Traditional automation follows explicit rules: if a condition is met, execute an action. AI-assisted automation in PTC Orbit goes further by analyzing historical patterns to predict future outcomes, scoring asset health based on weighted factors, and interpreting natural language queries to generate charts and tables on demand. These capabilities reduce the time service organizations spend on manual analysis and proactive planning.

Three AI-driven features form this automation layer. Demand Forecasting predicts work order volumes. Scores quantify asset risk and health. Conversational Analytics translates plain-text questions into visual data insights. Each feature operates independently, and together they provide a data-driven foundation for service planning decisions.

Demand Forecasting. Describe a forecasting goal in plain text, or specify criteria manually: asset filters (account, location, product), a forecast period (up to two years), and a historical data range (up to five years). The AI-assisted builder parses natural language inputs and populates the criteria automatically. Both paths converge at a review screen where you confirm parameters and generate the forecast. Results display as charts, tables, and exportable datasets.

Scores. Administrators create scoring models on the Scores page by defining weighted factors and criteria. PTC Orbit calculates scores across the installed base during scheduled batch jobs. Each score tracks how many assets it covers and when it was last recalculated. Disabled scores stop calculating during batch runs but remain available for reactivation.

Conversational Analytics. Open the AI Hub and type a question in natural language: "Show me work order volume by region for the last quarter." PTC Orbit interprets the prompt and generates a chart, summary, or data table from your asset data. You can refine the generated visualization by adjusting filters, chart types, and grouping criteria directly in the interface.