AI Human Interaction Guide · reference
AI Human Interaction Guide.
An encyclopedic synthesis of the AI literature, the AI-human-interaction research, and the methodology underneath enterprise AI adoption.
The promise is straightforward: we read this material so you don’t have to. Roughly 50 cited works in the part-by-part bibliographies — the diffusion canon (Rogers, Granovetter, Burt, Centola, Christakis & Fowler, Watts, and adjacent) plus peer-reviewed papers and working papers in AI and organizational science — together with 81 enterprise-AI white papers from the major firms, 40 deep-research syntheses, and 33 AHI program topic reviews. Approximately 9,000 pages of source material, distilled into seven structured, cited, cross-referenced parts. Most parts are encyclopedic synthesis; Part V is explicitly position-taking; Part VII is the load-bearing argument the rest of the guide sets up.
Contents
- I.
Foundations of AI and Data Strategies
The definitions, the four kinds of intelligence the field actually builds, the data substrate underneath modern AI, and the methodology gap that breaks downstream rollouts.
- II.
AI in the Workforce
The 12-factor framework for AI adoption — HR, talent, and org-design implications, anchored in the self-assessment instruments + the network-readiness reframe of the framework.
- III.
AI in Customer Experience and Marketing
Where AI changes the customer-facing surface — hyper-personalization, campaign optimization, and the methodological caveats underneath.
- IV.
AI in Product, Operations, and Decision Support
How AI is changing the way decisions get made — agentic systems, product lifecycles, and the sociotechnical embedding patterns that work.
- V.
The Research Frontier — Concerns, Inquiries, and Product Features That Address Them
The encyclopedia's most opinionated chapter. The AHI program's public face: what we don't yet know about AI-human interaction, what we're doing about it, and the safety / product positions the research supports.
- VI.
Governance, Privacy, and Compliance
Regulatory and ethical scaffolding for enterprise AI — the EU AI Act, executive orders, privacy regimes, and the calibration questions underneath.
- VII.
Network-Mediated AI Adoption
The load-bearing synthesis. Why adoption is a network-topology problem, not a per-user one — and what the diffusion canon predicts about what comes next.
- A.
Case Studies Catalog
Appendix A. A reference catalog of the cases the seven parts draw on — aggregate sector studies, documented enterprise failures, successful people-analytics work, diffusion-mechanism field experiments, and workforce-side adoption signals.
- B.
Practitioner's Toolkit + Further Reading
Appendix B. The measurement instruments (12-factor, CAMS, NAV, Three A's, Penwright Measurement Framework), the methodological frameworks (Principal-Issues Thesis, Four-S synthesis, RCI, Lean People Analytics, Full-Stack systems, Network-Mediated Adoption), and the further-reading anchors organized by part.
- C.
Master Glossary
Appendix C. Every term defined inside the seven parts, alphabetized and sourced. Built from the union of the part-end glossaries.