CLEVELAND (Apr. 13, 2026) – Collective Data announced the launch of embedded artificial intelligence capabilities within its fleet and asset management platform at the NAFA 2026 Institute & Expo.
The new enhancements integrate AI directly into existing system workflows, positioning intelligence as a core component of daily operations rather than a standalone add-on. The capabilities will be highlighted during an Innovations Showcase presentation led by Jason Wonase, CEO of Collective Data, and demonstrated throughout the event at booth 379.
The embedded AI tools are designed to improve productivity, accelerate operational insight and support user-driven configuration within the platform.
“The value of AI depends on the strength of the system behind it,” Wonase said. “Because our platform is configurable at its core, the intelligence we are embedding adapts to each customer’s workflows and delivers insight that is relevant actionable.”
One of the primary features being introduced is an interactive “Ask Anything” tool. The feature allows users to submit natural-language questions and receive contextual responses based on their operational data. Built on a reusable prompt framework within the platform, the tool delivers structured outputs aligned with each organization’s configured data environment.
Demonstrated use cases include:
- AI-generated activity summaries tailored to operational context
- Identification of potential work order comebacks with supporting explanations
- Daily or weekly operational snapshots on demand
Collective Data is also introducing new AI capabilities designed to simplify document processing, data import, and maintenance coding accuracy. Users can upload a PDF such as a repair invoice or service document and automatically generate a structured work order. The system interprets the issue description and translates it into standardized work tasks with properly aligned VMRS codes.
By converting documents into accurate, consistent work and VMRS codes at the point of entry, this new functionality improves the reliability of maintenance data. It significantly reduces manual data entry and administrative workload while strengthening reporting accuracy, trend analysis, and cost tracking.
With more precise coding from the start, organizations gain clearer visibility into failure patterns, recurring issues, labor allocation, and component-level costs. The result is better data, stronger reporting, and more informed decision making across maintenance planning, budgeting, warranty recovery, and fleet replacement strategy.
According to Collective Data, its adaptable schema architecture supports the accuracy and relevance of AI-generated results. Because the platform enables rapid configuration and evolution of data structures, the data feeding AI tools remains organized and contextual.
Future development will focus on expanding AI-assisted reporting, intelligent data importing and workflow configurations.
Collective Data will demonstrate the new capabilities during its NAFA Innovations Showcase session and throughout exhibit hours at booth 379. Learn more about our embedded AI capabilities here: https://collectivedata.com/artificial-intelligence/
About Collective Data
Collective Data is the most adaptable asset, fleet, and inventory management platform on the market, purpose-built for public safety, government, and enterprise organizations. For more than 25 years, we have helped agencies replace fragmented systems with a single, fully configurable solution designed to evolve alongside their needs.
Our cohesive, no-code platform empowers organizations to streamline operations, improve accountability, and gain complete visibility across assets, fleet, and inventory, all within one centralized system. Built for scalability and long-term flexibility, Collective Data delivers the control, productivity, and confidence today’s agencies require to operate at their best.
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Media Contact:
Robert Edilson
Director of Marketing
[email protected]
319-297-2128













































































