“AI ought to be thought of less as an emerging technology, rather, it ought to be thought more as a co-pilot, a co-pilot that is augmenting.” 

Intro:
Welcome to the Agile Digital Transformation podcast, where we explore different aspects of digital transformation and digital experience with your host, Tim Butara, content and community manager at Agiledrop.

Tim Butara: Hello everyone. Thanks for tuning in. Our guest today is Nitin Mittal, award-winning Head of the AI Business at the leading professional services company Deloitte. Nitin has just published All in on AI: How Smart Companies Win Big with Artificial Intelligence on Harvard Business Review together with Tom Davenport. 

And we figured this would be the perfect opportunity to discuss this topic, along with some of the key insights from their book here on our podcast. So, Nitin, welcome to the show. It’s great having you here with us. Do you want to add anything here or shall we just go ahead with our discussion about winning big with AI?

Nitin Mittal: Well, thank you, Tim, for inviting me. I’m certainly looking forward to the conversation. And happy to share my perspectives and observation, and why I actually co-authored this book.

Tim Butara: Awesome. Then let’s right down to it. And my first question for you today is: how can artificial intelligence enable innovative and scalable business strategies and processes?

Nitin Mittal: What we have been observing progressively is that what started out as an emerging technology is becoming more and more mainstream. It’s becoming more and more mainstream because of a few things that are actually happening. One, we now live in a digital economy. In that digital economy practically every enterprise aspires to be a digital organization. And the very essence of a digital organization is the digital interactions, the experiences and how they actually enhance productivity and efficiency, as well as figure out new pathways to grow in this digital economy.

All that is now more and more powered by artificial intelligence. And that is literally leading a tsunami in the world of business. It’s also leading a tsunami in the popular press, as we have I think read about ChatGPT as an example. But ChatGPT is just the tip of the iceberg, and probably the one that is pertinent right now in our society and in people’s popular imagination.

But the broader topic of AI is something that is getting embedded in a scalable manner in many business processes, workflows, tasks, interactions, experiences that are created – and consequently, augmenting the workplace, augmenting the worker, and augmenting the work environment. All that is fueling the productionalization, the scaling and the ubiquitiseness of AI in business.

Tim Butara: We’re definitely in this period right now where maybe a few years ago we knew that AI was already used, we knew certain areas where AI was maybe more dominant, but it wasn’t as embedded as society and in business, as you pointed out, as it is now.

And we’re certainly heading in that direction where it’s not just a tool in companies’ toolboxes, but an actual key part or a core part of their strategies. And I’m wondering, I think this is the right place to ask here, what would be the key considerations and challenges to developing this more comprehensive AI strategy as opposed to just using it as a tool in your toolbox, as I said before?

Nitin Mittal: The first recognition ought to be that this is not just a technology. It’s not just experimentation or a project that could be undertaken. Rather, it has to be viewed from a completely different lens. That lens is: today, we as individuals, work in enterprises. We as individuals interact in society. And we as individuals interact with each other.

But now, more and more, our coworker is an artificially fueled machine, or an algorithm. The entity that we could be interacting with may or may not be a specimen of the homo sapiens race. It could be an artificially intelligent system, even a chatbot. The form of interaction that we’re having may or may not be just with another human being, it could also be with an intelligent machine.

That’s the lens that we have to put on. A lens where machines, in a very liberal sense of the word, and that’s inclusive of systems and algorithms and intelligent processes etc. have started to essentially mimic in some form or shape human cognitive ability. And it is upon us to essentially harness that, enable it to augment our work productivity, enable it to augment experiences, enable it to augment interactions and enable it to augment our quality of life in a safe, ethical and trustworthy manner. That I think is the biggest promise in front of us.

But therein also lies the challenge – the challenge of, how do you trust the algorithm? How do we understand the underlying data sources that might be coded? How do we have a view into how a particular algorithm or the AI system may be working, and consequently what is the explainability, and do we have a perspective in terms of how data may actually be protected and the privacy norms around it?

So, we need to be very thoughtful around what I would call the trustworthy applications of AI as opposed to just the focus on the technological implementation and the technological application of AI. So we as a society have to progress down the path of both harnessing the promise of AI, but also having the necessary guardrails in place, whether it is through regulation, whether it is through other forms or whether it is through norms that we establish so that we can force a trust with our “new intelligent coworkers” and intelligent machines that are now proliferating in our digital economy.

Tim Butara: That was very well said, and some really great points here. We need to leverage AI, but we need to do so responsibly and transparently, I think that this was also one of your key points here, right, you alluded to this black-box nature of a lot of AI technologies and especially data sources, which are obviously super closely intertwined, and issues in data sources are often the root cause for issues with certain algorithms and stuff like that.

Definitely, ethical AI is, to me, one of the most important topics for discussion when we talk about AI, when we talk about AI implementation. Especially, since we’re talking right now about scaling AI, about using it in all layers of society – even if it was just for some chatbots or for some low-level use cases, ethics would still have to be a primary consideration, but even more so now.

Nitin Mittal: Absolutely. Spot on with that.

Tim Butara: And maybe another key point here, or a key question here, what kind of role should a company’s leadership and its culture play in a successful, effective and responsible AI-powered transformation?

Nitin Mittal: Yeah, this is a question that is very very pertinent in organizations and how they actually become all in on AI. I’ve actually written about this in terms of the human side, wherein organizations and enterprises have to be thoughtful around leadership, culture and people associated with the proliferation of AI. We have actually seen many examples that have been cited in the book, but what is key is that there absolutely has to be executive sponsorship. There has to be executive sponsorship. 

In fact, one of the executives, and we have cited his studies in the book, as it relates to CEOs who have taken up the mantle of artificial intelligence, and viewed it not from a lens of a technological revolution, but rather from the lens of a systematic revolution that is transforming their organizations, wherein it’s not just about the art and science and the mathematics of artificial intelligence, it’s about the promise, the benefits, the means to differentiate, and the basis to compete – as well as, how do you actually increase worker productivity but at the same time open up new avenues, new channels and new vistas to grow revenue, grow market share and offer a level of personalization and experience to your customers that perhaps would not have been possible?

That vision, that passion and that drive has to come from senior executives, in terms of what is the art of the possible and also the fortitude to see it being executed. So that part is absolutely critical. The next part is the culture that is established. It’s not just good enough to have the technical skillset of a data scientist or a model developer or someone who could in Python as an example. Those are critical, but those are not sufficient by themselves.

There also has to be a culture in terms of the insights that are generated by the algorithms, how are those insights woven into the business decision making fabric of an organization? How are those being adopted? How are those being relied on and acted upon? What are the business managers following up on based on those insights? So there has to be equal focus on the business skillsets to adopt, embrace, harness and progress with AI, as opposed to just the technological skillsets.

That also requires workforce training. And I absolutely acknowledge there are many occasions where there’s a bit of a fear factor, the fear factor of whether AI would replace my job. And what we actually find, it’s less about replacement, it’s actually more about augmentation. If you point AI in the right areas for the appropriate use cases and determine the usages of AI in conjunction with your workforce, you will be able to augment, you will be able to enhance, you will be able to increase the quality of work that is being done, and you will drive greater satisfaction because of what is made available as opposed to the fear of replacement.

Tim Butara: Some awesome points here again. And it’s definitely, I think this was one of the first things we talked about on one of the earliest episodes of our podcast was how we shouldn’t be so afraid of AI replacing humans, because the optimal implementations of AI, the most effective ones, the most responsible ones, will aim at augmenting the work that we do with the use of AI.

We also talked recently about the need to not have decisions rely solely on AI, but have AI be one crucial factor in the decision making process. So, it’s very nice to underscore things here, especially with you, Nitin, who are obviously an expert here and are drawing on insights from the book that you’ve no doubt had extensively in your head for the past– I don’t know, how long have you been writing it? How long did it take?

Nitin Mittal: Well, the whole process does take a while, it’s been about a 18 month or so journey. Writing is one part of it, but then there’s also the cross-referencing, there’s the editing, there’s the process that you have to go through with the publisher, so it’s about an 18 months plus journey in terms of the whole experience.

We did have the benefit, both my co-author and myself, we absolutely had the benefit of being in this field, certainly having quite a bit of experience. Obviously I’ve had both the benefit and the pleasure of working with many global organizations and clients of Deloitte as it relates to the applicability of AI, how it could be harnessed and consequently what does it mean for those enterprises. So I was able to, along with my co-author, essentially be able to crystallize it and distill it in the book, so that it could benefit the audience at large and anyone who is looking at practical ways of thinking and applying AI in a business environment.

Tim Butara: I guess this is now the perfect point to ask you if there are any industries or maybe types of companies that are particularly well positioned for an AI-powered transformation?

Nitin Mittal: I would certainly take a look at the case studies in the book. There’s many companies that we have cited. But as a brush, what I would say is that for the most part we end up finding that most of the companies fall in four levels of maturity. We actually call those four levels of maturity as underachievers, starters, pathseekers and transformers. And sometimes, companies go through that level of maturity, sometimes they take a leap of faith, they view the beneficial impact in a much more systematic manner and are applying AI across their organization.

DBS Bank out of Singapore happens to be one of those examples. Their CEO has actually been at the forefront of applying this as it relates to capturing their own place and their own position in a digital economy. Ping An in China happens to be another organization that has actually gone all in with respect to AI, and have started realizing the benefits and the promise of it in terms of new markets, new experiences, new services, new products.

The reason I cite them as examples is there’s a lot of ink that has already been written in terms of how tech companies and tech giants have been pioneers and early adopters of AI. The focus of this book is not to necessarily regurgitate that ink that has already been published, but rather, what are many of the traditional organizations in the vastness of our economy and in practically every industry, what are they doing? How are they thinking, how are they applying, how are they progressing, and consequently, where do they fall with respect to being either an underachiever, or they’re starting, or they’re seeking their own, or they’re truly transforming and going all in?

Tim Butara: That was a really great point. As you said, usually when we think and talk about successful AI implementations, we think about something like Google, Netflix, or, on the other hand, something almost dystopian and SciFi. But we don’t really think about traditional organizations not just leveraging AI to its full potential for them, but taking it to the next level, maybe uncovering new use cases and new strategies and new avenues that they never even would’ve thought existed if they hadn’t adopted AI so thoroughly. 

So, awesome, I really hope that everybody listening right now who’s interested in learning more will check out Nitin’s and Tom’s book and will get to learn more about this, so, thanks again, Nitin.

Nitin Mittal: There are companies who are profiled in the book that are across practically every industry. There’s Shell, we have talked about Krüger, Loblaw in Canada, Airbus – in fact, I also talk about my own company, Deloitte. And we happen to be a 175+ company with a long history and heritage from accounting but now into professional services at large.

So, these are the type of companies we have actually provided case studies, and I would say that there’s a lot to learn, even for my co-author and myself, in terms of what we experienced at these companies, what we learned from these companies, what we observed at these companies, and our hope is that it benefits everyone as well.

Tim Butara: Well, you’re definitely giving us an awesome taste for the plethora of knowledge and insights that the book still contains. But before I ask you for more info about the book, before I let our listeners go and get it off Amazon and anxiously wait for it, I just have one final question for this great conversation, Nitin. And that is – if you could give just one tip to business leaders who are considering an AI transformation, what would that be?

Nitin Mittal: The one tip that I would give is that AI ought to be thought of less as an emerging technology, rather, it ought to be thought more as a co-pilot, a co-pilot that is augmenting. Whether it is augmenting our workplace, whether it is augmenting productivity, whether it is augmenting experiences, interactions, processes, decision making, it at the end of the day augments.

And if we adopt that philosophy that AI is a co-pilot that augments, there’s less anxiety, there’s less fear, there is more trust, there’s more benefits, and it benefits not only the businesses at large, but society as a whole.

Tim Butara: That was a great note to finish on, Nitin. Just before we do, if listeners would like to reach out or learn more about the book or order the book, where would you point them to?

Nitin Mittal: I would definitely go to my profile on LinkedIn, that is one option. There’s always the option of ordering the book from many of the ecommerce platforms that are out there. Or simplistically just go to any search engine, type in “All in on AI” with my name and you will be able to order that book from a whole variety of channels.

Tim Butara: Ok, awesome. Nitin, thanks again for joining us today, this was an absolutely awesome discussion. I’m glad we got to discuss the book with you, and I hope it leads to some nice sales.

Nitin Mittal: Thank you very much. It was a pleasure talking to you, Tim.

Tim Butara: Have an awesome day, Nitin, and let’s meet up again some time.

Nitin Mittal: Thank you.

Tim Butara: And to our listeners. That's all for this episode. Have a great day, everyone, and stay safe.

Outro:
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