When algorithms design: Generative AI in retail

10 de October de 2023

Artificial intelligence is changing the face of the retail sector in a significant way. Among the various branches of AI, generative artificial intelligence stands out for its ability to create new content from existing data. In the retail world, this translates into a variety of applications that can drive efficiency, innovation and personalisation, thereby improving customer experience and business profitability.

What is generative artificial intelligence?

Generative Artificial Intelligence is a type of artificial intelligence model that uses neural networks to generate new data and content from a training data set.

Unlike other types of artificial intelligence, which focus on performing specific tasks, generative AI focuses on creating new and unique things; capable of performing complex intellectual tasks in the same way that a human being does.

In fact, GAI is not limited to performing a specific task, but can learn and apply its knowledge to different areas autonomously and unsupervised. The theory is that these systems would be able to make judgements and reason under uncertainty, as well as communicate in natural language, plan or learn.

In other words, generative AI refers to a form of artificial intelligence that can “think” in a general and flexible way, rather than being limited to specific tasks for which it has been programmed or trained. GAI is considered the highest level of artificial intelligence and is a key focus of research in the field of artificial intelligence.

How does Generative Artificial Intelligence (GAI) work?

A GAI works by creating two neural networks: a generator and a discriminator. The generator creates new dummy data, while the discriminator evaluates whether this data is similar enough to the original training data.

To better understand generative AI, let’s identify where the different types of models that exist today start from and their usefulness for comparison:

  • Artificial intelligence mimics human behaviour by relying on machines to learn and execute tasks without explicit instructions on what to generate.
  • Machine learning models take external data, such as weather conditions, and fit it to the data of an algorithm in order to make predictions. For example, the sales a retailer will generate on a given day.
  • Deep learning models use layers of algorithms in the form of artificial neural networks that are composed of several input, output and hidden layers. Each layer contains units that transform the input data into information that the next layer can use to perform a given prediction task. Thanks to this structure, a machine can learn through its own data processing.
  • Generative AI models are a subset of deep learning models that can generate new content based on what is described in the input.

Applications of Generative AI in the retail sector

Generative AI is proving to be an essential ally for retailers in the quest for innovation and efficiency. With its ability to analyse and generate content from vast data sets, GAI is not only facilitating a more efficient operation, but is also improving the customer experience through personalisation and optimisation of shop space.

Here are just some of the many applications of GAI in this sector:

Product development and design

  • Rapid prototyping: GAI facilitates rapid prototyping of new products or variations of existing products, drastically reducing the time and costs associated with traditional design.
  • Personalisation: Generative algorithms can personalise products according to individual customer preferences, creating more attractive and competitive offerings.

Inventory management

  • Demand forecasting: by analysing large volumes of historical data, IAG can forecast future demand more accurately, helping to maintain optimal inventory and reducing associated costs.

Personalised marketing

  • Advertising campaigns: IAG enables the creation of personalised advertising campaigns by analysing customer shopping and browsing data, increasing likelihood of purchase and customer loyalty.

Optimisation of shop space

  • Shop design: generative algorithms can suggest shelf layouts and shop areas that maximise sales and customer satisfaction, based on analysis of traffic patterns and shopping preferences.

Optimise your decisions with CognoGPT

At Cognodata we have built CognoIA, an artificial intelligence accelerator platform based on advanced analytics, machine learning and generative AI that allows us to streamline execution and inject our expertise into the projects we undertake. Specifically, we have developed CognoGPT as a module within this platform for the application of capabilities to see, hear, speak, search, understand and accelerate advanced decision making in new use cases.