From Asset Data to Predictive Operations
Building the trusted operational foundation that municipal AI needs.
Event Summary
AI is only as useful as the operational data supporting it. Many Public Works organizations still manage information across disconnected spreadsheets, paper records, GIS layers, asset management systems, financial platforms, service-request applications and institutional knowledge held by individual employees.
This session explores how agencies can create an authoritative, location-based view of infrastructure and operations — connecting asset location, condition, inspection history, maintenance activity, costs, service requests and real-time sensor information.
The event demonstrates the progression from basic asset inventory to automated workflows, condition forecasting, predictive maintenance and AI-supported resource allocation.
Key Takeaways
Attendees will learn:
- 01
What makes an operational dataset ready for AI.
- 02
How to establish an authoritative asset inventory.
- 03
How to connect GIS, work orders, finance, inspections and service requests.
- 04
Where predictive maintenance can deliver early value.
- 05
How to identify and correct gaps in asset condition and maintenance history.
- 06
Why data quality, governance and ownership must precede automation.
- 07
How to develop a practical Public Works AI-readiness roadmap.
Resource Guide
Asset Data Readiness Guide
A practical worksheet for auditing your asset inventory, closing data gaps and sequencing a Public Works AI-readiness roadmap.
Data you should have on hand
- Authoritative asset inventory with location, install date and material
- Condition and inspection history for the last 3–5 years
- Work orders and maintenance activity linked to asset IDs
- Maintenance and capital cost history by asset class
- Service requests / 311 records with location
- Sensor and SCADA feeds currently in production
Readiness checklist
- Every asset class has a named data owner and update cadence
- Asset IDs are consistent across GIS, work orders and finance
- Condition scoring uses a documented, repeatable method
- Known gaps in condition history are catalogued, not hidden
- Data quality rules run automatically, not by memory
Questions to ask internally
- Which decisions would change tomorrow if our asset data were trusted?
- Where do staff keep the data that never made it into a system?
- Which asset class carries the highest failure cost per event?
- What governance has to exist before we automate anything?
Next 90 days
- Pick one asset class and make it authoritative end to end
- Join GIS to work orders for that class and publish one dashboard
- Draft a one-page data governance charter and get it signed
Metrics to track
- % of assets with current condition score
- % of work orders linked to an asset ID
- Average age of condition data
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