The energy sector produces more data than many other industries, generating information continuously at an unprecedented scale. Smart meter installations are projected to reach 3.9 billion globally by 2035, generating USD46 billion in annual revenue and could generate massive volumes of granular consumption data. Yet despite this abundance, many organisations still face the challenge of turning data into meaningful action.
For many energy leaders, the question is no longer whether AI has potential; it is whether AI-enabled talent has the potential to transform their organisation.
As pressure grows around decarbonisation targets, grid modernisation, energy security, and operational efficiency, the organisations that succeed will be those that can act on their data faster, more confidently, and at greater scale than their competitors.
How we got here
The energy industry has collected operational data for decades.
Long before AI became part of business conversations, energy companies were already using SCADA systems, telemetry feeds, sensor networks, and operational control systems to monitor critical infrastructure and maintain grid stability.
Historically, these systems were designed with one purpose in mind: operational reliability.
Operational teams collected data to monitor equipment, maintain uptime, and ensure safe delivery of energy services. They did not design these systems to support enterprise-wide analytics, machine learning models, or real-time business intelligence.
Many organisations now operate with layers of legacy infrastructure alongside newer digital technologies. Over time, this combination has created highly complex data environments, with information spread across multiple systems, teams, formats, and time horizons.
Against this backdrop, what has changed in recent years is the volume and speed of data generation.
The rise of smart meters, distributed energy resources, IoT-enabled devices, renewable infrastructure, and digital trading platforms have transformed the scale of information flowing through the sector. Modern energy ecosystems now generate enormous streams of both historical and real-time data.
Yet, more data does not automatically generate more insight.
In many cases, it can actually magnify existing challenges.
Multiple business functions hold valuable information,. Organisations need a single consolidated view of operations to make quick and confident business decisions.
David Harvey, Head of our Data & Analytics Practice, believes, “Once data is stored but unused, it accrues costs (storage, infrastructure, headcount to maintain it). A useful test: if no one in the business can name the owner, the use case, or the last time it was validated, it’s already a liability rather than an asset.”
Why so much energy data remains underutilised
Most organisations understand the barriers preventing them from fully leveraging their data. The challenge is that many of these issues are deeply embedded within operational structures and culture.
Fragmentation obstacles
Across the energy value chain, systems have traditionally evolved independently. Generation, transmission, distribution and trading functions often operate using separate platforms, processes, and governance models. Data generated in one area rarely flows efficiently into another.
This creates operational blind spots. For example, maintenance teams may have access to asset performance data, while commercial teams hold customer consumption insights, and trading teams monitor market volatility. Individually, each dataset provides value. Together, they could unlock far more powerful operational intelligence — but integration remains difficult.
Skills shortages
Demand for expertise in cloud engineering, data architecture, machine learning, and AI implementation continues to grow across every sector. In energy, this challenge is amplified by the need for professionals who can bridge both technical capability and operational understanding.
A successful AI initiative in energy requires more than strong data science skills alone. Teams also need deep knowledge of assets, infrastructure, grid operations, regulatory requirements, and operational risk.
Many organisations operate with inconsistent asset naming conventions, incomplete maintenance records, missing sensor data, and operational logs stored in unstructured formats. In some cases, the same asset may be labelled differently across sites or business units due to historical acquisitions or evolving operational standards.
David shares: “AI is only as good as the data feeding it, and in the energy sector, where decisions affect grid stability, safety, and trading positions, poor-quality inputs produce expensive outputs. Models trained on incomplete or inconsistent data will confidently surface the wrong recommendations, which is arguably worse than no recommendation at all. Getting trusted, defined and clean data sets across the business is the foundation that everything else depends on.”
Organisations struggle to trust the outputs from untrusted data.
The backlog problem facing the energy industry
These challenges have created a significant backlog of underutilised data across the sector.
Many organisations now sit on years — sometimes decades — of operational and historical information that has never been fully analysed or connected.
In practice, critical insights often remain inaccessible. For example, some teams could keep insights in isolated spreadsheets or dashboards or bury data within maintenance notes, engineer reports, or legacy systems that traditional analytics tools cannot easily interrogate.
The irony is that many organisations already possess the signals they need to predict equipment failure, improve maintenance scheduling, optimise energy distribution, or identify operational inefficiencies.
The divide between operational technology (OT) and information technology (IT)
The divide between operational technology (OT) and information technology (IT) remains one of the sector’s most persistent challenges.
Energy organisations rely heavily on OT environments such as SCADA systems, PLCs, and industrial control systems to manage and maintain critical physical infrastructure. However, the analytics, machine learning, and cloud platforms needed to generate business insight often sit within entirely separate IT environments.
Historically, these systems were built with different priorities, protocols and security models. Bridging the gap between them safely and effectively remains a major obstacle for organisations looking to scale AI and advanced analytics across the business.
Where AI is creating real value
AI is helping organisations improve the quality, accessibility, and usability of their underlying data.
This matters because preparing data for analytics can be one of the most resource-intensive stages. Data scientists and engineers have spent enormous amounts of time manually cleansing records, reconciling inconsistencies, and filling gaps before meaningful analysis could even begin.
In energy environments, where data may span decades of operational history and multiple acquisitions, that time can become even longer.
AI is changing the economics and possibilities of data processing not only by automating existing tasks and enabling large-scale pattern detection but also by extracting complex, unstructured data and turning it into actionable intelligence through AI-powered data processing.
Several AI capabilities are already helping energy organisations improve the accuracy, consistency, and usability of complex operational data at scale:
Machine learning models – Identify anomalies at scale, detecting unusual patterns before they compromise downstream analytics.
Natural language processing – Helps organisations standardise asset naming conventions and reconcile inconsistent records across sites, systems, and historical datasets.
AI models – Gather missing values from correlated operational signals, reducing the need to discard entire datasets.
Perhaps most significantly, AI is unlocking value from unstructured information.
Maintenance reports, engineer notes, inspection documents, and operational logs often contain highly valuable operational insights, but they can be difficult to analyse systematically. AI can now extract and structure this information far more efficiently than traditional manual approaches.
This represents a major shift.
Previously, high-quality data was viewed as a prerequisite for AI adoption.
Today, AI itself is increasingly becoming the tool organisations use to improve and prepare their AI-ready data strategy.
That changes the conversation significantly.
Why technology alone will not solve the data problem
Despite the growing capabilities of AI, technology itself is rarely the biggest barrier to success.
The greater challenge is organisational readiness.
Many transformation programmes stall not because the models fail technically, but because organisations struggle to operationalise the outputs.
Most models generate probabilistic insights based on patterns, correlations, and predicted outcomes. That requires organisations to become comfortable making operational decisions using informed probabilities rather than complete certainty.
For industries built around reliability and risk mitigation, this can represent a significant cultural shift.
Leadership confidence becomes critical.
Organisations need leaders who understand both the opportunities and limitations of AI, and who are prepared to embed data-driven decision-making into operational processes.
Cross-functional collaboration is equally important.
Successful AI adoption in energy requires close alignment between operational teams, data engineers, cloud specialists, cybersecurity professionals, and business leadership. When these groups operate independently, transformation efforts often lose momentum.
The workforce challenge also remains significant.
Technology transformation is ultimately powered by people. As AI adoption accelerates, demand for professionals who can bridge operational expertise and advanced technical capability will continue to increase. Organisations that invest early in skills development and cross-functional capability building are likely to gain a significant competitive advantage.
Looking ahead
Preparing for AI transformation requires executives to set clear priorities in several critical areas. They should focus on establishing strong data governance frameworks to ensure data quality and accessibility across the business. Investment in talent is essential, especially in building teams with both technical and operational expertise. Underpinning these efforts, organisations must develop the right digital infrastructure to support scalable analytics and integration. By addressing data governance, talent development, and robust infrastructure, leaders can put their organisations in a stronger position to realise the full value of AI.
Our consultants can successfully enhance an organisation’s operational efficiency. By reducing the manual data reconciliation process from hours to seconds, internal teams can allocate time to high-value tasks.
The cost-effectiveness of our consultants also makes them a scalable solution for organisations.