"The adoption of AI is a function of not just its availability but the maturity and readiness of the verticals," says Dr. Prashant Pradhan, Chief Developer Advocate, IBM India/South Asia
Dr. Prashant Pradhan, Chief Developer Advocate, IBM India/South Asia, started his career as a scientist in IBM. Since 2001, Dr. Pradhan has dabbled in several key transformation projects at IBM, notably smarter planet solutions, IBM Watson and IBM Cloud. During his interview with Shubhra Rishi, Associate Editor – CIO&Leader, he discussed the future of Watson globally and in India, which has evolved from being an IBM Research project to the world’s first and most-advanced AI platform. The company is conducting highly domain specific work and developing industry specialized AI in areas such as healthcare, financial services, among others.
What is the real promise for AI?
The true impact of AI is in the core business of an organization. Wherever expertise is short and information is being generated at a constant pace, AI will have a huge impact. In the enterprise context, AI will enable augmenting human expertise and codify intelligence, which is being seen as the biggest transformation that will happen through AI.
Is the India market ready for a product like Watson and the potential that it has?
By design, artificial intelligence is one of the most relevant technologies of the future. From an enterprise perspective, AI has a number of applications in the banking, healthcare and legal sector. Watson was built with the need to create a completely new computing paradigm. Currently the data that organizations are creating adds a lot of value; however they can't consume all of it, and there aren't systems that can continuously learn and handle unstructured data, which is becoming a greater percentage of new data that is being created.
Take for instance, the research on Cancer is over 15 million pages of text and new knowledge is continuously generated. For example, there are over 40,000 new studies and research that has clues to solving complex types of Cancer. To make sense of unstructured data and create domain-specific knowledge, you need AI. With Watson, our motive is to solve these hard problems which fundamentally cannot be solved unless you have the means of highly sophisticated technology. For us, the design point shifts to looking at these opportunities. This fits very well in cases which deal with expertise being less widely available and scale is very high.
India is characterised by issues in different industries where scale is very high and expertise is very limited. For instance, in verticals such as healthcare, we simply can't produce enough oncologists to accelerate cancer-related research or education, where we don't have the mechanism to deliver personalized education. Therefore, from a market position perspective, there is no debate that this business in India is going to be exceedingly important. The adoption of AI is a function of not just availability of this technology, but the maturity and readiness of the verticals as well.
Let me give you a scenario, I was once evaluating AI start-ups as an expert on an investment panel. There were over 70 start-ups pending for evaluation. We then narrowed them down to 7 or 8. Finally, the few AI start-ups we chose were on the basis of their maturity, understanding of business problems, and enduring a viable business model. Therefore, it is very clear that AI in India is still at an evolutionary space.
How do you plan to leverage Watson via start-ups?
Initially when we introduced Watson and API to the development community -- the interest was from a technology point of view. They were able to work on APIs and get the maximum utility out of it. Our maturity in the start-up space is evolving in two dimensions.
Today, our discussions with start-ups are more evolved. They are no longer saying, “Let's jump on to the coolest technology.” Instead, they want to focus on getting the business context right. At IBM, we realize that we can add value to that discourse and indulge in deeper conversation with start-ups. We work with some of the largest companies in every vertical. We try to make sure that they get the right platform – work alongside us – and provide a grounded environment to test and prove that their ideas can have a long ranging impact on businesses.
Do you see the potential of Watson in the government vertical?
Government is a very important vertical for us. For education, agriculture, healthcare, and commerce – all these elements are tightly linked to one another. Take for instance, smart cities; the focus is on the infrastructural elements of the smart cities. What’s the right sequence to actually to turn cities smart? Some may say that it is about planning, education, skilling, and creating jobs, and not so much about infrastructure. But I’m not getting into that debate. Organically, a trend will evolve on the basis of dynamics. Smart cities are evolving in a particular direction. But it is also clear that if the end goal is to impact lives of citizens, then this kind of technology is going to touch all the phases.
IBM, globally, has done a lot of work with schools, broadly in the area of skilling, and it is crucial for mass preparation of skills. Using AI, based on continuous feedback, you can be precise about the career pathway that is best suited for a particular child. In fact, applying AI in a close loop fashion has huge implications on how you can optimally skill an entire generation of people. Maybe the maturity level of securing a wide-range impact in the government space is in its early stages. The industry is also catching up in the usage of AI.
What role is Watson going to play an important role in accelerating hybrid cloud adoption and ensuring the GRC is taken care of?
In the context of hybrid cloud, the role of AI is very precise. For a company, the important decision is to administer the right public cloud policy that will meet all the compliance requirements. IBM, by virtue of our diverse clients, has to be spot on in terms of the advising companies on hybrid cloud transformation. For instance, we are helping large banks take a very well-informed, compliant-ready transition to hybrid cloud, such that they can benefit from the fundamental attributes of the cloud at the same time, do not compromise security, data privacy and regulatory guidelines.
Watson is designed to make sure that the insights developed via AI training with enterprise client data, are only made available to that client. We don't train a generic AI. This was a very explicit design point. There are specialised teams that we have –who engage with clients --to make sure that the transition to hybrid cloud is fully compliant. It was a deliberate design point.
Can you speak about how Watson applies to the digital transformation drive in organizations?
AI is not about how you are engaging but it is also about mass personalization. If your systems are not smart enough, you will still need a human to support the system, you still haven't got anything out of your digital program. The opportunity for Watson in any project from an enterprise context is very strong. You have the opportunity to replicate your best agent. If 40% of engagement can be done via AI or assisted by AI, that's a significant jump. Enterprises are adopting the tech and it is a slow process. We still have to find a clear anchor on how to introduce AI and we have to build on that. It won't happen overnight. When you are switching from a labour-intensive engagement to an AI-powered engagement, the transformation has to be done with care.