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

Artikel i vetenskaplig tidskrift, 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.