By Nandan Nilekani, co-founder and chair of Infosys, predicts a significant shift in the artificial intelligence (AI) landscape.
According to Nilekani, the future value of AI won’t be found in the models themselves, but in the applications built on top of them.
As AI models, such as large language models (LLMs), become increasingly widespread and commoditized, the real innovation and economic benefit will come from creating practical, enterprise-focused applications for specific use cases and regions.
Nilekani’s perspective highlights the evolving dynamics of AI, where companies are turning their attention from foundational AI models to enterprise-level solutions.
This shift underscores the need for businesses to focus on real-world applications that deliver tangible benefits, moving beyond the hype surrounding AI models.
Region-specific AI models could unlock new markets
LLMs, like those used in popular AI applications such as ChatGPT, are AI systems trained on vast amounts of data.
Leading tech giants like OpenAI, Meta, and Google have invested heavily in developing these models, creating a competitive market.
However, as the technology matures, Nilekani sees a future where these models become commoditized and tailored to regional needs.
For example, in India, companies are already developing LLMs specifically designed for Indian languages and local datasets.
This regional diversification is crucial because AI models perform better when trained on data relevant to the region in which they operate.
According to experts, companies focusing on region-specific AI models could unlock new markets and drive innovation by offering solutions tailored to local needs.
Enterprise AI solutions demand a tailored approach
As LLMs and AI models become more standardized and accessible, Nilekani argues that the true value in AI will shift to the application layer, where businesses can differentiate themselves by integrating AI into their core operations.
Experts suggest that industries like finance, healthcare, and retail are poised to benefit significantly from AI-powered applications that optimize workflows, enhance customer experiences, and improve decision-making processes.
Nilekani points out that businesses must focus on integrating AI into their existing processes for maximum impact.
While consumer AI applications, such as chatbots, can be deployed quickly, enterprise AI solutions demand a more strategic and tailored approach.
This focus on applications will drive a more sustainable and impactful adoption of AI technologies across industries, according to industry insiders.
Enterprise AI’s complex adoption cycle
While consumer-facing AI applications have gained widespread popularity, Nilekani emphasizes that enterprise AI has a longer and more complex adoption cycle.
Integrating AI into core business operations requires organizations to rethink their workflows, data management, and technology infrastructure.
Though time-consuming, this transformation offers significant competitive advantages for companies that successfully implement enterprise AI.
Experts say that companies aiming to capitalize on enterprise AI must invest in building internal capabilities and refining their technology stacks.
As businesses increasingly recognize AI’s potential to drive efficiency and innovation, the demand for customized, industry-specific AI applications will continue to grow.
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