Breaking Down Utility Silos
Alexina Jackson is a creative problem solver who thrives in collaboration, tackling big questions and setting transformational vision. As the founder of Seven Green Strategy and Senior Advisor at Clarum Advisors, Alexina leverages her experience in consulting, law, and technological and commercial innovation.
Molly Podolefsky is a Ph.D. economist and leader in the energy and sustainability industry with experience spanning decarbonization, the utilities and energy sector, finance and investment and business management. As a Managing Director with Clarum Advisors, she leverages her knowledge and experience working with utilities, startups, corporations and funds in the energy transition space.
Most utilities would like to know more about the needs, risks, and operations of the electrical grid — intelligence is a conceptual no-brainer. Sensors, analytics, and control software allow them to make better decisions around operational risks and maintenance, economic efficiency, meeting customer needs, and security. In light of these value streams, utilities should be building multi-year plans for incremental deployment of grid intelligence solutions, compounding insights, savings, and customer benefits over time.
Yet utilities frequently struggle with where to begin. Resilience and reliability technologies are obvious starting points but can carry significant price tags, making investments based on risk avoidance, projected operational savings or customer service improvements hard to justify.
Efficiency and customer experience benefits are hard to quantify, and regulators are increasingly wary of qualitative arguments as affordability becomes a dominant concern. The greatest blocker to grid intelligence investments is often not the lack of value, but the inability to assemble a persuasive business case.
Grid intelligence is typically deployed by utilities system wide, producing incremental value across multiple departments. Some of the most powerful gains come from co-optimizing processes and investments — precisely the kind of benefits that siloed functions, metrics, and budgets struggle to capture.
When each department views grid intelligence through its own narrow lens, it creates a collective action barrier — no department has a strong enough business case to implement the optimal solution on its own. Breaking down these silos is a prerequisite for valuing grid intelligence in a way that reflects its true system impact.
How and Where Silos Arise
Alexina Jackson: To make cross-department total value assessment repeatable, utilities should standardize the evaluation process for grid intelligence proposals. Each should identify all relevant value layers specifying which departments benefit and how and providing quantitative and qualitative evidence.
To break down silos it is important to understand how and where they arise. Work within a utility is divided among operational departments, customer-facing functions, and digital and administrative teams. Each has distinct objectives, budgets, and decision processes, leading them to interpret “value” in ways that reinforce separation rather than collaboration.
Operational activities are often the earliest adopters of grid intelligence, but they are also subdivided — generation, transmission, distribution, system operation, asset management, and engineering, each with its own priorities and mandates. The different insights needed for generation capacity, grid capacity, outage and contingency management, planning forecasts, and system design frequently lead to separate systems of sensors and software, each solving a local problem but failing to share data or value across organizational lines.
Customer activities such as program design, metering, billing, and revenue protection tend to have modest budgets and highly focused incentives. Given their importance to cost recovery and revenue assurance, associated grid intelligence investments are typically justified by improving billing accuracy, reducing losses, and better targeting customer programs. As a result, operational improvements through customer-side sensors are often considered secondary.
Digital and administrative functions are similarly siloed. Within digital teams, information technology, data analytics, and operational technology are frequently separated for security reasons. Regulatory, resource planning, and finance functions support operational and customer areas by building the qualitative and financial narratives underpinning investment decisions and program design.
Molly Podolefsky: Grid intelligence around performance and capacity of overhead lines illustrates how this value-stack approach can work. Line monitoring solutions drive benefits that can be valued and stacked; improving reliability and resilience, optimizing use of grid assets, and informing smarter capital decisions.
Here a chicken-and-egg problem emerges: Utilities need robust grid intelligence to produce strong business cases, but they also require strong business cases to secure approval for those grid intelligence investments in the first place.
Fragmented data and legacy systems can further compound these organizational divides. AMI, GIS/EAM, CIS, and SCADA systems often lack the degree of interoperability needed to manage the grid as an integrated whole. Segregated data, disconnected workflows and incomplete communication create inefficiencies and duplicate work. Meanwhile, budgetary and compliance requirements are often framed as inflexible mandates, rather than engaging operations, planning, and customer program teams in joint value creation.
A Value Stacking Approach
A more effective approach to valuing grid intelligence investments is deliberately looking across utility functions to assess total system value, rather than assigning each investment to a single “owner” responsible for building the business case in isolation. Instead of evaluating a proposal solely based on the benefits from one use case, utilities should catalogue the full set of operational, planning, customer, and compliance benefits.
Practically, this starts with defining cross-functional use cases and value objectives at the outset of technology selection. An investment in advanced distribution management software or field sensors, for example, should be scoped jointly by system operations, asset management, planning, customer programs, and regulatory teams.
Together, they can identify distinct but related value categories: reduced outage durations and truck rolls, avoided or deferred capital projects, more precise targeting of customer programs, improved forecasting, and better measurement of performance and compliance commitments. Treating these as separate value layers that can be quantified and stacked allows the organization to construct a complete and credible picture of benefits. A project that appears marginal from one department’s point of view may, when its data value is considered enterprise wide, become a foundational enabler of future capabilities.
This approach requires the utility to treat data as a strategic asset underpinning multiple value streams. When evaluating investments in advanced metering upgrades, data platforms, or integration between OT and IT systems, utilities should look beyond the immediate sponsor’s needs, such as billing accuracy or control-room visualization.
Improved data quality, granularity, and interoperability can enable downstream applications like predictive maintenance, dynamic hosting capacity analysis, locationally targeted customer offerings, and more robust evidence of avoided costs.
Total Value Assessment as Standard Practice
To make cross-department total value assessment repeatable rather than ad hoc, utilities should standardize the evaluation process for grid intelligence proposals. Each proposal should identify all relevant value layers — such as energy, capacity, grid services, resilience, environmental, customer, and regulatory — specifying which departments benefit and how and providing a mix of quantitative and qualitative evidence.
Where monetization is feasible (for example, avoided capital projects, reduced losses, lower outage costs), those values should be calculated. Where it is not yet practical (such as certain equity improvements or enhanced regulatory transparency), consistent metrics or scoring methods can still be defined. Cross-functional working groups should facilitate the process to facilitate shared assumptions and joint ownership of outcomes.
Over time, this way of working can reshape how utilities plan and justify investments in grid intelligence. Establishing cross-department value stacking as standard practice helps reframe the perception of grid intelligence from a discretionary add-on to core infrastructure supporting safe, reliable, and affordable power delivery.
Line Intelligence — A Case in Point
Grid intelligence around the performance and capacity of overhead lines illustrates how this value-stack approach can work in practice. Line monitoring solutions drive benefits that can be valued and stacked; improving reliability and resilience, optimizing use of grid assets, and informing smarter capital decisions.
Improving Reliability and Resilience: Sensors, weather-informed analytics, and condition monitoring tools provide operators with a clearer, real-time view of the network and unlock greater operational flexibility. They can detect emerging issues before they cause outages, prioritize field work where it most reduces risk, and refine protection and switching strategies.
During storms and other high-stress events, the same intelligence supports more targeted sectionalizing and restoration. These improvements can be valued through avoided outages, reduced truck rolls and emergency overtime, lower equipment failure rates, and stronger performance against reliability targets — benefits that resonate with both regulators and customers.
Optimizing Use of Grid Assets: Traditional planning practices often rely on conservative, static ratings for overhead lines, resulting in designs that leave significant headroom unused. Investments in line intelligence enable utilities to operate closer to real, dynamic limits while maintaining safety and reliability, increasing grid throughput, reducing congestion on critical corridors, maximizing efficient use of outage windows, and deferring costly upgrades.
They also support greater integration of energy into the grid, particularly from distributed and renewable resources that might otherwise be constrained by assumed capacity limits, thereby helping to service new loads more efficiently. By extracting more value from existing infrastructure, utilities can deliver more reliable and affordable electricity while moderating the need for new capital outlays.
Informing Smarter Capital Decisions: With a more accurate and granular understanding of overhead line performance, utilities can improve prioritization and timing of capital projects. Planners using real-world data from line intelligence solutions can differentiate between lines requiring near-term reinforcement and those where upgrades can be delayed or addressed through non-wires alternatives.
Line capacity intelligence supports more targeted capital deployment and reprioritization of investment to higher-value projects, aligning capital plans more closely with real system needs and customer outcomes rather than worst-case scenarios. In an environment where affordability is increasingly top of mind for regulators, having more granular real-world data to substantiate rising capital spend is likely to quickly become a baseline requirement.
When evaluated using a cross-department, value-stacked methodology, investments in overhead line intelligence — and grid intelligence more broadly — emerge not as discretionary technology projects, but as essential infrastructure for a modern, resilient, and economically optimized grid.
