Predictive AI is one of the most useful tools for leveraging business data to make decisions. But what is the reason for this prevalence, and how does it differ from traditional predictions?
The main reason is that it provides a roadmap for planning critical aspects such as sales and stock management, basing decisions on data rather than merely on individual experience or biased perspective.
It does not mean that the prediction results are unquestionable. On the contrary, the results become an additional tool that decision-makers can use to work more reliably, securely, and conveniently. In other words, human elements like experience and intuition are still necessary for making decisions with a higher success rate.
A key reason for AI and human experience to continue working together in decision-making is that each considers different elements of the environment. It makes them complementary, and ignoring either can lead to an incomplete view and less accurate decisions.
How does predictive AI work?
Before answering this question, it’s important to mention that before AI gained popularity, statistical models were used to plan sales, manage stock, etc. These models were the only data-based resource that allowed decision-makers to make informed decisions.
Today, AI combines statistical analysis with machine learning models. Machine learning mimics how humans learn, enabling AI to perform tasks like data classification and predicting future outcomes.
Machine learning is the set of algorithms that allows AI to learn from data. The accuracy of the prediction depends on factors like the quality and quantity of the data.
How does data quality affect the results of predictive AI?
To understand the impact of data quality on results, we could compare it to the learning of a student who reads books with outdated information versus a student who has access to updated information. The former is more likely to give incorrect answers on an exam, while the latter has a higher chance of success.
We can think of predictive AI as a student learning how the data in your company behaves and developing the ability to analyze it and make predictions. Continuing with the example, the more time the student spends learning, the better their results will be in exams.
In other words, learning is a skill that can be trained and has the potential to improve with the necessary resources for training. These resources are primarily high-quality data and time. Depending on the volume of data or the complexity of the prediction, training may require more time.
Another important aspect is having the right data for each prediction. For example, if we want to know the average ticket value for a customer profile, we need data that defines that profile, such as age, gender, or location.
In conclusion, predictive AI provides companies with tools to make data-driven decisions with a deeper level of analysis. However, the accuracy of the results still depends on human responsibility, such as the data provided and its quality.
At RCM Software, we work to ensure that companies can benefit from AI throughout the entire data lifecycle. That’s why our data orchestration platform and BI both feature integrated AI.
