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Understanding AI Careers to a T: Developing the Right Skills Profile
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Understanding AI Careers to a T: Developing the Right Skills Profile


What is the question that those looking to pursue or grow a career in AI should be asking? This is the question that Kian Katanforoosh, recently posed to a large-language AI. The AI’s answer was simple enough, and characteristically on-the-money: if you want a career in AI, you should be asking, “What are the most important skills for a successful career in AI?” In our latest edition of Ask AI, Kian and Workers’s data transformation specialist, Bernardo, discuss which skills–and what kinds of skills–are necessary for anyone who wants to become an ideal asset to an AI team.

"Success in an AI career requires a mix of durable and perishable skills" - Kian Katanforoosh

AI careers are becoming increasingly T-shaped, which means that those working in AI roles need competencies in both durable and perishable domains. Some skills, such as multiplying matrices, as well as many knowledge domains, like Python, SQL, business analytics, and machine learning, are likely to be just as relevant in ten years as they are today. Those domains, therefore, are placed along the horizontal bar, because they are baseline competencies required for anyone in an AI role.

However, many skills and domains are perishable. Take, for example, natural language processing (NLP). Each year, new NLP models make previous models obsolete, along with those models’ associated skills and knowledge. Or consider the fields of responsible AI and fairness in machine learning, which are domains that have been, and are certain to continue, experiencing radical developments. These skills and domains are placed along the vertical bar because they require an individual to constantly develop and deepen their understanding. So, in addition to maintaining competencies in horizontal fields, those in AI roles must also seek to become the “hero,” or company specialist, in particular vertical fields.

Unfortunately, when developing their AI teams, many company leaders have failed to take into account the importance of the vertical domains, using one-size-fits-all job descriptions based on competencies in horizontal fields to guide their hiring decisions. However, this approach often yields teams with gaps in their knowledge and skills. 

"Organizations must have a skills framework or matrix to identify the skills needed for different roles in their AI transformation" - Bernardo Nunes 

So how can companies mitigate this issue? First, they can begin by developing a skills framework or matrix that maps the team’s current competencies, allowing them to identify the fields in which they need specialists. The development and usefulness of this skills framework, then, depends entirely upon the accuracy and resolution of the assessment of the team. Once the framework has been produced, it can and should then be used to guide hiring decisions as well as to facilitate targetted upskilling among the existing team members.

Conclusion

  • AI careers are essential for digital transformation. Data leaders need to make sure they are managing and inspiring AI skills effectively. With the right skills, AI professionals can help drive digital transformation and success in organizations.
  • A better way forward is for Data Leaders to establish a Skills Framework or Matrix using the T-shaped Professional Model as input. This will allow them to identify the AI skills their team requires and help them understand which AI specialists they need in order to propel digital transformation initiatives. AI careers should no longer be considered a separate entity, but rather an integral part of any organisation's digital strategy.
  • By investing in AI skills and AI professionals, data leaders can ensure they are managing and inspiring AI careers effectively. With the right skills, AI professionals can propel digital transformation and boost success in organisations.

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