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SME Questionnaire Form - August 18

Publication Target Date: August 18


Submission Deadline: August 1


Article Working Title: Legacy infrastructure problem holding back AI in OT cybersecurity


Editorial Brief: AI deployment in industrial environments keeps running into the same wall that the infrastructure underneath it wasn't built for this. Takepoint Research's survey surfaces a familiar but stubborn set of barriers, including legacy systems, patchy data quality, thin staffing, and vendors still finding their footing in the OT space.

 

The obstacle isn't reluctance, but architecture. Most industrial facilities were built long before anyone imagined running AI on top of them, and the control systems underneath haven't caught up. Clean, consistent telemetry is something many OT environments still can't reliably produce. AI needs reliable data to function and brownfield OT environments often can't reliably deliver it. One of the harder questions the survey raises is whether organizations are fixing that foundation first, or piling AI on top of architectural debt and hoping for the best.

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Question Set

What infrastructure limitations most often prevent industrial organizations from deploying AI effectively in OT security environments?

How does poor data quality, inconsistent telemetry, or incomplete asset context affect the accuracy and trustworthiness of AI-enabled OT security tools?

What architectural weaknesses tend to become visible when organizations try to apply AI in brownfield industrial environments?

Which industrial sectors appear best positioned to use AI in OT security today, and which are likely to face the greatest implementation challenges?

What foundational OT security capabilities need to be in place before AI can scale beyond limited pilots or isolated use cases?

Are vendors sometimes overstating how ready industrial environments are for AI-driven security operations? What should buyers be looking for beneath the claims?

Thank you for your response!