<p>A professional service organization (PSO) must allocate workforce resources to project jobs, a process known as resource planning (RP). We propose an Intelligent Resource Planning Optimization (IRPO) framework that integrates natural language processing (NLP) with optimization techniques to enhance this task. Using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) classifier, we extract job attributes (skills, education, experience) from curriculum vitae (CVs) and job postings and compute resource–job matching scores via Sentence Transformers (ST) embeddings. To ensure high-quality matching, we compute a resource-job score that considers multiple job attributes and hiring manager priorities. These scores feed into a reformulated multi-period resource planning problem, expressed as a minimum-cost matching (MCM) model on a bipartite graph and solved to optimality with Bertsekas’ <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varepsilon \)</EquationSource> </InlineEquation>-scaling auction algorithm. Computational experiments with real CVs and job postings demonstrate that our custom NLP models achieve a CV ranking closely aligned with expert judgment, that IRPO is orders of magnitude faster than a Gurobi-based mixed-integer linear programming (MILP) approach, and that it achieves 99.7% demand fulfillment and 84% capacity utilization—outperforming greedy heuristics by over 20%. For practitioners, IRPO offers a pathway to superior workforce management by integrating assignment, training, and hiring decisions into a single optimization. This holistic approach directly translates to enhanced demand fulfillment and resource utilization, reducing project delays and staffing costs. By providing attribute-based, explainable scores, our framework also fosters trust in artificial intelligence (AI)-driven decision support and presents a scalable blueprint for workforce planning in other complex sectors.</p>