PODCAST: AI for the Digital Enterprise
Episode 4: The Changing Colour of Innovation
Listening time: 11 minutes
The Changing Colour of Innovation
In the 4th episode of the series, Host Aruna Babu talks to Pritam Kanti Paul – CTO and Co-Founder of BRIDGEi2i Analytics Solutions about the pace and scale of innovation in an AI-led world. Tune in to hear Pritam’s thoughts on the consumerization of AI, the importance of leveraging the right building blocks to solve different kinds of problems, and the value in building an ecosystem of partners externally while fostering a culture of innovation internally. Don’t forget to subscribe and share!
Aruna: Hi there, you’re listening to AI to Impact by BRIDGEi2i, a podcast on AI for the digital enterprise. My name is Aruna Babu, and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business, technology, and user needs. In this series, I’m going to be sitting down with business leaders and AI and analytics experts who’ll share their views on what AI for the digital enterprise means and how do you make AI real for an organization.
Today I’m going to be chatting with a CXO – a brilliant mind with legendary people skills, Pritam Kanti Paul. He has over 17 years of analytics consulting experience across diverse areas and functions, and Pritam’s been a flagbearer of innovation throughout his career. He’s the CTO & one of the co-founders of BRIDGEi2i Analytics Solutions – one of the fastest-growing AI and data science firms in India.
Aruna: Hi Pritam, thank you so much for making the time.
Pritam: Thanks for having me here. I’m looking forward to this conversation.
Aruna: So, in our previous episodes, we’ve been discussing how AI programs need multi-functional teams and diverse capabilities to come together. What about innovation in the context of enterprises adopting AI? How do you think the color of innovation is going to change? Is it going to change?
Pritam: Well, it’s definitely changing. I mean, if nothing else, it’s about pace. It’s about scale. I think what we are observing is the rate of change at which technology is changing around us is really phenomenal. And, you know, innovation has to really catch up with the trend. On top of that, with the advent of open source, community sharing, the shelf life of any IP is also becoming really short. I think there’s a lot of excitement about what we call a consumer version of AI. And you know, what’s important, is to really kind of make a parallel on what it means for a digital enterprise. We have to make it real; we have to make it impactful, and most importantly, we have to make it simplified for enterprises to really utilize and understand the benefit out of it.
Aruna: Right. So, what are some models for innovation that enterprises can look at?
Pritam: If I look across industries, there’s so many different areas where innovations can happen. And with the changes that’s happening so fast, you cannot really catch up with everything. And if we look at BRIDGEi2i probably in a small way, it’s really a good sample of how the industry is evolving.
We we’re working with multiple functions, multiple industries, and hence the possibilities are huge. The most important thing that probably all of us have to do is really to kind of realize and acknowledge the building blocks or Lego blocks that builds up some of these use cases because use cases can keep on changing. But once you have a framework and building blocks, you can probably create and keep on innovating inside those frameworks. In our world, we call them AI accelerators; they are algorithms with the right AI technology around it. But, you know, we can bring them together in some way, in some proportions to actually solve multiple different types of innovation problems.
The second important thing that we are kind of also observing is about being open and creating an ecosystem for innovation. I think we have seen enterprises not trying to do everything themselves because there’s so much of innovation happening around you. It’s important to partner and leverage and be vigilant of what’s happening around you, because by the time you are building something, somebody else might have already built something even more impactful. And that whole ecosystem is what allows people not to reinvent the wheel, but be able to kind of leverage the best of the worlds. I think we have seen us being called the innovation partner for many of our clients and being leveraged in a similar way.
Aruna: Right. So you said it’s important to pick use cases. What do you think are some of the important use cases that AI is helping industries solve today?
Pritam: There are huge amount of areas where people are investing in and so many number of industries, each one of them have their own exciting areas that they are investing in. I’ll probably just touch up on 4 areas – but in no way do they cover everything, but probably something connected with the world I live in.
One of the big areas where I’m seeing innovation in AI being leveraged is about being predictive and reacting to changes very fast, and we are observing, you know, use of large scale anomaly detection, pattern detection being leveraged to actually create predictive alerts, and being able to kind of react really fast.
We have seen that being used in kind of preventing out-of-stock, being used for maintenance of assets. Even we have seen situations where people are using concepts like that to actually control financial breaches. The second big area that probably, you know, what we see around is on personalization. Personalization has been there for some time now. But with newer innovation in technology and data science, we are seeing far more deeper and almost 1-on-1 level personalization happening. And this is one area where probably e-commerce started, but all the digital enterprises are learning from it and leverage that in different ways in both offline and online worlds. And this is where you are trying to figure out the next logical touch: What would be the best communication at a particular time? What products might work for specific customers? I’ve also seen situations where we are leveraging some of these to design personalized products for individuals.
The third area, what I’ve seen would be being able to manage planning better for the industries. And planning is really a big exercise for any enterprise and you have so many different teams starting to plan for consumptions, or sales. And then there are multiple groups where you are planning for your brands/ availability of brands/you’re talking about shipment, or supply chain. Now, all of these are happening in pockets. And what AI is allowing us to do is to really bring all of them together under same fold.
And not only about, you know, kind of bringing them into the same fold, but also being able to react to changes, understand what changes, you know, how to become self-learning around some of them and also allow them to, for lack of a better term, creating like a digital twin for the whole business model that allows you to simulate certain input decisions like budgets and how that really impacts your overall planning. Last but not least, I think, you know, this whole idea of Siri, Alexa, it created a lot of excitement around how people want to converse with systems. And we have seen a lot of companies, a lot of innovations around, you know, how that conversational element can really be taken to industrial or enterprise world. And that’s where we have seen situations where, you know, sales conversations, you know, helping sales people to be far more effective or in collecting information from multiple people using AI. Or even, using conversational agents to provide important data at the right point are all being used. These are some of them. In no way are these exhaustive. You know, there’s so much happening around drug discovery, around trafficking, militaries. I just kind of picked up a few of them, which might be a little bit closer to what we do. And interestingly, the Lego blocks that I talked about sometime back, you know, these are probably the areas that are Lego blocks for us. In our world, we call them watchtowers, recommenders, optimizers, and converses. Still, they are all spaces that can really be deeply invested on.
Aruna: Very interesting, very important use cases. How do you suggest an enterprise should actually drive innovation in the context of AI?
Pritam: So it’s very interesting point. I think, you know, as a startup, this becomes a very, very critical point for us also because we have to innovate and we have to keep on reinventing ourselves. And I feel that the whole industry today is also a broader, bigger startup. So, being able to innovate as a whole, as an ecosystem, is really critical. And we believe that innovation is not about a team. All of us have a Labs team. You know, we might be calling them AI labs but you might be calling them something else. Now that’s a team which probably focuses on some of the most critical set of priorities for you. But innovation needs to be driven as a culture. And for that, probably almost everybody inside the organization in some way – incrementally or in a large way has to be part of innovation. And that is one of the area, you know, in today’s world, the use of hackathons, the use of innovation events where you bring in master classes together becomes very critical because that’s how you kind of create an excitement around innovation. And at BRIDGEi2i in particular, I think that’s one area our capability development initiatives really focuses on and brings all of those cultural aspects together to drive innovation. That whole aspect of being able to drive culture through innovation so that everyone is involved in it becomes critical in today’s AI industry.
Aruna: Alright. That was absolutely fascinating. Thank you so much, Pritam, for taking the time.
Pritam: Thank you very much.
Aruna: Thank you so much for listening to this episode of the AI to Impact podcast. It was such an interesting conversation with Pritam, the CTO of BRIDGEi2i Analytics. We talked about the pace and scale of innovation in an AI-led world, the consumerization of AI the importance of leveraging the right building blocks to solve different kinds of problems. And the value in building an ecosystem of partners externally while fostering a culture of innovation internally. If you found this interesting, please share it forward and do subscribe if you’d like to stay in the know. Thank you once again for tuning in. We’ll talk to you soon. Bye-bye.
The upswing in AI adoption and the impact of Digital initiatives in enterprise transformation have reached staggering heights, but there is still a lot of skepticism around the value realization of AI. BRIDGEi2i, a transformation partner to several large enterprises, has been spearheading an enterprise-wide movement on “Making AI Real.” In this series, we bring together reputed thought leaders, practitioners, and influencers of the industry as they discuss trends, predictions, and best practices on extracting tangible value from AI to embark on transformational journeys.
Pritam Kanti Paul
Pritam Kanti Paul, CTO and Co-Founder of BRIDGEi2i Analytics, is a Gold Medalist in his batch of Masters in Statistics at the Indian Statistical Institute Calcutta. He has over 17 years of analytics consulting experience in target marketing, pricing, credit risk, audit analytics, fraud detection, forecasting, spend analytics and market research. Prior to BRIDGEi2i, Pritam served HP as the Director of marketing, customer and e-commerce analytics and led teams focused on risk and financial analytics at GE.