Predictive Analytics
Know what's coming before it happens.
Most analytics tools tell you what happened. ExCom.ai tells you what's likely to happen next — and can act on it automatically.
Forecasting
Revenue Projections — Forecast revenue based on pipeline, seasonality, and historical patterns.
Demand Planning — Predict demand to optimize inventory and production.
Cash Flow — Project cash position weeks or months ahead.
Resource Needs — Anticipate staffing and capacity requirements.
Ask questions like:
- "What's our projected revenue for Q2?"
- "When will we run low on Component X?"
- "Forecast customer growth for the next 6 months"
Anomaly Detection
Surface problems before they become crises.
The platform learns what "normal" looks like for your business. When something deviates, you know immediately.
Use Cases:
- Unusual spending patterns
- Production quality shifts
- Customer behaviour changes
- Network performance degradation
- Revenue anomalies
How It Works:
- AI builds a baseline from your historical data
- Continuous monitoring against that baseline
- Alerts when deviations exceed thresholds
- Context provided — not just "something's wrong" but "here's what changed"
What-If Scenarios
Model decisions before you make them.
- "What happens to margin if we increase prices 5%?"
- "Impact of adding a second shift?"
- "If we lose Customer X, what's the revenue impact?"
The AI uses your connected data to simulate outcomes, not generic benchmarks.
Automatic Decisions
Go beyond alerts to action.
Define rules, and the platform executes them:
| Trigger | Action |
|---|---|
| Inventory below threshold | Generate purchase order |
| Customer churn risk high | Alert account manager |
| Quality metric out of range | Pause production line |
| Cash flow projection negative | Flag for finance review |
Decisions can be fully automatic or require human approval — your choice.
Trend Analysis
Identify patterns you might miss.
Correlations — Find relationships between metrics. Does weather affect sales? Does team size correlate with delivery time?
Seasonality — Automatically detect and account for cyclical patterns.
Leading Indicators — Identify metrics that predict outcomes before they happen.
Confidence Levels
Every prediction comes with context.
- Confidence score — How certain is the forecast?
- Key drivers — What factors most influence the prediction?
- Sensitivity — How much would the forecast change if assumptions shift?
No black boxes. You understand why the AI predicts what it predicts.
Visualisation
When you need to see the data:
- Ask a question, get a chart
- Automatic format selection (line, bar, scatter — whatever fits)
- Pin important views for ongoing monitoring
- Share with your team
But the focus is insight, not dashboards.
Related Capabilities
- Decision Engine — When anomalies are detected, the Decision Engine can respond automatically