I often discuss the impact of AI on the industry with colleagues in the sector. Many point out that the impacts will take too long to reach us because our roles are highly specialized and our industry is heavily reliant on manual labor.
And I usually disagree. A recent study from the London School of Economics sends a stark message: the rapid advance of automation and artificial intelligence has significantly widened wage inequality in Spain, hitting the lowest-paid workers the hardest. Covering the period 2010–2019 (with effects persisting into recent years), the research uses household survey data from Spain’s National Statistics Institute to construct a dynamic automation index. Unlike static measures, this index takes into account how jobs evolve: some tasks become more routine and replaceable, while others shift toward non-automatable skills.
The main finding is clear and worrying. Without the displacement effects of new technologies, Spain's wage Gini coefficient (a measure of inequality where 0 is perfect equality and 100 is maximum inequality) would have been around 21.5% lower in 2019. In practical terms, this means that the top 10% of earners captured a larger share of the wage pie than they otherwise would have, while the bottom 50%, and especially the poorest 10%, saw their relative wage share shrink. The bottom 10% could have seen their wages rise by around 2.2% more, and the middle classes saw modest gains redirected upwards. Automation didn't just replace jobs. It strengthened the bargaining power of highly skilled workers, boosting their wages while devaluing routine tasks common in middle and low-wage jobs (data entry, standardized administration, and certain service tasks).
The impact of AI, especially since 2015, reflects this same pattern: it complements highly skilled work (driving wage premiums at the top) while replacing routine, mid-level tasks, further polarizing income distribution. Technology drives economic growth and overall productivity, but the benefits don't automatically trickle down. Trade, globalization, education levels, and profit margins play a secondary role in the face of technology-driven change. Vulnerable groups include young people with low levels of education (waiters), older women in low-skilled jobs (housekeeping), and middle-income routine workers who face direct replacement by machines and algorithms (reservations, reception).
In the hospitality and tourism sector, where many roles involve routine and predictable tasks (check-in processes, basic reservations, housekeeping coordination, standardized guest services), these dynamics are especially relevant. Entry-level and mid-level positions often fall into the zone of automation, putting pressure on salaries and job security. Meanwhile, roles that integrate AI for personalization, predictive analytics, dynamic pricing, or revenue optimization tend to be concentrated in more skilled (and higher-paid) functions, widening internal gaps.
This is not an inevitable fate. It is a call to action. The study highlights two priority policy levers to make innovation more inclusive:
1. A massive and sustained investment in education and lifelong learning
In line with what we discussed last week, short, modular, and accessible continuing education programs can help workers through the transition. In hospitality, this means training frontline staff in data interpretation, guest experience design, sustainability practices, and basic use of AI tools (chatbots, forecasting apps), rather than leaving them vulnerable to replacement.
2. Rebalance tax incentives between capital and labor
Many tax systems (including Spain's) favor investment in machinery and software over hiring people, subsidizing automation even when productivity gains are marginal. Adjusting capital taxes, offering credits for investment in human capital, or reducing biases toward technology capital expenditures could curb excessive displacement and foster innovation that creates jobs.
Technology is not the enemy. Unfairly distributed benefits are. By proactively reskilling, redesigning roles around human strengths, and supporting policies that share productivity dividends, the sector can transform AI from a wage-polarizing force into a broad-based opportunity. Low- and middle-wage workers in hospitality deserve pathways to higher-value, better-paid contributions, not gradual erosion.
The LSE study reminds us of something: innovation without equity risks deeper divisions, social friction, and even political polarization. In our industry, where people remain at the heart of the experience, getting this right is essential for sustainable growth.
