With the Omicron strain now wreaking havoc, digital transformation has become the need of the hour. In the light of Covid-induced lockdowns and a stop-start economy, businesses are now looking at developing strategies not just to survive, but thrive. 

As per market research firm Statista, the global spending on digital transformation is expected to reach $1.8 trillion by 2022 and $2.8 trillion by 2025. In another report, 58 percent of businesses that shifted to digital early on could offer digitally connected products and services seamlessly, while that number is about 17 percent for recent adopters of digitisation. 

According to McKinsey, 67 percent of companies that pivoted to digital in the wake of the pandemic claimed to have a head start over competitors that didn’t.

Over the last one year, Indian organisations have seen an increase in data science and AI adoption. Industry experts said data science and AI can boost the national growth rate by 1.3 percent and add $957 billion by 2025 to India’s economy. However, the study showed India still lags compared to other developed countries. 

So, what contributes to the slow AI and data science adoption? What should Indian companies do to address this problem? 

High cost and dearth of talent 

One point which comes to mind instantly is cost and skill. There isn’t enough in-house talent within the national corporations, and the niche consulting companies almost always have rate cards in dollars. So, it becomes extremely challenging for Indian companies to afford them.

Parikshit Nag, cluster lead, advanced analytics & data science at HUL & Unilever South Asia

Ineffective leadership

I think most AI use cases need upper management sponsorship. The leadership teams need to know how to leverage ML in specific verticals. I think this first adopter group is lower in Indian corporates.

Ashwin Swarup, vice president at data science at Digite, Inc.

Lack of proven RoI 

One other factor contributing to the lack of AI and data science adoption in Indian organisations is the lack of proven RoI. The lack of a process around clearly measuring and communicating the impact of AI interventions on key success metrics serves as a deterrent for new AI investment.

Swati Jain, vice president L2, analytics at EXL

Lack of business use cases 

Incoherent leadership buy-in on the potential business impact the adoption in analytics & AI. This leads to a lack of business use cases around the impact of analytics & AI. Also, inadequate and inconsistent investment in right skilling creates the talent gap. This doesn’t help the cause of rising to the challenge of the MNCs and global tech companies attracting the brightest talents in the space.

Satyamoy Chatterjee, executive vice president at Analyttica Datalab Inc.

What’s the solution here? 

Need for AI and data science 

To increase adoption of AI and data science in India, we first need to ask if all Indian organisations feel the need for AI and data science. While native Indian digital organisations such as Nykaa, Ola, Zomato, may already be adopting AI and data science, there are many traditional Indian companies on the other end of the spectrum. The two key factors that will help us determine if companies first have a need and thereby drive adoption of AI and data science, include – context and competitive landscape, and lack of data availability and appreciation.

Sunil Mirani, co-founder and CEO at Ugam Solutions

Bolstering AI adoption in mid and lower-tier businesses 

I expect increased adoption in the middle and bottom tier driven either by market opportunities or market pressure. When that happens, just as it happened with IT adoption, the ecosystem will scale up to enable management buy-in to support talent needs and other requirements. 

Mani (Subramanian M S), ​​head of category marketing and analytics at Bigbasket

Good data management and governance practises 

Need for executive sponsorship has become a critical requirement for a top AI adoption. To enable adoption and sustenance, good data management and governance practises should be in place.

Suresh Chintada, CTO at Subex

Explainable AI (XAI)

Explainable AI (XAI) can help solve the AI scalability problem. It helps in converting black-box models to easily understandable insights with the help of XAI so that organisations can bring trust and reliability in their data and technology-based decisions. By ensuring unbiased and fair decisions, XAI aims to bring together technocrats, customers, and regulators to increase the adoption of DS and AI across their organisations.

Anirban Nandi, head of analytics (vice president) at Rakuten India

Building data-driven culture 

One way to improve things is to build an org culture that is more data-driven. This includes hiring top executives from organisations that have more data-driven culture; working on ‘low-hanging’ AI use cases that could provide substantial business impact; a data team that can work hand-in-glove with the business to consistently improve key business metrics; scaling up the use cases to different businesses or geography to strengthen the position of AI; and establishing a dedicated set-up like ‘labs’ for long-term initiatives.

Mathangi Sri, VP data science and head of data at GoFood

Solving problems for the common man 

We might not need a sophisticated self-driving car algorithm right now – we are not that mature as a market and don’t have that kind of infrastructure. Instead, we should be focusing on solving problems for the common man, say, an AI-enabled wellness tracker for livestock that can predict the key vitals and events. Once we have the use cases which solve our key problems – they will act as proof of concept, and our AI ML adoption will grow significantly.

Ritesh Srivastava, associate director of data science, digital and advanced analytics at Novartis

Other key solutions

A few recommendations for organisations to leverage data science and AI solutions in their businesses include replying more on pay-as-you-go cloud-based solutions and out of the box, AI services for businesses who have a limited budget for infrastructure development for data science and AI solutions or are not in the position to extensively hire data science talent.Businesses need to study the economics of the solution not just from a short term perspective, but evaluate the long term revenue generated by the solution. Also, organisations can partner with universities and research facilities to increase the funding for research and offer them to work on real-world/business use-cases that are of interest to the organisation. 

Aishwarya Srinivasan, data scientist at Google Cloud

The catalysts that can catapult the growth of DS and AI are relevant use cases, local data and technology partnerships, data and analytics translators, analytics advocacy, embedded implementation driving business results etc. 

Hari Saravanabhavan, vice president of global analytics at Concentrix 

This article is a collation of quotes by members of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.