Definition and workings of predictive AI
Whether it is for the retail sector or other types of companies, optimizing the management of stocks, sales or even traffic is a major issue. Beyond the impact on turnover, this issue is also essential throughout the supply chain, in order to ensure a quality service and to perpetuate the various processes of the activity (purchase, delivery, etc.). All companies must now produce in the right quantities, especially in the age of the Internet and online purchasing.
Thanks to the evolution of technologies, solutions and other analysis tools are now more and more precise. Among them, we can count on predictive artificial intelligence, a technology based on "machine learning", capable of predicting, simulating and automating everything related to stocks, pricing, promotions, or the assortment of a company.
To better understand the main principles of predictive AI and its various fields of application, let's take a look at how it works and its main specificities.
What is predictive AI?
Generally speaking, and outside of any application, predictive AI is a method of data analysis, capable of predicting and anticipating the future needs or events of a company. This allows, among other things, to see trends coming, or to predict risks and their solutions. Of course, the functioning of predictive artificial intelligence is based on current and past information collected within the company. Without this data, it is of course impossible to model useful and effective predictions.
As you can see, predictive AI is a technology that is based exclusively on data, and in very large volumes, just like Big Data. If it is one of the most efficient solutions in many areas, such as for inventory management in the retail industry, predictive AI only gives predictions and hypotheses, which must always be analyzed according to the constraints and expectations of the company. To be implemented, predictive AI requires special attention, whether it is to sort out the different data retrieved, or to be used wisely in a more global strategy.
Be careful, predictive analysis should not be confused with descriptive or prescriptive analysis. Indeed, if in the case of predictive analysis, it is a question of anticipating and foreseeing certain events in the company, descriptive analysis details and observes an event in progress. Prescriptive analysis is about understanding why an event is likely to occur and determining what the company can do to implement effective solutions. The latter uses the data collected by predictive AI to answer the question that interests companies, "how to deal with an upcoming event?
How does predictive AI work?
Predictive artificial intelligence has many fields of application (marketing, finance, retail, etc.), both for predicting and simulating certain data, and for automating sometimes time-consuming tasks.
Predicting business development with predictive artificial intelligence
One of the first interests of predictive AI for a company is obviously to obtain precise forecasts on its future and that of the market in which it evolves. In a world in movement like ours, where sectors evolve very quickly, it becomes very difficult to position oneself on the long term. In this respect, predictive artificial intelligence is a great help, as it brings a large dose of reliability to all companies that need it.
In the sales area, predictive AI is really catching on, so much so that retail giants such as Amazon are already using it. Optimizing one's model through advanced analytics therefore appears to be a solution of the future. Machine learning is a process that is becoming more and more important, regardless of the industry. For example, predictive artificial intelligence can retrieve information related to the navigation of customers on a website, to predict other current or future needs. If this impacts the user experience in a positive way, the consequences on the company's sales figures are also significant.
However, it would be misleading to limit predictive AI to customer expectations, since it can also impact many other areas. For example, inventory management in retail outlets and warehouses can be significantly optimized through the analysis of different types of data.
Simulate scenarios with predictive artificial intelligence
While predictive AI is an analytical method capable of making predictions, it can also simulate a number of scenarios to sharpen a company's strategy. Whether it is about promotions, pricing or assortment, this predictive AI capability can impact many areas of a business. For profitability or optimization purposes, simulating the most likely future scenarios in its market allows the company to be more efficient and more competitive.
Of course, predictive AI does not just make general predictions, since it also and especially adapts to business constraints and objectives. Far from being trivial, simulation has many advantages, both in terms of taking into account factors absent from predictive data and in terms of simply refining the contours of initial predictions. Indeed, it allows, among other things, to refine the latter and to have a better understanding of their ins and outs.
It should be noted that simulating scenarios implies surrounding oneself with experts and specialists, capable of distinguishing and deconstructing the information retrieved.
Automate tasks with predictive artificial intelligence
Beyond predictions and simulation, predictive AI can also automate a number of tasks within the company. If this relieves the teams normally in charge of these tasks, especially since most of the time they are time-consuming, this solution gives everyone the possibility to focus on their core business.
Among the tasks that AI can automate, we find for example the preventive maintenance of buildings. Indeed, artificial intelligence allows a company to collect precise information on the state of an infrastructure, or even on the functioning of a machine. Predicting in advance the different maintenance exercises therefore potentially saves money, avoiding more important damages and problems. This automation task can include several aspects, such as:
- data recovery;
- data analysis;
- evaluation of overall performance.
It goes without saying that the algorithms used for automation meet the concrete needs of companies, regardless of their profile. They are versatile and take into account existing infrastructures, such as databases, applications and storage files.
How to set up a predictive analysis?
Although it may seem obvious, using predictive AI within a company is not something you can improvise. Indeed, it requires sufficient skills, which is far from always being the case, partly because of the great complexity of these solutions. It is all the more useful to call upon specialists, as predictive analysis can be a source of errors and drifts that must be detected.
In order for predictive artificial intelligence to reveal its full potential, it is therefore important to clarify the expected objectives in advance, to know which tools and software to use, and to anticipate the integration of predictions into operations already underway.
In any case, many companies have entered the predictive AI market, offering complete solutions based on high precision algorithms. It is therefore best to use them to ensure that the analysis process yields the best possible results.
Which companies are using predictive AI?
As we have seen, a large number of companies can use predictive AI in different areas. Some of the industries include:
- companies of the mass distribution;
- the big names in luxury;
- fashion and ready-to-wear brands;
- pharmaceutical industry companies;
- multinational computer companies;
- the catering sector;
- the tourism sector;
- event management companies;
- insurance companies;
- etc.
Stock optimization, promotion optimization, pricing optimization, or even point of sale traffic forecasting, predictive AI algorithms can truly optimize all aspects of a company's operations. This is the reason why many major retailers have already chosen this method, such as the Casino group, U stores, or even Monoprix. Among the big names in the pharmaceutical industry, Bayer and Alter Pharma have also turned to these solutions. In the transport sector, giants such as SNCF and Corsica Ferries are using these technologies. Finally, multinational IT companies are not left out, since companies like Cisco, Oracle, or Microsoft have also adopted predictive AI.