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My learning journey
“You get associated with the top minds and you nourish those relationships even years after you finish the course.”
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“Ultimately, AI is not the master. Human intervention is necessary to provide the right feedback to the machine.”
Sharmistha Chatterjee
Senior VP Advanced Analytics Chapter Area Lead, Commonwealth Bank
I’m now at a level where I should be able to influence stakeholders to take up my AI and invest in the workforce. So, I knew it was time to search for courses that will give me a solid foundation on making a strong use case for AI. I had become a very good technical expert, but it was difficult to convince others that this is where we need to invest in order to impact business revenue streams.
I’ve always had an interest in machine learning ever since I realised that it would dominate the industry for the next few decades. Not a single decade, but decades. I decided to do a Master’s in Computer Science and Engineering and I saw the importance of data becoming really valued even before machine learning was widely adopted. My thesis was all about using machine learning to improve customer experience for mobile phone users. This was the time when machine learning models were beginning to be adopted which , saw a quick transformation when traditional and deep learning algorithms began to get adopted very fast and at scale.
In the last five years, there has been a tremendous change in responsible AI industry with regards to privacy. We’re asking questions like: how should we be anonymising our data? Where should it be stored? How can we make the machine learning models private too, so that both and models are safe from attackers? Everybody started talking about AI and regulations started to be imposed in different countries.
Now I lead a large team at Commonwealth Bank of Australia. Whether it’s home buying, retail services or business banking, we use data and machine learning to give the users customers the best experience. My team develops frameworks, provide platforms and scalable infrastructure to run machine learning models, making sure they’re always ethical and fully compliant with industry guidelines. At the moment, we’re making sure our customers get the right offers and messages in real time.
Banking has adopted AI and machine learning very quickly, but I needed to know how to write a proposal and how to talk to CEOs. Many of the clients I’ve worked with have not heard of AI. I needed to help them see how AI can revolutionize a business at tremendous speed. That’s why I enrolled on The Business of AI course with LBS online.
The course was all online, so you have access to the platform for a year and complete the course at your own pace. There was a one hour slot every week where we had live teaching, and we’d have discussions and ask questions. We started with an overview of why AI is important and how it could be used in different businesses like banking, telecommunications and healthcare.
Then, our dedicated learning manager, began to take us through the process of making a pitch and taking an organisation on a journey. We covered how to write a proposal and show CEOs and executives that you need to use AI to transform a business. We looked at the bottlenecks and the stages you would go through, and being delicate with the challenges you might face.
One moment that triggered a shift in my thinking was when we touched on the ethics of AI. I knew the technical side of things, but I thought more about the damage that could be caused. For example, , could deny someone a loan who is actually eligible. Or deny house rental services. Or a good telephone connection. We learned to be aware of the way AI is used so that it benefits people rather than causing harm. I saw that there were lots of complexities involved and that ultimately, AI is not the master. Human intervention is necessary to provide the right feedback to the machine.
As responsible AI becomes a major player in the industry, the course made me think about how I could position myself as a thought leader in this area. I have co-authored a book Platform and Model Design for Responsible AI. I started by researching more around the privacy element that was taught on the course. I found that there are numerous ways a well formulated algorithm can prevent bias. Not only that, but I started looking at the problem holistically. It starts the moment data gets captured, right through to the predictions that are made, an algorithm needs to be private and fair.
The course has helped me see that I could become an AI advisor and beyond that, how I can be a responsible citizen as well. I wasn’t always aware of the best practices to train models in the past. But now, I make sure my people are all aware of the right way of doing things, so we cultivate the right habits in the organisation. The course gives you all the tools and techniques to make sure models come out with the best results.
The Business of AI has improved my end to end thinking – not only as a data scientist, but from a stakeholder management point of view too. I can think through the logistics and technicalities, but I also know how to give better results to the business. That means I can pitch better solutions. I can point out the roadblocks and find ways around them. When I develop any AI or machine learning solutions, I look at the conversions and quantify everything in terms of revenue . I know not to overpromise but instead, to know the challenges and paint them a full picture.
There were lots of CEOs who joined the course wanting to transform their businesses using AI. They shared their experiences – the failures they’ve been through and what they learned from them. For example, when you begin to scale AI and machine learning models in a larger organisation, workforce becomes a challenge. We talked about planning the hires you need and what the right balance was. So, it wasn’t just about completing the course or attending the lecture, but about sharing our experiences together. Those insights showed me how to stay ahead of the curve and make full use of the course in real life.
I’m still in touch with some of my peers. We had a WhatsApp group where you could ask questions and that group is still active. I used to do a podcast about data, AI and machine learning so I have invited some of them to be on it. Some of them even read my book and feedback. Studying with LBS, you get associated with the top minds and you nourish those relationships even years after you finish the course. You meet people with diverse experiences, and you can learn a lot from them.
I did face challenges in the field of AI. It’s not very welcoming. But you have to make yourself welcomed if you’re not welcomed.
One piece of advice I would give to women in business is invest in yourself. As women, we often feel like we have family responsibilities and that can be very hard to manage. But if you can, make time for yourself. every day in you.
I did face challenges in the field of AI. It’s not very welcoming. But you have to make yourself welcomed if you’re not welcomed. I have had hundreds of failures and I’m not afraid to talk about them. I used to fail interviews and make notes on why I failed. There is no substitute for hard work. However, sometimes you have to ask why a woman has been rejected. For example, the Amazon hiring tool was shown to be very biased, so it rejected female candidates. The tool was corrupt. So, we have to think, where else is this happening?
One thing I’ve seen missing in our industry for women is mentorship. We need to mentor people and have a growth plan for them. And we need to remove that false assumption that, if a woman is at the top, she has come through a diversity quota. If a woman is there, she has earned it. So, CXOs and senior leaders need to think about how we can make it more transparent and make sure we have a clear roadmap. And as we become successful women in AI and in business, we should also think about creating a path for other women.
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