Georgia Tech partners with Avid Solutions and IBM to revolutionize Stonecrest's asset management, energy distribution and workforce coordination
The Partnership for Inclusive Innovation (PIN) is driving applied, community-oriented innovation across Georgia, having supported 20 communities and activated over 140 technologies statewide. Inspired by PIN’s mission, Avid Solutions, an IBM Business Partner, launched a national initiative focused on development-level solutions to food and energy insecurity—spanning construction, manufacturing, and energy systems. This work, piloted in Stonecrest, Georgia, laid the foundation for a collaboration with Georgia Institute of Technology (Georgia Tech), which joined the initiative as a research partner.
Georgia Tech expressed enthusiasm for working alongside Avid Solutions on this forward-looking initiative. The project’s emphasis on sustainability, workforce development, and community resilience offers students a meaningful opportunity to apply their skills toward equitable, long-term impact. The university looks forward to a strong and productive collaboration.
Stonecrest presented a unique opportunity to address systemic challenges through innovation. The city faced limitations in infrastructure that hindered predictive maintenance, efficient energy distribution, and coordinated workforce development. These gaps underscored the urgency for a more integrated, technology-driven approach—one that could support long-term resilience and equitable growth across the community.
in unplanned asset downtime across 3 counties with IBM Maximo Application Suite
in manual field response times through smart workflows using watsonx Orchestrate
in peak load costs through energy monitoring
in water waste in agriculture through automation
in deployment speed compared to earlier projects
To bring the vision of a resilient, intelligent infrastructure to life, Avid Solutions led the project, with ZNAPZ providing licensing support for IBM® Maximo® Application Suite. The initiative centered on deploying the IBM Maximo Application Suite asset management solution and the IBM watsonx Orchestrate® automation solution, chosen for their scalability, interoperability and ability to support public-private integration.
The implementation was carried out in carefully coordinated stages—from site preparation and stakeholder alignment to system integration, sensor and drone deployment, and full-scale validation. Throughout the process, Avid Solutions managed the delivery of IBM’s technologies, ensuring seamless integration with Georgia Tech’s existing systems and alignment with their community goals.
There were three key components of the transformation:
- Condition-based monitoring: The use of Maximo was extended beyond traditional facilities to monitor solar panels, irrigation systems and drone assets using real-time sensor data, enabling predictive maintenance and reducing downtime.
- Clean energy optimization: AI agents balanced energy loads and adjusted irrigation schedules based on solar yield and grid demand, helping reduce peak energy costs.
- AI-driven field operations: Drones captured multispectral imaging, which AI agents then translated into actionable insights on soil, moisture and crop health. These insights triggered automated workflows, supporting job creation in drone operations, data tagging and system monitoring.
Challenges such as data silos were addressed through Georgia Tech’s coordination with stakeholders, while workforce limitations were mitigated through partnerships that created local green jobs. The system built on watsonx Orchestrate and Maximo has a modular, API-driven architecture—aligned with IBM protocols—making it possible to be adapted to other regions by modifying energy models, crop databases and regulatory layers.
The deployment of Maximo and watsonx Orchestrate has delivered measurable, community-wide impact for Avid Solutions’ initiative in Stonecrest.
By leveraging predictive analytics, the initiative achieved a 40% reduction in unplanned asset downtime across three counties. Smart workflows enabled by watsonx Orchestrate led to a 60% decrease in manual field response times, significantly improving operational agility. Clean energy monitoring contributed to an 18% reduction in peak load costs, while automation in agriculture drove a 30% decrease in water waste. The initiative also delivered a 30% increase in deployment speed compared to previous projects, and it is projected to generate USD 2.3 million in cost avoidance over three years, with a 10x return on investment.
Beyond operational gains, the project also created 120 new jobs in sustainability and smart infrastructure, empowering local talent and reinforcing Avid Solutions’ and Georgia Tech’s role as drivers of inclusive innovation.
This transformation marks a shift from reactive to predictive governance, where intelligent automation enables faster, data-driven decisions. By extending Maximo into biological and environmental assets, Avid Solutions has redefined asset management and positioned the initiative as a national model for scalable, resilient infrastructure. Looking ahead, Avid Solutions and Georgia Tech will deepen their involvement in solutions across the state of Georgia in 2025 by launching a capstone collaboration with senior-level students, supporting the next phase of system optimization and data integration.
The Georgia Institute of Technology is one of the top public research universities in the US. It has more than 50,000 students across the main campus in Atlanta, at its off-campus instructional site Georgia Tech—Europe in Metz, France, and through distance and online learning.
Avid Solutions Intl is an IBM Business Partner delivering innovative, scalable solutions across a wide range of industries. With expertise in digital transformation, automation and intelligent systems integration, Avid Solutions helps organizations modernize operations, enhance sustainability and drive measurable impact through advanced technologies and strategic collaboration.
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Examples presented as illustrative only. Actual results will vary based on client configurations and conditions and, therefore, generally expected results cannot be provided.