The year 2025 saw AI move from boardroom chatter to a business imperative. Startups and enterprises began embedding GenAI tools and copilots into core functions, unlocking tangible gains in efficiency and innovation. So, what shaped the Indian AI ecosystem’s journey this year?
Localising AI For India: After years of leaning on globally trained models, companies are now prioritising systems that understand India’s realities – local languages, patchy infrastructure and business realities. Multilingual AI shifted from nice-to-have to non-negotiable, and demand zoomed for AI platforms that work with limited internet.
In response, big tech giants expanded AI infrastructure and sovereign cloud capabilities in India, enabling local training, deployment and data residency.
GenAI Matures: Enterprises also moved away from force-fitting GenAI everywhere. Instead, they began layering classic AI, domain-specific models and GenAI more thoughtfully based on use case, risk and cost. Organisations became sharper about where GenAI truly adds value, especially given its propensity to hallucinate and higher compute costs.
The Next Frontier: Agentic AI emerged as the next big theme. Interest is high, but adoption remains a concern. Real scale is rare as organisations hesitate to hand over end-to-end processes to autonomous agents. However, smaller startups, with fewer legacy constraints, are pulling ahead by redesigning workflows and capturing early efficiency gains.
Surface-Level Adoption? Despite growing experimentation, genuine enterprise-wide integration is yet to materialise. Fragmented data, legacy systems, internal resistance, limited AI literacy and weak governance continue to slow progress.
The Risk Factors: Meanwhile, AI risk is rising faster than governance. Control and audit structures remain uneven, and companies are already encountering inaccurate outputs and incomplete analysis in high-stakes areas like underwriting, fraud detection and compliance.
As AI moves deeper into mission-critical decisions, can Indian enterprises marry bold experimentation with the discipline needed to turn AI into a true operating layer? Let’s find out…
Sisir Radar Takes Off With $7 Mn FundingRunning complex AI workloads demands high-performance chips. However, existing solutions either consume too much power or lack the specialised compute needed for real-time industrial and energy applications. Trying to solve this problem is Azimuth AI.
Custom-Made Chips: Founded in 2022, Azimuth AI builds application-specific integrated circuits (ASICs) optimised for edge AI and low-power operation. Its tailor-made chips utilise software-defined architectures to deliver high inference performance while keeping energy consumption minimal.
Azimuth Charges Up: In partnership with Cyient Semiconductors, the startup recently launched ARKA GKT-1, a platform-on-a-chip designed for edge intelligence in the energy and utilities sector. The offering combines high-performance inference with power efficiency, addressing a critical gap in industrial edge deployments.
Scaling Custom Silicon: With its approach, Azimuth is targeting markets that require robust on-device compute, like smart utilities, energy management, and battery systems, where cloud dependency or frequent recharging isn’t viable. Overall, the startup is eyeing a piece of the ASIC-based edge accelerators market, which is projected to hit $9.28 Bn by 2030.
As India pushes deeper into local AI infrastructure, can Azimuth rule the global ASICs market?

In 2025, as many as 18 homegrown startups went public, collectively raising nearly INR 33,000 Cr and delivering landmark returns for early backers. So, which VCs cashed in on the startup IPO mania?

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