Key facts
- Indian enterprises are rapidly advancing in AI adoption, embedding it into core operations.
- Measurable returns are being seen in customer service, software development, analytics, product engineering, and innovation.
- Challenges to deeper AI integration include legacy data infrastructure, unequal access to computing resources, and sustainability concerns.
- Technical debt and the capital-intensive nature of infrastructure investments are significant blockers.
- Revenue-generation use cases for AI are less mature, requiring fundamental business model and workflow changes.
- Only 6-8% of AI projects reportedly achieve their intended business outcomes.
Indian enterprises are rapidly advancing in artificial intelligence adoption, with a growing number embedding AI into the core of their operations. Functions such as customer service, software development, analytics, product engineering, and innovation are already delivering measurable returns, according to industry leaders speaking at The Economic Times AI Vantage Roundtable.
Sandhya Arun, chief technology officer at Wipro, noted that software development life cycles are transforming, leading to faster, better, and cheaper outcomes. However, she also highlighted the need for a significant mindset shift, as technology is increasingly viewed as a colleague rather than just a tool. Balaji Thiagarajan, chief technology and product officer at Flipkart, added that the skill set required by knowledge workers is fundamentally changing, and organizations must address staff concerns about future work and potential displacement.
Panelists identified several challenges hindering deeper AI integration. Daisy Chittilapilly, president of Cisco India and South Asia, stated that many organizations are attempting to build a "21st-century AI economy on a 20th-century infrastructure," creating a gap between ambitions and execution. Technical debt is a major blocker, making AI adoption a capital-intensive conversation focused on infrastructure investments, equitable access to compute resources, and energy sustainability. While productivity benefits are visible, revenue-generation use cases remain less mature and may require fundamental rethinking of business models.
Amiteshwar Seth, senior vice-president and global delivery head of AI and data at Cognizant, emphasized that AI amplifies existing organizational capabilities and weaknesses, meaning messy data can lead to bigger problems. He cited industry estimates suggesting only 6-8% of AI projects achieve their intended business outcomes, often failing at the proof-of-concept stage due to poorly defined objectives, integration challenges, and underestimated operational costs.
Mritunjay Singh, chief operating officer at L&T Technology Services, described the current AI wave as more disruptive than previous technology transitions, with the potential to reshape business economics, cost structures, revenue models, and profitability. Lakshminarayanan Ramalingam, chief operating officer at Quest Global, argued that AI adoption should be measured by material changes in business models, cost structures, and new revenue streams, not just pilot projects.
Regarding India's specific opportunity, Chittilapilly suggested that the country's strength lies in developing domain-specific applications and smaller language models tailored to industry needs, rather than solely focusing on frontier AI models. Thiagarajan likened AI to a once-in-a-generation opportunity for India to leapfrog, similar to its transition to widespread mobile connectivity. Kishore Alva, president and executive director of Adani Group in Karnataka, stressed that AI infrastructure should be treated as a long-term strategic investment, considering factors like power availability, renewable energy, water, and land. Ramalingam also advocated for India to develop its own large language models to avoid royalty payments and to pursue manufacturing technology for chip making.