Tell me about your AI journey so far. What inspired you to carve out a career in data science/AI?

I was interested in computer science and coding from a young age. I also studied business at university and always dreamed of starting a business. When working in Silicon Valley, my co-founders and I decided to take on the mission of increasing productivity through technology. We envisioned freeing employees to be more creative and strategic; we wanted to use technology to enable them to reduce or get eliminate repetitive and manual tasks. That’s the power of automation and AI; it takes the ‘robot’ out of human workers.

What’s your area of expertise in AI and why did you choose this?

My focus in AI is on ethics; using artificial intelligence to both advance business productivity and efficiency, as well as using AI to remove inherent biases in our societal behaviour. We have a unique opportunity to train bots with intelligence. We can help shine a spotlight on biased data collection and in turn, on biased decision-making that that has long been part of human culture and history. We can then use AI to remove those biases and train bots to truly be anti-biased. This can help bridge a huge gap in human decision-making that has so much of unconscious bias built into its behaviours. At Automation Anywhere, our mission is also to make automation simpler by integrating Robotic Process Automation (RPA) with AI, making these tools more accessible to all humans and in turn driving benefits across all industries and verticals.

What challenges have you faced in creating a niche for yourself in the field of tech?

Automation is not new. For the last 30 or 40 years, automation-driven software has helped people become more efficient. However, traditionally, this automation was not economically viable or desirable for most business processes. RPA technology allowed any user, whether in business or IT, to use low code, drag-and-drop, and wizard-based functionality to access automation technologies more easily, economically and creatively. Automation Anywhere is a pioneer in this field and we have created a whole industry using RPA. Now augmenting RPA with AI and ML, we are making intelligent automation accessible to everyone, everywhere.

How will women in tech roles help mitigate bias and promote inclusion?

Inclusion and diversity are important in a team, division, company and at every level where there are decisions being made. Women in technology therefore have an important role to play, because they bring a different perspective and mindset to all technology solutions being developed. Taking this a bit further, any data that is gathered and analysed needs to also have different perspectives built in. Bots that are being trained to automate, similarly, have to be trained on many perspectives. Women in tech can help analyse and help make better decisions because of the diversity they bring. Inclusion also means these bots will have a more robust and inclusive platform on which they are being trained. A lack of diversity just fosters inherent biases in AI further and narrows down technology’s field of vision. Women in tech roles need to have a seat at the decision table so that we can have the best, most robust technology products and services out there.

What do you think are the biggest limiting factors for women not to advance their careers in tech, especially product development? What can change?

Women can, must and will. There are so many women who have helped drive change and progress. Frequent limiting factors for many women include not having visibility to other women trendsetters – as inspiration, mentors or sponsors. It’s important to have more women share, talk and show the possibilities of having both families and careers,. their career choices, promotions and even failures. Having women know that it’s OK to prioritize their own learning is important. It’s important in product development specifically, that women know it’s alright to think differently. The more visibility we bring to this and the more we celebrate diversity and inclusion and the benefits it clearly brings—the greater the change we will drive.

How do you think corporates are moving the needle in terms of supporting more women to participate in tech building/development?

Many companies are moving the needle to train and support more women. But we need even more companies to focus on this to increase the current percentage: 22% of AI professionals who are women, compared with 78%, who are male AI professionals. Giving women even more STEM training opportunities, access to mentors and a dedicated focus to unconscious bias training for all hiring managers are some ways companies can help.

What’s the one thing that you see AI transforming completely?

AI and automation will change the ways humans work. The future of work will be even more creative, innovative and efficient because of technology. Humans will be able to focus more on intelligent and strategic tasks; build more creative solutions that will enable even better products and services, and construct a better society and world. Better healthcare, less poverty and disease, a whole plethora of new products and innovative, human-centric customer services will become possible. It has also been reported that by 2022, automation will create 133 million new jobs. Simply put, we will enable progress.

Your biggest AI nightmare?

AI, like all other new technologies and endeavours, has to be strategically planned with checks and balances. Ethics in artificial intelligence is something I am very passionate about. Having a human-centric approach while creating AI technologies is a must. Having checks and balances for responsible AI usage is a must. If AI is created and utilized without the social responsibility lens on it, it can lead to misuse and misappropriation.

What’s your advice for other women who want a journey similar to yours?

Dream big. Take small steps every day. Work hard and don’t be afraid to fail. Everyone’s story is different and if you believe you can achieve something, you will. Women can, must and will.