Services
IoT and Digital Engineering
Bringing Together IoT and Digital Engineering to Create Rich Experiences
Highlights
- Utilities face increasing mandates to modernize aging infrastructure and improve service reliability. Emerging technologies offer predictive insights, automation, and enhanced field operations, making modernization not just beneficial but essential.
- We suggest tailored strategies for brownfield and greenfield systems ensuring flexibility and risk mitigation.
- Few guiding principles ensure consistent, scalable GIS modernization by aligning AI automation, data integrity, and smart operations with strategic goals for resilient utility transformation.
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A strategic shift
Transitioning to UNM marks a paradigm shift from legacy geometric network-based systems to a modern, service-oriented, object-based data model.
Utility organizations are under significant pressure from government and regulatory bodies to modernize their GIS-enabled network management platforms. UNM offers a standardized, scalable, and interoperable framework that supports advanced spatial analytics, real-time system integration, and future-ready innovations such as digital twins. This modernization aims to enhance efficiency, resilience, scalability, and productivity across operations. Whether implementing greenfield (new) environments or migrating brownfield (existing legacy) systems, the process involves navigating significant data, operational, and integration complexities. Let’s explore these in more detail:
Brownfield systems:
Organizations must address inconsistent schemas, missing metadata, and siloed systems to ensure data accuracy and accessibility. Minimizing the impact on critical operations during migration is essential, and leadership must implement strategies to mitigate downtime risks. Overcoming organizational inertia and unfamiliarity with AI/ML methods requires strong leadership and effective change management. Addressing limited in-house expertise on UNM, AI/ML, and cloud infrastructure is crucial, and leaders must invest in training and development programs.
Greenfield systems:
Ensuring future scalability and data model flexibility is vital, and organizations must prioritize robust design principles. Generating training data for AI models in the absence of real data is a challenge, and leaders must explore innovative solutions to address this need. Architecting interoperability from day one is essential, along with ensuring that integration plans are comprehensive and forward-looking. Avoiding overengineering or misalignment of genAI tools requires strategic oversight, and decision makers must ensure that AI strategies are aligned with organizational goals.
Modernizing the GIS landscape presents several challenges that must be addressed strategically to ensure successful implementation and long-term benefits. These challenges can be categorized into various domains, each requiring targeted mitigation strategies.
Data: Validate complexities of inconsistent schemas, topological errors, and missing associations. Ensuring data integrity and consistency is crucial for accurate decision-making and operational efficiency.
People: Reskilling staff and overcoming change resistance are pivotal for successful modernization. Leadership must foster a culture of continuous learning and adaptability, ensuring that employees are well-equipped to handle new workflows and technologies.
Integration: Ensuring compatibility with OMS, SAP, SCADA, and API upgrades is essential for a cohesive system. Leadership must drive efforts to achieve seamless integration across various platforms, enhancing overall system efficiency.
Cost: Addressing licensing, infrastructure upgrades, and hidden migration expenses are critical for budget management. Leaders must strategically allocate resources and optimize costs to achieve a balance between investment and returns.
Trends to watch
Key considerations for advanced tech and strategic planning in utility GIS modernization.
- AI/ML and genAI integration: These technologies help utilities predict equipment failures, automatically extract assets from drone and satellite images, and assist field crews with smart documentation tools.
- Greenfield and brownfield projects: Adopting dual-track GIS modernization strategies—greenfield projects emphasize building future-ready systems with robust data models and real-time connectivity, while brownfield transformations focus on phased, low-risk migrations from legacy platforms to capture early operational gains.
Ethical considerations: GIS modernization is increasingly shaped by emerging technologies like AI/ML and genAI, prompting utilities to address ethical considerations around data use and transparency.
Future-proofing businesses: Utilities are embracing phased GIS transformation roadmaps to turn geospatial systems into strategic assets for operational and decision-making.
Migration strategy
GIS modernization needs tailored strategies—distinct approaches for brownfield and greenfield projects.
A potential brownfield strategy could adopt an incremental migration approach, where asset classes—such as pipes and valves—are transitioned individually. AI models might be leveraged to prioritize assets based on operational data, ensuring that critical components are addressed first.
To support data integrity, AI/ML techniques could be employed for data cleansing, including:
- Supervised learning for entity resolution
- Unsupervised learning for anomaly detection, particularly to uncover inconsistencies in spatial topology
- Change detection by comparing datasets before and after migration
GenAI may play a key role in automating schema mapping, generating natural language documentation for migration logs, and producing ETL scripts and QA rules.
Deployment could begin with pilot implementations in low-risk zones, followed by phase-wise scaling informed by performance feedback and operational learnings.
A possible greenfield strategy could adopt a design-first methodology, emphasizing the creation of a flexible and scalable network schema informed by domain expertise and AI-driven insights.
AI/ML technologies might be utilized to enhance various aspects of the design process, including:
- Predictive modelling to forecast asset loads and service demand
- Optimization algorithms to refine network architecture
- Synthetic data generation to support robust model validation
GenAI could assist in automating the creation of templates and scripts for feature classes, while Natural Language Processing (NLP) models may be used to convert stakeholder feedback into actionable configuration specifications.
The deployment strategy may involve simulated environments powered by synthetic data for stress testing, with continuous improvement enabled through feedback loops and retraining of models to adapt to evolving requirements.
Guiding principles
Driven by core principles, delivering a smart, scalable, and cost-effective path to utility GIS modernization.
Subnetwork-based tracing automation: Automated tracing tools enhanced with AI/ML simulate upstream/downstream flows, detect loops, and isolate faults faster in complex networks, aiding both the initial greenfield design and brownfield upgrades.
Rule-based topology with AI validation: AI models are used to validate complex UNM rules (e.g., containment, connectivity, structural attachment), helping ensure data accuracy and integrity during schema configuration or migration.
AI-assisted network rule authoring: GenAI tools generate and validate network rules (e.g., what can connect to what), reducing manual setup time and improving consistency, particularly in greenfield implementations.
Automated asset loading into UNM schema: ML pipelines classify and load assets (e.g., transformers, valves, switches) into proper UNM feature classes, speeding up data migration and reducing manual mapping in brownfield systems.
Dynamic UNM-driven field operations: Field apps tied to UNM layers dynamically reflect trace results, asset associations, and connectivity status, with AI-enhanced editing and validation improving real-time accuracy.
Future-ready
AI-infused services deliver a strategic blend of business value and technical innovation that empowers utility organizations to prepare for a digital future.
- AI integration boosts operational efficiency by predicting maintenance needs and allocating resources intelligently. This helps utilities prevent issues before they become outages, reducing downtime and improving service levels. AI also enhances decision-making with advanced dashboards, natural language query tools like MapGPT, and scenario-based simulations for planning and investment strategies.
- AI automation cuts costs by reducing the need for manual data validation and quality assurance, freeing up resources for strategic work. AI-powered tools speed up migration by aligning legacy schemas with the UNM structure and validating data integrity, leading to faster rollouts and quicker ROI.
- AI extends utility GIS foundation by detecting anomalies in connectivity, asset placement, or spatial geometry, and providing real-time feedback loops for data correction. It also helps organizations to build scalable analytics pipelines for simulating network behavior, performing predictive analytics, and forecasting customer impacts.
The AI-infused UNM migration approach ensures accurate representation of utility assets and transforms the GIS platform into an intelligent system for smart grid and infrastructure goals.
To sum it up, the utility network model is not just an upgrade—it’s a foundational shift toward intelligent utility networks. With the right blend of strategy, automation, and AI/GenAI innovation, utilities can overcome migration challenges and future-proof their GIS infrastructure. Whether it's a fresh start in a greenfield deployment or a complex brownfield transformation, intelligent automation is the key to a successful UNM journey, ensuring future-proof GIS investments and enabling smarter grid operations.
