Indian Gen AI Investment report
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Indian Gen AI Investment report

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Q1. What is the market & business opportunity in the Indian AI space ? (think from an Early-Stage investment perspective) ​ Q2. What are the challenges in these market opportunities?

Gen AI market overview

Globally, investments in AI have seen a 24% CAGR since 2019, with 2023 seeing close to $83 billion invested. The majority of this was made in horizontal AI applications in data analytics, GenAI, and ML algorithms and platforms.
India’s AI market is growing at a CAGR of 25-35% and is projected to reach around $17 billion by 2027, according to a new report by BCG and IT industry apex body Nasscom.
Notably, around 93% of the investments made by Indian tech services and made-in-India product players focus on digital content, data analytics and supply chain.
This includes the development of proprietary AI & GenAI platforms, tools for automation, data analytics solutions, and bespoke AI applications for specific industry verticals such as healthcare, banking & finance, and retail.
The demand for AI talent in India is also expected to grow at 15% CAGR by 2027
India today has the second highest installed talent base with 420,000 employees working in AI job functions. India also has the highest skills penetration with three times more AI-skilled talent than other countries.
90% of the top 25 providers were found to have made large-scale Gen AI skilling commitments. Around 80% companies are proactively working to incorporate new AI specific roles across the organisation and ensure necessary upskilling for existing workforce. These include Chief AI Officer, AI Architect, AI/ML Governance Specialist and AI Ethics & Compliance Officer.

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Technical Architecture
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Layer
Description
Examples
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1
Infrastructure layer
Provide Hardware and compute power required to run AI workloads
Nvidia, AWS, GCP, Azure
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2
Foundational models
GenAI models on top of which the entire stack is being built. They are AI neural networks trained on massive unlabelled datasets, enabling them to perform a diverse range of tasks, including test/ image/ audio/ code generation, text translation, and summarization.
GPT, PaLM2, LLaMa, Midjourney, Runway ML, Kritrum, Sarvam AI
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3
Enablers
These are the tools & frameworks that enable developers to streamline the development process, optimizing resource utilization, enhance scalability & performance, seamlessly integrate with other systems. This reduces the time & cost required to create AI-powered applications with higher performance and compatibility.
Pinecone and Langchain, Tensorflow, H2O, etc.
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Application Layer
Vertical Models
Horizontal Models
Vertical LLMs are trained on curated high-quality data from a specific industry. This allows Vertical LLMs to generate more accurate and relevant results. Legal, health, and finance are among the industries where a lot of knowledge resides in the massive historic data, which is the play of Vertical LLMs.
Customer-facing applications are built on top of GenAI LLMs. It includes tools that serve a specific purpose and can be applied to any industry with minor modifications
Hippocratic in healthcare and Evenup and Harvey.ai in legal.
Jasper, Glean, Copy.ai, Rephrase.ai.
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