AI for the Energy Industry

Applicable across all business areas, also on-premises for sensitive data

Beyond Tech implements AI in energy companies where it provides noticeable relief in everyday operations: in customer service, sales and marketing, grid and technology, Asset Operations, and administration. There are two ways to achieve this: AI that is already built into your business applications, and a dedicated AI platform for applications with sensitive data that can also run in your own data center. On this page, you will learn which path is suitable for what, what you need for on-premises AI, and which rules apply to AI in the energy industry.

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Where AI makes an impact in energy companies

Customer Service

AI automatically categorizes incoming inquiries, summarizes concerns, and suggests responses. Chatbots and voicebots answer standard questions around the clock, while your team handles cases that require personal attention.

Sales and Marketing

AI identifies which leads are most likely to close, drafts proposals and campaign copy, and helps identify which customers might be interested in a new product.

Grid and Technology

A knowledge assistant answers questions about technical guidelines and grid connection requirements, citing the source. Incoming applications are automatically checked for completeness.

Generation and Assets

AI identifies causes for performance losses in wind and PV plants as well as storage systems and suggests specific measures.

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Energy Procurement and Forecasting

Consumption, generation, and grid load can be predicted more accurately when measurement, asset, and weather data are combined.

Finance and Procurement

Invoices, delivery notes, and contracts are automatically read, verified, and transferred into the correct systems. This saves manual entry and reduces errors.

HR and Organization

An internal assistant answers questions about policies, processes, and templates. New employees find what they need faster.

Legal and Regulation

AI searches through contracts, administrative orders, and regulations, summarizes content, and shows where changes affect your company.

Across all areas: AI Agents

Agents don't just answer questions; they complete tasks: they create processes, retrieve information from multiple systems, and initiate follow-up steps. Where it matters, your team continues to make the decisions.

On-Premises AI: When operating in your own data center pays off

With on-premises AI, AI applications and language models run on servers in your own data center instead of a provider's cloud. All data, inputs, and results remain within the company. Beyond Tech implements this for all applications on its own AI platform. For energy companies, this is often more than just a technical decision. They work with data from customers, grids, and assets and are part of the critical infrastructure. Therefore, four reasons support AI in your own data center:

Sensible Daten bleiben im Haus. Kund:innendaten, Verbrauchswerte, Netzinformationen und Verträge werden ausschließlich in Ihrem Rechenzentrum verarbeitet.
Kritische Infrastruktur braucht Kontrolle. Energieunternehmen müssen hohe Anforderungen an die Informationssicherheit erfüllen, in Österreich seit Oktober 2026 auch nach dem neuen NISG. Im eigenen Rechenzentrum bestimmen Sie, wo und wie Daten verarbeitet werden.
Unabhängig bleiben. Ihre KI hängt nicht von Preisen, Verfügbarkeit oder Nutzungsbedingungen eines externen KI-Dienstes ab. Sprachmodelle lassen sich austauschen, wenn bessere verfügbar sind.
Kosten planbar machen. Statt nutzungsabhängiger Gebühren investieren Sie in eigene Infrastruktur. Bei intensiver Nutzung über viele Mitarbeiter:innen hinweg kann das wirtschaftlicher sein.

What you need for on-premises AI

Computing Power

Language models run on servers with GPUs. The number required depends on how many employees use the AI simultaneously, for example in grid planning, maintenance, or customer service. These servers require more power and cooling than standard IT systems.

Technical Platform

The AI software runs on a container platform set up in your data center. It ensures that AI applications are operated separately, securely, and scalably. If such a platform already exists, the AI can build upon it.

Data

The AI needs access to the information it is supposed to work with: technical guidelines, grid connection requirements, contracts, or measurement and asset data. These sources are connected to the platform; your existing storage and systems remain intact.

Operations

Like every system in critical infrastructure, the AI platform requires regular updates, monitoring, and security. This is handled by your IT department or a partner, according to the security specifications applicable in your company.

Access Rights

Grid data, customer data, and contracts are not intended for all employees. The AI platform is connected to your existing user management so that each person only receives answers from sources they are authorized to see. This ensures data protection and information security are maintained.

Lizenzen

Auch im eigenen Rechenzentrum fallen Lizenzen für die KI-Software und die technische Plattform an. Statt nutzungsabhängiger Gebühren planen Sie damit Kosten, die sich gut in Ihre Budget- und Investitionsplanung einfügen. Die Höhe richtet sich nach Umfang und Rechenleistung.

Five steps to implementing AI in your company

1

Identify Use Case

Where does a process currently take up a lot of time, and where can AI demonstrably help? We evaluate the benefits and effort together with you.
2

Define the Path

AI in your business applications, a dedicated AI platform, or both? For on-premises AI, we plan the necessary infrastructure together.
3

Test with Real Data

A first prototype works with your own data. This allows you to see early on what the solution achieves in everyday operations.
4

Move into Operations

The application is integrated into your systems, monitored, and documented. Where decisions are important, humans remain responsible.
5

Empower Your Team

Training ensures that your employees use AI safely and effectively.

AI for your energy company

In an initial consultation, we will work together to find out where AI brings the greatest benefit to your company and which path is right for you.

Frequently asked questions about AI in the energy industry

What does on-premises AI mean?

On-premises AI means that AI applications and language models run on servers in your own data center instead of in a provider’s cloud. All data, inputs, and results remain within the company.

For which energy companies is 360° Digitalization suitable?

For energy suppliers, grid operators, municipal utilities, plant operators, and energy service providers of all sizes. The decisive factor is not the division, but whether processes run across multiple departments or systems.

Which AI applications can be operated on-premises?

All applications on the dedicated AI platform, such as knowledge assistants for technical guidelines and grid connection requirements, automatic checking of applications and documents, forecasts for consumption and generation, and AI agents. In contrast, the AI built into business applications for sales, customer service, and marketing runs in the software provider’s cloud.

Does AI always have to run in your own data center?

No. For many daily tasks, AI in business applications is the fastest route. On-premises AI is particularly worthwhile where sensitive data from customers, grids, or assets is processed or where special security requirements apply.

Will AI replace our employees?

No. AI takes over routine tasks such as categorizing inquiries or reading documents. Responsibility for decisions remains with your team, especially where security of supply or grid operations are concerned.

What does the EU AI Act mean for energy companies?

The AI Act regulates the use of AI based on risk. Transparency obligations have applied since August 2, 2026: chatbots and voicebots must identify themselves to customers as AI. Additional obligations apply to high-risk AI systems, which will take effect from December 2, 2027, following a postponement by the Digital Omnibus.

Does our AI application fall under high-risk AI?

That depends on the specific use. AI systems used as safety components in the power supply can be considered high-risk AI. Systems that support employees in routine tasks, such as in customer service or when checking applications, generally do not fall into this category. A binding classification should be legally reviewed on a case-by-case basis.

What does NISG 2026 mean for AI applications?

Since October 1, 2026, the new Network and Information System Security Act (NISG) has been in force in Austria. Energy companies considered essential or important entities must register by December 31, 2026, and meet higher information security requirements. This also affects the question of where and how AI applications process data.