Mr Tan shows off a robot which is currently being trialled. The robot is aimed at helping driver-partners save on delivery time.
Grab is accelerating its shift beyond transaction-based services to become an “intelligent everyday guide”, evolving from a super-app into a more integrated, AI-driven companion. The strategy is underpinned by its advanced intelligence layer and predictive models, enabling more personalised, seamless user experiences while opening up new monetisation opportunities beyond its core ride-hailing and delivery businesses.
Earlier, Grab revealed that it had set goals for the next three years to grow revenue by more than 20% annually and triple earnings before interest, taxes, depreciation and amortisation to US$1.5 billion in 2028 from last year’s level, according to Reuters.
“Today, Grab gets an upgrade. We’re moving beyond facility-based transactions and adding new product features that make us your intelligent everyday guide,” said Anthony Tan, Grab’s chief executive and co-founder.
Guided by an “AI first with heart” philosophy, Grab is absorbing the costs of these AI tools, providing them for free to ensure that everyday workers — regardless of their technical skills or financial status — are not displaced by the AI revolution.
The new products that Grab introduced this week are powered by the Grab Intelligence Layer, the company’s AI infrastructure built on insights from 20 billion rides and orders. Through an intelligence layer that powers digital tools such as the “Coach” driver assistant and physical innovations such as autonomous robots, the company aims to boost worker efficiency and safety.
Grab has also debuted a robot designed to act as a “human extension” for delivery drivers. As drivers currently lose 10% of their earning time navigating large malls for restaurants or waiting for customers at office buildings, the robot will take over these wait times and physical navigation tasks, allowing drivers to move on to their next job much more quickly.
“We are moving into hardware to improve the messy physical parts of the job that software alone cannot fit,” Mr Tan said.
Mr Tan emphasised a commitment to human-centric technology that empowers people to thrive amidst a rapidly shifting global landscape.
“As a product builder, I believe AI should work the hardest for the people who need it most,” said Philipp Kandal, chief product officer at Grab.
Grab introduced 13 AI-powered features at the GrabX 2026 annual product launch in Indonesia on Wednesday. The features serve three core user groups — consumers, travellers, and business partners.
The new features for consumers include shared mobility options such as Group Ride, multi-merchant ordering via Grab More, and the Grab AI assistant that acts as a personal concierge for food, shopping and bookings. Other features include GrabMaps and Cash Loan services, further expanding its role beyond transport and delivery into a broader lifestyle platform.
The new features for travellers include GrabStays for hotel bookings, and Discover by Grab for AI-curated dining recommendations. The GrabPay for Travel feature also enables seamless cross-border payments via QR codes.
For merchants and driver-partners, the Virtual Store Manager transforms existing CCTV hardware into AI-powered computer vision for real-time monitoring and hygiene detection of each store, a cloud printer to streamline order handling and eliminate the manual handoff, and Tap to Pay to turn smartphones into contactless payment terminals that accept credit cards and QR payments. Meanwhile, a Driver AI Assistant provides hands-free guidance to optimise routes and earnings.
Mr Kandal said Grab is scaling its existing business model by enhancing affordability rather than introducing entirely new monetisation structures. Moreover, the company is developing new revenue streams on the merchant side, with hardware and Internet of Things solutions — including cloud printers and virtual store management tools — set to transition from free trials to a subscription-based model covering equipment and operational costs.