Open questions
The cross-topic questions driving this research program. Each spans several topics; questions that belong to a single topic live on that topic’s page under Contradictions & open questions. When a question narrows into one construct, it becomes a topic and leaves this list.
- Automation, augmentation, or protection. Which tasks are candidates for automation vs. augmentation vs. protected human work — how is that decided, and where do worker preferences and moral boundaries diverge from technical capability? → automation-and-substitution, human-ai-task-allocation, human-ai-task-taxonomy, moral-boundaries-of-ai-automation, agentic-delegation
- What makes adoption durable. Which I-O constructs and change mechanisms best explain sustained, well-calibrated adoption of AI at work — beyond initial access or individual experimentation? → ai-adoption, task-level-ai-adoption, ai-receptivity, ai-literacy, ai-use-image-concerns, employee-change-support, behavioral-human-centered-ai
- Measuring impact. What does good measurement of AI workforce impact look like beyond productivity anecdotes? → ai-workforce-impact-measurement, ai-agent-benchmark-validity, construct-validity, job-analysis
- Measuring capability. How should organizations measure human capital, AI capability, and combined human-AI capability without collapsing distinct constructs into one performance-relevant but uninterpretable index? → human-capital-resource-measurement, competency-modeling, construct-validity
- The effort paradox. When does improving AI quality reduce human effort, attention, or practice enough that oversight, learning, and expertise maintenance suffer — and which designs restore judgment without wasting effort? → automation-complacency, cognitive-surrender, cognitive-debt, ai-induced-skill-erosion, ai-mediated-learning, cognitive-load-in-ai-assisted-work
- Work experience under AI. When does AI-mediated work improve versus degrade autonomy, competence, relatedness, identity, and felt job quality—and how do resource gains interact with shifted demands, verification work, and long-run capability? → ai-mediated-work-experience, ai-as-a-job-resource, digital-work-strain, ai-mediated-choice-and-identity, ai-mediated-learning, ai-enabled-job-crafting
- Construct validity in AI-mediated assessment. What does construct validity require in AI-mediated assessment and people analytics — and when does governance substitute for it rather than complement it? → construct-validity, algorithmic-assessment, evidence-based-management, responsible-ai-deployment
- Redefining work. How does AI reshape job analysis and the definition of work itself, before HR systems are built on it? → job-analysis, work-redesign, competency-modeling, human-ai-task-taxonomy
- From experiments to organization. Which organizational-learning and managerial capabilities turn AI experimentation into redesigned workflows and durable organizational knowledge rather than disconnected pilots? → organizational-learning-capability, ai-workforce-transformation-implementation, ai-mediated-organizational-networks, ai-mediated-teamwork
- Responsible deployment under uncertainty. What evidence, governance, stakeholder voice, and reversal mechanisms make workforce-facing AI deployment responsible when validation evidence is incomplete but deployment pressure is real? → responsible-ai-deployment, algorithmic-assessment, moral-boundaries-of-ai-automation