There has been much talk about having an AI-Ready Workforce, yet there seems to be more focus on the need than on how we can achieve this.
This is the third in a series of articles stemming from the National Academy of Public Administration’s Standing Panel on Technology Leadership as part of its Call to Action on Responsibly Using AI to Benefit Public Service at all Levels of Government. Please see our first blog, “A Call to Action: The Future of Artificial Intelligence and Public Service” and second blog, “Artificial Intelligence and Public Service: Key New Challenges.“
We also know that AI is dramatically different from any other
form of technology that has emerged since the advent of the open Internet. Given AI’s speed of
transactions, complexity, and potential for mistakes and bias, this nascent technology cannot be entirely
left in the domain of science and technology. The National Academy of Public Administration identified
“Make Government AI Ready” as one of its Grand Challenges in 2019. In addressing AI Ready
Government, we recognize that government functionality still depends on humans and human systems.
This means we must identify the main characteristics of an AI-Ready Workforce.
A broad definition of an AI-Ready Workforce might refer to a group of individuals who possess the
necessary skills, knowledge, and mindset to effectively collaborate with and leverage artificial
intelligence (AI) technologies in their work environments. To make our current workforce AI-ready, we
need to create and implement required training and development for AI that could range from a simple
online required course to a more comprehensive certification program.
We must also look at how schools of higher learning are adapting their curricula to include these newer
skillsets aimed at addressing government needs. Changing or modifying the highly entrenched college
curriculum is itself a monumental challenge. Teaching AI in college should provide students with a
comprehensive understanding of artificial intelligence concepts, methodologies, and applications while
equipping them with practical skills to work with AI technologies. Ideally this should be infused
throughout existing curriculums and at all levels. Given the rapidly evolving nature of AI, the curriculum
should be adaptable and updated regularly to incorporate new techniques, technologies, and best
practices. Here are some examples of how AI can be taught:
- Multidisciplinary Approach: AI is an interdisciplinary field, and its teaching should reflect that. It
should integrate concepts from computer science, mathematics, statistics, engineering, cognitive
science, public policy and administration, ethics, and more.
- Fundamental Concepts: Begin with teaching the fundamental concepts of AI, including machine
learning, neural networks, natural language processing, robotics, computer vision, and
- Hands-On Projects: Encourage practical learning through hands-on projects. Students should
work on real-world AI problems, implement algorithms, and integrate problem-solving based on
real-world problems through existing AI offerings to gain valuable and practical experience.
- AI Ethics and Responsible AI: Emphasize the importance of ethical considerations in AI
development. Teach students about potential biases, fairness, privacy, and accountability issues
that can arise with AI applications.
- Industry Collaboration and Internships: Foster partnerships with industry to provide students
with opportunities for internships, real-world projects, and exposure to the practical applications
of AI in different domains.
- Collaborative Projects: Encourage group projects and team collaboration, as AI development
often involves interdisciplinary teamwork.
- Guest Lectures and Seminars: Invite AI experts from academia and industry to deliver guest
lectures and seminars, giving students insights into cutting-edge research and industry trends.
- Research Opportunities: Provide avenues for students interested in AI research to engage in
research projects and contribute to the advancement of AI knowledge.
- Practical Applications: Demonstrate real-world applications of AI across various domains, such
as healthcare, finance, autonomous vehicles, and customer service, to showcase the potential
impact of AI.
- Explainable AI: Teach students about the importance of explainable AI methods to promote
transparency and trust in AI systems.
- AI and Society: Discuss the broader societal implications of AI, including its impact on the job
market, privacy concerns, and potential for social good.
By incorporating these elements into the existing curriculum, colleges can provide students with a well-
rounded education in AI that prepares them to contribute meaningfully to the field and adapt to the
ever-changing landscape of AI technologies. This means courses in policy, public administration, the
humanities, and organizational development are a few disciplines that should easily absorb and embrace
In addition to infusing AI technologies into existing curriculums, colleges might consider interdisciplinary
courses, programs, and degrees. An interdisciplinary approach acknowledges that AI often bridges
different domains, and an AI-Ready workforce benefits from having diverse expertise and understanding
across multiple disciplines.
Beyond those employees who are specifically trained or credentialed in AI, the rest of an AI-Ready
Workforce, must be skilled to operate in an AI-augmented environment. These skills might include the
- Digital Literacy: Employees should have a solid understanding of digital technologies and be
comfortable using digital tools. This includes basic computer skills, familiarity with software
applications, and an ability to adapt to new technologies quickly.
- Data Literacy: AI relies heavily on data, so an AI-Ready workforce should be data literate. This
means understanding how to gather, interpret, analyze, and draw insights from data.
- Continuous Learning: AI technology is rapidly evolving, and an AI-Ready workforce must be
committed to ongoing learning and skill development to keep up with the latest advancements.
- Problem-Solving Skills: AI can be applied to various complex problems, but it’s essential for
employees to have strong problem-solving abilities to identify the right issues to address and to
interpret AI-generated insights effectively.
- Critical Thinking: With AI-generated outputs, it’s crucial for the workforce to have critical
thinking skills to assess the reliability and accuracy of AI results, avoiding blind reliance on AI-
- Creativity: While AI can handle repetitive tasks, creativity remains a human skill that
complements AI. Employees should be encouraged to think creatively and explore innovative
- Ethics and Responsible AI Use: AI can raise ethical concerns, such as privacy, bias, and fairness.
An AI-Ready workforce should be aware of these issues and apply AI responsibly in their
- Collaboration and Communication: AI projects often involve cross-functional teams, requiring
effective communication and collaboration skills to work together efficiently.
- Adaptability: AI implementation may lead to changes in workflows and processes. An AI-Ready
workforce should be adaptable to embrace and integrate these changes.
- Resilience to AI Disruption: AI can automate certain tasks, leading to job changes. An AI-Ready
workforce should demonstrate resilience to adapt to these shifts in the job landscape.
In creating an AI-Ready workforce, organizations will need to invest in training and upskilling initiatives,
promote a culture of continuous learning, and foster an environment that encourages experimentation
and collaboration with AI technologies. Keep in mind that the field of AI is continually evolving, so the
characteristics of an AI-Ready workforce will continue to evolve as well.
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