STORIES · ENERGY
Data creates value when it helps predict what happens next.
Energy brings together distributed infrastructure, IT and OT environments, security, regulation and data generated across multiple locations. Transformation begins when this complexity becomes a shared view of the organization.
Energy is a good test of transformation.
The scale is large. Infrastructure is distributed. Technology lifecycles are long. And IT and OT environments have to operate side by side.
Add cybersecurity, regulatory requirements, business continuity and the growing potential of AI/ML and predictive capabilities.
Here, technology cannot remain an experiment. It has to work.
3 STORIES
STRATEGY & ORGANIZATIONAL INTELLIGENCE
AI/ML · PREDICTION · IT/OT · KSC · SOC · RPA
How to turn technology from infrastructure into an organization's ability to predict and act
CONTEXT
A nationwide organization operating critical infrastructure, distributed assets, multiple IT and OT systems and large volumes of operational and technology data.
PROBLEM
Technology developed in isolation can build capabilities without necessarily creating transformation. Data remains fragmented, predictive potential goes unused, while the development of AI/ML, automation and IT/OT integration must simultaneously address critical infrastructure security and KSC requirements.
CHANGE
Building and developing a technology transformation strategy combining AI/ML, predictive capabilities, RPA, IoT, ERP and BPM with IT/OT integration. In parallel, developing the cybersecurity approach, monitoring capabilities and incident response supported by SOC capabilities.
MY ROLE
Responsibility for technology transformation strategy, AI and innovation, translating emerging technologies into practical business and operational applications.
RESULT
Technology, data, automation and security became part of a shared transformation logic rather than operating as separate initiatives.
ADVANTAGE
Using data and AI not only to describe what has already happened, but to predict what may happen next, automate decisions and respond to change earlier.
FROM THE PROJECT
For years, the organization had been building procedures, instructions, forms and processes.
Eventually there were so many of them that a new need emerged: processes for managing processes.
First, you had to know that the right document existed. Then find it in SharePoint. Then recognize it by a formal title that often meant very little to the person actually looking for an answer.
This was exactly the kind of environment where AI demonstrated its practical value.
A model could read, understand and connect hundreds of documents. Instead of knowing the repository structure, procedure name or form number, the user could simply describe what they needed in their own words.
AI did not simplify the documents. It simplified people's access to the knowledge hidden inside their complexity.
IN THIS PROJECT
- Technology Strategy
- AI/ML
- Predictive Capabilities
- IT/OT
- RPA
- KSC
- SOC
- Cybersecurity
DATA & DISTRIBUTED INFRASTRUCTURE
DATA · ANALYTICS · PREDICTION · IT/OT · INTEGRATION
How to turn many local worlds into one view of the organization
CONTEXT
A nationwide energy producer operating distributed renewable energy sources across multiple local installations, operators, contractors and technology environments.
PROBLEM
Each region operated with its own procedures, standards, data formats and local conditions. From the perspective of an individual installation, everything could work correctly. From the perspective of the organization as a whole, there was no common language.
CHANGE
Integration of production and technology data, standardization of data aggregation and creation of a centralized view of operations. This provided the foundation for shared analytics and predictive models across distributed infrastructure.
MY ROLE
Designing the data integration logic, approach to information standardization and the use of technology data for operational and predictive management.
RESULT
Instead of multiple local views, the organization gained shared operational and management information.
ADVANTAGE
Distributed infrastructure could be managed as one system, shortening the path from data to decision and improving the ability to identify change and deviation earlier.
FROM THE PROJECT
Distributed infrastructure had another dimension of complexity.
Each energy region had its own procedures, operators, contractors, data formats and local standards.
The problem was that locally, every one of those standards was considered the right one.
From the perspective of an individual installation, everything therefore made sense.
From the perspective of the organization managing the whole, not necessarily.
Real value emerged only when those different local worlds could be brought into a common data model, shared analytics and a single management logic.
Only a common language of data made it possible to see distributed infrastructure as one system and begin managing it online and predictively.
IN THIS PROJECT
- IT/OT
- Data Integration
- Analytics
- Predictive Capabilities
- Standardization
- Distributed Energy
- Operations Management
PREDICTION AND SYSTEM INERTIA
COGENERATION · SCADA · EAM · ERP · PREDICTIVE AI
How to forecast demand before physics has time to react
CONTEXT
An integrated combined heat and power network with gas-fired cogeneration, district heating and power export to the national electricity system. Operational data was already available across SCADA, EAM and ERP, but it primarily described what had already happened.
PROBLEM
In cogeneration, production decisions must precede demand. The inertia of generating units and the heating network made reactive corrections expensive, while planning based on historical patterns and weather forecasts failed to capture local deviations where value was being lost.
CHANGE
A predictive layer was built across SCADA, EAM and ERP data. Forecasting operated simultaneously at network and individual-node level, while the thermal capacity of the heating network became a buffer charged in advance of predicted demand peaks.
MY ROLE
Solution concept, SCADA-EAM-ERP integration architecture and the data model supporting prediction. Leading the analytical work and defining where decisions could be delegated to the model and where they should remain with the dispatcher.
RESULT
Production started to follow predicted demand rather than historical averages. In the analysed part of the network, this reduced generated but unconsumed energy by 18% and improved the effective utilisation of both outputs of cogeneration.
ADVANTAGE
The ability to stay ahead of the system's own inertia - decisions began to precede changes in demand instead of following them.
FROM THE PROJECT
The largest deviations were explained not only by weather and time of day, but also by holidays, local events, traffic intensity and major television broadcasts.
An energy network does not only forecast the weather. It also forecasts when people will come home and what they will switch on.
IN THIS PROJECT
- Strategy
- Energy
- Cogeneration
- SCADA
- EAM
- ERP
- IT/OT
- Predictive AI
- Data Modelling
- Cost Optimisation
COMMON DENOMINATOR
Infrastructure is physical. Advantage is created through information.
A power plant, renewable energy source, OT system, procedure, AI model or SOC may each solve a different problem.
But only when they work together can an organization understand what is happening now, anticipate what may happen next and respond before a problem becomes an incident.
Data without context is a record. Data connected to process, technology and accountability becomes a decision.
EXPLORE OTHER STORIES
Technology creates value when it increases an organization's ability to act.
If you see a problem, an opportunity or a change that cannot be addressed with another standard project, let's talk about the result you want to achieve.