Key Insights

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  AI is transforming not only products but entire service ecologies, blending tangible and intangible elements via datafication.

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  A large majority of AI products fail due to poor model performance, lack of clear service value, low desirability, or ethical risks.

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  Designers often join AI product development too late, after data and models are already chosen, limiting innovation potential.

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  New design methods are needed that start with AI data sets or capabilities instead of user-centered approaches alone.

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  Algorithmic management in hospitality reduces worker autonomy and risks poor task assignment without contextual understanding.

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  Involving workers in co-design processes can increase their sense of agency, improve transparency, and lead to better AI tool adoption.

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  AI performance does not need to be perfect; moderately accurate AI applied wisely can create significant user value.

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  Privacy and ethical risk management in AI development is often unassigned but should be integral and early in product design.

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  There is a taxonomy of AI capabilities that can serve as design patterns to help translate technical possibilities into user interactions.

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  The hospitality industry’s complexity—multiple management entities and social-emotional labor—makes AI implementation especially challenging and in need of holistic design thinking.

Notable Quotes