We are not yet in the AI Era or Age of AI, defined here as the period when artificial intelligence runs the world while humans fade into the background. That tipping point arrives when we are no longer capable of independent execution, even with well-intended “human-in-the-loop” approaches, because our capability has eroded from a lack of authentic practice, exacerbated by convenience and a shift from “I can do this” to “Why bother doing it at all?”
When everything in society becomes perfectly efficient, learned laziness and cognitive offloading become inevitable across both cognitive and physical domains (Lin & Sohail, 2026; Risko & Gilbert, 2016). If perfect optimization is achieved, you erase the capacity to learn and grow, offloading critical thinking entirely and intentionally without recognizing what is at stake.
Among other learning and performance professionals in schools and workplaces, organizational Learning and Development (L&D) professionals serve as one of our most vital defenses against the emergence of the AI Era. According to the U.S. Bureau of Labor Statistics (2024), adults spend anywhere from 34.3 to 41.5 hours per week at work. A similar case can be made for the amount of time school-aged humans spend in academic environments, which is why academic educators are equally critical to averting this outcome. However, to stay focused on the core scope of this article, we spend a significant portion of our lives working. While some posit that the sole purpose of work is output, the best employers care deeply about both human growth and performance.
Research across organizational psychology, management science, and human resource development demonstrates that organizations shifting toward a learning- and growth-centered culture, rather than a performance-only focus, achieve superior long-term financial outcomes, higher innovation, and substantially lower turnover (Edmondson, 1999; Gallup, 2024; Kegan & Lahey, 2016). That evidence alone should silence arguments that workplace strategy is strictly about short-term performance rather than continuous learning. It is about both human learning and human performance. Without either, we cannot thrive.
As AI risks deskilling workers (El Tarhouny, 2025; Huseynova, 2025) and stripping meaning from work and life, L&D professionals, as champions of human capability, are essential to promoting human learning, readiness, and growth before the Human Era descends into the AI Era. Meaning in Life (MIL), defined by Steger et al. (2006, p. 80) as “the sense made of, and significance felt regarding, the nature of one’s being and existence,” is established in psychological and psychiatric literature as a central pillar of health. When individuals experience a disruption or decline in this sense of meaning, the negative impacts across emotional, cognitive, and physical domains are severe, ranging from psychological distress to severe mental health disorders, suicidal ideation, and self-harm risk (Hooker et al., 2018; Steger et al., 2006). To be explicit, a global loss of meaning in life will lead to loss of life.
If an entire population were to experience a universal loss of meaning, public health, sociological, and psychological research indicates the consequences would be systemically calamitous, compounding across individual, relational, and institutional levels. Empirical research shows that MIL is a core structural variable that maintains health, prosocial behavior, and social stability (Brassai et al., 2011; Steger, 2012). Scaling a loss of meaning across an entire population leads to predictable, evidence-backed outcomes, including public health cascades, “deaths of despair” (Case & Deaton, 2020; Rehder et al., 2021), the breakdown of social cohesion, and widespread economic and institutional stagnation.
Working within a public behavioral health agency, I can attest firsthand to the mounting demand on our systems, even though we have not yet reached the extreme scenario outlined in this paper. Public health, health economics, and psychiatric epidemiology document a dramatic, sustained escalation in the demand for mental health and substance use disorder services over the last two decades, a trend that accelerated sharply during and following the COVID-19 pandemic (Pancani et al., 2021; Substance Abuse and Mental Health Services Administration, 2023; Trilliant Health, 2026).
Before the pandemic, behavioral health demand was already steadily increasing across the United States due to reduced stigma, broader primary care screenings, expanded insurance coverage mandates, and rising baselines of distress. The onset of the pandemic acted as an unprecedented demand shock driven by social isolation, economic instability, grief, and systemic disruption, which is a small microcosm of what society could experience in a true AI Era. Since the pandemic, demand has remained elevated. We did not return to pre-pandemic baselines. Instead, we face sustained high utilization, expanded reliance on telehealth, workforce strain, and persistent access bottlenecks (Trilliant Health, 2026).
Holding all of this data, I return to my original premise that learning professionals must be one of your primary safeguards against the AI Era. This is not the time to reduce learning to low-friction “easy buttons.” On the contrary, we must create intentional space for real learning, knowledge-sharing, and human collaboration. L&D professionals, fully inclusive of Organization Development (OD) practitioners, are precisely the leaders organizations must leverage during this pivotal moment in human history. We must tap into their expertise to ensure humans do not become history.
Strategic Action: How L&D Shifts to Capability Preservation
Audit Workflow and Cognitive Offloading: Evaluate where AI integration enhances decision-making versus where it induces cognitive atrophy, ensuring critical domain practice remains within human hands.
Prioritize High-Friction, Intentional Learning: Move away from superficial, automated “easy buttons” and protect dedicated spaces for collaborative problem-solving, critical analysis, and peer-to-peer knowledge sharing.
Integrate Purpose into Job and Learning Design: Collaborate across Organization Development (OD) to build workflows and development pathways that protect employee autonomy, mastery, and meaningful engagement at work.
Align Performance Metrics with Continuous Growth: Measure organizational success not solely by automated output speed, but by workforce adaptability, skill retention, and deep problem-solving readiness.
Closing Engagement Prompt:
What is one conversation you can start today at work to better leverage L&D and protect human capability?
References
Brassai, L., Piko, B. F., & Steger, M. F. (2011). Meaning in life: Is it a protective factor for adolescent health risk behaviors? International Journal of Behavioral Medicine, 18(1), 44–51. https://doi.org/10.1007/s12529-010-9089-6
Case, A., & Deaton, A. (2020). Deaths of despair and the future of capitalism. Princeton University Press.
Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
El Tarhouny, S. (2025). Deskilling dilemma: Brain over automation. Postgraduate Medical Journal, 101(1192), 120–125.
Gallup. (2024). State of the global workplace: 2024 report. Gallup Press.
Hooker, S. A., Masters, K. S., & Park, C. L. (2018). A meaningful life is a healthy life: A conceptual model linking meaning and purpose in life to physical health outcomes. Review of General Psychology, 22(1), 11–24. https://doi.org/10.1037/gpr0000115
Huseynova, F. (2025). Addressing deskilling as a result of human-AI augmentation in organizational environments. CEUR Workshop Proceedings, 3901, 45–52.
Kegan, R., & Lahey, L. L. (2016). An everyone culture: Becoming a deliberately developmental organization. Harvard Business Review Press.
Lin, C., & Sohail, M. (2026). Cognitive offloading and technology dependency in AI-mediated learning environments. Journal of Educational Technology & Society, 29(1), 88–102.
Pancani, L., Marinucci, M., Aureli, N., & Riva, P. (2021). Forced social isolation and mental health during the COVID-19 pandemic. Frontiers in Psychology, 12, 636548. https://doi.org/10.3389/fpsyg.2021.636548
Rehder, K., Lusk, J., & Chen, J. I. (2021). Deaths of despair: Conceptual and clinical implications. Cognitive and Behavioral Practice, 28(1), 40–52. https://doi.org/10.1016/j.cbpra.2019.10.002
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Steger, M. F. (2012). Making meaning in life. Psychological Inquiry, 23(4), 381–385. https://doi.org/10.1080/1047840X.2012.720832
Steger, M. F., Frazier, P., Oishi, S., & Kaler, M. (2006). The Meaning in Life Questionnaire: Assessing the presence of and search for meaning in life. Journal of Counseling Psychology, 53(1), 80–93. https://doi.org/10.1037/0022-0167.53.1.80
Substance Abuse and Mental Health Services Administration. (2023). Key substance use and mental health indicators in the United States: Results from the 2022 National Survey on Drug Use and Health (HHS Publication No. PEP23-07-01-006). Center for Behavioral Health Statistics and Quality.
Trilliant Health. (2026). 2026 Behavioral health report: National utilization, demand, and market trends. Trilliant Health Research.
U.S. Bureau of Labor Statistics. (2024). American Time Use Survey — 2023 results (USDL-24-1230). U.S. Department of Labor.
Dr. Treca Bourne 
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