Augmentation or overwhelm? GenAI and the recoding of supply chain planning's DNA

Journal article, 2026

Purpose Supply chain management literature describes generative AI (GenAI) as transformative for operations, but its socio-technical consequences for the professional workforce remain underexplored. This study investigates how GenAI adoption reshapes core supply chain planning (SCP) roles.
Design/methodology/approach Employing an exploratory multi-case study design, the study compares job specifications from GenAI adopter firms (Amazon, Tesla, Colgate-Palmolive and The Warehouse Group) with those of matched non-adopter firms across three deployment architectures. A strict separation between classification data (strategic documents, executive statements and technical publications) and analysis data (job specifications) prevents circular reasoning. Semi-structured interviews with senior SCP leaders were triangulated with the textual analysis to reveal day-to-day practices that formal documentation does not capture.
Findings Two different archetypes emerge: the process guardian, who executes procedures within transaction-focused systems and the supply chain architect, who orchestrates adaptive planning across AI-enabled platforms. GenAI adoption produces an autonomy-ambiguity paradox, whereby planner authority expands while the decision space becomes harder to define. Formal hiring documentation lags behind operational deployment across firms. Four transition-specific paradoxes characterize the progression from early to advanced GenAI maturity in SCP roles.
Originality/value A transformation framework models pathways from GenAI deployment to augmentation or overwhelm. A three-category typology of deployment maturity (GenAI-native, GenAI-augmented and build-phase) captures variations that binary adopter/non-adopter classification would collapse. A maturity model operationalizes this framework through diagnostic stages that comprise transition paradoxes and resolution requirements. Nine propositions structure future research on human-AI collaboration in SCP.