By Faiz Shakir, VP & MD – Sales, Southern Asia at Nutanix
As far as hype levels go, no technology has approached the heights of artificial intelligence (AI).
Based on the Deloitte Tech Trends 2024 report , Majority of Indian businesses are utilizing Gen AI/AI technologies to streamline operations, enhance customer experiences and gain a competitive edge. These advancements have the potential to accelerate India’s digital transformation with businesses coming forward to strategically invest in the new-age tech to maximise benefits by adapting to evolving market dynamics.
Now, a year after ChatGPT ignited the AI hype cycle, the initial excitement in India has transitioned into a more pragmatic understanding of the challenges and opportunities in implementing the technology.
In the early days, many Indian businesses approached AI as ‘science projects.’ Technology teams often found themselves experimenting with AI capabilities, seeking to understand how the technology could integrate into their unique operational contexts.
From discussions with local enterprises, it’s clear that these early initiatives typically took place in isolated environments. This allowed teams to build essential skills while identifying gaps in talent and resources. However, the journey has revealed that AI is not just about experimentation; it requires strategic alignment with business objectives and a commitment to scaling successful projects.
AI-for-AI’s sake is over. Enterprise AI is now the focus.
As businesses look to move from practice to praxis, there are a number of hurdles they have to overcome. While there are numerous challenges, the three most prominent for enterprise AI are ensuring the necessary skills are in place, convincing the board of the real-world business value, and ensuring the infrastructure can provide the necessary computing power.
Syncing up skills
India will need 30 million digitally skilled professionals by 2026, with 50% of the current workforce needing to re-skill in emerging technologies, according to the TeamLease report. This skills gap makes it unlikely for organizations to develop their own large language models (LLMs), leading 90% of enterprises to plan on leveraging existing LLMs, as mentioned in the Nutanix State of Enterprise AI Report.
The most in-demand skills include generative AI and prompt engineering (45%) and data science and analytics (44%). While companies have shifted away from creating proprietary LLMs, substantial effort is still needed to deploy and manage these technologies effectively. For an enterprise LLM to succeed, it must access organizational data, often requiring significant investment to extract this data from legacy systems, which necessitates board approval.
Bringing the board on board
Contrary to the hype, enterprise Generative AI and LLMs can’t simply be downloaded. Strict governance and privacy regulations necessitate implementing AI in secure environments to protect sensitive data from unauthorized use. For an enterprise LLM to provide real value, it must access comprehensive organizational data; otherwise, its insights may lack accuracy due to blind spots.
However, enabling this access is challenging, especially for organizations relying on legacy systems. Data must be rationalized, and applications modernized before AI can be effectively deployed. Thus, any proposal to implement AI should prioritize infrastructure modernization; without this, the potential benefits of AI will be significantly limited, as decisions will be based on incomplete data.
Intelligent Infrastructure
A recent Nutanix Enterprise Cloud Index (ECI) report shows that while 90% of organizations recognize AI as a priority, one-third find their IT infrastructure inadequate for AI applications. Over the next year, 84% plan to invest in modernizing their IT.
A major focus will be on adopting hybrid multicloud environments, which combine edge, private, and public clouds. Currently, one in five organizations in the APJ region uses a hybrid multicloud model, with two in five planning to adopt one soon. This infrastructure offers the flexibility and scalability essential for AI, bridging legacy systems with cloud-native services.
Moreover, modern hybrid multicloud environments are highly automated, reducing the management burden on IT teams. This allows skilled engineers to shift from maintenance roles to driving AI initiatives. When implemented effectively, AI can deliver substantial benefits, transitioning from experimentation to a strategic, value-driven enterprise model.
