Predictive maintenance only works
if the asset data is trustworthy.
Power generation operators understand that asset failure is not acceptable. But the AI tools promised to prevent it require data foundations that most operators have not yet built. We close that gap, methodically, and without vendor lock-in.
Where power generation AI investments stall
Asset Performance Management needs clean data
APM platforms are only as predictive as the maintenance history, condition data, and failure records fed into them. Most EAM systems in the sector are incomplete.
Regulatory reporting demands auditability
IEC and OFGEM compliance requirements mean asset data must be traceable, consistent, and governed. Ad hoc AI deployments rarely meet this standard.
AI procurement is outpacing readiness
Boards are approving AI budgets before data maturity has been assessed. We help operators sequence investments correctly, data foundations first.
Our services for Power Generation
Strategic Advisory
AI readiness assessments, data maturity benchmarks, and sector-specific roadmaps aligned to your operational reality.
Learn more →Data Transformation
Asset data cleansing, taxonomy alignment, EAM and IWMS modernisation, and structured data foundations that AI can actually use.
Learn more →Managed Services
Data Governance as a Service (DGaaS), continuous AI oversight, model monitoring, and responsible governance on a retainer.
Learn more →Built on the Asset Intelligence Framework
Three pillars. One integrated approach. Governance, Data Maturity, and AI Readiness, assessed together and delivered with SAFE-AI™ governance active whenever AI is in scope.
What organisations in Power Generation ask us
What makes power generation asset data particularly complex for AI readiness?
Power generation assets operate across long lifecycles — often 30 to 60 years — with maintenance histories spanning multiple owners, operating regimes, and record-keeping systems. OT systems (SCADA, DCS) generate high-volume sensor data that is rarely integrated with EAM records at the asset level. The result is that the organisation has significant data volume but very poor data coherence — sensors attached to assets with incomplete records, maintenance history split across legacy systems, and condition data that cannot be reliably attributed to specific components.
How does AI readiness for renewable generation differ from conventional power?
Renewable assets — wind, solar, hydro — operate at scale with high asset counts and relatively short operational histories. The AI readiness challenge is typically one of taxonomy standardisation and data aggregation across large fleets, rather than the long-history integration problems of conventional generation. However, both asset types share the same fundamental requirement: clean, consistently structured asset records with accurate location hierarchy, equipment specifications, and maintenance history before AI tools can be deployed reliably.
What regulatory frameworks affect AI adoption in power generation?
Power generation operators in Great Britain are subject to National Grid ESO requirements, Ofgem licence conditions, and where applicable the NIS Regulations (and their NIS2-derived successors). AI systems used in operational decision-making are in scope for governance requirements under these frameworks. The operator must be able to demonstrate that AI-assisted decisions were made on reliable data, with documented oversight. Asset data quality is therefore a compliance requirement, not just an operational preference.
Can predictive maintenance AI work with ageing OT infrastructure?
It can, but it requires a data foundation that does not currently exist in most legacy OT environments. Ageing SCADA and DCS systems were not designed to share data with modern AI platforms, and the asset records associated with them are typically incomplete. The path to AI-driven predictive maintenance in these environments runs through data integration and governance first — establishing what data exists, what quality it is at, and what can be reliably surfaced to AI systems — before any model is trained or deployed.
Ready to make predictive AI
a credible investment, not a gamble?
Be ready.