Choosing between predictive and generative AI depends on the specific needs of companies, combining them makes it possible to optimize decision -making and content creation.
At a time when it transforms industries, improves decision -making processes and creates new opportunities in multiple areas of activity, choosing the right artificial intelligence (AI) as specific needs is not always simple. The deployment of chatgpt highlighted the powerful possibilities of AI and allowed to all, companies as collaborators, to make it a choice of choice.
In two stages, three movements, the AI has raised a cloud of dust and exerted strong pressure on companies to identify the way they could use it. This dust and this pressure blur the tracks between predictive and generative AI applications. These AIs are both trained and trained using historical data. However, it is essential to understand the difference between these two forms of artificial intelligence and the corresponding applications to fully exploit the potential.
From predictive AI to prescriptive AI
As its name suggests, predictive AI is designed to predict results on the historic database. Most organizational challenges can be translated into a question on which a predictive model may be formed, subject to the necessary historical information. These scenarios are transcribed into questions by classifying data into binary categories or different classes, or by measuring a result on a continuous set of values. This AI learns the limits between the different categories based on the data on which it was trained and uses this knowledge to establish predictions about new and unpublished data.
The algorithms commonly used in predictive AI are as follows: logistical regression, decision trees, random forests, support vector machines (also called “separators with large margin”) and neural networks.
Using this technique for more reliable projections is an exceptional power that provides a prospective vision of important results. However, it can be just as crucial, if not more, to understand why these results are likely to occur. It is precisely for this reason that any predictive application must incorporate the concept of explanability. Without an explanation, no clear action can be undertaken. Take the example of attrition, that is to say the loss of customers or subscribers: if a company ignores the cause of a potential increase in this risk, how can it limit it?
The incorporation of the concept of explanability in the predictive AI makes it possible to go to the prescriptive AI to explore certain actions and measure the impact on the desired result. It is possible to model the probable results by integrating the customer into a particular support plan, increasing the engagement rate and lowering prices, without applying these measures.
Consequently, an appropriate action derived from AI can be implemented. Going from an isolated action to a coordinated set of shares allows companies to develop business optimizations. It is necessary to define the optimal result by allowing predictive AI models to explore the data entered and the many possible iterations in order to inform a set of prescriptive actions and to optimize the probability of the expected result.
Fill the ditch by combining predictive and generative
The capacity of the generative AI (GENAI) to generate text, images, music and many other data makes it a powerful tool for content creation and natural language treatment. Generative AI models learn the underlying patterns and structures to the data on which they are trained and use this knowledge to produce original results.
Although serving distinct objectives, predictive AI and generative AI can give birth to more robust and more complete solutions. For example, a predictive model will identify the risk of attrition of a customer, while a generative model will be used to develop personalized communication capable of treating this risk and mitigating it. This synergy improves the overall efficiency of AI applications by providing precise data doubled with reliable information.
Choosing the right AI, whether predictive or generative, depends on the needs and objectives of an organization. If predictive AI is ideal for making precise predictions that are based on historical data, generative AI excels in the creation of new content adapted to a given context. It is by understanding the strengths and applications of one and the other that companies will be able to exploit the potential of the AI to stimulate innovation, improve decision -making and offer exceptional experiences.
The predictive AI and the generative AI both conceal a tremendous potential – and the data represent the starting point of this adventure. As companies get used to AI, new opportunities and new use cases will be created. The use of AI from one end to the other of companies will help to accelerate innovation and will allow you to discover applications hitherto ignored, thus expanding the field of action of this technology.
