It’s been two years since the term ‘generative AI’ started gaining widespread attention. Although it wasn’t new—it appeared on Gartner’s ‘hype cycle’ as early as 2020—by the end of summer 2022, excitement was mounting around genAI tools capable of generating text, images and computer code.
The estimated $1-trillion investment in generative AI has yet to deliver widespread returns. As with every tech revolution, the hype will fade and real work will begin. This is where India’s tech industry stands today—at the crossroads of immense opportunity and significant challenges.
In 2013, the M D Anderson Cancer Center at University of Texas launched a moonshot project to use IBM’s AI-powered Watson system to diagnose certain cancers. By 2017, the $62-million project was paused without being used on patients. Meanwhile, the centre’s IT team explored smaller-scale cognitive tools for tasks like assisting patients’ families, identifying financial aid needs, etc. These efforts proved far more successful. The stark contrast between these two approaches offers valuable insights for anyone planning AI initiatives.
The Indian market is still in its early stages. There has been a 50 percent decline in the Indian genAI startup funding in the first half of 2024, compared to that of 2023. While there has been a seven-fold increase in activity by the Indian industry, over half are focused on new product launches.
Most Indian organisations are progressing towards mid-level AI/genAI maturity, with defined strategies and initial use cases aimed at scaling. While 75 percent of the 500 companies surveyed by Nasscom have AI strategies at the proof-of-concept (PoC) stage, only 40 percent demonstrate significant progress in moving to production. Collaborative efforts are driving advancements in telecom, enterprise tools and retail applications. There is growing emphasis on domain-specific fine-tuning and development of custom, small language models.
However, genAI’s appeal comes from its transformative potential, often amplified by success stories. Despite billions poured into AI/genAI projects, Forbes reports that less than 15 percent of these initiatives make it to production. While there is a clear surge in genAI activities led by a strong uptick in product and go-to-market partnerships, the realisation of substantial, measurable impact remains in early stages.
The question arises: why does AI, especially with advances in genAI, fail to deliver expected returns? This reflects a set of broader challenges.
● Complex implementation: GenAI integration demands overhaul of existing systems and workflows, often requiring significant investments in infrastructure and process redesign. For unprepared businesses, it becomes a costly experiment.
● Data dependency: AI systems thrive on quality data, but many organisations grapple with fragmented, biased or inadequate datasets. Without data governance, outputs risk being unreliable or counterproductive.
● Talent deficit: Building and deploying genAI solutions requires a specialised workforce—data scientists, machine learning engineers and AI ethicists—who are in high demand and short supply.
● Ethical and regulatory challenges: From bias in algorithms to compliance with data protection laws, the ethical landscape of AI is fraught with challenges. Navigating these while ensuring business alignment is a tightrope act.
Despite these obstacles, the Indian tech industry has a solid foundation to harness AI for transformative change. With a median age of just 28—significantly younger than Europe’s 44—India’s demographic dividend is a powerful asset. This workforce is increasingly connected, supported by over 790 million mobile broadband connections, enabling rapid digital adoption.
Adding momentum is India’s vibrant tech ecosystem, fuelled by thriving exports and a burgeoning deep-tech startup landscape. Indian developers rank among one of the largest contributors to platforms like GitHub, reflecting a strong base for innovation and collaboration. When paired with its robust digital public infrastructure, this ecosystem provides a strong foundation for emerging technologies to flourish.
India also stands as one of the world’s largest markets for AI, backed by the second-largest AI talent pool with 4,20,000 professionals—manifolds larger than in most nations. As global players invest in the genAI race, India’s vast tech talent and rapidly growing domestic market position it as a pivotal force in the expanding landscape.
The question for businesses, therefore, is not whether to embrace this wave, but how to do so effectively. For Indian enterprises to lead in this generational shift, a strategic pivot is necessary. Here’s a roadmap to move from being genAI-ready to genAI-first.
● Shift from PoC to production: Focus on high-impact use cases with clear returns. Collaborating with disruptors and scaling successful pilots can accelerate this.
● Build talent and partnerships: Address talent shortages through continuous accelerated upskilling initiatives, partnerships with smaller firms and collaborations with academia to nurture expertise.
● Enhance infrastructure: Strengthen data governance frameworks to ensure security, accessibility and compliance. The industry is driving accessibility to affordable compute resources. The Telangana AI Mission offers startups time on CDAC’s AI supercomputer AIRAWAT-PSAI; a recent partnership unlocked $200,000 in compute credits for deep-tech startups. Future efforts include collaborating with INDIAai Mission to set up 10,000 graphics processing units nationwide.
● Foster innovation through collaboration: Large enterprises should engage with startups for co-innovation, while small and medium businesses can leverage partnerships with similarly-sized tech firms to jumpstart AI initiatives.
● Prioritise measurable outcomes: Clear success criterions and a focus on returns are critical to validate genAI’s value and sustain investments.
GenAI’s journey mirrors that of other transformative technologies: a cycle of inflated expectations tempered by realities of implementation. The question is not whether this technology can deliver value; it is how businesses can align their investments with realistic, measurable outcomes.
Those who succeed will not only navigate the hype, but also emerge as leaders in the AI-driven economy. For these organisations, AI will not be a costly experiment but a cornerstone of sustainable growth.
(Views are personal)
Rajesh Nambiar | President, Nasscom
