Key Considerations for AI Upskilling in Organizations

Artificial intelligence (AI) is rapidly transforming industries by enhancing decision-making, increasing efficiency, and driving innovation. For companies, upskilling the workforce in AI has shifted from being a competitive edge to a critical necessity for staying relevant in today’s market. Earlier this year, I led my company’s AI upskilling program. Through a process of trial and refinement, we identified five key considerations that can help guide organizations as they embark on their own upskilling journey.

Start With a Skills Needs Assessment

Before launching an AI upskilling initiative, it’s essential to assess the organization’s current AI literacy through skills needs assessment. This process establishes a baseline of knowledge and helps to identify specific learning gaps.

A survey was used to conduct this assessment. A particularly effective approach involved asking employees to rate both their knowledge and competence in specific AI topics, ranging from foundational concepts such as AI basics and generative AI to more advanced areas like machine learning (ML).

Technical roles may require specialized training, while others can benefit from understanding how AI can improve workflows. Identifying these needs allows for the development of a tailored and relevant upskilling program.

Build a Curriculum That Caters to Different Learning Styles and Levels

After completing the skills assessment, the next step is to design an AI curriculum that addresses the identified gaps and accommodates different learning styles and experience levels. A blended learning approach proved to be most effective, combining curated eLearning courses via LinkedIn Learning, videos and articles, instructor-led workshops, and collaborative learning opportunities.

eLearning platforms like LinkedIn Learning offer flexibility, allowing employees to learn at their own pace, while interactive videos and articles simplify complex AI concepts. Instructor-led workshops provide hands-on learning for those who prefer a classroom environment, and best-practice sharing sessions allow employees to learn from peers with AI experience.

A cohort-based learning model, where employees collaborate over several weeks, proved especially impactful, fostering deeper understanding and creating a supportive community of AI learners within the organization.

Establish Guidelines for Responsible AI Use

When embarking on an AI upskilling journey, ensuring responsible AI use is essential, including implementing an AI Policy.

An AI Policy outlines rules, regulations, and expectations for data privacy, security, and the use of approved tools, ensuring compliance and safeguarding sensitive information.

Meanwhile, the AI Guidelines offer best practices for ethical AI use, addressing issues like bias, hallucinations, and intellectual property. Together, these frameworks help employees navigate AI responsibly and effectively.

Leverage External Experts and Internal Talent for Upskilling

Identifying and leveraging both external experts and internal AI talent is a powerful strategy in upskilling efforts. External experts are crucial for keeping the organization at the forefront of AI advancements, while internal talent brings a deep understanding of the company’s specific workflows, challenges, and culture.

When it comes to internal talent, it’s important to recognize that AI expertise doesn’t always come from the most obvious places—employees with a curiosity for AI and a willingness to learn can be as valuable as those with prior technical experience.

When our AI upskilling project was launched, employees from all departments were encouraged to volunteer, regardless of their AI background. This approach not only formed a diverse team with varying levels of experience but also fostered a strong sense of ownership and enthusiasm for AI learning.

Non-experts contributed by organizing best-practice sharing sessions, helping to identify AI opportunities within various departments, and offering practical insights on how AI could improve workflows. Meanwhile, existing experts supported the initiative by mentoring others, providing technical solutions to address AI opportunities within departments, curating advanced learning content, and designing specialized AI courses. For example, we launched a multi-week AI & ML cohort program led by an internal expert, which deepened technical expertise and aligned employee learning with the company’s long-term AI strategy.

Measure Success Through Business Impact

To truly assess the effectiveness of AI upskilling, it’s important to go beyond just measuring the completion of learning initiatives. Success should be measured in terms of the application of learned skills and the business impact those skills create.

Some key performance indicators (KPI) to consider include increased productivity or efficiency, improved quality, reduced error rates, and optimized customer experience, to name a few.

These metrics can be tracked through employee feedback, performance reviews, and by monitoring specific business outcomes.

Conclusion

AI upskilling is essential for future-proofing the workforce. By conducting a skills assessment, building a tailored curriculum, establishing guidelines for responsible AI, leveraging both external expertise and internal talent, and measuring business impact, organizations can ensure their AI initiatives deliver real value. Now is the time to embrace AI’s opportunities and empower employees to thrive in the AI-driven future.

Zelna McGee is VP talent and organizational development at L.A.-based Centerfield, which supercharges customer acquisition for leading residential service, insurance, e-commerce and B2B organizations. Connect with her via LinkedIn.

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