AI's most successful enterprise use cases, customer service and coding, target opposite ends of the labor cost spectrum. It either replaces easily quantifiable, lower-cost roles or provides significant leverage to the most expensive employees like software engineers.

Indians are more optimistic about AI than Westerners because AI is seen less as a threat to the workforce (which has proportionally fewer white-collar jobs) and more as a crucial national opportunity. AI is viewed as a "leapfrog" technology to accelerate development and close the economic gap.

Contrary to expectations, analysis shows that sectors with low profit per employee, such as healthcare and consumer staples, stand to gain the most from AI. High-tech firms already have very high profit per employee, so the relative impact of AI-driven efficiency is smaller.

Contrary to expectations, professions that are typically slow to adopt new technology (medicine, law) are showing massive enthusiasm for AI. This is because it directly addresses their core need to reason with and manage large volumes of unstructured data, improving their daily work.

Unlike previous top-down technology waves (e.g., mainframes), AI is being adopted bottom-up. Individuals and small businesses are the first adopters, while large companies and governments lag due to bureaucracy. This gives a massive speed advantage to smaller, more agile players.

Instead of competing to build sovereign AI stacks from the chip up, India's strategic edge is in applying commoditized AI models to its unique, population-scale problems. This leverages the country's deep experience with real-world, large-scale implementation.

The initial enterprise AI wave of scattered, small-scale proofs-of-concept is over. Companies are now consolidating efforts around a few high-conviction use cases and deploying them at massive scale across tens of thousands of employees, moving from exploration to production.

C-suites are more motivated to adopt AI for revenue-generating "front office" activities (like investment analysis) than for cost-saving "back office" automation. The direct, tangible impact on making more money overcomes the organizational inertia that often stalls efficiency-focused technology deployments.

Indian startups are carving a competitive niche by focusing on the AI application layer. Instead of building foundational models, their strength lies in developing and deploying practical AI solutions that solve real-world problems, which is where they can effectively compete on a global scale.