Amazon Tests Next-Gen AI Tools in India Before Global Rollout

Amazon is increasingly turning to India as a real-world laboratory for its next wave of artificial intelligence innovation. The country’s vast scale, diverse consumer base, and blend of first-time internet users and experienced online shoppers make it an ideal environment to test, refine and scale AI-driven tools.

According to Rajeev Rastogi, vice-president–machine learning (ML) at Amazon, who oversees ML technology for emerging markets, many AI breakthroughs originate in India because the challenges are often more complex and immediately visible.

India’s unique mix of linguistic, regional, and purchasing diversity allows Amazon to identify product gaps early — and once successful solutions are developed, they are often rolled out globally.

India as a Live Testing Ground for AI Innovation

Why India Matters in Amazon’s AI Strategy

India’s e-commerce ecosystem presents a unique combination of conditions:

• A large base of mobile-first users

• Rapidly digitising small sellers

• High product catalogue variability

• Strong regional and cultural preferences

This environment forces Amazon to build AI systems that are adaptable, scalable and context-aware.

Rastogi explained that sharper consumer preferences and operational challenges in India often push teams to innovate faster than in more mature markets.

2% Background Lift — Generative AI in Product Images

One of the most striking examples of India-led experimentation comes from product imagery in categories like apparel.

Indian shoppers were found to prefer more realistic product visuals over plain white backgrounds, especially in fashion categories. In response, Amazon deployed generative AI to create neutral-tone backgrounds that maintained shadows, depth and realism without distracting from the product.

Rastogi described the outcome:

“We did an experiment and we saw that the sales went up 2% from just changing the background colour for these categories like apparel, certain categories and so on,” Rastogi said.

A 2% lift in sales from a background adjustment demonstrates how seemingly minor visual tweaks, powered by generative AI, can deliver measurable commercial impact.

AI-Driven Catalogue Automation

Bridging Digital Gaps for Sellers

Emerging markets like India often have sellers with limited digital infrastructure or product documentation. To address catalogue inconsistencies, Amazon developed AI tools that automatically extract product attributes — such as colour, sleeve type, or collar style — from images and minimal text inputs.

Initially prioritised in India to improve catalogue quality, these tools are now influencing automation systems in other global marketplaces.

By reducing manual listing efforts and improving search accuracy, the technology enhances discoverability and improves the overall shopping experience.

Customer-Facing AI Experiences Inspired by Indian Shoppers

Troubleshooting Chatbots

India’s preference for assisted shopping — similar to in-store guidance — led Amazon to develop AI-powered troubleshooting chatbots that help customers resolve post-purchase concerns.

These systems were first refined in India before being integrated into Rufus, Amazon’s AI shopping assistant, and deployed in additional regions.

The model demonstrates how user behaviour in one market can shape AI features globally.

Beyond Search — Adaptive Interfaces and Personalisation

Dynamic User Profiling

India’s heterogeneous shopper base has also enabled Amazon to refine personalised search and adaptive interfaces.

By analysing in-session behaviour, the company can determine user proficiency levels and modify the experience accordingly.

Rastogi explained:

“The more proficient customers, see ads, different widgets, subscribe and save options, Prime (offerings), and so on. For the less proficient users, there are tutorials, different language options and simplified navigation for instance,” Rastogi said.

This dynamic segmentation ensures:

• Experienced users receive advanced tools and promotional visibility

• New users benefit from simplified navigation and educational prompts

Local Signals, Global Impact

Search algorithms are also tailored using signals such as:

• Regional product popularity

• Brand affinity

• Price sensitivity

These methods, tested extensively in India due to its consumer diversity, are being adapted for other markets.

Generative AI and the Future of Product Videos

Amazon is also leveraging GenAI to automate the creation of short product videos. Current systems can assemble:

• Product images

• Feature highlights

• Script narration

• Voiceovers

into short clips explaining product benefits.

While fully automated, studio-quality video generation from simple prompts remains under development, Amazon expects rapid progress as AI models continue to mature.

This evolution could significantly reduce content production costs for sellers worldwide.

Data, Compliance and Regulatory Constraints

Balancing Innovation with Governance

Behind these AI systems lies the challenge of data governance. Rastogi noted that machine learning teams operate using datasets approved for use, while compliance teams separately determine what can be accessed under country-specific regulations.

This layered governance structure ensures experimentation continues responsibly, although some product launches may be delayed or restructured depending on evolving legal requirements.

India’s regulatory environment — including evolving data localisation and privacy norms — requires Amazon to build adaptable compliance frameworks that can later be replicated globally.

Strategic Significance of India in Amazon’s AI Roadmap

India’s scale makes it a high-stakes market not only for revenue but also for innovation testing. The company’s experience in solving for:

• Multilingual interfaces

• Infrastructure variability

• First-time digital adoption

• Hyperlocal preferences

provides insights that can be translated to other emerging markets in Asia, Africa and Latin America.

As global e-commerce competition intensifies and AI-driven personalisation becomes a key differentiator, lessons learned in India are shaping Amazon’s broader AI playbook.

Conclusion

Amazon’s AI strategy increasingly reflects a “build in India, scale globally” model. From a 2% sales lift through AI-generated backgrounds to adaptive search interfaces and catalogue automation, India has become a proving ground for AI innovation.

By testing solutions in one of the world’s most complex and diverse consumer markets, Amazon is refining tools that later influence its global operations. As generative AI reshapes e-commerce experiences — from search and visuals to customer support and video content — India’s role as an AI innovation hub within Amazon’s ecosystem is set to grow even further.