The Biggest Challenges of AI/ML Solutions Slowing Down the Pace of AI Adoption

AI and ML products and services are emerging in India, as India is witnessing massive digitalisation and digital transformation among the industries. AI is transforming the operational, functional, and strategic landscape of several industries and their products, ranging from smartphones to surgical robots to aircraft. AI is one of the attractive sectors for the development and investment by the Government and other enterprises, during the last 5 years Indian Government has launched numerous initiatives to promote AI. For instance, in May 2020, the Indian government launched “INDIAai”, a national AI portal for India jointly developed by the MeitY, NeGD, and NASSCOM. “INDIAai” features AI resources such as articles, start-ups, investment funds in AI, resources, companies, and educational institutions related to AI in India.

However, with the advancement in AI technology, enterprises need to tackle with few challenges.

  1. Ethics and integrity issue with AI & ML

With the advent of advanced AI solutions, ethics and integrity issue have been a major concern among users. AL and ML solution works on the instances based on the data feed into it and its self-learning capacity, however, AI solution cannot always be trusted to be fair and neutral, and sometimes AI judgment are intangible to humans. There have been several instances, where AI and ML algorithm ignores the accuracy of the data and give an ambiguous result. For instance, in October 2020, during a football match (Scottish Championship – “Inverness Caledonian Thistle F.C.” vs. “Ayr United F.C.”) in Scotland, an AI-based camera mistakenly assumed lineman’s bald head as the ball and follow it through the match

According to The Alan Turing Institute[1]AI ethics is a set of values, principles, and techniques that employ widely accepted standards of right and wrong to guide moral conduct in the development and use of AI technologies”.

Tech companies, research institutes, and universities are working proactively to mitigate the ethics issue (misuse, abuse, and negative unintended consequences of AI systems) and to develop a trustworthy AI framework and guidelines for the ethical use of AI. ‘Institute for Ethics in Artificial Intelligence’, ‘Montreal AI Ethics Institute’, and ‘The Institute for Ethical AI & ML’ are a few institutes working towards the ethics issue in AI, these institutes are creating community, organizing events, and publishing content on AI ethics.

2. Huge investment and advanced IT infrastructure is required to enable AI solution

High implementation costs and poor IT infrastructure resists numerous organizations to implement AI solutions. AI processes a huge amount of data to give precise results, hence, it requires high-performing hardware. When it comes to ML and DL, a large set of data is trained, for instance, autonomous vehicles will require thousands of hours of video and millions of images to train the model, hence, to enable the same enterprises requires high performance computing.

In an order to drive successful AI strategy enterprises require a robust IT infrastructure and advanced computing systems, which are expensive and require huge capital. Also, the AI hardware requires regular maintenance and upgradation to drive a seamless operation. For instance, to transform a manufacturing plant from ‘manual operation’ to an ‘automated AI-enabled operation’ would require high capital investment— to integrate IIoT, high performance computing, and other components of industry 4.0.

The government is working towards helping the research institute with AI and Cloud infrastructure, for instance, in January 2020, NITI Aayog announced to set up “AIRAWAT”, a platform to boost AI and cloud infrastructure. Under AIRAWAT government aims to work on the challenges associated with the computing resources, going forward government will build an AI computing infrastructure that will help to accomplish the computing need of “Centres of Research Excellence” (COREs), “International Centers Transformational AI” (ICRAIs), and Innovation Hubs.

3. AI Expertise in India

Getting the right set of talent for AI and the digital program is difficult for enterprises. In India, there is a huge demand for AI professionals. Upskilling and reskilling professionals is the way forward to address the rising demand for AI experts in India. This will also help to bridge the gap between AI expertise supply and demand. Enterprises are exploring numerous ways to fill the talent gap in both the short and long term by hiring external talent, building in-house capabilities, and partnering (buying or licensing capabilities) with large technology firms for AI solutions.

A few of the initiatives for AI skilling taken by the Indian Government, technology firms, and industry body are listed below:

  • In June 2021, IIT Roorkee established “Centre for Artificial Intelligence and Data Science” with two new M.Tech programs in AI and Data Science
  • In November 2020, Telangana Govt partners with NASSCOM and Microsoft to upskill 30000 youth in AI
  • In November 2020, NASSCOM launched ‘FutureSkills Prime’ a digital learning platform, a joint initiative by MeitY and NASSCOM

4. Data security and privacy issues with AI solution

AI/ML/DL solution process a homogonous amount of data including confidential and personal data, which need to be secured to eliminate enterprises exposure to cyber risk and geopolitical risk. Customers are paying more attention to safeguard their personal data; the AI products need to incorporate security from concept to complete lifecycle, also, AI products should be capable of surviving an evolving threat landscape.

With the introduction of HIPPA, GDBR, and PDP bill, the data privacy issue is anticipated to be reduced, as these framework/regulations set up regulatory guidelines regarding collecting, storing and cross-border transfer of data.

To know more about AI and ML market, please read TechSagar, whitepaper on “Landscape of AI and ML in India”. This paper outlines the top AI and ML applications, opportunities, key development, government initiatives, and leading AI and ML challenges that enterprises must address:


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