Anuja More is a product leader and an engineer with over a decade of experience building products that deliver transformative impact at scale. Currently, she is the Product Lead at Meta for the WhatsApp Business Platform where I oversee the development of building conversational experiences between businesses and WhatsApp's 2+ billion global users. Most recently, she has been focusing on the application of GenAI in messaging to enhance communications and scale businesses on WhatsApp.

Can you tell us about your AI journey? 

My career started more than a decade ago, as a big-data engineer with Persistent Systems India, where I learnt to build machine learning algorithms on top of map reduce and hadoop technologies. One of my milestone projects was designing and planning a content filtering, ranking and tagging system for the data analysis of the national television show 'Satyamev Jayate'. The flagship show generated more than one billion unstructured responses across Twitter, Facebook, YouTube, SMS polls and the analysis helped in real time to understand the impact the show had at a governmental, social and individual level.

After completing my Masters in Information Technology and my MBA in the US, I started working as a product manager for large corporations such as Fujitsu and Juniper Networks. At Juniper, I was responsible for drafting the company’s cloud network data analytics strategy. As a product manager, I helped build and led a cloud orchestration product that leveraged AI and machine learning to provide advanced monitoring, scheduling and performance management for cloud infrastructures.  

After a few years, I joined Meta’s (formerly Facebook’s) Connectivity organization where I worked in developing next-generation ML driven network technologies that enabled 300M people to have access to faster internet. Currently, I am the product lead for Meta’s WhatsApp business platform where we're now building generative AI experiences between businesses and WhatsApp's 2 billion plus global users. 

My journey in AI thus started at the infrastructure layer to then building software platforms and now into the application layer. 

What is your area of expertise in AI, and what made you choose it?

My primary areas of expertise are ML and AI driven cloud automation, data analytics and Generative AI. I am passionate about building data-driven products. Harnessing the power of data and the ability to predict outcomes that can drive efficiency, automate tasks and connect people through meaningful experiences has driven my interest in AI. Being at the intersection of technology and business, I enjoy applying creativity and humanization to the world's most technical problems.

How did generative AI impact your field of work?  

Generative AI has revolutionized network and communication technologies. In my career, two areas stand out where generative AI has been transformative:

Self-driven autonomous networks

Generative AI helped pave the way for intelligent networks by connecting complex AI/ML models used across network planning and operations with large language models (LLMs) that can understand network behaviors.  Customer data was then used to train these models to build better prediction models for network capacity planning performance and proactive remediation of cloud issues.

GenAI in Conversational AI 

Conversational systems are becoming an integral part of our daily routines. Every day, millions of people use natural-language interfaces via in-home devices, phones, or messaging channels. GenAI is transforming the chatbot experiences (rule-based, non-interactive responses) to conversational AI for a more human-like experience, handling variance, and recognizing intent.

Describe some challenges you have faced in reaching where you are now.  

Constant need to prove competence

As a woman in technology, there is a constant need to prove competence. The biggest hurdle with women leaders is that they don't believe they deserve success, attributing it to luck, help from others, or hard work. I overcame this imposter syndrome by owning my confidence to 'sit at the table' and reach out for opportunities in the workplace, whether it be driving a new project, initiative or learning new technologies. 

Navigating a new technology space 

Problem spaces are always evolving and keeping up with technology to solve them has always been challenging. It's like navigating directions after moving to a new city. As a product manager you want to be an expert right off the bat, but that’s just not realistic. It’s important to understand how to add value and not just implement a new trend for the fear of missing out. You start with defining a purpose or a problem and the value a new technology like AI can bring. Learning from industry experts, listening to customers, prototyping and measurement are ways by which I have learnt to leverage any new technology.

Do you see enough female leadership roles in corporates? In your opinion, what should change?  

More recently, companies are increasing women representation at the top and we’re seeing more women in C suite positions. However, there is still a huge drop-off in middle to senior leadership roles. According to Mckinsey’s women in workplace report, in 2023, for every 100 men promoted from entry level to manager, only 87 women were promoted. This drop-off creates less women representation for leadership opportunities. Companies need to create a strong culture of inclusion to encourage women leaders. They need to implement levers such as flexible and remote work options, better child-care and mental health benefits to support ambitious women who want to grow in their career as they go through phases of growth in their personal life. 

What do you want to say to women who wish to build careers in AI and other tech-related fields? 

Start now!

You do not require expensive courseware to learn new technologies. For example, there are many open source softwares and LLMs such as ChatGPT plugins, Llama, Hugging Face, MidJourney etc. that you can start learning, implementing and applying AI to your ideas. Learning AI does not have to be expensive and it's never too late.

Think of failure as an experiment

We often view failure as a roadblock to success. Next time you fail at something, ask what can I take away from this and instead of avoiding failure, focus on recovery. Considering failure as an experiment will enable you to take more risks and grow from our mistakes. 

Ask for help

If you question some of the most successful people on what they do in order to grow, they constantly ask for help. Make that a competitive advantage for yourself.  Most people are really excited to help others out, but aren’t sure how. Whether it's about building a career in AI or seeking for a solution to a problem, seek out people, communities, forums and mentors and ask for help!