AI adoption strategy for businesses in India: the need to move beyond LLM hype

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AI strategy in India for businesses

Highlights

The article states that, due to the impact of FOMO and hype, Indian businesses are not making a balanced assessment of costs, reliability, and operational risks when adopting AI.
For India, the authors emphasize adopting local solutions based on low-cost or open models rather than expensive sovereign LLMs, and focusing on practical applications in sectors such as agriculture and healthcare.
Adopting AI solely under pressure from valuations and cost-cutting may negatively affect jobs, energy consumption, security, and long-term stability.

This article was translated from the original. Read the original version by our correspondent here.

Risks and limitations behind the LLM boom

Forbes India published this article, shared with permission from IIM Bangalore, in which the authors state that business decisions in the current AI boom are often driven by FOMO—the fear of missing out—without adequate assessment of costs, reliability, and operational risks. According to them, much like the dot-com era of the internet age, growing enthusiasm around new technology can push even enterprises with strong business models in the wrong direction.

Tracing AI's evolution through expert systems, artificial neural networks, deep learning, CNNs, and transformer models, the article explains that AI's usefulness is not limited to LLMs. Applications such as robotics, quality inspection, medical imaging, and automated systems have long existed, but LLMs became the focus of public discourse following ChatGPT in 2022.

According to the authors, the new wave of agent-based AI is making decision-making and task execution more autonomous, but it may also increase hallucinations, boundary failures, uncontrolled behaviour, API abuse, and cyber risks. They argue that promises of improvements in the next release will not be sufficient until robust accountability and punitive frameworks are established.

The path to practical AI investment for India

The article suggests a different strategic direction for India, emphasizing the development of solutions for local needs using open or low-cost models instead of making major investments in expensive "sovereign LLMs." Using agriculture as an example, the authors say that directly replicating AI tools developed for large farms in Western countries would not be useful in India, where most farmers own less than three acres of land.

According to them, if small agricultural equipment manufacturers such as KisanKraft develop affordable AI-enabled machines, features such as soil analysis, weed detection, and crop disease identification could reach Indian farmers at practical costs. Similarly, open models such as AlphaFold 2 can support early research in public health, but investment will still be necessary in lengthy processes such as chemical synthesis, clinical trials, and regulatory approval for drug development.

The article concludes that business leaders, investors, and governments should adopt AI from the perspective of responsible, sustainable, and measurable use rather than attributing human qualities to it. The authors warn that if companies continue adopting AI solely under pressure from valuations, hype, and cost-cutting, it could affect jobs, energy consumption, operational security, and long-term business stability.

In our previous report, former HCL Technologies CEO Vineet Nayar said that market frenzy around AI in India is often outpacing real business results and sustainable business models. He warned that many companies are adopting AI tools without a clear strategy and ROI, driven by investor expectations and the fear of being left behind (FOMO), while examples of proven results and cash flow at scale remain limited.

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