Resource allocation in project-based organizations is a constant negotiation. Project managers compete for the same skilled people, and the allocation decision often comes down to who asks first or who has the most organizational leverage rather than what is optimal for the business.

The Problem

Allocating people to projects requires balancing skill requirements, availability, project priority, team composition, and development goals. This multi-variable optimization is done manually in spreadsheets, producing allocations that are feasible but rarely optimal. Over-allocation and under-utilization coexist.

The AI Approach

An LLM can evaluate allocation options against multiple constraints simultaneously: matching skills to requirements, respecting availability, balancing workload, and aligning with business priorities. It produces recommendations that a human would take hours to derive manually.

Three-Step Build

Step 1 - Data assembly. Compile project requirements (skills needed, effort estimates, timeline), resource profiles (skills, current allocation, availability), and project priorities from your PMO.

Step 2 - Allocation optimization. Feed the data to an LLM with instructions to propose allocations that maximize skill-fit while respecting utilization targets (typically 75-85%). Flag conflicts where demand exceeds supply for specific skills.

Step 3 - Scenario comparison. Generate two or three allocation scenarios with trade-off analysis: Scenario A optimizes for skill-fit, Scenario B optimizes for timeline, Scenario C balances both. Present to decision-makers with the implications of each.

Where It Breaks

Resource allocation involves human factors (team dynamics, career development, personal preferences) that data does not capture. The model cannot predict that two specific people work poorly together or that someone is about to resign.

The Production Path

Use AI recommendations as a starting point for allocation discussions, not as final decisions. Track actual project outcomes against recommended vs. actual allocations to validate the model’s recommendations over time.