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Grab and OpenAI Partner to Upskill 30,000 Grab Partners in AI

The regional super-app is integrating AI-focused training into its existing GrabAcademy curriculum to boost productivity for its merchant and driver ecosystem.

Grab has officially announced a new collaboration with OpenAI to integrate artificial intelligence training into its regional partner ecosystem, targeting a goal of upskilling 30,000 Grab partners. According to the original publisher, this initiative represents a significant expansion of the company’s existing educational infrastructure, GrabAcademy, which currently facilitates online training modules for merchants across the platform.

The training program is designed to provide partners with the skills to leverage AI tools, aiming to improve operational efficiency and digital literacy. By embedding OpenAI’s expertise into its existing digital portal, Grab intends to streamline how its thousands of drivers and merchants interact with technology. While the specific rollout timeline for the Malaysian market remains subject to regional deployment schedules, the partnership marks a structural shift in how Grab approaches partner development.

The initiative moves beyond basic onboarding by introducing concepts of generative AI to everyday business operations. For merchants, this could translate to better inventory management or customer communication, while drivers may benefit from AI-optimised navigation and task management features. The mechanics of the training involve modular, self-paced content hosted on the GrabAcademy platform, ensuring that partners can learn without significant downtime from their daily earnings.

This initiative is particularly timely for the Malaysian workforce. With the country reporting a stable unemployment rate of 3.0% as of July 2026, comprising approximately 520,300 individuals, any effort to increase the digital capabilities of the gig economy serves as a buffer against broader labour market shifts. For the average Malaysian driver navigating a complex fuel environment—where RON95 sits at RM1.99 under the BUDI95 scheme and diesel costs RM5.27—any increase in productivity or efficiency gained through AI tools could be a critical factor in maintaining take-home pay.

For Malaysian SMEs operating on the Grab platform, the adoption of AI training is a significant development. As the economy records a real GDP growth of 6.0% year-on-year, businesses are under pressure to scale efficiently amidst a headline inflation rate of 1.9% as of August 2026. If the OpenAI-backed training allows local merchants to automate customer service or marketing tasks, it could help them remain competitive against rising operational costs and the non-subsidised fuel prices of RM4.37 for RON95.

This partnership sits within a broader trend of technology giants investing in the "human element" of the digital economy. While many tech firms have historically focused on consumer-facing AI, Grab’s pivot toward upskilling its supply-side partners—the drivers and merchants—suggests a strategy to solidify its ecosystem’s resilience. By digitising the workforce, the company is effectively raising the barrier to entry for its competitors while simultaneously creating a more capable base of partners.

Moving forward, stakeholders should watch for how the curriculum is localised for the Malaysian market, specifically in terms of language accessibility and relevance to local merchant needs. The integration of AI into such a massive user base is a significant undertaking that reflects the maturing relationship between global AI developers and regional super-apps.

What remains unconfirmed is the specific date for the Malaysian rollout and whether this training will eventually become a mandatory requirement or remain an optional certification. Additionally, the platform has not yet disclosed whether the tools learned in these sessions will be integrated directly into the Grab partner application or if they will remain separate, external digital assets.

Source

Originally reported by Techinasia. Read the original report →

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