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In my daily work as CEO of AIMANA, I’ve noticed a growing interest among organizations in implementing AI – an interest that brings new challenges and doubts about how to conduct this process adequately.

One of the first steps that companies and professionals must take is to understand their level of proficiency and understanding of AI-related processes and themes, so they can map the competencies they need to develop.

In this article, I’ll discuss the importance of developing skills for professionals involved in AI projects and product creation, as well as existing frameworks and models that can be of great value to businesses.

Bridging the gap

Developing capacities and competencies in AI is no longer a preparation for the future; it’s already clear from the demands presented by companies in the current job market – demands that present a gap to be filled, either through training or capacity building.

According to a research conducted by Microsoft in partnership with LinkedIn, the demand for AI-skilled professionals has increased by 323% over the past eight years; meanwhile, more than half of business leaders expressed concern about finding sufficient talent by 2025.

Naturally, this gap affects the implementation of AI in companies structurally, as the preparation of professionals and leaders is more than necessary for the equation to work.

Frameworks for developing AI competencies

Considering these needs, created by the evolution of technology and its growing presence in the corporate sphere, different institutions, organizations, and companies have built various frameworks and structures to address the issue, focusing on developing competencies and literacy – both hard and soft skills – in AI.

The Digital Promise, a non-profit organization, has developed an AI literacy structure that consists of three interconnected engagement modes. This structure, which emphasizes the importance of understanding and evaluating AI in the decision-making process, considers the following aspects:

• Understand (Understand): Basic understanding of AI can do and how it works, enabling informed decision-making about the use of AI systems and tools;
• Evaluate (Evaluate): Centralizing human judgment and justice issues to make critical considerations about AI benefits and costs to people, society, and the environment;
• Use (Use): Interacting, creating, and solving problems using AI, thinking about a progression of use for different contexts and purposes.

  Naturally, as more frameworks and structures are built, the more they are absorbed and understood by the market, especially when thinking about an AI implementation that can ensure not only security and efficiency but also scalable and aligned with the business strategy.

  Hard skills and soft skills

  From the perspective that AI represents an extension of human capabilities, I believe that the real value of AI is only captured when there are qualified professionals to translate AI possibilities into concrete solutions. Therefore, it’s necessary to evaluate the requirements that exist within teams to address them adequately through training, capacity building, and development.

  In the framework developed by AIMANA – built upon existing models in the market and our experience with companies – we understand the importance of analyzing competencies from two aspects: hard skills and soft skills, worked conjointly.

  Thus, we evaluate the following competencies and skills, categorized into 5 levels – from beginner to fluent:

  • Hard skills: Understanding AI; using AI tools; creating with AI; critical evaluation of AI; AI ethics;
  • Soft skills: Adaptability; collaboration with AI; communication about AI; continuous learning; digital empathy.

    This type of evaluation allows not only a comprehensive and complete perspective on the level of preparedness but also the design of personalized development plans and paths – considering that each collaborator possesses different skills and levels of proficiency.

    Conclusion

    Finally, I believe it’s important to point out that adopting frameworks for AI implementation does not stifle innovation but enables focus, security, and scalability.

    Because organizations that understand AI as a central part of their strategy need more than isolated initiatives: they need structure, organization, and clarity. And, within this need, well-adjusted models and frameworks can represent a bridge, a path that leads to real business transformation.