Future-proofing Human Resources. Strategic Foresight and AI in the revolution of Talent Management
Bibliographic record
Abstract
Abstract This article examines the transformative role of Strategic Foresight and Generative AI (GenAI) in Talent Management (TM), providing a framework to future-proof human resources. As organizations navigate digital disruption and evolving workforce dynamics, integrating Strategic Foresight and AI-driven solutions becomes imperative for sustaining competitive advantage. Strategic Foresight enables organizations to anticipate workforce trends, proactively address emerging skill demands, and develop adaptive HR strategies. This forward-looking approach enhances decision-making by leveraging scenario planning, trend analysis, and visioning to align TM with future business needs. Simultaneously, GenAI revolutionizes HR functions by automating processes, generating predictive insights, and personalizing employee experiences, fostering efficiency and innovation. This research proposes an AI-enabled TM framework that combines Strategic Foresight with GenAI to enhance workforce agility and resilience. It underscores AI’s role as a strategic enabler rather than a simple automation tool, redefining talent acquisition, learning pathways, and succession planning. By integrating Strategic Foresight and AI, organizations can enhance decision intelligence, optimize workforce strategies, and ensure long-term adaptability. This approach positions HR as a catalyst for business transformation, leveraging AI’s potential to support continuous learning, foster internal mobility, and drive talent-centric innovation. Ultimately, future-proofing HR requires a paradigm shift in TM, where AI augments human-centric decision-making rather than replacing it. Organizations that embrace this dual approach - balancing technological advancements with strategic foresight - will cultivate a resilient workforce prepared for the complexities of the future of work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".