06/01/2026

AI Literacy is Not Enough

By Safaa Amer

The Shifting Career Development Landscape
Artificial Intelligence (AI) has a double‑edged effect on career development. It streamlines processes but also creates job insecurity, career disruption, and ethical concerns, including “blind trust” in AI advice that can misdirect careers (Pandya & Wang, 2024). AI tools are reshaping the employee lifecycle from within organizations, influencing who gets hired, how performance is evaluated, which employees are visible for advancement, and whose career pathways are shaped by algorithms rather than human judgment (Wilson et al., 2022).

For career practitioners, the shift is reflected in the tools procured by their client’s current or prospective organizations, the decisions those tools influence, and the employees whose careers are shaped by processes they are not even aware of. The gap between AI implementation speed and workforce readiness is widening. Organizations are adopting AI tools to increase efficiency and reduce costs across hiring, performance management, and talent development often faster than managers, employees, or HR can evaluate them (Stone et al., 2024).  

Career practitioners are uniquely positioned to see this gap, since they have insights into employment trends and understand job seekers’ priorities through client interactions.

AI Literacy as an Organizational Competency
Addressing that gap requires expanding AI literacy well beyond information technology. At the organizational level, meaningful AI literacy operates across three tiers (Chee et al., 2024):

1. Practitioners interpreting tools, advocating for employees, flagging harm, and translating ethics upward and agency downward
2. Leaders making strategic decisions about AI adoption, governance, and accountability (Budianto et al., 2025)
3. Employees navigating AI-mediated career pathways, performance systems, and development tools (Liu et al., 2025)

Career practitioners are the critical connective tissue across all three. AI literacy is critical, but not enough. Career practitioners who understand AI tools without the ethical grounding to question them, or the organizational standing to advocate when something is wrong, are equipped to observe harm, not prevent it. Literacy is the foundation. Ethics and advocacy are what make it actionable.

AI literacy is also a workforce equity issue. Without a careful organizational strategy, upskilling is offered to those already advantaged. Building organization-wide AI literacy is not simply a technology investment; it is vital for workforce development (Chen, 2024).

Ethics at the Workplace Level
Organizational AI ethics are different from individual coaching ethics (Madanchian & Taherdoost, 2025). AI recommendations about who is selected for hiring and advancement can reinforce existing organizational inequities without any individual decision-maker recognizing their role in the outcome (Chen, 2023).

In organizations, the risks are systemic. For example, a biased promotion algorithm does not harm one person, it profiles an entire talent pipeline. A surveillance tool that monitors employee productivity does not raise one ethical question, it erodes psychological safety across a workforce (Paul & Horwitz, 2026).

Accountability sits at the center stage of AI ethics. When a career decision is shaped by an AI recommendation, who is responsible for the outcome: the organization or the vendor who built the model? Currently, that question is rarely answered clearly before tools are deployed.

Equity and Access
Organizational AI inequity rarely begins on the job; it often begins before a candidate walks through the door. Resume-screening algorithms, video interview analysis tools, and predictive hiring scores are widely deployed, yet candidates seldom know that AI shaped their outcome, and HR professionals often lack the technical literacy to interrogate the tools they rely on (Li & Kim, 2024). The equity stakes of organizational AI are highest precisely where human oversight is lowest. The ethics and literacy demands of AI in hiring deserve their own examination.

Inside the workplace, inequity compounds. AI-driven workforce tools frequently encode existing disparities across neurodiversity, disability, language, and cultural representation. The boundary between organizational responsibility and vendor accountability remains blurred (Wang et al., 2024). Equity must be a design requirement for organizational AI, not an afterthought.

The Practitioner’s Role: Connector, Advocate, and Early Warning System

AI literacy is emerging as a core competency for job seekers and practitioners alike, as essential as digital literacy and cultural competence (Chiu et al., 2024). In practice, it means that career practitioners should:

• Interpret AI-generated results and explain them in plain language that clients can act on. For example, explaining why an algorithm flagged certain skills gaps or ranked a career path
• Stay current with evolving AI capabilities, privacy practices, and ethics frameworks that may impact the client’s work performance, job search strategy, and more
• Model responsible, critically informed use of AI insights.

Literacy equips practitioners to understand the tools, while ethics and advocacy determine what they do with that understanding.

Ethical practice requires informed and persistent advocacy across all tiers:

• For the career practitioner - understanding how AI tools work well enough to interrogate vendor claims, recognizing bias signals in AI-generated recommendations, knowing when and how to escalate concerns, and documenting AI-related dilemmas through insightful supervision while helping clients understand and navigate the landscape (Biagini, 2025).
• For the employee - understanding how AI influences their visibility and leaning on the career practitioner to ensure they are not navigating the system alone (Westover, 2024).
• For leadership - translating the equity and accountability risks into language that executives recognize as organizational and legal exposure, bringing workforce impact data to governance conversations, and pushing for human oversight protocols before the next tool is deployed.

Looking Ahead 
Career practitioners are strategically positioned to become a voice in workplace AI policy. They should be advocating at the tool level by auditing systems before deployment, at the policy level by contributing to governance frameworks, and at the field level by connecting with NCDA and professional peers to build collective standards. The question is no longer how quickly everyone can deploy AI (Rakova et al., 2020), it is whether AI tools can be used wisely while understanding their impact. AI literacy is where that work begins. Ethics and advocacy are where it becomes meaningful.

References
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Budianto, S., Rahadian, D., & Yunita, I. (2025). The emerging landscape of AI-powered leadership: Transforming roles and organizations. Journal of Lifestyle and SDGs Review. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe04139 

Chee, H., Ahn, S., & Lee, J. (2024). A competency framework for AI literacy: Variations by different learner groups and an implied learning pathway. British Journal of Educational Technology, 56, 2146-2182. https://doi.org/10.1111/bjet.13556 

Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10, 1-12. https://doi.org/10.1057/s41599-023-02079-x

Chen, Z. (2024). Responsible AI in organizational training: Applications, implications, and recommendations for future development. Human Resource Development Review, 23, 498 - 521. https://doi.org/10.1177/15344843241273316 

Chiu, T., Ahmad, Z., Ismailov, M., & Sanusi, I. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open. https://doi.org/10.1016/j.caeo.2024.100171 

Li, H., & Kim, S. (2024). Developing AI literacy in HRD: Competencies, approaches, and implications. Human Resource Development International, 27, 345 - 366. https://doi.org/10.1080/13678868.2024.2337962 

Liu, X., Zhang, L., & Wei, X. (2025). Generative artificial intelligence literacy: Scale development and its effect on job performance. Behavioral Sciences, 15. https://doi.org/10.3390/bs15060811 

Madanchian, M., & Taherdoost, H. (2025). Ethical theories, governance models, and strategic frameworks for responsible AI adoption and organizational success. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1619029 

Pandya, S., & Wang, J. (2024). Artificial intelligence in career development: A scoping review. Human Resource Development International, 27, 324 -344.  https://doi.org/10.1080/13678868.2024.2336881 

Paul, K., & Horwitz, J. (April, 2026). Exclusive: meta to start capturing employee mouse movements, keystrokes for AI training data. Reuters. https://www.reuters.com/sustainability/boards-policy-regulation/meta-start-capturing-employee-mouse-movements-keystrokes-ai-training-data-2026-04-21/ 

Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2020). Where responsible AI meets reality. Proceedings of the ACM on Human-Computer Interaction, 5, 1 - 23. https://doi.org/10.1145/3449081 

Stone, D., Lukaszewski, K., & Johnson, R. (2024). Will artificial intelligence radically change human resources management processes? Organizational Dynamics. https://doi.org/10.1016/j.orgdyn.2024.101034 

Wang, X., Wu, Y., Ji, X., & Fu, H. (2024). Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1320277 

Westover, J. (2024). Preparing the workforce for ethical, responsible and trustworthy AI. Human Capital Leadership Review. https://doi.org/10.70175/hclreview.2020.13.4.7 

Wilson, M., Robertson, P., Cruickshank, P., & Gkatzia, D. (2022). Opportunities and risks in the use of AI in career development practice. Journal of the National Institute for Career Education and Counselling, 48(1). https://doi.org/10.20856/jnicec.4807 

Safaa R. Amer, PhD, is a C-suite policy advisor and career development professional with expertise in data analytics, AI integration, and economic research to empower people and organizations globally. Dr. Amer has led multidisciplinary initiatives across government, academia, Fortune 500 companies, and international organizations. She has guided teams through transformation, uncertainty, and innovation with focus on building capacity. Dr. Amer is passionate about bridging data-driven insights with human-centered coaching to prepare diverse talent for the future of work. Connect with her at https://www.linkedin.com/in/safaa-r-amer/ or by email at Safaa@Cleveragroup.com