In a recent episode of the AI Business podcast, Kunal Purohit, Chief Digital Services Officer at Tech Mahindra, delved into the intricacies of scaling AI in the enterprise. With a focus on the opportunities and challenges presented by artificial intelligence, Purohit outlined key factors hindering rapid deployment and shared insights on the common mistakes enterprises make in adopting AI.
Tech Mahindra's Approach to AI and Digital Transformation
Purohit wears two hats at Tech Mahindra, leading initiatives to harness emerging technologies for creating innovative solutions while also contributing to the establishment and scaling of new businesses within the organization. His primary goal is to facilitate the transition from traditional operating models to digital and cognitive operating models, leveraging the power of AI for faster outcomes and increased client satisfaction.
The Evolution of Digital Transformation with AI
The conversation delved into the evolving landscape of digital transformation, where enterprises are increasingly realizing the untapped potential of their data. Purohit highlighted the shift from automating processes and creating scalable applications to harnessing AI for personalized customer experiences, enhanced service delivery, and improved predictability across various business functions.
Stages of AI Maturity in Enterprises
Purohit discussed the four stages of AI maturity that enterprises typically traverse. Starting with automation, the journey progresses through intelligent automation, the integration of AI techniques into business value chains, and finally, the exploration of generative AI. He emphasized that most enterprises currently find themselves between stages two and three, with some forward-looking companies venturing into generative AI.
Challenges in Scaling AI and Potential Solutions
The conversation pivoted to the challenges faced by enterprises in scaling AI. Purohit identified several factors, including data culture, risk aversion, technological complexities, and the human element. He stressed the importance of finding champions within organizations, fostering a data-driven culture, and overcoming the fear of experimentation.
To address these challenges, Purohit introduced Tech Mahindra's Generative AI Studio, allowing enterprises to take incremental steps in understanding the power of generative AI through experimentation without significant upfront investment.
Real-world Examples of AI Implementation
Purohit shared examples of AI use cases, both horizontal and vertical, illustrating how enterprises are leveraging generative AI. From enhancing knowledge search and management to specific applications in industries like oil and gas, the discussion showcased the transformative impact of AI on business processes.
Common Mistakes and Advice for AI Implementation
Drawing from Tech Mahindra's experiences, Purohit highlighted common mistakes made by clients in their AI journeys. These included delayed starts due to fear of responsibility, underestimating the effort required for successful outcomes, and not grasping the importance of quick, well-implemented initial steps. He emphasized the need for clients to engage with technology that has been adequately trained and tested.
Tech Mahindra's Startup Incubator – Garage4.0
The conversation concluded with insights into Garage4.0, Tech Mahindra's startup incubator. Purohit explained the selection criteria for startups, focusing on creating ventures that operate independently and eventually contribute value either by monetization or integration back into Tech Mahindra's services.
In summary, Kunal Purohit's discussion shed light on the evolving landscape of AI in enterprise settings, emphasizing the importance of overcoming challenges through a balanced approach that combines technology, culture, and strategic experimentation.
