Digital Transformation Depends on diversity

Digital Transformation Depends on diversity

Across the industry, businesses are now technology and data companies. The sooner they realize and survive, the sooner they will meet the needs and expectations of their customers, create more business value and grow.

It is increasingly important to reimagine businesses and use digital technology to create new business processes, cultures, customer experiences, and opportunities. A myth about digital transformation is that it’s all about the use of technology. It’s not. To be successful, digital transformation is inherently necessary and depends on diversity. Artificial Intelligence (AI) is the result of human intelligence, which is enabled by its huge talent and also sensitive to its limitations.

Therefore, it is essential for organizations and parties to prioritize diversity and think outside the box. For me, diversity surrounds three main pillars.

Humans are the most important part of artificial intelligence; the reality is that people create artificial intelligence. Human diversity the decision-makers in creating the AI ​​algorithm need to reflect the diversity of the team the general public. This is beyond ensuring opportunities for women in the role of AI and technology. Also, it includes full dimensions of gender, race, ethnicity, skill set, experience, geography, education, perspective, interest, and much more.

Why? When your different teams review and analyze data for decision making, you blind their own and unique human experience, privileges, and the possibility of limitations to the experience of others. Collectively, we have the opportunity to apply AI and machine learning to advance and improve our future. It starts by reflecting on our diverse team and the rich outlook of our world.

The diversity of skills, perspectives, experiences, and geography has played a key role in our digital transformation. At Levi Strauss & Co., our growth strategy and AI team includes more than just data and machine learning scientists and engineers. We’ve recently tapped employees of organizations around the world and deliberately started training people with no previous experience in coding or statistics.

We have taken people into retail operations, distribution centers, and warehouses, and design and planning, and built their expert retail skills through our first machine learning boot camp, and supercharged them with coding and statistics. We did not limit the required background; we’ve only found people who are curious problem solvers, naturally analytical, and persistent in looking for different ways to approach business issues.

The combination of existing specialist retail skills and machine learning knowledge means employees who have graduated from the program now have meaningful new perspectives on business value. The first such initiative in the retail industry has helped our team members create a talented and diverse bench.

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