The present time is moving rapidly towards digital and the use digital technologies creating new business processes, cultures, customer experiences and opportunities. Generating more business value and grow by meeting customer requirements and expectations.
Although a common mindset is that digital transformation is all about getting into technology. For instance, Artificial intelligence (AI) is the outcome of human intelligence, allowed by its huge talents and also liable to its limitations. Rather success depends on integrating diversity in the business.
Thus, it is imperious for organizations – especially the apparel industry to make diversity an urgency and ponder about it outside the old-style sense to incorporating digital transformation.
If diversity is put under a magnifying glass, it centers around three key pillars.
People
People are the utmost vital part of artificial intelligence; as it is the people who created the AI and other digital technologies. The diversity of people — the team of decision-makers in the construction of AI algorithms — must replicate the diversity of the overall population.
Including essential human realms like full dimensions of gender, race, ethnicity, skill set, experience, geography, education, perspectives, interests – cause this diverse teams reviewing and analyzing data will ensure mitigating the odds of their own individual and distinctively human experiences, rights and limitations striking them to the experiences of others.
As diversity of skills, views, experiences and geographies has significantly played a vital role in the digital transformation.
A global retail brand did research on employees. It intentionally trained people with no prior experience in coding or statistics.
Participated people were taken in retail operations, distribution centers and warehouses, and design and planning and put them through machine learning bootcamp, building on their skilled retail skills and boosting them with coding and statistics. As a result, it showed the importance of diversity to generate a balanced AI output.
Data
It all depends on how vast data a system has to generate AI and machine learning capabilities. These are good as the data is put into the system. The general misconception is that data is structured tables — numbers and figures — but data is anything that can be digitized.
100 or more years of apparel making of a company is data. The customer service conversations are data. The heatmaps from how people interchange in stores are data. The consumer reviews are data. Currently, everything that can be digitized becomes data.
Having said that, the general conception needs to widen regarding data and need to ensure that all data is constantly feed into AI work.
A lot of analytical models use data from the past to forecast the future. But because the fashion clothing industry is still in the blossoming stages of digital, data and AI adoption, having past data to set parameter is often creates a common bottleneck. In fashion, the industry is looking ahead to forecast trends and demand for totally new products, that have no sales history. Raising the question of how it is done?
By using more data than ever before, for instance, both images of the new products and a database of an apparel product from past seasons. Then apply computer vision algorithms to detect similarity between past and new fashion products, which helps in predicting demand for those new products.
These applications provide ample more accurate estimates than experience or perception do, adding previous practices with data- and AI-powered predictions.
Some leading apparel brands use digital images and 3D assets to simulate how garments feel and even create new fashion.
For instance, they train neural networks to recognize the shades around many jean styles like tapered legs, whisker patterns and distressed looks, and detect the physical properties of the components that affect the drapes, folds and creases.
Then combine this with market data, where brands can tailor our product collections to meet changing consumer needs and desires and focus on the inclusiveness of our brand across demographics.
Tools and techniques
Beside people and data, apparel brands need to confirm diversity in the tools and techniques we use in the creation and production of algorithms. Some AI systems and products use classification techniques, which can preserve gender or racial prejudice.
Like classification techniques accept gender is binary and commonly assign people as ‘male’ or ‘female’ based on bodily appearance and conventional assumptions, denotating any other forms of gender identity are removed.
To solve this brands take race out of the data to try and render an algorithm race-blind while continuously safeguarding against bias. For example, to diversity AI clothing products and systems, fashion brands use open-source tools.
Open-source tools and libraries by their nature are more diverse because they are available to everyone around the world and people from all backgrounds and fields work to enhance and advance them, enriching with their experiences and thus limiting bias.
By diversifying people, data, and techniques and tools will greatly aid fashion brands in eradicating and revolutionize its businesses and the entire apparel industry. Transforming manual to automated, analog to digital, and intuitive to predictive.
