In this sense, a survey carried out by Baufest to understand what the market in general thinks about how the advancement of AI will affect it in the short term, revealed that 71% of those surveyed believe that these algorithms will make their work easier. Optimism diminishes with a view to the future: 18% believe that in two decades it will replace a large part of the positions that people occupy today, and 11% believe that in 5 to 10 years their work could be threatened by the development of this technology.
But let’s go back to the present: what would happen if, for example, someone told you that an algorithm can anticipate how much your company is going to sell during the year, would you trust the results of this technology?
Predictive Artificial Intelligence (AI) is a method of data analysis that allows predicting and anticipating the future needs or events of an organization, on different fronts such as marketing, inventory management, logistics, sales, finance, and equipment maintenance, among others. others. This technology can also simulate a set of scenarios to refine the company’s strategy, for example, at the price level, promotions, and assortment, to name just a few.
Currently, artificial intelligence tools have reached such a level of evolution that the most pragmatic companies already share many success stories, make more investment, more conferences, more job descriptions, and more news. These results allow us to clearly visualize the opportunities generated by this technology. Its operation is based on current and past information, collected within the company. Without this data it would be impossible to model useful and effective predictions.
When it comes specifically to the flow of sales or revenue, there are several tasks that predictive artificial intelligence using machine learning (ML or machine learning) tools are already doing for organizations. With ML it is possible to predict, simulate and automate different aspects of the financial management of a company. Thanks to their evolution, these solutions are now more and more precise.
Financial forecasting processes are linked to financial, historical and market data, which reflect and affect the performance of the company. But since business realities are rarely static over time, financial forecasting must also take changing circumstances into account. Machine learning tools allow you to add inputs and larger volumes of data to the forecasting equation to generate more accurate predictions, using data such as purchasing patterns, historical fraud records, real-time stock information, customer habits, and more.
Artificial Intelligence Applications
We said that predictive analytics uses historical data (or external data) to anticipate future events. Unlike traditional software where data is processed with a previously written program generating results, in machine learning the program is generated from the data and historical results of that data, meaning; the machine learns. That “learned” predictive model is then used on the current data to project what will happen next, or to suggest actions to take for optimal results.
These solutions allow you to speed up the forecasting process, handle a large amount and variety of data, as well as continuously improve accuracy, creating a robust system.
Naturally there will always be peripheral factors that distort the data. But the more data sources the company has, whether internal or external, the more accurate its predictions will be when using artificial intelligence and predictive analytics.
Artificial intelligence and machine learning
According to an international study by the consultancy Future Market Insights, predictive analytics market revenue reached $10.5 billion in 2021 and is expected to grow at a compound annual rate of 15.8% between 2022 and 2032. By the end of 2032, it is estimated that this market will reach a valuation of $ 55.5 billion. he Markets & Markets firm expects that several factors, such as the increase in the use of ML as well as the acquisitions and launches of products in this market, will drive the adoption of predictive analysis software and services.
As a discipline derived from data science, big data and artificial intelligence, today’s predictive analytics puts the spotlight on the business relevance of the resulting insights. With interactive and easy-to-use software, this discipline is no longer just the domain of mathematicians and statisticians: business analysts and line-of-business experts can easily benefit from these technologies today.
We have already commented that at a corporate level these solutions can be used in various departments and activities, but to focus on financial and sales issues, they are often used, for example, to optimize marketing campaigns (determine customer responses or purchases, as well as to maximize cross-selling opportunities). In this sense, predictive models help companies attract, retain, and grow their most profitable customers. In addition, retailers around the world are using predictive analytics for merchandise planning and price optimization to analyze the effectiveness of promotional events and determine which offers are most appropriate for consumers.
In the area of sales, retail giants like Amazon have been using predictive AI for a long time. The solutions allow optimizing the business model and strategies through advanced analysis. For example, predictive AI makes it possible to retrieve information related to customer navigation on a website, to predict other current or future needs. This seeks to impact the user experience in a positive way, and the consequences on the company’s sales figures are usually very significant. A popular example is Netflix using AI algorithms to recommend shows to its users, Netflix’s vice president of product innovation Todd Yellin says that 80% of content viewed comes from these recommendations.
With machine learning, companies can process more data from more sources and perform more complex and sophisticated queries on that data, producing more accurate forecasts faster. Anyone who has played a game of chess on Windows will know that it is practically impossible to beat the CPU on hard mode, this is due to the artificial intelligence behind this game, which will give the best possible move in the shortest amount of time.
Future of Artificial Intelligence
Predicting profit and loss data with machine learning is a common reality today. Having a data set available that includes a variety of income and expense variables over a considerable period, it is possible to anticipate the net benefit, considering the seasonality trends collected in historical data. With these tools you can also see which variables most affect profits and net profit. And then different scenarios can be planned by modifying those variables.
In times of uncertainty and rapid change, predictive AI allows you to add a new approach that collaborates with business forecasting. By providing your finance team with ML or AI solutions, you will be providing them with tools that can speed up and improve the accuracy of their financial forecasting work.
At Baufest we offer a comprehensive applied AI solution that adjusts to what each company needs, to generate an impact on the business.