Reference
Key concepts in project profitability management, AI forecasting, and services delivery. Each term is defined in the context of how Promapp uses it.
Scope drift (also called scope creep) is the gradual, often unnoticed expansion of a project's scope beyond its original boundaries. Unlike formal change requests, scope drift happens incrementally — small additions, extra revisions, or undocumented requirements that collectively erode project margins. AI-powered tools can detect scope drift early by comparing planned vs actual task creation rates and flagging unplanned work.
Estimate at Completion (EAC) is a projection of the total cost of a project when it finishes. EAC combines actual costs incurred to date with a forecast of remaining work, using metrics like Cost Performance Index (CPI) and Schedule Performance Index (SPI). Probabilistic EAC extends this by computing optimistic (p20), expected (p50), and pessimistic (p80) scenarios rather than a single number.
Project health signals are real-time indicators that measure the overall condition of a project across multiple dimensions. In Promapp, 6 signals are computed continuously: velocity trend (delivery speed), scope drift (unplanned work), burn rate (cost efficiency), estimate inflation (accuracy of remaining estimates), mapping quality (time entry coverage), and unplanned work ratio. Each signal is scored green/amber/red.
Margin forecasting predicts the profit margin a project will achieve at completion, based on current trajectory and historical patterns. Unlike revenue forecasting, which only projects billing, margin forecasting accounts for both revenue and costs — giving a true picture of profitability. Probabilistic margin forecasting produces confidence bands (p20/p50/p80) instead of a single estimate.
Root cause attribution identifies and ranks the specific factors causing margin erosion in a project. Rather than surfacing a generic 'project is over budget' alert, root cause attribution pinpoints whether the issue stems from scope drift, estimate inflation, rate mismatches, staffing inefficiencies, or timeline slippage — and quantifies each factor's margin impact in euros or percentage points.
Budget vs actual (BvA) tracking compares planned project costs against actual costs incurred. In services companies, this means comparing estimated hours and rates against logged time entries and actual expenses. Real-time BvA tracking reveals cost overruns as they happen, rather than at month-end when it's too late to course-correct.
Probabilistic forecasting generates a range of outcomes with associated confidence levels, rather than a single point estimate. For project management, this means computing p20 (optimistic — 80% chance of being better), p50 (expected — median outcome), and p80 (pessimistic — 80% chance of being worse) margin bands. This approach gives leadership a realistic view of best, expected, and worst case scenarios.
The Cost Performance Index (CPI) measures cost efficiency by dividing earned value by actual costs. A CPI of 1.0 means the project is on budget; below 1.0 means over budget; above 1.0 means under budget. CPI is a key input to EAC forecasting and is one of the strongest predictors of final project cost overruns.
The Schedule Performance Index (SPI) measures schedule efficiency by dividing earned value by planned value. An SPI of 1.0 means the project is on schedule; below 1.0 means behind schedule; above 1.0 means ahead of schedule. SPI combined with CPI provides a comprehensive view of project health and is used in probabilistic forecasting models.
Structured interventions are AI-proposed corrective actions designed to recover margin on at-risk projects. Unlike ad-hoc firefighting, each intervention is a specific, actionable recommendation — such as rebaselining the plan, requesting a change order, adjusting staffing, or de-scoping low-priority deliverables. Each intervention includes an estimated margin recovery percentage so managers can prioritize by impact.
Policy guardrails are configurable rules that define what the AI autopilot can propose and what requires human approval. They ensure that AI recommends but humans decide. Examples include cost thresholds above which a change order must be escalated, approval chains for staffing changes, and alert rules for margin drops beyond a configured percentage. Guardrails are customizable per project type (fixed-price, T&M, blended).
A unified data model connects all project-related entities — clients, opportunities, offers, projects, tasks, time entries, budgets, rates, expenses, milestones, and team resources — in a single interconnected graph. This means every hour logged automatically impacts margin calculations, every scope change triggers health signals, and the AI has full context to make accurate predictions without manual data reconciliation across multiple tools.
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