Adobe’s new report shows GenAI is aiding and shaping how India’s creators scale, monetize, and express creativity.
Adobe’s inaugural Creators’ Toolkit Report reveals how deeply GenAI is enhancing India’s creator economy. Based on a global survey of 16,000 creators, the report shows 97% of Indian creators view GenAI as a positive force. The challenge creators once faced was scale. High production costs, limited tools, and time-intensive workflows constrained growth. GenAI now removes those barriers by embedding intelligence across ideation, creation, and distribution.
Adoption in India is nearly universal. The report shows 99% of Indian creators actively use creative GenAI tools today. About 95% say AI accelerates brand growth, audience reach, or business outcomes. Another 85% say GenAI enables content creation previously impossible. Editing, enhancement, and upscaling lead adoption, used by 77% of creators. Asset generation for images and video follows closely. GenAI solves quality and speed challenges without requiring advanced technical skills.
Creators increasingly combine multiple AI tools to optimize output quality. Nearly 89% used more than one GenAI tool recently. However, challenges remain around trust and transparency. About 78% worry their content may train models without consent. High costs, inconsistent output quality, and unclear training data limit deeper adoption. These concerns push creators to research tools carefully before committing to platforms.
Looking ahead, agentic AI represents the next growth phase. Around 90% of Indian creators are optimistic about AI that manages multi-step creative tasks. Nearly 96% would adopt AI agents that learn their creative style while preserving human control. Desired benefits include automating repetitive tasks, improving ideation, and delivering performance insights. As mobile-first creation grows, GenAI is becoming the strategic backbone of India’s creator-driven digital economy.
This case matters because it shows GenAI has moved from experimentation to core infrastructure, directly driving productivity, speed, and economic participation at scale. Beyond Adobe, the findings reflect a broader enterprise shift in how AI is adopted, where trust, transparency, and cost now influence decisions as much as raw capability. The case also represents a common enterprise problem: scaling high-quality output without increasing complexity or headcount. GenAI addresses fragmented workflows, skill gaps, and time constraints, while simultaneously exposing governance challenges around data use, intellectual property ownership, and model accountability.
