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What skills do we need when dealing with artificial intelligence systems? What skills should we equip our teams with in order to be able to use AI systems to their full potential and enjoy using them?
The skill mix
Even if you are not a technician, there are some skills that can be helpful when dealing with AI. In our opinion, a mix of the following skills is important:
Problem solving: Problem-solving skills are necessary when using AI, as they enable you to recognize and solve problems that may arise when using an AI system and thus improve the effectiveness and reliability of the system.
Critical thinking: Critical thinking is an important skill when using AI because it involves analyzing and evaluating information. When using AI, critical thinking is required to assess the accuracy and validity of the results generated by the system and to ensure that the system is used effectively and ethically. Critical thinking thus also includes the ability to evaluate the quality of the data and the models created by AI systems.
Creativity: Creative thinking is important when dealing with AI as it helps to develop innovative solutions to complex problems. While AI systems can process large amounts of data and recognize patterns and correlations, they still need human input to recognize and solve problems that have not yet occurred.
Communication skills: Good communication skills are required to deal with AI. This includes the ability to explain complex ideas and algorithms to non-technical stakeholders. Communication continues to be an important skill when using AI as it involves interacting with others, both inside and outside an organization. Effective communication is necessary to ensure that AI systems are used effectively, efficiently and ethically, and that their potential benefits are understood by all stakeholders.
Decision-making: The ability to make informed decisions is essential when dealing with AI. As AI systems often make decisions that impact business operations, customer experience and other important aspects of an organization. It's about understanding the strengths and limitations of AI systems and using this knowledge to make informed decisions.
Continuous learning: AI is a rapidly evolving field and it is important to keep up with the latest developments. The ability to continuously learn and adapt to new technologies and ideas is essential.
An example of the skill mix
Suppose you work for a company that uses AI to recommend products to customers. As a non-technical person, your job could be to evaluate the quality of the AI system and its recommendations. What are your skills then?
Problem solving: You realize that some customers are not satisfied with the recommendations they receive from the AI system. You work with the technical team to identify the problem and develop a strategy to improve the recommendations.
Critical thinking: You evaluate the quality of the data used by the AI system to make recommendations. You realize that some data is outdated and not relevant to current customers. You will work with the technical team to update the data and improve the accuracy of the recommendations.
You are creative: you have new ideas on how the AI system can be used to improve customer satisfaction. For example, you suggest training the AI system using customer feedback and improving its recommendations.
Communication skills: You will explain the benefits and limitations of the AI system to non-technical stakeholders, such as the sales and marketing teams. You will also communicate customer feedback to the technical team and work with them to improve the system.
Decision-making: You use your knowledge of the strengths and limitations of the AI system to make informed decisions. For example, you decide whether the AI system should only recommend products for certain types of customers based on their previous purchasing behavior.
Continuous learning: You keep up to date with the latest developments in AI and use this knowledge to improve the AI system. For example, they suggest the use of a new machine learning algorithm to improve the accuracy of recommendations.
Questions about handling the AI
When it comes to dealing with AI solutions, there are some important questions you should ask an AI system to ensure its efficiency and reliability. Here are some examples:
How was the AI system trained? If you know how the AI system was trained and the quality of the data used for training, you can better assess the accuracy and reliability of the results.
What are the scope and limitations of the AI system? It is important to know what the AI system can and cannot do so that you can assess its usefulness and identify areas where human input is still required.
How does the AI system make decisions? If you understand the decision-making process of the AI system, you can better assess the fairness and transparency of its results.
What are the potential risks and disadvantages of the AI system? It is important to consider the potential risks and negative consequences of using an AI system, such as privacy concerns or unintended bias.
What happens if the AI system makes a mistake? It is important to know how the AI system deals with errors or mistakes and how they can be corrected.
How can the AI system be improved? By understanding the potential for improvement of the AI system, you can identify areas for further development or research.
Overall, these questions allow you to better understand and evaluate an AI system and ensure its effectiveness and reliability for the task at hand.
More info
can also be found in our podcast https://open.spotify.com/episode/2UjtgGAyRlgLxVBTQ6jqoS
or in the book "Digital Competence and Future Skills. How companies prepare themselves for the digital future" by Dr. Philipp Ramin
