Growing Your AI IQ: A Guide to Building AI Competence

By IEEE Computer Society Team on

March 24, 2025

Explicit competences are built by intentionally learning information, such as when we study and learn words in a new language.

Implicit competences grow “in the absence of conscious awareness” in discrete episodes over time, as when we learn how to speak a new language.

To highlight the challenges AI entails, the authors compare the competences that exist in the workforce today with those that are urgently needed; both are growing in importance and will continue to do so.

Existing AI competences include:

hard competences such as data science, algorithms, security, and AI frameworks; and
soft competences such as problem solving, abstraction, and communication.

AI competences that are less common in the workforce today include:

hard competences such as systems engineering, model selection and evaluation, and copiloting; and
soft competences such as empathy, creativity, and ethics.

Enterprise Generative AI Summit in San Jose, California

Analyze knowledge gaps. Develop a tailored curriculum that identifies specific learning needs and skill gaps using standardized questions and small case studies.

Create the training. Use GenAI to synthesize and aggregate existing training materials—as well as materials on corporate rules, standards, and so on—into individualized training formats that coherently address Step 1 gaps.

Deliver the training. Use GenAI to enhance existing e-learning platforms and deliver digestible Step 2 content that is automatically adjusted to the trainee’s available time, knowledge gaps, learning styles, learning goals, project role, and the project’s needs.

Get feedback. Use GenAI to offer immediate data-driven feedback on learning progress, then use that feedback to automatically update training materials. Examples of updates might include restating learning questions and selecting sources to more thoroughly explore specific types of content.

What’s at Stake?

In various areas, increasing AI competence is not only a good idea but an urgent need on the part of companies, institutions, and societies. For example, Ebert and Hemel provide a compelling discussion of AI ethics, including how AI mirrors the values and thinking of its human creators, who form the foundation of “the morality of AI” through the data they choose and the algorithms they create.

The authors also discuss the stakes of the current regulatory chaos surrounding AI, offering a cautionary analogy regarding the introduction of automobiles: At first, only experts drove, and there was little need for regulation. Once driving gradually became a more common option, rules were implemented and licenses required to prove driving competency.

In contrast, AI has been quickly released into the wild, fueled by corporate pursuits of growth, competition, and engagement at all costs as governments and standardization bodies struggle to catch up.

Dig Deeper

To learn more about these stakes and approaches to developing AI competence, read “Grow Your Artificial Intelligence Competence.”

To dig even deeper, attend the international IEEE Conference on Artificial Intelligence (IEEE CAI) May 5–7 in Santa Clara, California. In addition to showcasing the latest AI research and breakthroughs, IEEE CAI emphasizes applications and key subject areas, from human-centered AI and sustainability to issues and industry-specific applications in healthcare, transportation, and engineering and manufacturing.