One phenomenon that knocked the wind out of everyone’s lungs in 2023 was artificial intelligence! Right from chatbots to AI tools for video and content creation, it has caught everyone’s fancy.
India wasn’t far behind, as several companies and apps mushroomed, reflecting the global scenario. A NASSCOM report shows that more than 60 Indian startups started shop in April and June 2023. The optimism surrounding AI has rubbed off on investors, too.
NASSCOM’s India Data Science & AI Skills Report shows spending on AI in India topped $3 billion in 2022 and is expected to jump to $4.2 billion by 2024.
In fact, Goldman Sachs sees investments in the AI sector topping $160 billion globally by 2025, with India having an advantage of “resilient growth and strong demographics,” which could attract investments from global investors and corporations.
“The intricate dance of AI and ML capabilities, coupled with multi-channel integration, has propelled businesses toward a future where agility and scalability are paramount,” said Abhijit Dutta, Chief Strategy Officer of Hostbook, a cloud-based accounting services firm that also offers automated business solutions.
Despite the rapid growth of AI companies, challenges remain when it comes to integrating AI into traditional corporate processes.
In this article, StartupTalky speaks to a few AI consulting companies that shed light on AI integration in India.
AI Awareness
Data Mining Strategy
Identifying Use Cases
AI Training
AI Awareness
The top challenge faced by AI companies is to dispel myths surrounding AI integration, said Agam Chaudhary, founder and CEO of Two99, a collective of agencies with a focus on advanced e-commerce, technology, and marketing.
Explaining how they tackle the problem, Chaudhry said, “We show them it’s not a sci-fi flick but a practical tool that can make their lives easier. We bring out the success stories custom-made for their industry. We lay it all out on the table—the good, the bad, and the ethical considerations. Building trust is crucial. We’re like AI consultants, working hand-in-hand with them, understanding their worries, and customizing solutions that fit like a glove,” Chaudhary said.
Based on client experiences, PwC had listed some myths that clients seemed to express about AI: ‘ businesses don’t need AI, and they are ‘too risky, to name a few.’
Over time, there seems to be a gradual attitudinal shift towards AI. A survey of 54,000 workers conducted by PwC in September showed that a third of respondents believe AI will help increase productivity and efficiency. More than a quarter said it would help them learn valuable new skills.
An AI survey by Uplekha found that 61% of Indian companies feel AI will make work more efficient.
Abira Dutta
Data Mining Strategy
Data mining is the cornerstone of AI and ML. AI heavily leans on vast sets of existing data and statistics to come up with a near-perfect AI model of content or analysis, which can then be applied to the problems in question.
According to E&Y India Chairman and CEO Rajiv Memani, India is the second largest generator of data after China, which is an added advantage for training AI models.
Yet, the availability of clean data sets has been an issue.
“All three aspects, i.e., clean data, relevant data, and a sufficient amount of data, are important. The models need sufficient data, and for financial risk use cases, they should cover historical data from at least 1-2 economic cycles. In the absence of such data, these AI and ML models produce suboptimal results and end up losing user confidence in using these models,” said Abhinava Bajpai, Co-founder and Head, Acies TechWorks.
The government is in the process of developing India’s comprehensive AI strategy, which involves building an India Dataset Platform and an AI Compute Platform.
Information and Technology Minister Rajeev Chandrasekhar recently elaborated on these, saying that the Indian dataset platform will be one of the largest and most diverse collections of anonymized datasets to train multi-parameter AI models. Meanwhile, the India Compute Platform will create a substantial graphic processing unit (GPU) capacity for enterprises to train AI models under a public-private partnership.
Muskaan Kapoor
Identifying Use Cases
In a bid to jump onto the AI bandwagon, companies are struggling to adopt specific use cases, AI experts said.
“I would say that you know, if organizations are looking at adopting AI, look at some of those achievable use cases, which they can then take right and partner with companies to achieve those,” said Rohit Yadava, Chief Operating Officer, MSys Technologies, which offers digitalization services to companies.
Echoing this view, Sairam Vedam, Chief Marketing Officer of Cigniti Technologies, says, “Our approach has always been to understand the existing data strategy of the company. Also, what is the existing automation strategy of the company because we are a born-testing quality engineering company? So, we look at AI applications through those two lenses. And then, as I said, educate, experiment, experiment in the sense of experiment on use cases.”
Another report by Deloitte outlined the use cases of AI across six major industries: consumer, energy, resources, and industrial; financial services; government and public services; life sciences and health care; technology; media; and telecommunications.
For instance, use cases within the consumer goods segment could include aiding content generation, trade promotions, creating new product prototypes, creating an immersive marketing experience, market intelligence through data access, on-demand customer support, and shopping assistants.
“Performing cost-benefit analysis of AI and ML models before implementing them is important for the continuous and persistent use of such models… The size of the business and revenue and cost impacts need to be considered before implementing AI and ML models,” said Bajpai from Acies Consulting.
AI Training
Training staff with AI know-how has now become imperative. This is apparent from the rise in demand for AI training and AI-related upskilling courses.
“It has become essential for executives to learn AI. Data science training is specifically helpful to train aspirants in AI, and by ensuring the holistic development of executives, these programs can become a game-changer in helping industries realise the true potential of AI,” said Piyush Arora, senior director of business strategy at AI-based learning platform Edvancer.
Edvancer has seen a 4x rise in applications for AI courses and a 100% increase in interview opportunities for students with data science and AI qualifications.
A NASSCOM report shows India is currently ranking 2nd in training and hiring AI talent in the world.
“A major portion of the future talent demand will come from the existing tech workforce through upskilling; learning curves are becoming shorter, and skills are becoming redundant in 18 months,” NASSCOM said, adding that “design thinking” is a key skill to implement AI and not merely the ability to build and run complex algorithms.
However, all this training comes at a heavy cost.
A study conducted by the Boston Consulting Group and the Indian Institute of Management-Ahmedabad estimated that just the top 500 Indian companies would require “at least one million hours of training.”
“Organizations must invest in significant upskilling of mid- and senior-level management on the business aspects of AI, digital transformation, ‘agile’ ways of working, and more. Companies have a choice to prioritize AI and adopt it or perish—and the nature of this technology is such that either scenario would come about very quickly,” the report said.
The top 500 listed companies would need at least 25,000–30,000 advanced practitioners of AI and ML in the next 3–5 years, including AI professionals, data scientists, data engineers, and enterprise architects, the report said.
Conclusion
AI maturity has been a buzzword in 2023, given the boom in AI and its peripheries. According to AI watchers and experts, this maturity will accrue over a period of time with enough use cases to innovate, experiment, and smartly apply collated data. However, the large boom in AI within the country has laid bare the talent and skills gap. Hence, training and upskilling pertinent AI skills must become a priority for companies going forward.
