The danger of AI in industrial PR: speed kills trust
AI tools can pump out press releases, brochures, and website copy in seconds. That's why LinkedIn is full of corporate posts that didn't exist a year ago-because they were too time-consuming to write well.
But scale comes with a cost: trust. PR expert and technology journalist Patrick Schroeder has a blunt warning for industrial companies: «Most readers now recognize AI-generated texts immediately. Not just by the frequently quoted dash. Also by the above-average sharp, but always similar logical structure.» The outcome? «Many people, including customers and specialist media, lose trust in the company at this point.»
AI at scale can make you invisible
This hits thought leadership hardest. Once it's unclear which ideas are human and which are machine-made, the sender blurs. «The sender itself becomes nebulous and no longer tangible, making it almost impossible to build trust,» says Schroeder.
People start skipping AI-ish posts the way they skip banner ads. That's real: "banner blindness" has been documented for years. «It's a paradox: companies want to become more visible through more AI content, but achieve the opposite. This is a worrying development. Especially in economically challenging times, when trust is more important than ever.»
Storytelling makes industrial brands credible again
Schroeder's recommendation for 2026 is simple: publish real stories from real work. «Storytelling offers the opportunity to put the focus back on people. Companies become tangible and credible through this real insight into their everyday life. And that is essential in economically turbulent times.» Customer projects, service calls, production fixes-these beat generic thought pieces every day of the week.
How PR teams can use AI without losing the room
- Set a "human-first" rule: ideas originate with people. AI can assist, but it doesn't lead. If AI meaningfully helps, disclose it.
- Audit for AI tells: repetitive sentence rhythm, identical paragraph length, empty adjectives, and an overuse of the em dash. Vary sentence lengths and use concrete detail.
- Trade volume for specificity: publish fewer posts, but make each one name people, places, numbers, and constraints. Specifics signal truth.
- Build a voice guide: words you use, words you avoid, tone sliders (technical ↔ simple, formal ↔ conversational), and "never say" claims.
- Source stories from the field: interview engineers, sales, and service teams weekly. Capture frictions, not just wins.
- Make leaders visible: first-person posts from accountable voices beat faceless corporate statements. Sign them.
- Media outreach: pitch a sharp angle with access (data, photos, site visits). Kill the AI boilerplate.
- Measure trust, not just reach: track replies, saves, journalist callbacks, and demo requests tied to content-not just impressions.
A simple industrial story structure
- Situation: the customer's constraint, environment, and stakes.
- People: who did the work, what they believed, and why it mattered.
- Process: choices made, trade-offs, and what was tried and rejected.
- Proof: numbers, before/after photos, timelines, quotes from both sides.
- Payoff: impact on uptime, quality, safety, or cost-and what's next.
Where AI still helps (safely)
- Research support, outline options, transcript clean-up, and language checks.
- Drafting interview questions and turning notes into a messy first pass-then a human rewrites for voice and truth.
- QA: spotting jargon, contradictions, or missing proof points before publish.
Bottom line
Trust is your moat. AI is a useful assistant, but a poor spokesperson. If your content sounds like everyone else, you vanish. Put people back at the center, publish fewer but richer stories, and let proof carry the message.
If your team needs practical upskilling to use AI without losing brand voice, see our curated learning paths for communicators: AI courses by job.
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