourced map data.

The expanded pin (in red) displays additional information to guide delivery partners to their destinations like informal landmarks or regional names. Delivery partners can expect to see a combination of information including nearby landmarks, a specific meeting point, or the unit number of an apartment. Source

GenAI continuously learns from driver and customer interactions to refine its maps, ensuring that drivers can find specific locations more easily, even in complex or less structured environments.

2. Optimizing Operational Efficiency for Drivers and Merchant-Partners

In addition to improving employee productivity, Grab’s GenAI also supports driver-partners and merchant partners by streamlining their operations. For drivers, the AI-powered mapping and route optimization tools mentioned earlier ensure they spend less time navigating traffic and more time completing jobs. By dynamically rerouting based on real-time traffic conditions and integrating customer feedback into navigation, drivers are able to reduce fuel consumption, improve punctuality, and complete more jobs in less time.

Merchant partners, especially small businesses and restaurants, also benefit from AI-driven insights. These tools help them optimize inventory management, predict demand, and even adjust their menus or product offerings based on customer preferences. GenAI analyzes transaction trends and customer behavior to offer actionable recommendations, enabling merchants to better tailor their services and improve profitability.

3. Natural Language Querying for Internal Data: Empowering Employees and Partners

Generative AI at Grab is also transforming internal workflows by enabling natural language querying of internal data. Employees and merchant partners can now ask complex questions about product specs, customer metrics, or user cases using simple natural language queries. Rather than navigating cumbersome databases or manually compiling data, they can retrieve relevant information instantly.

For example, a Grab employee in product development can ask, “What were the key pain points for users in the last food delivery campaign?” or “What features improved driver retention?” and the AI will return the most relevant data quickly. This accelerates decision-making and shortens the product development cycle. Teams no longer need to spend time extracting and analyzing data manually, allowing them to focus on more strategic tasks.

This AI-driven data access also enables more agile collaboration across departments, helping employees make better-informed decisions in real time. The speed and ease of these queries enhance overall productivity and responsiveness to evolving market needs.

4. GenAI-Driven UI Design: Overcoming Language Challenges and Device Limitations for Regional Expansion

Grab has leveraged Generative AI (GenAI) to design user interfaces that cater to the unique demands of its expanding user base across Southeast Asia, especially in regions where low-end phones dominate. As part of its expansion into countries like Thailand and Cambodia, Grab identified challenges in providing a consistent, user-friendly experience across diverse devices with varying processing capabilities. To address this, Grab utilized AI to streamline the development of adaptive user interfaces that are optimized for lower-end smartphones, ensuring accessibility for a wide audience.

For example, Grab redesigned its typefaces to better suit the complex Thai and Cambodian scripts, enhancing readability and user experience on devices with limited screen resolutions. This AI-driven approach not only accelerates product development cycles but also ensures that UI elements are efficiently customized to meet both cultural and technical needs in each region.

The Interconnected Nature of Grab`s GenAI Deployments

What makes Grab’s GenAI deployments stand out is the interconnected nature of these use cases impacting their end Customer, the Enterprise in more than one area (Capabilities, Design, Decision Making), and its Ecosystem (Drivers and Merchants).

GenAI tools across its entire ecosystem. AI is not siloed in one department but is integrated seamlessly across the app, benefiting multiple stakeholders simultaneously.

- Cross-functional Efficiency: The natural language querying system is used by employees across various departments, from product development to operations. By improving access to internal data, GenAI accelerates workflows in every corner of the business.

- Ecosystem Synergy: Improved mapping doesn’t just help drivers; it enhances the entire service experience. Accurate maps reduce customer wait times, improve delivery accuracy, and support merchants in managing deliveries, creating a ripple effect of efficiency throughout the ecosystem.

-Real-time Feedback Loops: GenAI’s ability to process and act on real-time feedback from customers and drivers ensures continuous improvements in service delivery. Every successful transaction feeds into the AI models, refining future routes, product features, and customer interactions.

Looking through the lens of our AI-Native Transformation framework maps out the breadth of Grab`s current use cases.

Explore AI Opportunities with Our Guidance

If you’re considering how to effectively integrate Generative AI into your business strategy, we invite you to explore our comprehensive framework. Our workshops are designed to provide insightful evaluations of your current roadmap alongside introducing our pioneering framework.

Learn how we help companies and leaders transform towards becoming AI Native and reach out to us. The outline of our framework from `Thinking like an AI-native (co-authored with Hari Abburi) from our new Wiley book: 𝐓𝐡𝐞 𝐅𝐚𝐬𝐭 𝐅𝐮𝐭𝐮𝐫𝐞 𝐁𝐥𝐮𝐫𝐬: 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞 𝐈𝐧𝐭𝐞𝐫𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐒𝐡𝐚𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 captures the breadth and potential of such a transformation.

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