Background
Sam Leonard, the Facilities Process Manager for Building Services in the City of Austin, Texas, recounts how the asset management team relied on outdated pen-and-paper methods when he joined the department years ago. Needing a more modern solution, the city began its transformation in 2014 with the implementation of a legacy work order system from Brightly, which was later acquired by Siemens.
While this was a start, early attempts to gain a holistic view of city assets were still fragmented. Facilities condition assessments (FCAs), for example, were only conducted for certain buildings like fire stations. This limited scope meant that Austin lacked a comprehensive understanding of its entire building portfolio, hindering effective capital planning and resulting in costly reactive decisions based on immediate needs rather than strategic insights.
Without robust data or organizational goal alignment, Leonard and his team faced challenges effectively justifying capital budget requests to key stakeholders. This led to insufficient funding for addressing critical deferred maintenance. The writing was on the wall: the City of Austin needed better data.
Upleveling from a work order system to enterprise asset management
The City of Austin’s pivotal shift toward efficiency, data-driven insights and capital planning started with the adoption of Brightly’s enterprise asset management (EAM) solution (now a part of the Siemens portfolio), which became the centralized hub of their new asset lifecycle management (ALM) strategy.
In a crucial move, the city undertook a massive effort to conduct FCAs on all buildings, a significant expansion from its earlier program. This comprehensive effort led to the meticulous cataloging of 10,000 assets into the EAM solution, finally allowing an accurate, 360-degree view of the city’s vast infrastructure. Investing in this foundational data was a gamechanger for the building services team, enabling a shift toward proactive maintenance and informed decision-making.
Technicians, now equipped with tablets and mobile devices, could access work orders, parts information and manuals directly in the field, drastically reducing response times and administrative burdens and virtually eliminating paper-based workflows. The team customized the software by adding a new “asset status” field, allowing them to track precisely when assets were replaced, rebuilt or decommissioned, further enhancing their ALM initiatives.
Securing funding with capital planning data and reporting
With rich data at their fingertips, the City of Austin then utilized capital planning capabilities. This allowed the city to move beyond reactive planning to a truly data-driven capital strategy.
Leonard generated high-level reports illustrating asset age, criticality and condition projected over a 20-year span, modeling various funding scenarios. Complete with risk analyses and heat maps, the reports told a compelling story to stakeholders.
The culmination was a presentation to the CFO in 2025, where the city requested $25 million per year for deferred maintenance. The impact was immediate and profound. Leonard noted, “The CFO was incredibly impressed. Having the data made all the difference; without the proof, we would have gotten nothing.”
With strong data backing their case, the team not only secured approval but received an additional $50 million per year for five years to tackle the deferred maintenance backlog, a substantial commitment that will make a huge dent in long-standing infrastructure needs.
The future is clear
Looking ahead, the City of Austin views this success as a vital first step in their ongoing ALM journey. They are committed to fully optimizing the capital forecasting model, continuously updating asset information as deferred maintenance projects are completed, and regularly rerunning analyses to demonstrate results and refine future strategies.
Despite challenges like staffing constraints for work order input requiring temporary support, the team is effectively leveraging the system’s capabilities, constantly finding workarounds and optimizing their processes.
Their vision is clear: to continuously utilize data and expand system functionalities, supporting long-term sustainability and operational excellence.