Women leaders in AI
We have for a long time embraced the idea that women and men are different. As true as it is, we tend to map these differences to gender stereotypes.

Women leaders in AI

We have for a long time embraced the idea that women and men are different. As true as it is, we tend to map these difference to gender stereotypes.

A research by Zenger Folkman published two key findings:

1. Women were rated better than men on 12 of the 16 leadership competencies.

Data shows that more women were rated as better ‘overall leaders’ by their peers, managers, direct reports, and fellow associates.

2. There are more men than women in leadership roles.

Research data shows that there are more men in the upper rung of the hierarchy and more women in the lower rung of the hierarchy.

These findings are intriguing because though women are rated better than men in leadership competencies, we don’t see many women in leadership roles. This is because most leadership competencies are closely tied to common stereotypes.

tableSource: David Schneider, ‘The Psychology of Stereotyping’

Gender stereotypes are notoriously sticky, in part because we’re often unaware that we hold them ―Dr. Tiina Likki

Though holding gender stereotypes saves us time and energy in understanding and differentiating people, they may emerge more strongly under certain conditions such as culture roles or leadership competencies.

None of this has stopped women from reaching newer heights in their respective areas of expertise. Consider, the highly innovative and technical domain of artificial intelligence. Despite being an advanced technology, you often hear about gender bias and stereotypes within this industry as well. Today AI is a key differentiator for businesses of all sizes and across all industries. If AI is the future, it has to first and foremost be all-inclusive and gender-neutral. This means you don’t just need the technology but also great leaders (including women) who can put your organization in the forefront.

Here are a few women leaders, influencers, and strategists (in no particular order) who’ve broken the glass ceiling in artificial intelligence space that Brainalyzed, as a company, is so passionate about!

Elenita Elinon

Executive Director Quantitative Research,

JP Morgan Chase & Company, USA 

LinkedIn connect

“AI can have a significant role in enabling pattern discovery (fraud, issues), issue classification and categorization, data quality identification and remediation, and overall reduction of the cost of doing business.”

Victoria Stasiewicz

Global Information Systems – Manager Information Management,

Harley-Davidson, USA 

LinkedIn connect

“I learned that a full discovery phase is key to success [in AI]; and conducting that prior to formal project planning is a best practice. Ensuring that readiness target dates are accurate and concise and that everyone is in agreement is also key.”

Kyoka Nakagawa

Chief Engineer, Value Creation Department,

Digital Transformation Division,

Honda R&D Co., Ltd., Japan 

LinkedIn connect

“AI planning requires special skills, and not every project ends in success.”

Carolyn Staats

Director of Innovation,

Information Systems Department, Sonoma County, USA

“Don’t be afraid to fail… and don’t be afraid to let others fail. It’s often the best source of learning and provides the very means of moving forward.”

Claudia Pohlink

Head of Artificial Intelligence,

Deutsche Telekom/T-Labs, Germany 

LinkedIn connect

“What is often surprising to me is the versatility of AI: We can apply our methods to many different business problems and come up with improvements.”

Gail Blum

Manager, Talent Acquisition Operations,

NBCUniversal, USA 

LinkedIn connect

“AI is not necessarily a standalone solution to all recruiting process problems. You still must have a strong foundation in your recruiting workflow and use AI as a complement. If your current recruiting process is not set up to incorporate AI, then you need to start by implementing the right changes with your people processes before turning on automation.”

Seema Gaur

Executive VP & Head IT,

IFFCO Tokio General Insurance Company, India 

LinkedIn connect

“Proper data input is essential for machine learning, and continuous data feedback is essential to increase the accuracy of the AI engine.”

Patricia Maqetuka

Chief Data Architecture & Operations Officer,

Nedbank, South Africa 

LinkedIn connect

“While you can go deep with AI quickly, it’s important to bring the people who will be affected along for the ride. It’s easy to want to land a new disruptive technology, expecting that people will immediately see the benefits and run towards the solution, but old habits are hard to break. It’s important that stakeholders are involved at all points in the journey.”

Tanja Richter

Director, Consumer Products and Services,

Vodafone, UK 

LinkedIn connect

“Start small. Get your hands dirty by experimenting and scale only after you have a good grasp on the technology. Be clear from the beginning how you measure success: Start with a well-contained problem and don’t try immediately to save the world.”

Donna Hill

Program Manager

CVP (Customer Value Partners), Washington D.C. 

LinkedIn connect

“Understand your audience and its idiosyncrasies. People approach questions very differently. It is easy for humans to make conversation using all of their senses, but AI does not have that ability and relies on keywords and training to make those connections. If a chatbot is not trained to consider the various ways a question can be asked, then the value of the output goes down. In addition to slang, it is important to have a diverse testing committee to ensure that unconscious bias has not been inadvertently programmed into the chatbot.”

Yimei Guo

Managing Director, Global Head of Research Technology,

Morgan Stanley, USA 

LinkedIn connect

“There are considerable opportunities to use AI and machine learning to extract meaningful data from vast amounts of unstructured data and generate business signals. The key success factor is the close partnership among domain experts, data scientists and data engineers. Business domain knowledge is essential as machine-learning models are very specific to each industry.”

Lee Hatton

Chief Executive Officer Banking & Wealth

Suncorp Group, Australia 

LinkedIn connect

“You’re never going to get to a perfect moment with AI—by its very nature, it will always continue to learn the more it interacts with customers. Don’t be afraid of that journey. Embrace it and take your customers along with you. Also, it’s critically important to not lose sight of the problem you’re solving. It’s easy to get wrapped up in the technology and the excitement of doing something new, which is great, but you need to think about why you’re doing it and never stray from that focus.”

Séverine Marquay

AI Experience, Digital Support & Innovation,

Orange France, France 

LinkedIn connect

“You have to know what you want from your AI project. Start small and grow fast. Focus on one use case or one small team. Also, choose open-minded people who are willing to challenge themselves, learn and take quick decisions in agile ways. Having technical expertise is important, but passion, customer understanding and the knowledge of your company processes are keys.”

Laura Bellamy

Director, Information Experience,

VMWare, USA

“In such a technically [AI] rich field, I think success is actually related to culture. Machine learning brings the opportunity to answer key questions for your business, but it also shows patterns and brings insights that you might not be prepared for. It’s important that your team trusts the data used to build the model and has confidence in the model predictions, so they are prepared to trust the insights and the results that machine learning might deliver.”

Siew Choo Soh

Managing Director,

DBS Bank, Singapore 

LinkedIn connect

“We always begin solving problems for our customers by understanding their everyday needs and pain points.”

Jennifer Edgin

CTO, Deputy Commandant Information,

U.S. Marine Corps, USA 

LinkedIn connect

“Each effort has provided an opportunity to learn how we want to employ a capability, to learn how we want to establish a continuous DEVOPS pipeline, and to learn even more ways that AI can assist Marines. The biggest thing that we have learned through each effort is that we are at the beginning of a transformational journey.”

Ona Juodkiene

Co-Head of IT Operations,

Danske Bank, Denmark 

LinkedIn connect

“Setting up AI technology takes time—and human brains. The quality of the output you get from the data is very much dependent on the data you put in. Highly skilled employees calibrate the system in an ongoing iterative process and help teach the system. It takes time and talented IT professionals to tailor and adjust and integrate these systems into the organization’s IT environment. But when you invest in this, it makes it possible to create a positive culture across the organization.”

Shelley Kalms

Chief Digital Officer,

Woodside Energy, Australia 

LinkedIn connect

“My own learning journey with AI is ongoing, but at this point I can reflect that being able to deliver a technically viable solution is only one part of the challenge. Embedding AI in how people work requires an investment well beyond project delivery, and that’s what we’re focusing on now.”

Harmeen Mehta

CIO & Head Cloudand Security Business,

Bharti Airtel, India 

LinkedIn connect

“Technology is changing at a fast pace and with the play of AI, if we don’t catch up we will be left behind. AI is definitely one of the areas where we cannot miss out by actually not being fully involved. It’s the power of AI and human intelligence that’s going to create a world of tomorrow.”

This is just a tiny sample of the incredible women contributing to the AI movement. Which power women are influencing you? Let us know in the comments.

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