AI at Work Varies by Power, Culture, and History: Why Uniform Adoption Misses Structural Inequities
Original framing: “Why employee AI adoption isn't one-size-fits-all” — Phys.org
The Texas A&M study employs surveys and psychometric scales, offering valuable data on individual variance but lacking longitudinal, intersectional, and organizational‑level analyses. Robust mixed‑methods research would better capture systemic effects. Scientific rigor is present but limited.
The headline’s focus on individual variance masks a deeper web of power, culture, and history that shapes AI’s workplace impact.
By integrating Indigenous stewardship, historical labor struggles, and cross‑cultural attitudes, we reveal that AI adoption is a contested site of governance rather than a neutral tool. Scientific rigor must be expanded to longitudinal, intersectional studies, while artistic and spiritual lenses surface ethical dimensions that numbers alone cannot capture. Trickster logic reminds us that AI can both liberate and imprison, depending on who writes the rules. Systemic solutions—participatory governance, equitable training, transparent regulation, and forward‑looking scenario planning—offer pathways to align AI with democratic, just, and sustainable futures.