By Jonathan H. Westover, PhD
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Abstract: This article explores how generative artificial intelligence (AI) technologies such as machine learning and deep learning can be applied to enhance strategic human resource (HR) planning. Drawing from academic literature and industry case studies, the article outlines key techniques of gen AI and principles for responsible implementation. It then discusses specific opportunities for gen AI to elevate HR processes like recruiting, training, performance management, compensation planning, and cultural transformation from descriptive to prescriptive levels through data-driven decision making and insights. Aligning AI goals with organizational values around diversity, ethics and worker well-being is emphasized. The essay aims to translate technical concepts into practical recommendations while arguing that when guided properly, gen AI shows strong potential to empower HR leaders and elevate their people strategy through personalization, prediction, continuous improvement and robust measurement of ROI.
As a long-time HR strategist and management consultant focused on people and organizational issues, one of the most exciting new developments I've seen is the rise of artificial intelligence (AI) and its potential to positively transform how companies approach human resources. While terms like "AI" and "machine learning" have taken on an aura of mystery and even fear for some, I believe - based on my research and experience helping various organizations apply new technologies - that properly harnessing these advances can empower HR and other leaders to gain unprecedented insights about their workforce and partners. Done right, AI allows data-driven decision making at a scale simply not possible through human effort alone.
Today we will explore the practitioner's perspective on how generalized or "gen" AI (as opposed to narrow, task-specific algorithms) can help organizations achieve strategic workforce planning goals. Drawing from relevant academic literature as well as case studies, I will outline both how AI works and specific ways it can be applied to enhance key HR processes like recruiting, performance management, training and development, culture building, and more. Throughout, my objective is to translate sometimes dense technical concepts into practical takeaways and recommendations HR professionals and their teams can apply. While no technology is a panacea, I believe gen AI has immense promise to elevate your people strategy when guided by a set of core principles focused on empowering employees.
Gen AI Foundations and Principles
Before delving into applications, it is important to establish a common understanding of what gen AI is and how it operates. At its core, AI refers to systems that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, and decision making (Russell and Norvig, 2020). A generalized approach aims to build systems that can accomplish a wide range of goals, as opposed to narrow AI focused on a single application like facial recognition.
Two key techniques that power most gen AI are machine learning and deep learning. Machine learning involves algorithms that can learn from large datasets without being explicitly programmed, detecting patterns and correlations to make predictions on new data (Marr, 2018). Deep learning takes this a step further by using artificial neural networks modeled after the human brain, with many hidden processing layers, to complete highly complex tasks (Shrestha and Mahmood, 2019). By exposing these networks to massive volumes of real-world data, they gain the ability to "teach themselves" to perform human-like analyses in areas such as natural language processing and computer vision.
However, as with any technology, important safeguards must be in place to ensure AI augment human effort and judgment rather than replace it (Arnold and Scheutz, 2018). Some guiding principles when applying gen AI within HR functions include:
Explainability: Models should be able to clearly explain their recommendations to avoid potential bias, lack of transparency, or loss of trust.
Oversight: Humans must maintain oversight of AI systems and be able to correct undesirable outcomes. This requires suitable monitoring metrics and controls.
Fairness: Data and algorithms should be regularly audited to ensure all groups are treated fairly and potential discrimination is avoided.
Privacy: Sensitive employee data must be kept confidential according to applicable regulations like GDPR.
Alignment: AI goals and incentives need to align with an organization's values like diversity, ethics and worker well-being.
With proper governance grounded in these principles, gen AI can become a responsible and powerful strategic partner for HR teams. Let's now explore some opportunities for application.
Applying Gen AI to Strategic HR Planning
In their book Advanced Analytics: Opportunities and Challenges, Anthropic (2021) outline three levels of HR analytics maturity: descriptive, predictive and prescriptive. Descriptive looks at trends in the past, predictive examines patterns to anticipate the future, and prescriptive recommends optimal actions. Gen AI has the potential to elevate organizations to this highest prescriptive level across the critical functions that define strategic workforce planning.
Recruiting & Selection
Gone are the days when effective recruiting relied purely on keyword searches, basic applicant tracking and one-size-fits-all hiring processes. AI has revolutionized how companies source and evaluate candidates at scale. For example, Anthropic (2021) profile an industrial supply company that partnered with Pymetrics to incorporate game-based assessments measuring things like memory, attention and problem-solving into their hiring process. By analyzing how candidates performed, compared to current high-performing employees, they were able to dramatically improve the quality of hires while streamlining the overall process.
Training & Development
Many organizations struggle to meaningfully develop their employees due to lack of tailored programs, scarce resources and limited insights into skill gaps. However, using AI to analyze trends in individual performance reviews, feedback across projects, skills utilized in work products like documents and code repositories, even behaviors exhibited in collaboration tools begins to paint a granular picture of strengths and opportunities on both micro and macro scales (Bersin, 2018). This informs highly personalized learning pathways addressing precise developmental needs, whether assigning online courses, peer mentoring, rotations or other interventions. Machine learning also enables predictive modeling of which training modes tend to yield the biggest impacts on different role types or demographic segments.
Performance Management
Traditional annual or biannual reviews struggle to capture real-time performance and provide actionable coaching. Forward-thinking companies are experimenting with continuous feedback systems using conversational AI assistants which employees can ask questions to or directly enter feedback for peers on demand (Marr, 2021). AI identifies themes and patterns across this structured and unstructured input. Combined with OKRs, project tracking, one-on-one notes and other digital breadcrumbs, visual dashboards emerge detailing how individuals and teams are tracking against goals. Automated nudges keep performance conversations ongoing versus a once-per-year event. Prescriptive suggestions based on best practices optimize talent management plans and retention.
Compensation Planning
Determining appropriate salary ranges, bonus structures and other rewards requires weighing internal pay equity alongside external market benchmarks. By amalgamating diverse compensation datasets using privacy-preserving techniques, AI is able to provide a personalized, real-time view of an individual's total compensation relative to peers with similar roles, skills, experience, performance and other relevant factors within and beyond the organization (Salary.com, 2021). This empowers data-driven decisions around pay adjustments, equity grants, incentive plans and other tailored rewards keeping top performers engaged and fairly compensated compared to opportunities elsewhere. Advanced modeling also predicts the retention impacts of various what-if compensation scenarios.
Cultural Transformation
Perhaps no factor influences whether talent thrives more than company culture and the day-to-day employee experience it shapes. Yet defining culture can feel nebulous without clear metrics (Glassdoor, 2021). AI uncovers tangible dimensions like collaboration levels between teams, tenure trends over time, sentiment in surveys, reviews and other communications, work-life balance patterns from device and calendar access. Clustering techniques group employees exhibiting similar behaviors to surface subcultures. Management can then strategically target initiatives strengthening desired cultural traits or remediating undesirable ones left unaddressed. AI also tests hypotheses around relationships between cultural health and key business outcomes like productivity, customer satisfaction and financial performance specific to an organization's unique context. This brings much-needed rigor and data-driven focus areas for cultural stewardship initiatives.
Measuring ROI & Continuous Improvement
Ultimately, an HR function must demonstrate concrete value and return on any investments. AI plays an invaluable role here through attribution modeling linking talent programs and outcomes back to business results like revenue growth, costs reduced, quality improved. For example, a leading manufacturer of semiconductor equipment found AI revealed its training on Six Sigma methodologies directly correlated to a 15% increase in first-pass yields and $7 million in annual savings (McKinsey, 2017). Robust measurement also supports continuous improvement by pinpointing where strategy or execution may need recalibrating. AI becomes an ongoing strategic advisor keeping workforce initiatives razor-sharp and aligned to the organization's evolving priorities.
Conclusion
Gen AI holds immense promise for HR leaders seeking to elevate strategic planning to prescriptive levels through data-driven decision making. However, carefully integrating these emerging techniques requires establishing appropriate governance, controls, transparency and employee safeguards from the outset. When guided by a set of core human-centered design principles, I am convinced AI can become a formidable yet responsible partner for HR teams. Properly applied across critical functions like recruiting, performance management and cultural transformation, organizations gain the insights to unlock their people's fullest potential in driving business success. While change inevitably brings uncertainties, the opportunities of AI outweigh the risks in skilled, ethics-grounded hands. For HR strategists pursuing excellence, this technology represents an immensely powerful lever to take workforce initiatives to new heights. The path forward starts with small, careful experiments that build momentum one responsible application at a time.
References
Anthropic. (2021). Advanced analytics: Opportunities and challenges. https://www.anthropic.com/papers/advanced-analytics
Arnold, T., & Scheutz, M. (2018). Against unfairness by unawareness in AI assistants. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 59-65). https://doi.org/10.1145/3278721.3278779
Bersin, J. (2018, July 11). AI for learning: What to expect in 2018 and beyond. LinkedIn. https://www.linkedin.com/pulse/ai-learning-what-expect-2018-beyond-josh-bersin/
Glassdoor. (2021). Using data and analytics to measure and improve company culture. https://www.glassdoor.com/employers/blog/using-data-analytics-measure-improve-company-culture/
Marr, B. (2018, December 2). What is machine learning really? A definition and overview. Forbes. https://www.forbes.com/sites/bernardmarr/2018/12/02/what-is-machine-learning-really-a-definition-and-overview/?sh=5cddf17a37dd
Marr, B. (2021, January 31). How conversational AI is transforming HR and the future of work. Forbes. https://www.forbes.com/sites/bernardmarr/2021/01/31/how-conversational-ai-is-transforming-hr-and-the-future-of-work/?sh=2f17ddd62f7d
McKinsey & Company. (2017). Artificial intelligence: The next digital frontier? https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Artificial%20intelligence%20The%20next%20digital%20frontier/MGI-Artificial-intelligence-Discussion-paper.ashx
Salary.com. (2021). Leveraging data and analytics for total rewards optimization. https://www.salary.com/insights/using-data-analytics-optimize-total-rewards
Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson Education.
Shrestha, Y. R., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040-53065. https://doi.org/10.1109/ACCESS.2019.2912200
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). Using Generative AI for Strategic HR Planning: How AI Can Elevate Your People Strategy. Human Capital Leadership Review, 12(4). doi.org/10.70175/hclreview.2020.13.2.8
