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fears of job displacement, and fostering a collaborative human - AI work environment are
crucial components for SMEs to truly harness AI's potential without compromising the
core values of hospitality (Bulchand-Gidumal, 2020).
2.3. Organizational adaptive capacity as a mediating mechanism
Adaptive capacity refers to the ability of a system, organization, or community to
modify its characteristics and behavior to better cope with existing or anticipated external
stresses and uncertainties. In the context of tourism SMEs, it manifests as the
organizational agility to swiftly restructure operations, update service delivery, and adjust
marketing strategies in response to market volatility or unexpected crises (Cakmak, 2023).
This study posits that AI readiness acts as a fundamental technological catalyst for
adaptive capacity. Real-time data processing, predictive analytics, and dynamic pricing
powered by AI allow SMEs to respond to fluctuating demand instantaneously, providing
businesses with the necessary dynamism in a fast-paced environment (Marczewska &
Weresa, 2023). For example, when faced with sudden disruptions, SMEs with high AI
readiness can employ natural language processing (NLP) chatbots and smart systems to
automatically manage customer communications, pivot their target segments, and
optimize resource allocation on the fly. Consequently, adaptive capacity serves as a
crucial mediating mechanism: while AI readiness equips a firm with the technological
means, it is the resulting adaptive capacity - the flexible and proactive organizational
agility - that actually translates these digital investments into tangible survival and
recovery outcomes during disruptions.
2.4. Destination resilience and regenerative tourism
While adaptive capacity operates primarily at the micro (organizational) level,
destination resilience and regenerative tourism represent the macro-level outcomes.
Destination resilience is conceptualized as the capacity of the entire tourism ecosystem to
absorb shocks, maintain essential socioeconomic functions (such as community
livelihoods), and reorganize adaptively without collapsing. The collective adaptive
capacities of micro-level actors (SMEs) aggregate to fortify the destination's overall
resilience, enabling a faster recovery of visitor trust and economic stability post-crisis
(Guo et al., 2018).
However, modern tourism demands a paradigm shift beyond mere resilience or
traditional sustainability - which primarily aims to minimize harm - towards regenerative
tourism. Regenerative tourism focuses on actively restoring, revitalizing, and flourishing
the socio-ecological systems and communities impacted by human activities. AI
technologies play an indispensable role in operationalizing these regenerative practices.
At the enterprise level, AI-driven smart energy management systems and IoT sensors can
significantly optimize electricity and water usage, predicting energy demand to reduce
the destination-wide carbon footprint. Furthermore, AI-powered predictive models are
increasingly utilized for intelligent waste management and transportation optimization,
effectively mitigating local pollution and reducing transport emissions. At the
environmental level, the integration of machine learning and remote sensing - such as AI-
equipped drones - enables the real-time monitoring of biodiversity, deforestation, and
habitat degradation, allowing for immediate conservation interventions. By leveraging AI
capabilities, tourism SMEs do not merely recover from systemic shocks; they become
proactive agents contributing directly to the ecological and social regeneration of their
host destinations (Maria Jesus Jerez- Jerez, Claudia Sevilla- Sevilla, 2025).

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