The opportunities for using AI in retail

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Article written in collaboration with Pixis Conseil

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Sundhar Pichai, CEO of Google, recently said that AI would have a stronger impact on society than electricity or fire. The contribution of AI to the global economy is estimated at around 15 trillion (15 million) dollars by 2030.

Since the mid-1950s, AI has met with some impressive victories (chess, game of go, machine translation, language comprehension, autonomous driving, chatbots) but whose daily applications remained relatively unclear and the societal impact marginal.

The first significant shift in artificial intelligence is in fact quite recent, it coincides with the multiplication of the data we produce and the explosion of the computing power of our equipment. Here we are talking about Datafication of our society and of machine learning or Machine Learning (ML).

This shift is decisive because it makes it possible to move from a “rear-view” management of our activity that prevailed until now (learning from the past so as not to reproduce it) to a predictive vision: knowing what will happen and acting accordingly. The opportunities for Machine Learning seem endless.

Prerequisites to master to get the most out of AI

The AI fantasy machine has been relaunched but technological reality still has some limitations:

1. Data availability

• The available data is not always of sufficient quality and quantity to develop an effective AI. For example, Stanford University's AI, developed to diagnose skin melanomas, has a level of success equivalent to a panel of 21 oncologists. But to do so, she needed a huge database of 130,000 images to develop her learning.
• In addition, the data may also have biases. For example, sales from one year to the next may have been influenced by random or integrable factors (weather, strikes...) that are not always easily identifiable. Thus, the predictive power of AI can be distorted by these biases that it is not always possible to neutralize.
• The other major bias of data is that it, generated by our behaviors and interactions, is likely to reproduce the discriminations of our society. In order to create equitable artificial intelligences, it is necessary to clean up the data that feeds the AI so that it does not reproduce these biases (ethnic, sexual or religious discrimination) on the exponential scale that is its own.

2. Scaling AI algorithms

‍The mathematical performance of machine learning algorithms cannot be an end in itself, the challenge is the maintenance of the algorithm, its efficiency and its resilience when it must undergo a colossal flow of data. This skill, which makes it possible to move from a prototype to an industrial application, is dependent on great maturity in the management of AI, its algorithm and its infrastructure. This maturity is still largely lacking.

According to a Gartner study, 85% of AI projects never make it past the prototype stage.

This ability to do:

1. business predictions based on data,
2. at an industrial level, i.e. capable of generating a reliable long-term ROI,
3. Do it within a reasonable time
4. at a reasonable cost is the real challenge of artificial intelligence today.

This is possible, especially for retail, thanks to MLOps.

Contraction of Machine Learning and Operations, MLOps is a set of tools and practices that allow Machine Learning projects to move from the stage of a prototype created from a controlled quantity of data to the stage of large-scale exploitation, dealing with exponential flows of data. Thanks to this, the AI sector is finally becoming mature. The MLOps is now the condition Sine qua non which will finally allow the massive deployment of AI by businesses.

The meeting of AI with the retail universe

This large-scale prediction tool allows four main types of applications that are particularly suited to the supply chain, customer relationship management or even customer/point of sale marketing:

1. Operational excellence
2. The creation of innovative products
3. Hyper-personalization
4. Risk management

Of these four main families of applications, an infinite number of actions can be undertaken on a case-by-case basis, including:

• The supply chain: the use of AI to predict flows within the Supply Chain would make it possible to optimize the volumes of upstream/downstream orders and the sizing of teams (in particular in warehouses) by taking into account variables such as weather or consumer trends
• Customer relationship management: taking care of the simplest customer interactions would make it possible to relieve human operators for the treatment of the most complex cases
• Customer/point of sale marketing: Analyzing customer flows, changing the location and predicting the products that a specific customer would be likely to buy during their next purchase (in store, online...) would boost transformation rates and increase Ca/m2 in stores.
  
  The average shopping carts of e-commerce sites could also be targeted. For example, a customer advisor in contact with the customer could offer complementary products based on the recommendations of an AI that would have analyzed the customer's profile and history.

Know how to identify what you want to target

Because of this proliferation, it is essential to choose the right application cases in order to aim for a tangible return on investment.

For example, the startup Craft AI has developed a 6-step methodology to support its customers in record time:

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To be able to use AI in your processes, you will finally have to consider these last conditions:

Business factors

• What data and what performance do we expect from AI?
• What data is available? In sufficient quantity and quality?
• What level of prediction is expected?
• Is it enough to carry out a POC?
• What are the expected benefits? What are the magnitudes to justify a POC?
• What business plan for AI projects? What ROI?

Human factors

Transversality

It is important to have cross-functional teams from the start of the project in order to have a global vision of the problem and to develop an efficient AI.

AI fears

Often seen as replacing existing teams, it can inspire fear. AI is at the service of decision support teams. The confidence of the teams in the results provided and the clarity of the models are essential.

Pixis Conseil is an independent consulting firm, retail expert in strategy and organization, created in 2005. It is made up of experienced women and men, with a double operational and consulting experience, to provide the efficiency necessary to carry out our missions.

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