Introduction to AI Adoption in Indian Enterprises
Artificial intelligence (AI) has been a buzzword in the Indian business landscape for several years now. As the technology continues to evolve, enterprises are recalibrating their AI strategies to focus on measurable returns, industry-specific use cases, and governance challenges. In a recent webinar hosted by S&P Global Market Intelligence and Accenture, executives outlined the shifting landscape of AI adoption in Indian enterprises.
Measurable Returns and Industry-Specific Use Cases
According to Justine Iverson of S&P Global Market Intelligence, the most consistent theme in AI adoption is the focus on measurable returns. Enterprises are no longer satisfied with proof-of-concept projects and are instead demanding clearer ROI and top- and bottom-line impact. This shift is reflected in the increasing number of AI mentions in earnings calls, which rose by 4.5% from Q3-Q4 2023 to the end of last year.
Francis Hintermann, Global Lead of Research at Accenture, reinforced the ROI theme, arguing that the narrative is expanding beyond horizontal productivity use cases to verticalization, where AI is applied to core industry value chains. He cited an Accenture survey of CXOs, which stated that 78% emphasized revenue growth as the priority in coming years. As an example of industry-specific impact, he pointed to life sciences, where AI can accelerate and reshape drug discovery and R&D processes.
Governance Challenges and Partnership Ecosystems
Despite the growing focus on measurable returns, governance challenges remain a significant concern for Indian enterprises. Elena Tesoni, who leads strategy and business transformation for S&P Global Market Intelligence, emphasized the importance of trust and governance in AI adoption. She outlined two main lenses used by S&P Global Market Intelligence: trust and governance, and human-in-the-loop decision-making.
Iverson also addressed hallucinations and guardrails, describing an approach of tightening model boundaries and grounding outputs in trusted data. She encouraged validation by users and emphasized the need for human-led decision-making in AI adoption.
Upskilling and Reskilling for AI Adoption
Hintermann warned about the risks of shadow AI, where employees use personal accounts when enterprise versions are unavailable, creating risks related to intellectual property, responsibility, and ethics. He emphasized the need for executives to provide sanctioned tools and pair them with upskilling and reskilling to close the growing skills mismatch.
For more information on upskilling and reskilling for AI adoption, visit our resources on AI training and development and AI skills mismatch.
Conclusion
In conclusion, Indian enterprises are recalibrating their AI strategies to focus on measurable returns, industry-specific use cases, and governance challenges. As AI adoption continues to accelerate, it is essential for businesses to prioritize trust and governance, upskilling and reskilling, and human-in-the-loop decision-making. By doing so, they can unlock the full potential of AI and drive business transformation in the years to come.
For more information on AI adoption in Indian enterprises, visit our resources on AI adoption in India and AI strategies for Indian businesses.