Collaborative robots promise agility and resilience in manufacturing, yet many deployments fail to deliver tangible relief for frontline employees. Addressing this gap, we present a transparent, LLM-supported procedure for “shift handover to the robot,” which systematically delegates repetitive, routine tasks to an unmanned robot shift while preserving human oversight and value creation during staffed hours. The framework operationalises ambidextrous automation: it protects the exploitation of proven processes while intentionally freeing human time and attention for exploration activities such as improvement, onboarding, observation, and learning. The central thesis is that transparent, data-grounded delegation can convert the potential of collaborative robotics into realised employee relief and stable productivity. This paper summarises context, approach, outcomes, and brief preliminary validation results of the procedure, including “exploration surplus” and practical SME adoption guidance.

At the core is an interactive, checklist-guided decision process that combines fourteen practical criteria with context data from production systems. Evidence includes cycle times, takt adherence, setup and changeover characteristics, error history, scheduling buffers, and environmental, health, and safety constraints. A retrieval-augmented LLM interprets this evidence and proposes candidate tasks for delegation, supporting both cooperative day-shift work and an autonomous, unmanned robot shift. Each recommendation carries a transparent decision trail – prompt-response pairs, data inputs, and scored criteria – that forms standardised handover documentation and enables auditability.

The procedure includes a lightweight task library, exception-handling rules (human fallback, safe stops, and alerts), and scheduling integration so that the robot shift is treated as a governed planning resource rather than ad-hoc automation. We anchor the design in ambidexterity theory by positioning exploration and exploitation as mutually reinforcing over time. Delegating monotony to the robot creates an “exploration surplus”: time and cognitive bandwidth that humans reinvest in improvement and innovation, which, in turn, yields more exploitable routines for subsequent robot shifts. This virtuous cycle is illustrated conceptually through an idealised productivity curve and can be tracked empirically using operational metrics.

We outline measurable targets for repeatable adoption in small and medium-sized enterprises: quantitative indicators include OEE deltas, interruption counts, idle time in unmanned shifts, error rates, and rework; qualitative indicators include perceived workload, trust, and collaboration quality. Expected outcomes are fewer operator interruptions, more stable after-hours execution, and higher acceptance due to clear, auditable decisions. Because the criteria and data interfaces are explicit, the approach is pragmatic to implement and portable across heterogeneous equipment bases. Limitations include data availability and quality, the need for robust safeguards in unmanned operation, and disciplined management of prompts and knowledge bases; we propose mitigations such as minimum data thresholds, watchdog mechanisms, and continuous review of decision logs.

Taken together, the procedure reframes robot handover from a narrow technical integration to a socio-technical management practice. It clarifies which tasks are suitable, why they are suitable, and how their delegation will be evaluated, making automation choices legible to engineers, planners, and operators alike. By turning the robot shift into an accountable planning instrument, the framework helps factories convert potential into realised benefits with traceability, safety, and human-centred relief at scale.