Predictive AI, like all emerging technologies, presents challenges, and understanding them will bring us closer to more correct use.

In the previous article, I gave you an overview of the Predictive Artificial IntelligenceFrom its benefits to use cases across different industries, I want to go a step further and delve into its operation and the challenges it faces.

How does this technology make predictions?

To access the powerful insights that AI can provide, there are a series of preliminary steps that will ensure the information delivered is accurate and truly beneficial to businesses:

1. Data collection: This technology needs to be fed with large amounts of information, which can come from various sources, such as internal databases, transactions, IoT devices, social networks, etc.

2. Data processing: Cleaning and organizing the data, before AI starts using it, is key to avoiding erroneous, duplicate, biased, inconsistencies, and other problems that could cause model failures when analyzing it.

3. Modeling: Machine learning algorithms are implemented to build a model that adapts to the type of data, the nature of the problem, and the desired results. This allows us to identify complex patterns.

Some of the most common models are: Neural networks, regression models, and decision trees.

4. Training: Once the data has been processed, the model can be trained to learn patterns and relationships within the data. It's important to adjust its internal parameters to improve the accuracy of the predictions, and it should be noted that this process can be time-consuming and computationally demanding.

5. Evaluation, validation and adjustments: To perform a correct evaluation of the model, after its training, different data from the previous process should be used to ensure that the model generalizes well and memorizes new data.

6. Predictions: Once the model is validated, it can make predictions based on new data, providing valuable information for business decision-making and strategy planning. These predictions can be in the form of rankings or continuous values.

Understanding how predictive AI performs its function and contributes to business success is key, but we cannot ignore the fact that it is a technology that faces many challenges. challenges and riskswhich, in many aspects, depends directly on us and the use we make of it.

Below I'll tell you about some of those challenges and the considerations we need to keep in mind:

• Working with quality data: If you use incorrect or biased data, you will get erroneous predictions; that's why data quality should be paramount when creating and implementing AI.

• Ethics and privacy: Many concerns remain about data privacy and security within Artificial Intelligence, as well as its potential misuse. To address this, governments are working on legislation to regulate its use, prioritizing ethics and ensuring we can responsibly harness its benefits.

• Bias and discrimination: Processing data incorrectly can introduce biases into the models, which can be amplified, affecting the results.

• Adaptability: Models must be able to be constantly updated and adapted to new data and consumption patterns, so that their implementation can last over time.

Similarly, another challenge I must highlight is the transparencyWhen we consume models that are already trained and highly complex, it can be difficult to understand how they arrive at their predictions, hindering confidence in the results.

I hope this information is useful to you and gives you a more accurate picture of the process by which your business can obtain precise data to improve its performance. success and the competitive advantage.

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References:

Ref 1: https://cdetech.org/predictive-artificial-intelligence-in-the-stock-market

Ref 2: https://keytrends.ai/es/academy/glosario/inteligencia-artificial/analisis-predictivo

Ref 3: https://geekflare.com/es/predictive-ai-vs-generative-ai/