Major capital projects in energy and utilities are increasingly digital as well as physical investments. By considering data, interoperability and AI readiness from the outset, organizations can create infrastructure that delivers greater operational value long after commissioning.
For decades, major energy infrastructure projects have been measured against three fundamental expectations: safe delivery, on time and on budget.
Those measures remain critical. But for assets expected to operate for decades, leaders also need to consider what happens after commissioning.
Energy and utility organizations are investing in generation, grids, renewable energy, storage, hydrogen, water infrastructure and other critical assets. At the same time, they are looking to improve productivity and resilience, modernize operations and realize measurable value from artificial intelligence (AI).
The connection between these priorities starts with decisions made much earlier than many organizations might expect.
Choices about data, architecture, operational technology (OT), integration, cybersecurity and governance during a capital project can determine how easily an asset can be monitored, maintained and optimized years after it enters service.
This changes the question leaders need to ask. Beyond whether an asset will be ready to operate, will it also be ready to evolve and deliver the data needed to support future operational decisions?
Look beyond the traditional capital project finish line
Capital projects typically focus on delivering a defined physical capability, such as generating power, transporting energy, treating water or expanding network capacity.
Increasingly, the business case also needs to account for the asset’s operational life and its ability to provide the OT data needed to improve operations.
Operators need reliable access to information about asset performance. Operational and enterprise data need to connect securely where appropriate. Organizations need the ability to identify potential issues, optimize processes as conditions change and introduce new capabilities without rebuilding the underlying technology environment.
For infrastructure that may remain in service for decades, these capabilities can have lasting consequences.
A major capital investment therefore creates more than a physical asset. It also establishes a digital foundation that influences how effectively the asset can be operated, maintained and optimized throughout its life cycle.
This broadens the conversation about return on investment. Success includes delivering the initial project business case while establishing the capabilities needed to improve operational performance and adapt as technologies, markets and business requirements change.
Treat data as part of the infrastructure
Every modern energy asset generates data. Generating it, however, does not automatically create value.
Operational information can remain fragmented across equipment, control systems, engineering environments, applications and enterprise platforms. Without appropriate architecture and governance, organizations can collect large quantities of data while still struggling to turn it into timely, actionable information.
Data therefore needs to be considered part of the infrastructure itself.
During design, organizations need to consider how operational data will be collected, contextualized, governed, shared and used. Waiting until an analytics or AI initiative requires that data can make integration more difficult later.
This means establishing appropriate data quality, governance and lineage while enabling interoperability between systems and greater collaboration across organizational silos. It also means designing architectures that can securely support operational and business requirements.
A trusted data foundation can give operators greater visibility into asset performance, support better decisions and enable predictive analytics. It also establishes a fundamental requirement for AI: reliable data with the context and governance needed to use it responsibly will turn insights into drive trusted actions.
Build AI readiness into the operating environment
The rapid evolution of AI can create pressure to start with a model, platform or use case. In capital-intensive, mission-critical environments, the starting point is the operating foundation.
Organizations first need to understand whether the environment can support AI safely and effectively.
That requires reliable and accessible data, appropriate information exchange between IT and OT, cybersecurity embedded in the architecture and clear governance and accountability. New capabilities also need to be introduced without compromising the safety, reliability or resilience of critical operations.
Energy and utility infrastructure brings together enterprise IT with operational environments that can include supervisory control and data acquisition (SCADA), distributed control systems, industrial Internet of Things technologies, advanced metering and other critical systems. As connectivity increases, decisions about data, cybersecurity, cloud and edge technologies and access management become more closely linked.
AI readiness is therefore an architectural and operational capability, not simply access to an AI platform. Building that capability into capital programs and modernization initiatives gives organizations greater flexibility to introduce AI where it can deliver measurable operational value.
Design energy infrastructure to evolve
No organization can know which technologies will be available halfway through the operating life of an asset commissioned today.
Trying to design around a specific future technology is therefore unlikely to be the answer. A more durable approach is to create an environment that can accommodate change.
That means considering interoperability, scalability and modularity where they support business and operational requirements. It also means making operational information securely available to new applications without destabilizing mission-critical systems.
With these foundations in place, organizations can progressively introduce capabilities such as predictive analytics, automation and more sophisticated operational optimization as requirements and technologies evolve.
The same principle applies to existing assets. Modernization does not necessarily require replacing physical infrastructure. Organizations can introduce integration, analytics, automation and operational intelligence around critical systems in stages, helping extend their value and useful life.
This is where capital investment and operational transformation converge.
Capital project, operational and technology teams bring different priorities and expertise to infrastructure decisions. Involving these perspectives early can help organizations avoid an important long-term constraint: an asset that performs as designed physically but is difficult to optimize digitally. Bringing these teams together creates a more complete view of the asset and the value it needs to deliver throughout its operating life.
Make digital requirements part of capital planning
For leaders overseeing major infrastructure investments, the practical implication is to bring long-term operational requirements into the capital program early enough to influence design.
Three questions can help focus that discussion:
- What operational data will we need? Define what information the asset needs to produce, how it will be contextualized and who will need access to it.
- How will systems work together securely? Consider interoperability, cybersecurity, governance and IT-OT integration alongside physical and engineering requirements.
- How will the asset accommodate change? Design for appropriate flexibility so new analytics, automation and AI capabilities can be introduced as operational needs evolve.
These are not simply technology decisions. They affect how organizations will operate, maintain and extract value from infrastructure throughout its working life.
From infrastructure that works to infrastructure that evolves
The energy transition will require significant investment in physical infrastructure. The opportunity is to make those investments work harder over time.
That requires looking beyond the traditional finish line of a capital project and bringing together the operational, technology and capital project expertise needed to consider the asset across its full life cycle.
An asset needs to perform as designed when it is commissioned. Its digital foundation also needs to support trusted data, secure interoperability and the ability to introduce new operational capabilities as requirements change.
Organizations that establish these foundations today will be better prepared to apply AI where it can create practical, measurable value rather than introducing technology for its own sake.
For leaders making long-term infrastructure decisions, this adds another measure of success to safe, on-time and on-budget delivery: Don’t just ask whether the asset is ready to operate. Ask whether it is ready to evolve.
