HAVE MORE WOMEN FIRST, TO SCALE-UP AI AND DATA SCIENCE

The global artificial intelligence (AI) market size was valued at US$87.04 billion in 2021. Artificial intelligence technology has gained rapid traction since its introduction in the market.  However, the World Economic Forum data suggests women account for only 22 percent of the global AI jobs. Moreover, in India, around 43 percent of Indian women students graduate is in STEM and only 14% employed as engineers, scientists, and technologists in research development institutions. Does that mean there is a need for more female leaders in the field of AI and Data Science? Yes, when women become leaders, they bring talents, and new views, alongside structural and cultural diversity to the companies they work for, resulting in more successful solutions. Women can study more minor details to see what is going on underneath the surface with diverse views and a sense of awareness. And that’s exactly what this world needs when it comes to scaling up AI and Data Science.

No doubt, Artificial Intelligence is one of the prime fields in which women can experience huge success, especially with the right push towards female participation in the industry. Women are a necessary force that organizations must integrate in order to accelerate the AI maturity of enterprises. AI has the potential to mitigate the company gender and leadership gaps by removing bias in recruiting, evaluation, and promotion decisions; by helping improve retention of women employees; and, potentially, by intervening in the everyday interactions that affect employees’ sense of inclusion. If companies want to achieve the highest AI maturity levels, it is crucial to mobilize women on a mass scale and include them as part of all enterprise endeavors in artificial intelligence, from research to product launch.

For example, in the screening stage, AI can provide you with data points on how many of your applicants are men versus women. If there is a large disparity, you can then cast a wider net to target more women. Tracking this data over time will help you build a more gender-equitable screening process.

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