AI Resource Allocation System
Consulting Firm, London
Context & Objective
We designed an intelligent resourcing solution to replace static spreadsheets and subjective manager decisions. The objective was to automate project allocation, balance workloads, and provide real-time visibility of team capacity across departments.
Challenges Faced
Limited Visibility
There was no centralised system showing who was available or over-allocated, which made planning reactive and inconsistent.
Manual Skill Matching
Managers relied on memory and emails to match people to projects, which was slow and did not scale effectively.
Disconnected Tools
Approvals, timesheets, and reporting were handled in separate systems, creating duplication and data silos.
Approach & Solution
AI-Led Intake
Incoming project requests analysed by AI Builder and GPT-4 to extract skills, timelines, and effort estimates automatically.
Optimised Matching Engine
Dataverse stored structured staff profiles. An optimiser assessed availability, capability, and workload to recommend balanced teams.
Seamless Approvals
Power Automate integrated with Teams and Outlook so managers could review or adjust AI-generated suggestions instantly.
Continuous Monitoring
Power BI tracked utilisation and forecasted capacity while anomaly detection identified workload imbalances early.
Results & Impact
- Reduced allocation time from several hours to minutes through automation
- Improved organisation-wide visibility of workloads, resulting in better balance and retention
- Enhanced forecasting accuracy by around 40%, enabling earlier interventions
- Model incorporated holidays, learning objectives, and project criticality for fair decisions