Czy AI zastąpi zawód: szatniarz/szatniarka?

Szatniarz/szatniarka faces a very high AI disruption risk with a score of 81/100, driven primarily by automation of inventory tracking and item allocation tasks. However, complete replacement is unlikely in the near term because human judgment remains essential for customer service, emergency response, and handling exceptions. The role will transform rather than disappear.

Czym zajmuje się szatniarz/szatniarka?

Szatniarz/szatniarka is a cloakroom attendant responsible for the safe storage and retrieval of clients' outerwear, bags, and personal items. They receive articles from guests, issue tokens as receipts, match tokens to items upon departure, and return belongings to their owners. Beyond basic item management, they handle customer inquiries, process complaints, and may assist with special requests or facility information. This role combines custodial responsibility with direct customer interaction in hospitality and leisure settings.

Jak AI wpływa na ten zawód?

The 81/100 disruption score reflects a sharp divide between vulnerable and resilient components of this work. Vulnerable tasks—allocating numbers to belongings (core inventory logistics), maintaining administrative records, and managing lost-and-found systems—are highly susceptible to automation via RFID tracking, barcode systems, and inventory management software. These represent approximately 60% of routine daily work. Conversely, resilient skills including assisting clients with special needs, managing emergency evacuations, and greeting guests rely on empathy, judgment, and real-time problem-solving that AI cannot yet reliably handle. The modest AI Complementarity score (31.72/100) indicates limited opportunities for AI to enhance existing tasks—surveillance equipment use and customer communication represent exceptions. Near-term outlook (2-5 years): expect introduction of automated check-in/check-out systems and digital inventory tracking, reducing staffing in high-volume venues. Long-term (5-10 years): hybrid roles may emerge, with attendants focusing on customer service while AI handles logistics. Small venues and premium hospitality will retain human staff longer due to personalized service value.

Najważniejsze wnioski

• Inventory and administrative tasks are highly automatable; AI systems can track belongings more accurately than manual allocation (72/100 automation proxy).
• Customer service, special needs support, and emergency response remain distinctly human skills and provide job security in interactive hospitality settings.
• Technological adoption will vary by venue size and business model—large facilities will automate faster than boutique establishments.
• Reskilling toward premium customer experience and conflict resolution will be essential for role survival in AI-augmented environments.
• The occupation will likely shrink overall but persist in roles emphasizing human interaction and judgment over logistics.

Wynik zakłócenia AI NestorBot obliczany jest na podstawie 3-czynnikowego modelu wykorzystującego taksonomię umiejętności ESCO: podatność umiejętności na automatyzację, wskaźnik automatyzacji zadań oraz komplementarność z AI.