Here&#8217;s How India Needs To Prepare For The Age Of AI
Has AI made an impact in India? According to the latest report titled Leveraging AI And Robotics For India’s Economic Transformation by PwC, about 68 percent of Indian business decision makers believe that AI will help their business in ways such as boosting productivity, generating growth and addressing societal issues. Machine learning solutions were also listed at the very top across every industry for having the potential to automate repetitive jobs. Sudipta Ghosh, partner and leader, data and analytics at PwC India, said, “While AI holds the potential to truly transform our lives as individuals and enterprises, its growth and adoption are wholly dependent on overcoming challenges related to reducing costs, securing the right talent and data, and addressing concerns around privacy and trust”. India is ready for an AI-led economic transformation, and there is a lot of potential for companies to set up AI-focused innovation centres in India. Here are some of the key findings from the report: India is seeing a boom in the AI-focused startups space which are in turn seeing a good investor response. With ML becoming a mainstay and the novelty factor wearing off, startups should start differentiating their offerings from the market on dimensions like ease of use, interoperability, robustness and support to be competitive When it comes to AI adoption in India, 36 percent large financial establishments have already invested in emergent technologies and around 70 percent plan to embrace it in the near future Financial sector boasts of the highest AI use cases in AI and ML. Establishing data access frameworks and guidelines for open application interfaces from financial institutions will act as enablers for increased adoption of AI in the sector Environment, cybersecurity, national security and defense and developing accessible technology for the differently-abled, are some of the sectors where AI could play a pivotal role in India Top Challenges In India’s Growth Story While there is a lot of enthusiasm around the AI growth trajectory in India with numerous developments in the field of AI, ML and robotics, there are still a lot of challenges to be tackled. Even though Indian startup founders are seeing institutional support from the Indian government, there are still a lot of implementation issues to overcome. Overcoming The Cost Issues For Implementing AI-based Solutions: AI-based solutions and products are costly to train. The technology lacks maturity and is yet to give a solid return on investment in many sectors. Besides, large companies are still grappling with the culture change that is required to adopt AI. Lack Of Talent: India’s talent crunch problem has been well-chronicled, and according to recruitment startup Belong.co, roles in data science and data engineering are at the intersection of maths, statistics and programming, which usually isn’t a part of the engineering curriculum in Indian universities. Students passing out of Tier-I academic institutions like IITs and IIITs end up specialising in disciplines in sub-topics of AI like statistics and information retrieval. As a result, only less than two percent of professionals who call themselves data scientists or data engineers have a PhD in AI-related technologies. Lack Of Data Conundrum: Given that data is the new oil, big tech giants are evolving into data oligarchs, many of them even buying out companies for their data. For example, Amazon’s acquisition of Whole Foods was motivated by data. Back home, the Flipkart-eBay partnership was driven by the need for a large consumer base wherein the deal enabled eBay sellers outside India access to Indian consumers. Lack Of Innovation: According to the Global Innovation Index 2017, India ranks 60 (for the last few years, it has been continuously improving its position) but is yet to catch up with superpowers like China, Japan and US. According to a news report, India filed 1,423 international patents in 2015-16. Top MNCs like IBM and Microsoft filed the majority of patents (70 percent) while academia filed around 30 percent. Lack Of Strong Business Model: India Inc has traditionally not been product-driven and it can be hard to crack the code of building the right product that tackles industry-specific problems. It’s not possible for all AI startups to scale their products, let alone stand the test of time. As Adarsh Natarajan, founder of AIndra had said, the published results in the field of AI may not translate into in-field results. “In a nascent field like AI, working without help of prior literature is quite hard and can be challenging to deal with at times,” he said. Understanding Importance Of Research: It is important to have a solid understanding of current research and be able to build products with that knowledge. Involvement of potential customers in order to understand their problems and to get their data is also important. Access to data is the key. Once you have the data, it has to be cleaned, structured and labeled. Without data you cannot train your models. And if your data is not of sufficient quality, you need a lot of capital and time to fix it with human labour. Outlook The AI Task Force recently released their roadmap which was panned for its lack of vision. However, Ghosh said that the Government too had a favourable attitude towards the use of AI to meet these goals. AI is expected to create new areas of economic opportunity and wealth creation, which will be an ingredient in retaining key sectoral competitiveness and, in turn, jobs.
Has AI made an impact in India? According to the latest report titled Leveraging AI And Robotics For India’s Economic Transformation by PwC, about 68 percent of Indian business decision makers believe that AI will help their business in ways such as boosting productivity, generating growth and addressing societal issues. Machine learning solutions were also […]
India is seeing a boom in the AI-focused startups space which are in turn seeing a good investor response. With ML becoming a mainstay and the novelty factor wearing off, startups should start differentiating their offerings from the market on dimensions like ease of use and interoperability.
["AI Task Force", "NITI Aayog"]
["AI Task Force", "data science", "Go", "API", "machine learning", "AI", "ML", "RAG", "analytics", "Rust", "R", "NITI Aayog"]
["AI", "machine learning", "ML", "data science", "analytics", "RAG", "R", "Go", "Rust", "API"]
Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.