Client
KEGOC
Selected work / KEGOC
A focused AI session for the energy sector, connecting generative AI to productivity, safety learning, grid decisions, and infrastructure monitoring.

Client
KEGOC
Sector
Energy
Format
Briefing
KEGOC
The session explained AI evolution, the difference between classical ML and generative models, and how ChatGPT can support knowledge work and management decisions.
The energy context changed the conversation: grid load, infrastructure inspection, safety learning, and operational support mattered as much as productivity.
A tailored presentation, live examples, and a practical discussion of where generative AI could fit into KEGOC workflows.
Case study
For KEGOC, the training had to connect two worlds: fast-moving generative AI and the concrete work of an energy infrastructure company. The program covered AI history, transformer models, ChatGPT, productivity research, energy-sector examples, infrastructure monitoring with drones, and a discussion of responsible AI use inside KEGOC workflows.
Need
A generic ChatGPT lecture would have missed the point. The audience needed to understand the technology and see examples that make sense for an energy operator.
Program
The training moved from basics to application: ChatGPT for employee learning, internal knowledge, document automation, safety training, and management decisions. The energy-specific part covered classical ML in load prediction, grid optimization, and AI-assisted inspection of power lines and infrastructure.
Content design
The presentation kept the technical explanation credible while returning every block to decisions the organization could actually consider next.
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