Harrick Vin, CTO of TCS, emphasized the challenges of building an effective organizational strategy for AI adoption, which requires collaboration among diverse teams. He noted that low AI adoption rates are often due to misalignment with organizational needs and insufficient knowledge on driving structural changes. TCS maintains a steady R&D expenditure to address these challenges and improve AI deployment across industries, highlighting innovations in sensing technologies, quantum computing, and other areas.
Harrick Vin, the global chief technology officer of Tata Consultancy Services (TCS), highlighted that establishing an effective organizational strategy for artificial intelligence (AI) is a significant hurdle for businesses. In an exclusive interview with Mint, he emphasized the importance of fostering collaboration among various teams, which is essential for creating a cohesive AI strategy. The challenge lies in aligning different teams, each with unique needs and philosophies, to ensure a unified approach toward AI maturity.
Vin explained that the low adoption rates of AI stem from inadequate alignment with company requirements and insufficient knowledge on implementing organizational changes to transition AI from experimental use to value-driven production. TCS has maintained a consistent research and development (R&D) expenditure at approximately 1.2% of its annual revenue, which stood at ₹2,751 crore in FY24, aimed at supporting these necessary developments in AI technology.
To bolster AI advancements, TCS is strategically investing in R&D to tackle emerging challenges. Vin elaborated that their R&D focus is on innovative technologies and the future of work, aiming to address areas like agricultural development, waste management, robotic automation, and more. He also identified key innovation areas in the tech landscape, including sensing technologies, quantum computing, data management, and advanced communications systems like 6G.
Highlighting successful applications of TCS’s R&D initiatives, Vin mentioned projects such as leveraging hyperspectral imaging for city planning and utilizing local AI models in connected vehicles to assess driver fatigue. The effectiveness of these innovations is reliant on the evolution of AI models and the correct application of these models in appropriate contexts.
Vin further clarified that traditional AI models are predictable and efficient, while generative AI models offer adaptability but also introduce challenges due to their non-deterministic nature. Identifying the correct contexts for deploying each type of model is a significant aspect of navigating the complexities presented by AI.
Despite the current hype surrounding generative AI, its direct impact on revenue for tech exporters has yet to align with expectations. As of December 19, Accenture reported generating $500 million in revenue from generative AI within the last quarter, while TCS revealed 600 active generative AI projects without disclosing specific revenue figures for this segment. This indicates potential for growth in AI revenue for TCS, even amidst a steady R&D investment trajectory.
The topic of organizational strategy in AI adoption highlights the complexities businesses face when integrating AI technologies into their operations. For effective implementation, companies must adapt their structures and processes, ensuring collaboration among diverse teams. As organizations strive to leverage AI capabilities, understanding the balance between value creation, complexity, and associated risks becomes crucial. The insights from TCS’s CTO underscore the necessity of tailored strategies that align with corporate objectives and industry dynamics.
In conclusion, effective AI adoption hinges on the development of a robust organizational strategy that prioritizes collaboration and alignment among teams. TCS exemplifies a consistent research approach aimed at overcoming technical challenges in AI integration. Continued investment in R&D indicates a commitment to enhancing AI capabilities while navigating the complexities associated with generative AI. As businesses seek to accelerate their AI initiatives, understanding the distinctive advantages and limitations of different AI models will prove essential for achieving substantial growth and revenue impact.
Original Source: www.livemint.com
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