Mine reconciliation is a critical process for aligning predicted Mineral Resource Estimates or Ore Reserve estimates, as defined under JORC (The Joint Ore Reserves Committee of The Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia), with actual production outcomes across the mining value chain. It provides quantitative measures of operational performance, identifies sources of ore loss and dilution, and underpins confidence in mine planning, reporting, and decision-making. However, reconciliation workflows are traditionally spreadsheet-based and delivered on month-end cycles, constraining operational teams’ ability to respond to emerging issues within relevant operational time frames. This paper compares how an AI assistant processes and reports F2 reconciliation data relative to conventional, human driven methodologies. It presents an AI-assisted reconciliation workflow focused on F-style factors at the mine interface, including F2, and demonstrates how increasing reporting cadence from monthly to weekly and daily intervals improves operational agility without compromising governance, accountability, or auditability. A de-identified open pit copper data set was used to demonstrate the workflow, comprising daily records of mined tonnes and grade compared with modelled values for sulfide and marginal material. Results show that short-cadence reporting reveals trends and deviations that may be masked by month-end averages, enabling earlier investigation and intervention. The workflow provides a practical blueprint for implementing AI-assisted mine reconciliation within existing assurance frameworks while supporting future scalability. AI-assisted F2 reconciliation represents a step change in how mining operations validate data, maintain resource accountability, and translate reconciliation outcomes into operational action. When reconciliation calculations remain deterministic, governed, and auditable, AI can be safely applied to high-effort tasks such as data handling, validation support, variance triage, and report generation within a structured human-in-the-loop framework. In the case study, the AI assistant completed the F2 reconciliation in approximately ten minutes, compared with two hours using a traditional manual workflow, producing clear, decision-oriented outputs that highlighted key variances. Although human oversight continued to play a crucial role, this method significantly boosted productivity by lessening manual work and shortening reporting delays. As a result, professionals could dedicate their attention to identifying root causes and implementing corrective measures, which improved operational efficiency. With appropriate data pipelines, uncertainty frameworks, and governance controls, AI-assisted F2 reconciliation offers a scalable bridge between spreadsheet-based methods and near-real-time operational control while preserving technical rigour.