There is much talk about the new intelligent technologies that have emerged in recent years. But what exactly is artificial intelligence (AI)? It is the ability of a machine to replicate human activities such as classifying items, making decisions, analyzing facts, understanding and communicating in a human language (English, Spanish, Portuguese, etc.), learning, identifying an object through a photo, etc. In short, there are several functions in which artificial intelligence can act.

The ChatGPT tool has recently become very famous for providing various responses in natural language, but not all responses are reliable, and this technique still needs some evolution to be used in a corporation without risks of the tool saying something that is not aligned with the corporation’s guidelines. This tool is part of a group of tools called “ChatBot”.

In addition to ChatBots, there are a multitude of other tools that use artificial intelligence techniques covering various areas of knowledge. And which of them can be useful for HR?

When you conduct a selection interview and want to save it in a practical way for later analysis, you can use a tool that allows you to store and compare candidates more easily without forgetting key elements of the conversation.

I would like to focus on another utility in which artificial intelligence can help HR. It is at the moment of an evaluation of competencies and performance. Imagine you are in a company with several thousand people and you do not have a sufficient team to help all managers define individual action plans (IDPs) and how they should lead their team.

The Efix – Artificial Intelligence has come to solve this problem. By exposing it to a database, it learns and brings answers.

And how does this help? To explain in a didactic way, let’s think of a simple scenario. Suppose a particular team has 27 people, and each person was evaluated in only 2 competencies: Engagement and Negotiation. Each of these people chose a single IDP from the options formal training, “on the job” training, and coaching.

Thus, each of these people obtained a trio of data:

• Evaluation of engagement competency
• Evaluation of negotiation competency
• Choice of IDP that best suits their reality

In the example, for didactic reasons, the IDPs were chosen randomly, but in real life, PDIs are usually more grouped. For a better visualization, let’s provide this historical data in the graph below, considering the evaluation of the negotiation competency on the X-axis, the evaluation of engagement on the Y-axis, and the color of the point as the chosen IDP. We will use the following colors to represent the IDPs:

• Blue: Formal training
• Red: “On the job” training
• Green: Coaching

With this, we finish the first cycle of competency evaluations. When starting the second cycle, we have the data from the first cycle as a base.

Suppose a certain person has just finished their competency evaluation and is deciding which IDP best suits their reality. For example, their evaluation fell right in the middle between the green and red points of the central box (meets x meets).

At this moment, the Efix – Artificial Intelligence comes in to suggest the IDP to be adopted. And how does it do that?

The system builds a map with historical data with the best solution it can position all the trio of data. After this analysis, the system will arrive at a map like the one shown in the figure below.

In this way, the system transforms punctual historical data into suggestion regions. Note that the system arrived at a map in which 100% of the points are located within the suggestion area corresponding to its color.

In the case of this evaluated person, their evaluation fell into the central box between the green and red historical data, indicated by an X in the figure. As this point is near the boundary, the system indicates to the end user that the best suggestion is the red region, but the green one can also be considered.

Returning from the colors to what each color represents, the system’s response will be: “Considering the historical data from the previous period, I suggest you do ‘on the job’ training (red) with a 60% priority. Additionally, I have a second suggestion, with a 40% priority, which would be a coaching program (green).”

Once the concept behind the system is understood, let’s delve deeper into the idea. With 2 competencies, this map is very easy to draw, but what if, instead of 2 competencies, the evaluation model considers 3 competencies? In this case, the map would be three-dimensional. A three-dimensional map is much more complex, but we can still imagine what it would be like.

In a real case, we evaluate several competencies and still have to consider the goal data. In this case, we will have a map with between 10 and 20 dimensions. Humans have difficulty imagining something in 10 dimensions, but a computer does not.

Furthermore, we have other complexities in this analysis. Usually, an evaluation cycle generates more than 3 different IDPs. When we think of a population of thousands of people, we can easily have hundreds of IDPs.

Expanding a little more, the system will make several graphs, considering the various realities, such as position, board of directors, department, division, unit, etc.

It is in this context that the Efix – Artificial Intelligence system helps the HR team have a more assertive result with the definition of IDPs for its employees.

Furthermore, in the same way described above, the system can bring other types of suggestions, such as actions that a manager should adopt with their team or that a department should adopt as a result of a climate survey.

In this way, artificial intelligence is much more than a ChatBot and can provide more direct and tangible returns that can save time and money and, furthermore, enable actions that previously seemed impossible