Data is the business of the future, for large industry. But concretely, what are the contexts in which data becomes useful to create predictive analysis? And above all: can AI help us in carrying out predictive analysis?

Below we will go into detail on the concept of predictive analysis carried out by AI, with practical examples from the world of companies in different sectors.

What does predictive analytics mean?

Predictive analytics is a field of data analysis that focuses on the use of statistical models. Based on historical data and current trends, predictive analytics predicts future outcomes, which in the computer science industry is possible thanks to techniques such as data mining and machine learning. 

With the company data mining we can analyze large datasets, extracting useful information such as patterns, relationships, and anomalies in the data.

Instead the machine learning algorithm , to use a simplified but immediate definition, is a branch ofartificial intelligence which deals with the development of algorithms and techniques that allow computers to learn from data without being programmed.

Predictive analytics in the enterprise to make informed decisions

Let's think for example of predictive maintenance: through the analysis of data coming from sensors installed on machines, artificial intelligence algorithms can predict when a machine is most likely to break down. 

For example, a manufacturing company can monitor the temperature, vibration, and other parameters of its machines to predict failures and plan maintenance accordingly.

But predictive analytics is also used to optimize production processes, improving efficiency and reducing waste in order management and the supply chain, for example by planning logistics routes more carefully. 

By predicting product demand, AI helps in predictive analytics and enables companies to optimize inventory levels. This means reduce stock-outs, but also limit problems with goods in customs, and managing each of these two scenarios with targeted suggestions given by the AI, which are based on the experience gained in the past.

To draw a parallel, AI capable of predictive analytics is like a highly trusted and experienced employee who knows common errors in processes and notices anomalies before damage occurs.

But let's think about the potential of a machine, which, compared to a human employee, never gets tired, always works with maximum attention and does not miss any important data. 

Or, let's think about those sectors, like that energetic, where human control would be insufficient. Utilities, with an AI that takes care of predictive analysis, could predict demand peaks and adjust production as needed, improving efficiency and reducing operating costs. 

AI, still in the energy sector, is also capable of identify areas of inefficiency and implement solutions to reduce overall energy consumption.

Quality control with predictive analytics

By analyzing production data, predictive AI can perform preventive quality control. Reducing the error rate is useful for many sectors, but think about the huge benefit also in terms of public health, if predictive AI began to be implemented on a large scale also in the food and pharmaceutical industries.

Safety at work

Another area where predictive analytics is making a difference is in workplace safety. By analyzing data about accidents and operating conditions, companies can identify risk factors and implement preventive measures to improve worker safety. For example, a mining company can analyze data from sensors installed in tunnels to predict collapses and improve miner safety.

Predictive AI in Administration and Human Resources

Already today, many Artificial Intelligence solutions are used by controllers and Human Resources, in different ways. 

For example, AI can be useful for cash flow forecasting, improving liquidity management and financial planning.

Additionally, AI can assess customer credit risk and make more informed decisions about payment terms and credit policies.

In HR, AI can analyze employee data to identify factors that influence performance and job satisfaction. This can improve recruitment. Or, AI can predict future workforce needs based on business growth and market changes, thus optimizing human resource allocation.

Predictive analytics in marketing

Identifying groups of customers with similar behaviors and characteristics is not an activity that can be entirely delegated to a machine, but a good part of it can. 

Or, AI helps analyze historical sales data to predict future sales trends, but also identify customers at risk of churn (churn analysis).

By adopting predictive analytics in your company, you can gain a significant competitive advantage by improving operational efficiency, increasing sales, and optimizing resource allocation.

All this is possible by correctly using the AI ​​tools already available on the market, but adapted to the needs of each specific company!