Responsible Use of Artificial Intelligence
Human Expertise. Transparent Use. Accountability.
At Prescher.com, we concentrate on one area: secure and reliable industrial data communications networks for critical infrastructure.
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Our work is grounded in field experience, practical engineering, operational requirements, and an understanding that uptime, safety, security, and maintainability matter more than technology for technology’s sake.
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Artificial Intelligence is becoming a powerful tool for technical professionals. We intend to use it.
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But we are equally clear about something else: AI does not replace experienced engineering judgment. It supports it.
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Our approach is influenced by two important perspectives on the rapidly changing relationship between people and technology. We think both ideas matter in OT.
Responsible AI Principles
Human Expertise Stays in the Loop
We use AI to support research, analysis, documentation, and problem-solving — but it never replaces engineering judgment.
Our experienced professionals validate findings, challenge assumptions, and ensure recommendations are technically sound and operationally appropriate for each customer's industrial environment.
We Will Tell You When We Use AI
When AI materially contributes to project analysis or a customer deliverable, we will identify where and how AI was used. We view that disclosure as good engineering discipline. AI-assisted work remains our work: our professionals review it, challenge it when appropriate, and take responsibility for the conclusions and recommendations we deliver.
We Will Keep Challenging AI Itself
Today’s AI capabilities will not be tomorrow’s capabilities. We will continually evaluate where AI is producing genuine technical value, where human expertise remains superior, and where using AI could introduce unnecessary uncertainty or risk. Our objective is to use it where it makes experienced people better at solving difficult OT problems.
Accountability Remains Human
Human Oversight
Experienced professionals remain responsible for questioning assumptions, validating conclusions, and deciding whether recommendations fit the real industrial environment.
Transparent Use
Customers should understand when and how AI materially contributes to project analysis or a deliverable. Disclosure is part of good engineering discipline.
Continuous Evaluation
We continually assess AI’s value, limitations, and risks so it strengthens experienced people rather than replacing their judgment.