Women are the key piece to the puzzle of realizing the highest maturity levels of digital enterprises, but unless we realize this, our progress in AI and technology will remain stagnant. To close the gender gap in science, technology, engineering, and math (STEM) and to accelerate advances in artificial intelligence and the sciences, we must encourage and support women on all levels, from the government to enterprise and establish equal employment opportunities for all.
Women make up a fraction of the artificial intelligence workforce, whether in the form of research and development or as employees at technology inclined firms. According to the World Economic Forum, “Non-homogeneous teams are more capable than homogenous teams of recognizing their biases and solving issues when interpreting data, testing solutions or making decisions.” In other words, diverse teams and especially those that emphasize women at their epicenter, are a necessary provision for enterprises to adopt, build, realize and accelerate enterprise AI maturity levels. At present, unfortunately, few enterprises understand the criticality of women to boost AI maturity levels.
STEM, data science, and AI fields experience a lack of female role models. Without female role models for girls to look up to, it becomes difficult for young women to envision future careers in science, technology, and engineering fields. A 2018 Microsoft survey shows that female STEM role models boost the interest of girls in STEM careers from 32 percent to 52 percent. Therefore, we must showcase the achievements of women in the sciences and engineering across the world to capture the attention of females everywhere.
One of the biggest pressures that females face in STEM careers is cutthroat competition amongst male counterparts and the toxic workplace culture that it creates. An HBR article found that three-fourths of female scientists support one another in their workplace to ease tensions. Moreover, women are likely to be demoted as inferior by men holding equivalent positions, whether those jobs are in engineering, data science, or AI. All of these factors contribute to females swiftly dismissing STEM jobs to avoid such disquieting workplace circumstances.
According to a survey conducted by BCG, when it comes to STEM, “Women place a higher premium on applied, impact-driven work than men do: 67% of women expressed a clear preference for such work, compared with 61% of men.” This finding highlights a significant fact: women are vastly more likely to pursue STEM roles that provide them with meaning, purpose and produce impactful results, but many women don’t perceive this purpose and impact in STEM jobs. Therefore, without a clear high impact-driven pathway insight, females tend to turn their heads on STEM, data science, and AI-related careers.
Studies have shown that communication is of the utmost importance when it comes to getting more women involved in STEM careers. According to BCG GAMMA, just 55% of women feel like they know enough about employment opportunities in data science. Furthermore, vague explanations of job qualifications, such as “being strong in data science,” and, conversely, incredibly in-depth job descriptions in search of data wizard talent, tend to steer females clear of STEM-related jobs. Moreover, an HBR study found that female engagement with STEM employers falls far behind men and that this should come as no surprise as, “Given the selection bias that accompanies personal work networks, especially in a young and still male-dominated field.”
It isn’t enough to pique the interest of girls and young women to pursue STEM careers: the goal is to maintain, foster, and grow that interest. A study published in the Social Forces journal found that women in STEM are much more likely to abandon their jobs than if they held other careers. More precisely, the study highlights that some 50% of women holding STEM careers left after 12 years on the job, whereas that number dropped to 20% for women in other fields. On average, females tend to distance themselves from STEM after 5 years of industry involvement. But why? According to the same study, “Women with engineering degrees said they left engineering because of lack of advancement or low salary, along with other working conditions.” These facts show that retention of women in the STEM, data science and AI workforce is chief among challenges to address.
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