Lawise

By Ralf Haller
Founder | Product & Growth | GTM Leader in Enterprise AI & SaaS | Scaling Innovations in Europe and Globally

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Every week I hear it anew: A lawyer, an HR manager or a trustee says:
“AI must be 100% correct — otherwise it is useless. ”

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And in the next sentence, it is admitted: Even experienced professionals regularly make mistakes.
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This position reveals a fundamental contradiction:
We expect artificial intelligence to be error-free that we would never expect from humans.

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Where does this double standard come from?

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Perfection bias in machines

Studies from Stanford and MIT show that people overestimate the capabilities of AI — and as soon as it makes a mistake, trust falls disproportionately (MIT Tech Review, 2023). Even if AI is significantly more accurate than humans, it is often considered “not reliable” after the first mistake.

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The illusion of control

We are more likely to accept human mistakes because we believe we can control, coach and “lead” people. AI, on the other hand, acts like a black box. When it fails, it seems uncontrollable — and scares (Harvard Business Review, 2021).

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Cognitive dissonance among professionals

Many see AI as a threat to their professional identity. If an AI can answer legal, tax, or HR questions — what does that say about our education?
The demand for perfection thus becomes a psychological protective mechanism.

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What are the consequences of this attitude?

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Missed opportunities
AI systems with 95% accuracy could already save time, money and risks today — but the claim to be error-free is delaying implementation.
wasting resources
Many companies stick to outdated, manual processes — even though AI-supported workflows deliver better results in many areas.
Regulatory imbalance
If legislators adopt the same perfection myth, there is a risk of excessive demands on “imperfect” AI — while people are judged more leniently for mistakes. Ironically, that can the Decrease safety instead of increasing.

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How do we create a realistic picture of AI?

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Compare with human accuracy, not with an ideal
Example: Under pressure, lawyers are correct in ~ 85% of cases. When an AI reaches 95% — and is auditable — that is progress, not a step backwards.
Enable human-AI collaboration
Let AI do the routine work People remain in control of borderline cases, interpretation, and context.
Explain how systems learn
Tools such as Jurilo.ch combine machine learning with legal validation — accuracy is constantly improving.
Start small, scale specifically
Perfection is not a starting point. Start in low-risk, repetitive areas — such as document analysis, HR policies, or legal FAQs for SMEs.

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Let's get in touch

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Have you experienced for yourself how teams or customers demand perfection from AI?
What helped to overcome this hurdle—what didn't?

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And when you develop or buy AI solutions:
What level of automation and control is realistic in your area?

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Let us not let the machines fail due to unrealistic demands.

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Let's just ask:
How useful and verifiable is AI — and how do we build trust together?

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👉 I look forward to hearing your perspectives in the comments — or directly by message.
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PS: Jurilo.ch verified answers (highlighted in green) are 100% correct and can also be used in court.