Use Cases for Generative AI in Workforce Development

Introduction 

Interest in artificial intelligence (AI) is soaring, and resources highlighting use cases for generative AI tools appear to abound for learners, educators, and employers. However, new Jobs for the Future (JFF) research found a lack of similar resources for workforce development. Training providers, public workforce boards, and organizations that provide career navigation services to jobseekers are hungry for ideas on how to use generative AI to support people in their searches for quality jobs. 

To help fill this gap, JFF’s Center for Artificial Intelligence & the Future of Work interviewed a wide range of stakeholders, including training providers, workforce board professionals, AI startup founders, venture capitalists, and young people.¹ We applied their insights to develop current and potential use cases for generative AI in workforce development and to identify existing tools.   

Our goal is to help workforce professionals adopt generative AI in their work and highlight market opportunities where investment in new solutions has the potential to drive equitable economic advancement. 

These observations are a snapshot of a rapidly evolving landscape and are not meant to be assessments of quality. Our research focused on two groups: jobseekers navigating the path from learning to earning (particularly people who are members of populations facing systemic barriers to advancement) and the professionals working for organizations that support those jobseekers.² 

Support for Individuals 

The use cases we identified span all phases of an individual’s career navigation journey, from finding careers of interest to succeeding on the job. They may be particularly helpful for supporting people experiencing workforce transitions, such as those who are looking for a first job, changing careers following a layoff, or reentering the workforce after an absence, such as time spent caring for children or serving in the military. We did not include use cases in the domain of employers, such as helping workers succeed in specific roles (through onboarding or on-the-job training, for example) or advancing one’s career at the same company. 

Some use cases, such as resume builders or job search aids, aim to reduce the time people spend finding and landing a job. Others, such as tools that validate skills gained through a variety of experiences or help people build relationships with mentors and develop social capital, have the potential to make a more significant impact and help people achieve economic advancement. (See Figure 1.) 

Use Cases for Jobseekers

Support for Organizations

The use cases we identified for professionals working for organizations that support jobseekers, such as workforce boards, training providers, and career services organizations, cover activities that include direct service, operations, and strategic leadership. We recognize that individual workforce development organizations are likely to vary in the ways they interact with jobseekers, but we hope our research is broadly representative of the work they do.  (We also recognize that workforce development organizations can use AI to assist with tasks such as budget creation, staffing, project management, and reporting to boards of directors, but these areas are outside the scope of this research.)  

Many use cases aim to significantly enhance efficiency by reducing the time workforce development professionals spend on manual tasks and processes, freeing them up for more impactful work. For example, generative AI can transcribe and summarize meeting notes in real time, enabling career coaches and case managers to focus more on providing tailored guidance to jobseekers. Generative AI can also help operations staffers do things like draft funding applications and grant proposals and reduce the time they spend manually tracking outcomes data and checking compliance requirements. (See Figure 2.) 

Use Cases for Professionals Who Assist Jobseekers

Market Maps

Tools for Jobseekers 

Many companies building generative AI tools for jobseekers and the organizations that support them initially focus on a single use case and plan to expand to others later. In mapping companies to our use cases, we identified areas where the market was already saturated and where it presented opportunities for developing new tools.  

Saturation:   

• Many resume and cover letter builders use generative AI to help jobseekers tailor materials for specific roles. These tools can be particularly beneficial to people who need more guidance with the job application process. However, we are already witnessing the ripple effects of the proliferation of these tools. Some employers use generative AI to sift through applications and then request demonstrations of skills, such as performance tasks. 
• Career navigation tools abound. They provide career-related content for jobseekers to explore their interests and potential career paths. Generative AI has augmented the functionality of existing tools by increasing quality, simulating human-like interactions, and improving available career information.   
• Many companies are building career coach and co-pilot tools. These tools help jobseekers understand their interests and explore potential career paths, identify ways to gain necessary skills and experiences. They often feature a chatbot-style platform with capabilities for human coaches and educators to view usage and interact with jobseekers. The success of these tools largely depends on the quality and delivery of information. The companies best positioned to succeed are those partnering closely with educational, community-based, and workforce development organizations.  

Opportunities: 

• Help jobseekers develop social capital. Generative AI has the potential to facilitate the process of connecting individuals with advocates and mentors, leading to more career opportunities. This could include helping jobseekers find people to connect with, providing coaching strategies for conversations, simulating or steering conversations, or managing follow-up communications. 
• Help individuals navigate support services and resources. Tools that enhance the effectiveness of resource navigators could comprehensively assess and address jobseekers’ needs by identifying relevant supports and facilitating their acquisition. For example, they could connect individuals with appropriate community-based organizations and automate some application processes.   
• Articulate jobseekers’ experiences and skills. Translating a jobseeker’s experiences into skills aligned with employer priorities can be challenging. This is especially true for people with relevant but uncredentialed lived experiences or jobseekers whose education and work backgrounds don’t align with traditional expectations. While some companies address this issue, more businesses could ease the way for job candidates by using generative AI to convey their experiences more clearly.   

Tools Supporting Organizations 

We found more generative AI tools supporting individual jobseekers than supporting organizations that work with jobseekers. In some cases, workforce development organizations can use general-purpose tools built for other industries or customers. However, we see significant opportunities for workforce-specific tools.  

Saturation: 

• General-purpose tools abound. We identified many generative AI tools that support note-taking, grant research and writing, managing contracts, and completing other administrative tasks. However, the stakeholders we interviewed reported uneven user experiences because the tools were not designed specifically for workforce development professionals.  

Opportunities:  

• Track outcomes. One major challenge facing workforce development organizations is monitoring what happens to individuals after they receive services. Tracking down information on each individual’s job placement, salary, and other aspects of their role requires significant staff time. Generative AI could potentially automate follow-up efforts by searching for information autonomously or using proxy data to obtain outcome metrics more efficiently. 
• Maintain program eligibility and compliance. Another time-consuming manual task for workforce organizations is monitoring changing eligibility criteria for funding through federal programs such as the Workforce Innovation and Opportunity Act (WIOA) or the Supplemental Nutrition Assistance Program (SNAP) and manually checking whether individuals qualify. A tool that could automate these processes or even apply for funding on a jobseeker’s behalf could reduce the amount of time workforce professionals spend on such tasks and increase the amount of time they help clients find work. 
• Coach case managers. Sales representatives use tools like Gong to receive real-time coaching before, during, and after customer conversations based on best practices and organizational data. Something similar, tailored for workforce direct service professionals, could improve the quality and consistency of job coaching outcomes.   
• Map regional assets. Workforce organizations frequently revise asset maps of partners in their region and their functions. Generative AI could make this information more easily accessible and up to date, saving time and facilitating collaboration across a region.  
• Recommend actions. Some tools offer tailored recommendations to workforce professionals regarding career pathways, training opportunities, or jobs for their clients to pursue, and we see additional opportunities for generative AI to support these stakeholders. For example, a tool could use eligibility and compliance data to identify a jobseeker’s eligibility for programs linked to various public and private funding sources and recommend how these sources might be effectively integrated.  

Future State

With the explosive growth of generative AI tools over the past year and a half, one might assume that the technology has been adopted at an equally swift rate. However, we continue to hear from our stakeholders that generative AI tools remain in the early stages of adoption. In our interviews for this scan, stakeholders primarily identified use cases related to their day-to-day operations and incremental improvements in efficiency. 

However, at the Center for Artificial Intelligence & the Future of Work, we are eager to move toward a future phase of adoption: having education and workforce professionals use generative AI to drive equitable economic advancement for all. We recognize that generative AI alone won’t resolve equity issues in workforce development. Collaboration and behavior change among employers, higher education institutions, and workforce organizations will be crucial. But we believe that generative AI has the potential to improve our collective impact. 

In this future state, we envision that generative AI could: 

• Facilitate human connection. Rather than replacing unique human interactions, generative AI could help us connect with others and improve the quality of those connections.  
• Use data to drive equitable pathways to careers. By making data on available career opportunities more transparent and using inference to understand the skills needed to succeed in these pathways, generative AI could support the development of a true skills-based economy.  
• Remove friction from career navigation. Generative AI could also address some of the challenges and stigmas associated with the job search process, such as accessing supportive services or understanding norms.  

We acknowledge that integrating these use cases into the workforce development ecosystem requires more work. First, generative AI tool providers should co-design solutions with people from populations typically excluded from technology development to counteract the inherent biases and lack of data representation within large language models. Second, ensuring AI literacy and digital access is crucial in preventing a double digital divide, especially when advanced AI tools are accessible primarily through paid models. In addition, organizations need effective change management strategies to integrate AI, including employee upskilling and thoughtful AI integration practices. 

We’re excited to see the learn and work ecosystem move toward the transformational use of generative AI in the future, and we’re eager to explore the opportunities and gaps we’ve identified in the market. As we continue this work, we aim to better understand the adoption and efficacy of existing tools, highlight innovators to watch, and incubate and test solutions in partnerships with organizations. 

We invite you to offer feedback, push our thinking, and share other use cases and tools you have been using or hope to see. Please contact us if you would like to collaborate with us on this work. 

Resources:

• Use Cases Definitions
• Workforce Tools List

Endnotes

1 Research methods: We interviewed 36 people. The sample was made up of training providers, workforce board professionals, workforce subject matter experts, AI startup founders, venture capitalists, and young people. We also conducted desk research on relevant frameworks and products that fit our use cases.

2 JFF defines “people facing systemic barriers to advancement” as: people without a four-year college degree, who struggle to secure quality jobs, regardless of their skills; most people of color, even those with a four-year degree, who face racial bias in school and in the workplace; most women, even those with a four-year degree, who earn less than men and are promoted at lower rates; and people with criminal records, who continue to be stigmatized in the job market after serving their sentences.