In wind energy, artificial intelligence has long since arrived - right inside the turbine. Across many sites, turbine types and weather conditions, hundreds of specialised machine-learning models are “listening” to the equipment: they detect anomalies, estimate remaining useful life and optimise day-to-day operations. Yet the decisive question for manufacturers and operators is a different one - and it reaches far beyond the individual turbine: how AI-ready is your company as a whole?
The energy transition is also a data transition
Companies in the wind industry face challenges similar to those of refineries: optimise assets, improve efficiency and profitability, advance decarbonisation. In both worlds, success is not decided by individual sensors but by the ability to form a consistent overall picture from a multitude of sources.
That is precisely why the energy transition is not only a question of technology and capital - it is also a data transition. Those who master their data can steer assets, grids and business processes intelligently. Those who don’t leave value on the table - no matter how powerful the individual AI models in use may be.
The real obstacle: fragmented data
Modern process industries reveal the scale of the challenge. A single refinery today runs 50 to 200 different software systems - spread across every level, from process control to enterprise management. This diversity is intentional: it keeps complex operations safe, efficient and compliant.
The problem lies elsewhere: these systems are often integrated with one another by less than 30%. Laboratory systems, document management, accounting, CAD, piping and calibration software - each speaks its own language, with its own nomenclatures, units and time references. Wind energy companies know the same pattern: SCADA, condition monitoring, weather data, maintenance systems, ERP and contract data exist side by side - but rarely connected.
And this is exactly where the central insight applies:
AI readiness is not a technical topic but a strategic one - and it begins with data.
Data integration as the foundation of AI readiness
Before a company can seriously consider AI tools and use cases, its data has to come together. boTec takes a horizontal integration approach for exactly this purpose, bringing the most diverse data types and sources together into an integrated data model:
- harmonising nomenclatures
- converting units
- aligning data in time
- aggregating and disaggregating
- resolving data references across system boundaries
The result is more than a technical interface: processes, assets and their interrelationships are represented as data-based digital twins - linked directly to the live data of operations. This creates a single, reliable view of the entire company that makes monitoring, simulation and well-founded decisions possible in the first place.
Talking to your own data
On this integrated foundation, a new way of accessing data becomes possible - one that requires no specialist knowledge. Through the Model Context Protocol (MCP), an open standard by Anthropic, a language model can be connected directly to the integrated data. Instead of building a dedicated interface for every combination of AI model and data source - the classic “M×N integration problem” - a universal, model-independent bridge emerges.
The difference is significant. Without access to external tools or data sources, a language model is limited to its available model context and supplied content. MCP provides a standardised way to connect models to operational data and tools. A language model with MCP, by contrast, accesses the actual operational data in a controlled way. A request can then sound as simple as:
“Load the daily raw, reconciled and forecasted values for flow MFlow since the start of the month and show the differences between forecasted and reconciled in a table.”
The advantages of this tool-based approach:
- Less hallucination, because the model draws on real tools and data instead of “guessing”
- No proprietary model training and no large language model strictly required
- Fully local deployment possible - crucial for data protection, operational security and critical infrastructure that must remain resilient against cloud outages
At the same time, the language model does not replace a precise formal language. Where reproducibility and speed matter, a domain-specific language (DSL) remains superior; the LLM is easier to use and supports exploratory interaction. The two approaches complement each other.
A broad data spectrum - from planning to sales
AI readiness means understanding the entire company as a data space. Along its value chain, a wind energy company generates an exceptionally broad spectrum of data:
- Asset planning & development - geospatial and wind resource data, environmental and permitting data, grid connection, financial and yield models. Foundational data that has an impact across the entire economic lifetime.
- Construction & commissioning - construction progress, component and serial numbers, quality and safety data for a clean handover into asset management.
- Operations & maintenance - SCADA data at second-level intervals, condition monitoring, weather and event data, maintenance histories, predictive-maintenance models. This is where most operational data arises.
- Grid & plant operations - feed-in and power output data, grid stability, energy trading and forecasts as the key to commercial real-time control.
- Asset & portfolio management - master data, KPIs, costs and revenues, contracts and compliance. This is where operational, financial and strategic perspectives converge.
- Sales, customers & stakeholders - contract and billing data, ESG and sustainability reporting, market and communication data.
- Cross-cutting data - staff and qualifications, HSE, IT/OT security, environment and sustainability.
Each of these stages is valuable in its own right. But the data unlocks its full potential only when connected.
From integrated data to concrete use cases
Once the data silos are dissolved, AI use cases that were previously out of reach become tangible:
- Maintenance & asset management: optimised planning of maintenance across assets, locations and distances to be travelled
- Grid & plant operations: better prediction of grid status at positions without their own sensors - based on neighbouring sensor, demand and weather data
- Sales & customers: higher customer satisfaction through automated analysis of feedback and problem reports
AI readiness is a strategic decision
The common thread is clear: data integration is AI readiness - and AI readiness is what makes AI tools and AI use cases possible in the first place.
AI readiness means integrating technical, commercial and organisational data silos in such a way that AI-supported decision processes become possible across all business processes - in order to achieve globally optimal results for the company.
In the end, then, the question is not one of technology but of business:
“What new possibilities would open up for our company if our data were fully integrated and connected?”
Those who take this question seriously don’t start with the next AI model - they start with their own data.