India AI Report Flashcards
NEWS
The report AI in India – A Strategic Necessity is published by BCG X, the AI and Digital Transformation unit of BCG, in collaboration with IIM-Ahmedabad’s Brij Disa Centre for Data Science and Artificial Intelligence (CDSA). The report seeks to assess the level of AI implementation within Indian enterprises and their achievements in translating it into tangible business outcomes. The report examines the AI maturity of organisations depending on how they have harnessed the technology’s usage. The findings are based from an examination of 130 firms spanning the Banking, Financial Services, and Insurance (BFSI), Consumer Goods (CG), and Industrial Goods (IG) sectors. This research is supplemented by comprehensive interviews and surveys carried out with CXOs across organizations of varying sizes, including large, medium, and small enterprises.
As the usage of AI is gaining momentum, the technology is poised to bring about a transformative effect on economies, societies, and overall civilization. In India, the effective integration of AI has the potential to contribute as much as 1.4 percentage points to the annual growth of real GDP. The report points out that a notable portion of participants in the Banking sector, along with a smaller subset of companies in Consumer Goods and Industrial Goods, demonstrate a high level of AI maturity that aligns with global standards. The companies have been categorized into 4 groups based on their maturity level— Leaders, Steady Followers, Leapfroggers and Laggards. Interestingly, leadership status was attributed to 11% of the companies who are constantly challenged by 9% of leapfroggers. Laggards account for two-thirds of surveyed participants with limited exposure to AI.
HIGHLIGHTS
The report also pointed out the skill-gap that India is going to face in future. In fact, should the top 500 listed companies in India prioritize AI as a strategic objective, they would require a minimum of 25,000 to 30,000 proficient AI-ML practitioners within the upcoming 3-5 years. Attaining AI maturity necessitates substantial training and enhancement across areas such as data engineering, enterprise architecture, product management, design thinking, and domain knowledge.
The report also touches upon the roadmap aimed at enhancing AI maturity, with a specific emphasis on an organization’s existing level of maturity and its corresponding industry.
RELEVANCE
The report is a practical guide for Indian enterprises to accelerate their AI maturity. The report presents the most recent insights into the influence of AI on organizations, along with exemplary real-world experiences of AI-driven transformations. It uncovers numerous positive discoveries and offers them to the reader for comprehension.
KEY TAKEAWAYS
Effective incorporation of AI by Indian enterprises has the potential to consistently contribute around 1.4 percentage points to the growth of the real GDP
On the whole, two-thirds of Indian companies are falling behind in terms of AI adoption and maturity
There is a difference between leaders and laggards when it comes to investing in AI technology. Leaders first prioritize the use-cases and then decide the optimal choice of technology while it is just the opposite for laggards
Indian BFSI companies, especially banks and emerging NBFCs, exhibit remarkably elevated levels of AI maturity, comparable to global leaders
Leaders acknowledge that the success of AI adoption hinges on several factors of which 70% stems from the interplay of individuals, organizations, and processes
The leading 500 Indian companies would necessitate a minimum of 1 million hours for training in enhancing the skills of mid and senior-level management concerning the business dimensions of AI and digital transformation
How is Global AI Currently Governed?
India:
NITI Aayog, has issued some guiding documents on AI Issues such as the National Strategy for AI and the Responsible AI for All report.
Emphasises social and economic inclusion, innovation, and trustworthiness.
United Kingdom:
Outlined a light-touch approach, asking regulators in different sectors to apply existing regulations to AI.
Published a white paper outlining five principles companies should follow: safety, security and robustness; transparency and explainability; fairness; accountability and governance; and contestability and redress.
US:
The US released a Blueprint for an AI Bill of Rights (AIBoR), outlining the harms of AI to economic and civil rights and lays down five principles for mitigating these harms.
The Blueprint, instead of a horizontal approach like the EU, endorses a sectorally specific approach to AI governance, with policy interventions for individual sectors such as health, labour, and education, leaving it to sectoral federal agencies to come out with their plans.
China:
In 2022, China came out with some of the world’s first nationally binding regulations targeting specific types of algorithms and AI.
It enacted a law to regulate recommendation algorithms with a focus on how they disseminate information.
What are the Differences Between AI, ML and DL?
The term AI, coined in the 1950s, refers to the simulation of human intelligence by machines. AI, ML and DL are common terms and are sometimes used interchangeably. But there are distinctions.
ML is a subset of AI that involves the development of algorithms that allow computers to learn from data without being explicitly programmed.
ML algorithms can analyze data, identify patterns, and make predictions based on the patterns they find.
DL is a subset of ML that uses artificial neural networks to learn from data in a way that is similar to how the human brain learns.
What are the Different Categories of AI?
Artificial intelligence can be divided into two different categories:
Weak AI/ Narrow AI: It is a type of AI that is limited to a specific or narrow area. Weak AI simulates human cognition.
It has the potential to benefit society by automating time-consuming tasks and by analyzing data in ways that humans sometimes can’t.
For example, video games such as chess and personal assistants such as Amazon’s Alexa and Apple’s Siri.
Strong AI: These are systems that carry on tasks considered to be human-like. These tend to be more complex and complicated systems.
They are programmed to handle situations in which they may be required to problem-solving without having a person intervene.
These kinds of systems can be found in applications like self-driving cars.
What are the Different Types of AI?
Reactive AI: It uses algorithms to optimize outputs based on a set of inputs. Chess-playing AI, for example, are reactive systems that optimize the best strategy to win the game.
Reactive AI tends to be fairly static, unable to learn or adapt to novel situations. Thus, it will produce the same output given identical inputs.
Limited Memory AI: It can adapt to past experiences or update itself based on new observations or data. Often, the amount of updating is limited, and the length of memory is relatively short.
Autonomous vehicles, for example, can read the road and adapt to novel situations, even learning from past experience.
Theory-of-mind AI: They are fully adaptive and have an extensive ability to learn and retain past experiences. These types of AI include advanced chat-bots that could pass the Turing Test, fooling a person into believing the AI was a human being.
A Turing test is a method of inquiry in AI for determining whether or not a computer is capable of thinking like a human being.
Self-aware AI: As the name suggests, become sentient and aware of their own existence. Still, in the realm of science fiction, some experts believe that an AI will never become conscious or alive.
What are the Applications of AI in Different Sectors?
Healthcare: It aims to enhance diagnosis accuracy, enable personalized treatment, improve patient outcomes, streamline healthcare operations, and accelerate medical research and innovation.
Recently, the Indian Council of Medical Research (ICMR) issued a guiding document- “The Ethical Guidelines for Application of AI in Biomedical Research and Health care”, which outlines 10 key patient-centric ethical principles for AI application in the health sector.
Business: AI in the business sector helps optimize operations, enhance decision-making, automate repetitive tasks, improve customer service, enable personalized marketing, analyze big data for insights, detect fraud and cybersecurity threats, streamline supply chain management, and drive innovation and competitiveness.
Education: AI could open new possibilities for innovative and personalised approaches catering to different learning abilities.
IIT Kharagpur has collaborated with Amazon Web Services to develop the National AI Resource Platform (NAIRP), the future possibilities of which include monitoring eye movement, motion and other parameters for better teaching and learning.
As demonstrated by ChatGPT, Bard and other large language models, generative AI can help educators and engage students in new ways.
Judiciary: It is used to improve legal research and analysis, automate documentation and case management, enhance court processes and scheduling, facilitate online dispute resolution, assist in legal decision-making through predictive analytics, and increase access to justice by providing virtual legal assistance and resources.
SUVAS (Supreme Court Vidhik Anuvaad Software): It is an AI system that can assist in the translation of judgments into regional languages.
This is another landmark effort to increase access to justice.
SUPACE (Supreme Court Portal for Assistance in Court Efficiency): It was recently launched by the Supreme Court of India.
Cybersecurity/Security
