Unlocking value through integrated digital, data and AI foundations
The energy sector is undergoing unprecedented internal and external change. This extends beyond technology, as the underlying dynamics of the energy system are being fundamentally reshaped. Rising demand, electrification, distributed and consumer energy resources (DERs & CERs), microgrids and new loads such as electric vehicles (EVs) require far greater real-time visibility and intelligence across the energy ecosystem.
This shift places significant demands on how utilities integrate, govern and act on data across operational and organizational boundaries. As a result, data has become a core corporate asset, with value equal to or, in some cases, exceeding that of physical assets.
To respond, utilities need a Digital Backbone: an integrated, intelligent foundation that connects data, platforms and business processes with people to improve reliability, safety while meeting both regulatory and societal expectations. Further, internal pressure is increasing to unlock data for advanced analytics and AI. When implemented effectively, a Digital Backbone turns siloed data into shared value by enabling AI to be applied responsibly and at scale in support of business and regulatory compliancy imperatives.
Our approach brings these together through a Digital and Data Backbone that is business-led and rooted in culture, operating model, governance and technology. The objective is to provide trusted data that enables trusted actions, in a manner that reflects the realities of each organization’s operational and regulatory environment.
In this article, the term Digital Backbone refers to the core structural foundation, while Digital and Data Backbone reflects that same foundation when data, governance and AI-ready capabilities are explicitly designed into it.
What is the Digital Backbone?
The Digital Backbone is an end-to-end approach to digital transformation that helps utilities envision, design and operationalize technology in direct alignment with their strategic goals. As data volumes grow and operational complexity increases, the Digital Backbone must also provide the foundation for trusted data sharing and AI-enabled decision-making.
Built on a set of propositions—from digital strategy and cloud enablement to security, sustainability and enterprise service management—the framework ensures every investment is business-led and technology-enabled, not the other way around, and is grounded in each organization’s operational and functional realities.
In practical terms, this means establishing and implementing a digital data architecture that can:
- Support real-time grid visibility and situational awareness
- Enable integrated, omni-channel customer experiences
- Connect complex ecosystems of DSOs, TSOs, DER and CER aggregators, flexibility service providers, regulators, investors, developers and consumers
Each organization’s Digital Backbone is tailored to its business model and strategic imperatives. It is not a one-size-fits-all blueprint, but a structured approach that leverages existing strengths and culture to unify the enterprise and enable end-to-end operational data sharing at pace and scale.
Activating the Digital Backbone with data and AI
The next stage in realizing the value of the Digital Backbone is defined by how effectively data is shared and used, particularly to support advanced analytics and AI. Utilities are rich in data but often constrained in how consistently they convert it into insight or operational impact. Data remains fragmented across legacy platforms and organizational silos and is frequently inaccessible to the subject-matter experts who could use it to improve decision-making.
To change this, utilities need a modern, scalable data architecture that:
- Mirrors the operating model
- Handles increasing volume, variety and velocity of data
- Makes trusted data easily accessible to business experts
- Supports advanced analytics and AI use cases at enterprise scale
In other words, the Digital Backbone must be strengthened with AI-ready capabilities that turn fragmented information into trusted, usable assets, enabling data-driven decision-making across the organization and its wider ecosystem.
The Digital Backbone provides the enduring structural foundation, while trusted data and AI bring that foundation to life through insight, automation and trusted action.
Organizations that prioritize AI before data enablement often lose confidence in its ability to deliver value, as data quality issues and premature adoption of platforms that are difficult to sustain lead to outcomes that fall short of expectations.
Choosing the right data sharing and data platform architecture
There is no single “best” architecture. The right approach depends on regulatory context, governance requirements, ecosystem maturity and the utility’s operating model.
Importantly, data sharing does not mean data decentralization. In regulated, multi-actor energy systems, value is created by enabling secure access and interoperability, while data often remains where it is created rather than being duplicated.
Most utilities therefore combine multiple data-sharing patterns:
CENTRALIZED
A single source of truth where participants contribute to and consume from a common repository. This simplifies data access, discovery and governance, plus improves consistency in reporting and analytics. However, in multi-stakeholder and operational contexts it can introduce scalability, trust, sovereignty and data-timeliness trade-offs, particularly for near-real-time use cases.
FEDERATED
Data remains distributed across organizations or domains but follows shared governance principles and standards. This preserves local autonomy while enabling interoperability across DSOs, TSOs, flexibility service providers, energy retailers and regulators.
DATA MESH (AS A DISTRIBUTION PATTERN)
Data is treated as a strategic asset, with ownership and accountability placed in domain teams in line with their operational responsibilities. When managed with product-like discipline, this improves agility and responsiveness, while requiring mature governance, automation and cultural readiness in regulated utility environments.
HYBRID
In practice, most utilities adopt a hybrid approach, combining centralized governance and selected shared platforms with federated or mesh-style distribution depending on the domain, use case and maturity.
Platform patterns for AI-ready data
- DATA LAKEHOUSE
Combines the flexibility of a data lake with the structure and performance of a data warehouse. It brings structured, semi-structured and unstructured data together, services multiple personas from a single environment and reduces duplication across the data landscape.
- While data mesh can describe how data is distributed, it can also represent a broader shift in how data is owned and managed across the organization.
- DATA MESH (as an operating model and platform approach)
A broader socio-technical shift in which business domains are accountable for the data assets they produce and use, managing them with product-like discipline. In large utilities, generation, networks and customer operations may each steward distinct data domains aligned to their operational responsibilities, with clear ownership, quality expectations and defined consumers.
- This model supports faster response to regulatory changes, market signals and customer expectations, but works best when governance and operating models are sufficiently mature to support decentralization.
Across both approaches, the guiding principle remains the same: architecture must align with the operating model, not force the business to adapt to technology constraints.
Stop waiting for perfect data: Deliver value first
A common concern is whether utilities need “perfect data” before adopting advanced analytics or AI. In practice, waiting for perfect data often delays value indefinitely.
A more effective approach is value-led and iterative:
- Start with high-ROI, “low-hanging fruit” use cases that demonstrate tangible value
- Use early successes to fund and prioritize targeted data quality improvements
- Apply AI-enabled tools for automated data quality checks, anomaly detection and scenario simulation
We support utilities in moving away from open-ended proofs of concept (POCs) toward proofs of value (POVs) and minimum viable products (MVPs) that are designed to scale, align with enterprise governance standards and deliver measurable outcomes quickly.
While it may appear counterintuitive, establishing pragmatic data policies, fit-for-purpose data models and manageable governance early often accelerates AI adoption rather than slowing it down. Trusted outcomes drive user confidence, improve adoption and allow successful initiatives to scale responsibly. This approach fits naturally within the Digital Backbone, where architecture, governance, and delivery methods are all oriented toward continuous value realization rather than one-off projects.
Culture: the largest ROI driver
Technology alone does not determine success; culture and operating models are decisive. Shifting to modern architecture and sharing data across traditional silos is fundamentally a people and business model change journey enabled by digital transformation and access to data.
Key shifts include:
- Moving data ownership closer to business domains
- Building multidisciplinary teams that combine engineering, analytics and operational expertise
- Creating accountability for data quality, integrity and continuous improvement
Insights from the CGI Voice of Our Clients show that in the energy and utilities sector, organizations with more agile operating models are significantly more likely to realize value from their digital investments. The same traits that enable agility—empowerment, collaboration and learning—also underpin effective data and AI strategies.
Governance, trust and data sovereignty
As digital ecosystems expand and data is shared across organizational and national boundaries, trust becomes non-negotiable.
A strong Digital and Data Backbone embeds:
- Federated data governance with clear roles, standards and decision rights
- Defined data ownership and accountability
- Security, privacy, and sovereignty by design, aligned with evolving regulatory and market requirements
Without trust, even the most advanced platforms fail. With AI, the stakes are higher still: data quality and governance determine whether risk scales as quickly as value.
Operationalizing the Digital and Data backbone
When utilities align backbone, architecture and culture, they can unlock trusted, interoperable data across legacy and modern systems to:
- Create unified visibility across asset, grid and customer data
- Deploy advanced analytics and AI for predictive operations
- Improve resilience, compliance and customer engagement
- Reduce integration complexity and time-to-value
This is where our strengths come together. We help utilities connect business strategy, operating model and data and AI architecture; implement cloud, lakehouse or mesh approaches where appropriate; establish governance and culture that treat data as a strategic asset; and move confidently from pilots to production, with a focus on value and responsible AI.
Conclusion: Turning shared data into shared value
There is no single blueprint for digital success in energy and utilities organizations. The real challenge lies not in selecting tools but in building the skills, culture and confidence to share and use data responsibly across complex ecosystems.
Our Digital and Data Backbone approach helps utilities balance flexibility with control, design architectures that are secure and AI-ready and embed governance and culture that can unlock return on data and AI investments.
By connecting strategy, governance and technology, utilities can turn shared data into shared value. This accelerates the transition to a more sustainable, intelligent and customer-centric energy system and positions AI as a scalable, responsible force for long-term operational impact.
In doing so, organizations can realize the full value of their data assets by empowering people to uncover insights, test improvements and deliver lasting, measurable outcomes.
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