Upskilling and Reskilling in the Age of AI

UPSKILLING AND RESKILLING ARE NECESSARY

As AI enters the work environment, it may be necessary to learn new skills to take advantage of these advances:

Upskilling focuses on adding new skills that build on your existing foundation. The aim is to make you more proficient and valuable in your current position, potentially opening doors for advancement.

Reskilling involves acquiring an entirely new skill set to transition into a different job role. This could be because your current role is being automated or because you want to switch careers altogether.
Reskilling might involve a more intensive learning experience, such as a certificate program or even a degree, depending on the new field you’re targeting.

Employers should invest in upskilling and reskilling because companies with a skilled workforce that more quickly adapts to AI will gain a competitive edge. Upskilling and reskilling programs demonstrate a company’s commitment to employee development and future success. Investing in employee growth boosts morale and fosters a culture of continuous learning. This can improve employee retention and reduce the need for external recruitment.

Based on their upskilling and reskilling needs, employees can be partitioned into three classes:

Users of AI systems
Developers who design and create AI systems.
Managers of AI system applications

Unprioritized lists of skills are presented for each employee class so readers can identify new skills they may need to acquire.

NEW SKILLS NEEDED TO USE AI SYSTEMS

The new skills required to use an AI system depend on the complexity of the system and the way you intend to interact with it. Here’s a breakdown of the skills you may need:

Technical literacy. A basic understanding of computer technology is necessary.
Adaptability to new technologies. You must be able to learn to use new AI systems by reading instruction manuals, reviewing training videos, and experimenting with how systems can be used within your company.
Office productivity tools. Learn to use GenAI tools that create, organize, summarize, and search transcripts of voice and conference sessions; organize, summarize, search, and respond to emails and voice messages; and organize and schedule meetings.
Domain knowledge. Understanding the concepts and technical jargon used in your specific business will improve your ability to specify commands and understand the system’s outputs.
Data analysis skills. Basic data analysis skills can be helpful to effectively interpret reports generated by AI systems. This might involve understanding charts, graphs, and reports to determine what your business is doing.
Fraud detection. AI can rapidly generate realistic personalized text, audio, and video that may mislead people in becoming involved in fraudulent schemes. Users must be able to detect scams.
Prompt engineering. This is the art and science of crafting effective instructions for GenAI systems. Users need to know how to specify effective prompts using text or speech, and how to use retrieval-augmented generation (RAG), a process for seeding prompts with results from a database query.
Detect and correct errors and biases. GenAI systems may pick up and amplify biases (prejudice or inclination present in the data on which an AI system is trained).

GenAI systems may also generate hallucinations (incorrect or misleading results). You should always review the output of GenAI systems to validate its accuracy and judge if bias is present. Additional new skills may be needed as users enter the new world of AI systems.