By Rudradeb Mitra, CEO, Omdena

In this article, I share my thoughts on why the feminine leadership style will lead to more successful AI projects and enable an environment for collaboration and inclusion.

Part I: Differences between Masculinity and Femininity

I want to share a fascinating story I heard from a student while mentoring at the Start Summit in St. Gallen. The story as narrated:

“Last semester, our class got split into three different groups to develop a safety technology solution for Swiss or German brands:

• Group 1: Only women
• Group 2: Only men
• Group 3: Four women and one man

After four weeks of work, each team had to present their work.

Group 1, composed of only women, developed a safety solution for women who need to walk in the dark . As the jury consisted only with male, the group  decided to tell a story using a persona, music, and videos to make them feel what women are experiencing daily. They also emphasize that everyone has a mother, sister, or wife in their life and that they probably don’t want her/them to suffer. Ultimately, altough their solution was rather simple technologically (a system which use the light to provide safety), the group was able to   connect with the audience emotionally.

Group 2, included only men, presented a more high-tech solution using AI, GPS, and video conferences. Their presentation was based on  facts and numbers pointing out competitive advantages of the solution.

Group 3, with four women and one man, wasn’t able to finish their solution. The only man in the group could not agree to be led by women, and they, therefore, spend too much time discussing group dynamics instead of working.

The groups not only had different outputs but also approached the problem differently. Group 1 decided to start by defining each other’s work preferences and styles to distribute responsibilities and keep a hierarchy as flat as possible.

On the other hand, the two other groups elected a leader for the team. It turned out that these “leaders” were perceived as dictators, which led to heavy conflicts where the teams spent hours discussing and arguing while our group was just working and being productive”.

What science tells us about gender differences

The science landscape about gender differences and their effects on behavior is still evolving and has not come up with a clear set of scientific explanations for different behaviors yet. However, by compiling most of the research, two main factors influence behaviors:

1. Potential physiological differences between men and women

2. Social norms and pressures forming different behaviors

In the above story, women developed the solution in a Collaborative Leadership Style (adhocracy culture), adapting the leading position based on the tasks with an almost flat hierarchy. They derived their argumentation by involving all stakeholders (in this case, the mothers and wives = users), showing empathy for their problems. They saw the bigger picture and also built a simpler solution that was finished.

Through the story, we could connect the dots on why most AI projects never end up moving from the prototype phase to a real-world application.

“The most exciting breakthroughs of the twenty-first century will not occur because of technology, but because of an expanding concept of what it means to be human.”

— John Naisbitt

Part II: Making AI a success

There are three main reasons why most AI and Machine Learning (ML) solutions do not move from the prototyping phase to the real world:

1. Lack of trust: One of the biggest difficulties for AI or ML products is the lack of trust. Millions of dollars have been spent on prototyping, but with very little success in real-world launches. Essentially, one of the most fundamental values of doing business and providing value to customers is trust, and Artificial Intelligence is the most heavily debated technology when it comes to ethical concerns and related trust issues. Trust comes from involving different options and parties in the entire development phase, which is not done in the prototype phase.

2. The complexity of a launch: Building a prototype is easy, but there are tens of other external entities that need to be considered when moving into the real world. Besides technical challenges, there are other areas of focus that need to be integrated with the prototyping (such as marketing, design, and sales).

3. AI products often do not take into account all stakeholders. I heard the story that Alexa and Google Home are being used by men to lock out their spouses in instances of domestic violence. They are turning up the music loudly or locking them out of their homes. It is possible that in an environment with mostly male engineers building these products, no one is thinking about these scenarios. Additionally, there are much more instances in which artificial intelligence and data sensors can be biased, sexist, and racist.

Interestingly, none of the three points above relate to the technical challenges, and all of them can be overcome by creating the right team.

How to make AI be more successfully adopted?

To solve the above challenges and build more successful AI products, we need to focus on a more collaborative and community-driven approach.

This takes into account opinions from different stakeholders, especially those who are under-represented. Below are steps to achieve that:

Step 1. Involve diverse groups esp. include women

Access to knowledge has become ubiquitous, which has created a huge talent pool. Now, a company does not need to go to the top universities, where for primarily historical reasons, are fewer women, to get top talented people. There are people all over the world, who also bring in different perspectives and opinions.

Step 2. Build a communal and collaborative bottom-up team with different stakeholders

Next, we need more collaboration between men and women and different stakeholders to launch products successfully in the real market. This can be achieved through forming inclusive project communities that build AI products based on common values, beliefs, and often a bigger vision.

Step 3. Create the right Organizational Structure for Collaboration

What if we could create organizational structures and practices that don’t need empowerment because, by design, everybody is powerful and no one is powerless? I have seen that this can be achieved by connecting intrinsic and extrinsic motivations (which are not related to money) and creating an incentive structure that is not competitive.

The role of a leader is not to be a boss but to foster Collaborative Leadership. Such an organizational structure will decrease the need to control people and give opportunities to learn and grow together.

Part III: Connecting Part I and Part II.

Why AI team should be led with femininity qualities

While none of the mentioned qualities can be generalized, the following graphic summarizes some of the reasons why many women are a great fit for Collaborative Leadership.

In conclusion, I am arguing:

“We should think more holistically and do our best to create the right environment where we look beyond gender, race, and cultural background and focus on how we can collaborate as humans to build a better future”.