Today, advancements in technology are disrupting industries and has necessitated companies to rapidly adopt the latest solutions for their day to day business. This is also now relevant for India, as a major global economy, to be at the forefront of such technology adoption. IBM, the global leader in technology, is playing a key role in using technologies like AI, cloud, blockchain & IoT to transform business, industries and the world. Anil Bhasker, Business Unit Leader, Analytics Platform-India/South Asia, IBM briefs about their innovations and solutions in a recent interaction with DT.
Q: Please give an overview of Watson AI's deployment in India.
A: BFSI, telecommunications, healthcare, FMCG and retail are some of the sectors in India that are witnessing high adoption of IBM Watson, our suite of enterprise-ready AI services, applications, and tooling. Preference of consumers for personalization seems to be the primary impetus for adoption of AI. Hence companies in sectors that are focussed on this KPI are quicker to adopt AI to gain edge over their competition. Unlike earlier technology cycles, we see India being at par with the rest of the developed countries with respect to technologies such as AI, ML and others. For example, a majority of insurance companies and banks in India have AI driven chatbots which are becoming the first point of customer interactions. State Bank of India is using Watson AI tools to get better insights into customers so they can personalize their experience, no matter what platform they use to access the bank – online, mobile, etc. And they’re doing it at mass scale. Leading hospitals and healthcare institutions have implemented AI-enabled Watson for Oncology & Watson for Genomics to help physicians provide patients with personalized, evidence-based cancer care.
Q: What will be the scope of Watson AI a decade from now look like?
A: After having experimented with AI and moved simple workloads to the cloud and committing to ‘random acts of digital’, we believe that businesses are now ready to move to Chapter 2. Chapter 2 of Digital and AI is about scaling and moving from experimentation to transformation. We see business are pursuing two distinct approaches to digital transformation: outside-in and inside-out. While an outside-in approach is largely driven by the market and demand for new digital services, an inside-out approach is about modernizing core systems and architecting their business for change.
Q: What are some key challenges in the adoption of AI as a technology with business in India?
A: In the words of my colleague Rob Thomas, General Manager, IBM Data and AI, while the first automobile was driven down the streets of Detroit in 1890, it would take another 30 years before Henry Ford streamlined production and made cars available to the mass market. Over the first hundred years of the self-propelled vehicle during 1750-1850, essential building blocks were established – standard components like the combustion engine, steering wheel, and axel. These building blocks enabled scale, which led to wider adoption. Consider, that in the first generation of vehicles, if a person wanted a means of transport, they had to design and fabricate every component.
The evolution of the auto industry is similar in many aspects to the currently nascent world of AI. Like the auto industry, in order for AI to flourish, organizations must adopt and embrace a prerequisite set of conditions or building blocks.
For example, AI requires machine learning; machine learning requires analytics; and analytics requires the right data and information architecture. In other words, there is no AI without IA (information architecture). These capabilities form the solid rungs of what we call the AI Ladder – the increasing levels of analytic sophistication that lead to, and buttress, a thriving AI environment.
The lack of proper data is one of the challenges for enterprises in adopting AI at scale. One set of enterprises want ready-to-use solutions and not building blocks. However, without the above-mentioned AI ladder approach & quality data repositories, it may not be as successful.
