AI pitfalls
What to avoid when designing AI solutions.
When designing AI solutions, it’s essential to be aware of common pitfalls that can negatively impact user experience, security, and trust.
Recognising and proactively addressing these pitfalls helps maintain high-quality AI solutions that are accurate, transparent, secure, and user-friendly.
| Pitfall | Example | Prevention |
|---|---|---|
| Hallucinated information | AI shows incorrect vessel arrival times without actual port data. | Always validate AI outputs against authoritative backend data sources, clearly marking or excluding uncertain data. |
| Hidden automation | AI automatically approves shipment rerouting without clearly informing the logistics coordinator. | Require explicit user confirmation for critical automated actions and follow clear authorisation policies. |
| Unexplained AI decisions | AI automatically approves shipment rerouting without clearly informing the logistics coordinator. | Include clear and accessible explanations for AI-driven decisions, linking to detailed reasoning and underlying data. |
| Persistent biases | AI repeatedly recommends certain suppliers due to biased historical purchasing data. | Regularly audit training data for fairness, diversify inputs, and transparently disclose recommendation criteria. |
| Overgeneralization | AI suggests identical inventory replenishment levels regardless of seasonal demand fluctuations. | Contextualise AI recommendations based on roles, logistics tasks, real-time data, and specific contexts. |
| Data privacy ambiguity | AI uses customer data for personalized tracking without clearly communicating privacy implications. | Explicitly request user consent, transparently communicate data usage, and provide clear privacy preference settings. |
| Unrecoverable errors | AI misinterprets a user’s voice request without clearly guiding the user on how to correct it. | Design clear interactions with explicit prompts and easy correction or error recovery paths. |
| Friction in escalation paths | Users struggle to smoothly transition from AI chatbot interactions to human support. | Clearly and proactively present easy escalation options and ensure smooth hand-off experiences. |
| Security shortcuts | AI chatbot inadvertently reveals sensitive shipment information without proper authentication. | Enforce secure, role-based authentication and clearly indicate when sensitive actions require additional security steps. |
| Inconsistent AI personality and tone | AI conversations abruptly shift from formal to overly casual, confusing users and reducing trust. | Define and consistently apply a conversational style guide aligned with user expectations and company tone. |
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See also
Contributors
Mia Stigsnaes-Hansen
UX Designer
Martin Oliver Christensen
UX Designer
Fangyu Zhou
UX Designer