By Jonathan H. Westover, PhD
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Abstract: Artificial intelligence (AI) and advanced technologies are transforming the nature of work and presenting profound challenges for how organizations structure and develop talent. As many routine jobs are automated and new roles emerge, HR must strategically guide companies through this period of significant workforce transition and upskilling. This article explores HR's critical strategic role in navigating the "talent restructure" driven by AI. It outlines approaches for mapping how technologies will impact existing roles and skills needs over time. It then provides recommendations on designing large-scale reskilling and internal mobility programs focused on versatile, future-proof competencies. The article also discusses adaptations to traditional performance management and career pathing frameworks needed to support continuous learning and flexibility. With diligent planning and execution of skills analytics, reskilling initiatives, and modernized HR systems, the brief posits that organizations can empower their workforces to thrive amid technological disruption by helping workers continuously upskill themselves.
As an experienced HR professional and management consultant, I have seen first-hand how artificial intelligence (AI) and advanced technologies are increasingly transforming the workforce and fundamentally reshaping traditional roles and structures. While this brings opportunities for greater productivity, efficiency, and value creation, it also poses serious challenges for talent acquisition, development, and retention that must be addressed strategically.
Today we will explore HR’s critical role in navigating this AI-driven “talent restructure” and provide insights and examples for leading organizations through this transition.
Background: The Impact of AI on Jobs and Skills
There is widespread consensus that AI and related digital technologies will significantly alter the skills required of human workers. Several studies have estimated the potential scale of job transformation or displacement. For example, a 2017 report by McKinsey Global Institute estimated that up to 30% of work activities could be automated by 2030 using currently demonstrated technologies (Manyika et al., 2017). Meanwhile, the World Economic Forum projected that by 2022, AI will displace 75 million jobs worldwide while creating 133 million new roles (World Economic Forum, 2018).
While the precise impacts are uncertain, it is clear that many routine physical and cognitive tasks are most at risk of automation (Frey & Osborne, 2017). At the same time, human skills like creativity, social-emotional intelligence, complex decision-making, and adaptive problem-solving are seen as increasingly important and less susceptible to computerization (World Economic Forum, 2020). This talent restructure demands that organizations thoughtfully reskill and upskill their workforce to fill emerging roles while also internalizing the human capabilities needed to partner with intelligent technologies.
Strategically Mapping the Talent Impact
To effectively manage the AI-driven talent restructure, HR must take a strategic lead in mapping and monitoring how emerging technologies will impact existing jobs and skills requirements over time. Several proactive steps can be taken:
Conduct job task analyses to understand the degree of automation potential for each role based on the types of activities performed (Autor et al., 2003). Rate job functions on a scale of high to low risk.
Work with business and technology leaders to identify new roles, functions, and skill needs that will be created or significantly modified by digital transformation initiatives. Consider skills like data literacy, cloud computing proficiency, and machine interaction design.
Build talent maps that plot employees' existing skills and interests against future requirements to uncover potential skills gaps (Bersin, 2017). Use these maps to strategize reskilling/upskilling programs accordingly.
Routinely re-evaluate roles and skills forecasts as technologies advance. Remain vigilant of disruptions from adjacent industries and monitor emerging tools like robotic process automation that may reshape previously "safe" functions.
With thorough analyses, companies can proactively shape their talent pipelines rather than reacting to disruption. Maps also provide a basis for reskilling/career pathing initiatives that maximize existing talent investments.
Designing Targeted Reskilling and Upskilling Programs
Once skills gaps are identified, HR must design and implement reskilling and internal mobility programs at scale. Some considerations include:
Prioritize "Future-Proof" Skills: Focus initial efforts on developing versatile skills like digital literacy, collaboration, critical thinking and lifelong learning mindsets that maintain long-term employability (World Economic Forum, 2018).
Leverage Multiple Learning Modalities: Offer blended learning combining online micro-credentials, hands-on project work and coaching/mentorship. Coursera, Udacity and edX offer cost-effective online options (Gal et al., 2020).
Incentivize Participation: Use financial subsidies, paid time off, promotions/new roles and internal "skill markets" as motivators. Communicate programs as career investments rather than remediation.
Collaborate Internally: Partner with L&D, technology and business leaders to codevelop curricula aligned with strategic priorities and emerging job needs.
Measure Impact Rigorously: Evaluate pre- and post-learning metrics like skills assessments, productivity increases, retention and promotion rates to gauge ROI and continuously improve programs.
For example, Anthropic pairs research scientists with machine learning engineers for 6-12 month rotations to cross-train vital technical and soft skills (Anthropic, 2022).
Adapting Performance Management and Career Pathing
As jobs morph, HR must rethink traditional performance evaluation and career progression approaches. Possible adaptations include:
Shift from role-based evaluations to competency-based assessments of portable, future-focused skills actually used on projects.
Broaden performance discussions to include continuous learning achievements, technology adoptions, risk-taking and adaptability displayed.
Co-design individual development plans between managers and direct reports to encourage self-directed skill building, cross-training and internal mobility.
Establish clearly articulated career path frameworks that map non-linear, multi-disciplinary routes between functions to promote agility (Bersin, 2019).
Consider skill-based pay tied to competencies developed rather than tenure or roles held. Rewards continuing education investments.
Highlight lateral moves, project work, experiments and failures in addition to vertical promotions as valid career markers.
For instance, IBM restructured performance reviews around its 10 Foundational Competencies like collaboration and data fluency (IBM, n.d.).
Conclusion
Strategically managing the talent impacts of AI will be crucial for organizations to thrive in an increasingly technology-driven future. Done effectively, HR can help companies internalize needed digital skills, maximize existing talent investments, and foster a culture where workers can continually uplevel themselves. While the changes are significant, a focused, proactive approach centered on skills mapping, reskilling infrastructure, adaptive career frameworks and competency-based development offers a prudent path forward for navigating this “talent restructure.” With diligent guidance and execution, HR can empower both the workplace and workforce to realize new opportunities through intelligent technologies.
References
Anthropic. (2022). Fellowship program. Anthropic. https://www.anthropic.com/fellowship
Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801
Bersin, J. (2017). The organizational response model: Adapting to constant change through talent, process and technology. Deloitte Insights. https://www2.deloitte.com/us/en/insights/topics/talent/organizational-response-model.html
Bersin, J. (2019). Reinventing the performance review: The new systems and processes required for continuous feedback and growth. Deloitte Insights. https://www2.deloitte.com/us/en/insights/topics/talent/performance-management-system-reinvention.html
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Gal, U., Lyons, K., & Christen, M. (2020). Being human-centric about AI alignment: Towards cooperation between humans, AI, and organizations. Big Data & Society, 7(2), 2053951720948061. https://doi.org/10.1177/2053951720948061
IBM. (n.d.). Reinventing Talent Management with IBM Cloud Pak for Watson AIOps: A Practitioner's Guide. IBM. https://www.ibm.com/downloads/cas/PA8EG49N
Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages
World Economic Forum. (2018). The future of jobs report 2018. World Economic Forum. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf
World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum. http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf
Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.
Suggested Citation: Westover, J. H. (2024). HR's Strategic Role in Managing the AI-Driven Talent Restructure. Human Capital Leadership Review, 13(3). doi.org/10.70175/hclreview.2020.13.3.2
