Last Updated on 10. February 2026
Artificial intelligence has long since arrived in the energy industry – in the form of tools, pilot projects and initial productive applications. At the same time, we are seeing growing disillusionment and frustration among decision-makers and employees. AI has been “tried out” and is being used, but it is hardly changing everyday work. Tools are available, but only a few applications have a measurable impact on throughput times, quality, or costs.
Our findings from projects: The bottleneck rarely lies in the tool or model, but rather in the fact that AI is not consistently integrated into workflows and roles. Individual GenAI tools work well for individuals, but have no organizational impact without process integration.
What we are currently observing in AI projects in the energy industry
In practice, we encounter recurring patterns that slow down the successful use of AI. A key misunderstanding is that generative AI should be used immediately for every problem. In fact, many projects fail not because of a lack of model performance, but because of a lack of process clarity, non-digitized processes, or unstructured data. In such cases, automation or classic (rule-based) AI approaches often have a faster effect. GenAI is particularly effective when it is based on prepared, structured information and is used specifically for content classification, consolidation, and decision support.
A typical use case from our projects in regulated environments: regulatory requirements, supplier documents, or project documents are manually reviewed, summarized, and passed on through several stages. Most of the time is spent reading, sorting, and coordinating. In a first step, these documents are automatically captured, classified, and checked for completeness. Building on this, generative AI is used to summarize content, highlight deviations, or prepare decision-making bases. The result: less manual preparatory work, clearer handovers, and more time for technical evaluation – with full traceability and human approval.
The often underestimated “soft” part of AI transformation is particularly challenging. Not because it is unclear, but because it is rarely actively managed. Many organizations invest time in searching for technology and tools or defining use cases, but fail to agree on common goals, priorities, and responsibilities at an early stage. The result is many good initiatives – but no clear direction. Which AI projects actually contribute to strategic goals? Which ones have priority? And what criteria are used to decide what to pursue and what not to pursue? As long as these questions remain unanswered, AI will remain a side issue rather than a management tool.
In addition, AI is changing the basis for decision-making, role models, and expectations of employees. If this change is not actively supported, uncertainty arises – both at the management level and among employees. The supposedly “soft” part thus becomes the decisive lever: it is not the technology that determines the success of AI, but the organization’s ability to set goals, enable targeted learning, and thus scale AI safely.
How mgm brings AI from use case to impact
Against this backdrop, mgm supports companies throughout the entire AI life cycle. Our approach combines strategic clarity, technical feasibility, and organizational anchoring. The goal is to treat AI not as an isolated topic, but as an integrated part of value creation.
Specifically, our service portfolio ranges from the development of a robust AI strategy to the structured prioritization of suitable use cases to implementation, scaling, and secure operation. Among other things, we provide support in the following areas:
- Developing AI strategy, roadmap, and governance to clearly link use cases to business objectives and define decision-making structures
- Use case workshops and structured prioritization (AI Navigator) to systematically weigh impact and feasibility
- Implementation of PoCs and prototypes, including clear success criteria
- Scaling and integration into existing processes and system landscapes
- Change management and employee empowerment to ensure AI is accepted and used
- The establishment of secure, sovereign AI environments (e.g., mgm C12 Cloud, Private GPT, STACKIT, or Azure)
- — GDPR-compliant, ISO 27001-certified, and without the use of customer data for training
All solutions can be flexibly adapted to existing infrastructure requirements — whether cloud, hybrid, or data center — and, if necessary, supplemented by CIO Advisory for organizational integration.
What our customers get out of it
The added value for our customers does not come from individual AI applications, but from the ability to control AI in a targeted manner and scale it effectively. By clearly linking the use of AI to strategic goals, decision-making structures, and governance, companies create transparency about which initiatives are effective — and which are not.
Structured prioritization and implementation based on clear success criteria enable AI projects to be brought from concept to deployment in a controlled manner, rather than getting stuck in a multitude of isolated pilots. Scaling does not only mean technical expansion, but also reliable integration into existing processes, system landscapes, and responsibilities.
The result is not a selective increase in efficiency, but a resilient AI capability: measurable relief in day-to-day business, better decision-making bases, and an organization that can use AI in a comprehensible, scalable, and long-term manner.
Relevant fields of application for AI in the energy industry
The energy industry in particular offers numerous opportunities for the pragmatic use of AI. The potential is particularly great where time-consuming preparatory work dominates. This includes, for example, the automatic recognition, structuring, and pre-filling of content from PDFs, emails, or forms, as well as the rapid indexing of large volumes of documents—for example, in the case of regulatory requirements, approval procedures, or tenders.
Checking for completeness and consistency also plays a central role, for example in NIS2, DORA, or other compliance certificates. In addition, AI can support case classification and prioritization in customer service or market communication and largely automate recurring processing steps.
Typical introductory questions and what they reveal about the degree of AI maturity
In discussions with interested parties, the level of AI maturity often becomes apparent very quickly. Questions such as the following are helpful in this regard: Is the focus currently still on the individual use of individual tools or already on cross-departmental applications? Is it primarily about empowerment and experimentation — or about concrete relief in everyday life? Where is AI anchored organizationally: in IT, in a specialist department, or in a center of excellence?
It is also particularly revealing to look at existing pilot projects: Which applications have made it into productive operation—and which have not? Often, the reasons lie less in the technology than in a lack of integration, unclear responsibilities, or a lack of acceptance.
From technology to impact
We support companies in making AI effective—from the initial idea to stable, secure use in everyday life. The focus is always on measurable benefits, integrated processes, and an organization that understands, accepts, and further develops AI.
Here you will find all the information about mgm AI solutions.
