AI Shopping Apps Are Booming—But Can They Change the Way We Consume Fashion?

“The difference with AI-enabled smart apps is that users themselves are impacting what’s presented to them. ‘This is really: We think you’ll like this based on the fact that you gave us these 65 signals that allowed us to kind of figure out what your style is and what your price-point is and what type of fit you might like,’ says Boyle. ‘That level of complexity can’t be handled in the backend of a site, it has to be handled more dynamically by the technology.’”

Photographed by Acielle / Style du Monde

How artificial intelligence in eCommerce can be a harbinger of next-gen customer experience?

  • “The global E-commerce sales are projected to touch $4.8 billion by the year 2021, Gartner predicts that around 80% of all customer interactions will be managed by AI technologies (without any human agent) by the year 2020.”

Here’s How Fashion Retailers Can Leverage AI During Covid

  • “While retail sales have been recovering since the pandemic hit, fashion is still struggling. A September article from Deloitte found general retail sales have been recovering since they dropped 14.3 percent in April. In July, sales increased 1.2 percent to about $391 billion. However, the fashion sector has suffered more than most, with retail sales down about 20 percent in July compared to February.
  • Since the pandemic began, 47 percent say they’re making ecommerce purchases more than they did previously, according to Cotton’s September 2020 Coronavirus Response Survey.
  • Deloitte’s State of the Consumer Tracker found worries about health due to the pandemic helped fuel a 33 percent surge in online spending in Q2 compared to 2020’s first quarter.”

Here’s How Fashion Retailers Can Leverage AI During Covid

The 2020 data and AI landscape

“Some noteworthy developments:The year of NLP

Transformers, which have been around for some time, and pre-trained language models continue to gain popularity. These are the model of choice for NLP as they permit much higher rates of parallelization and thus larger training data sets.

Google rolled out BERT, the NLP system underpinning Google Search, to 70 new languages.

Google also released ELECTRA, which performs similarly on benchmarks to language models such as GPT and masked language models such as BERT, while being much more compute efficient.

We are also seeing adoption of NLP products that make training models more accessible.

And, of course, the GPT-3 release was greeted with much fanfare. This is a 175 billion parameter model out of Open AI, more than two orders of magnitude larger than GPT-2.”

Gartner’s top 10 strategic predictions for “resetting everything” in 2021 and beyond

“By 2025, traditional computing technologies will hit a digital wall forcing the shift to new paradigms such as neuromorphic computing.

Current computing techniques will not be enough to allow CIOs and IT executives to deliver critical digital initiatives by 2025. Artificial intelligence (AI), computer vision and speech recognition will be used everywhere, and general-purpose processors will be unsuitable.”