Rakuten’s Affordable AI Strategy Offers a Lesson for Asia’s CMOs
As Big Tech chases scale, Rakuten focuses on efficiency. Here’s what Asian marketers can learn from its AI playbook.
As global technology giants pour billions into ever larger AI models, Rakuten is taking a more disciplined path. The Japanese e-commerce and services group is building artificial intelligence systems designed to deliver direct business impact while keeping costs tightly controlled.
That focus is already showing results. AI-related functions contributed approximately US$65 million to Rakuten’s operating income in 2024, and the company is targeting roughly US$130 million from AI in 2025, indicating a rare example of AI moving beyond experimentation into sustained profitability.
The effort is led by Ting Cai, a former Google engineer who joined Rakuten three years ago to build its AI capabilities from the ground up. His mandate is to use the latest AI techniques to improve customer experience and commercial efficiency, but do so at scale and at low cost.
Leighton Cosseboom
A Different AI Priority From Big Tech
Rakuten’s approach stands apart from Silicon Valley norms. Rather than competing to train the largest possible foundation models, the company focuses on task-specific performance. Cai oversees a team of around 1,000 engineers supported by thousands of Nvidia chips; yet, the goal is not a technical spectacle. It is a margin improvement.
This philosophy matters for Asian CMOs operating in highly competitive and price-sensitive markets. Many brands across Japan and Southeast Asia face pressure to justify AI spend quickly, especially as marketing budgets come under closer scrutiny.
Rakuten applies AI across its marketplace to improve ad targeting, product discovery, and personalization. The company says sellers using its AI-powered ad tools are seeing stronger returns on investment, while shoppers are engaging more frequently through improved search and recommendation features.
Leighton Cosseboom
Smaller Models With Commercial Intent
Earlier this month, Rakuten released the third version of its large language model. The company says it is around 90% cheaper to run than comparable systems. Instead of activating all parameters at once, the model uses a subtle design that turns on only what is needed for each task.
This allows Rakuten to deploy smaller, specialized models for different functions such as conversational shopping, semantic search, and ad placement. The result is lower compute usage without sacrificing practical performance.
Recent regional research supports this direction. Surveys of Asia Pacific marketing leaders published in 2024 by global consultancies show that more than half of CMOs now prioritize cost-efficient AI deployment over cutting-edge capabilities. The main driver is simple. AI must improve conversion, retention, or productivity fast enough to justify ongoing operational costs.
Leighton Cosseboom
Making Conversational Commerce Profitable
Rakuten has also been closely tracking the economics of AI-driven conversations. According to Cai, users who interact with the company’s AI shopping assistant return more often. The challenge is ensuring that every interaction leads to profitable outcomes rather than rising infrastructure costs.
The company is now focused on reducing the cost per conversation so that purchases made through AI-assisted interactions generate positive margins. For marketers experimenting with chat-based commerce and AI customer assistants, this emphasis offers a clear warning. Engagement alone is not enough if it does not translate into sustainable revenue.
Leighton Cosseboom
What Asia's Marketers Can Take Away
Rakuten’s longer-term ambition is to offer its AI tools and expertise directly to business customers, positioning the group as an AI enablement partner rooted in real commerce use cases.
For CMOs across Asia, the lesson is pragmatic. AI does not need to be massive or expensive to be effective. It needs to be targeted, measurable, and aligned with commercial outcomes.
As brands across the region move from AI pilots to scaled deployment, Rakuten’s cost-first strategy highlights a path that prioritizes efficiency, profitability, and long-term adoption over headline-grabbing scale.
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