AI Washing: How Firms Use Artificial Intelligence Narratives to Mask Offshoring, Recessionary Cost-Cutting, and Post-Pandemic Restructuring

When firms attribute workforce reductions to artificial intelligence capabilities they do not possess, have not deployed, or that did not cause the decision, the resulting information asymmetry harms every stakeholder group that relies on corporate communications to allocate resources. This article argues that AI washing, the strategic practice of misattributing organizational restructuring to AI, operates as a narrative pattern structurally incentivized by three convergent forces: offshoring of white-collar positions, recessionary cost-cutting driven by tariffs and credit tightening, and correction of pandemic-era over-hiring. Drawing on institutional theory, signaling theory, and the greenwashing literature, the article develops a Triple Concealment Model identifying these forces and proposes signal quality inversion as a mechanism explaining why capital markets reward unverified AI claims. A structured comparative case analysis across six independent evidence categories provides the empirical foundation. The article offers a five-question diagnostic framework enabling boards, investors, and workforce leaders to distinguish genuine AI transformation from strategic narrative packaging. The analysis reveals that in the cases examined, the gap between AI narrative and AI capability is not random but structurally incentivized, generating a form of credibility debt that degrades the information environment needed for genuine future transformation.