The accelerating integration of Artificial Intelligence (AI) into the tourism sector marks a paradigmatic shift from conventional digitalisation toward deeply data-driven, adaptive, and experience-centred operational models. This paper examines how AI-driven decision-making and personalisation mechanisms are transforming contemporary tourism ecosystems, redefining not only organisational processes but also the epistemological foundations of tourism management and experience design. The central argument advanced is that AI in tourism should not be conceptualised merely as a technological efficiency tool, but rather as a systemic mediator between data, human behaviour, and value co-creation. The study is grounded in an interdisciplinary theoretical framework combining data science, tourism studies, decision theory, and human-centred systems design. It positions tourism as a complex socio-technical ecosystem in which heterogeneous data streams—behavioural, transactional, spatial, and experiential—are continuously translated into operational knowledge. Within this context, AI acts as an interpretive layer that enables organisations to move beyond retrospective analytics toward anticipatory and adaptive decision-making. The paper problematises the widespread tendency to equate AI adoption with automation alone and instead argues for a reconceptualisation of AI as a cognitive infrastructure supporting strategic intelligence, situational awareness, and experience personalisation.

A core contribution of the paper lies in its analytical distinction between data accumulation and data interpretation. While contemporary tourism systems generate unprecedented volumes of data, the epistemic challenge lies in transforming these data into actionable insights that meaningfully enhance traveller experience and organisational decision quality. The paper analyses how machine learning models, predictive analytics, and recommendation systems enable tourism organisations to identify latent behavioural patterns, forecast demand dynamics, and design personalised interactions across the entire customer journey. Particular emphasis is placed on the transition from static segmentation to dynamic, context-aware personalisation, where experiences are continuously recalibrated in response to evolving traveller preferences, environmental conditions, and organisational constraints. The paper further explores AI-driven decision-making across key functional domains of tourism ecosystems, including customer relationship management, revenue management, marketing communication, and experience orchestration. It demonstrates how predictive models reshape pricing strategies, capacity planning, and service design by introducing probabilistic reasoning into traditionally heuristic decision processes. Rather than replacing managerial judgment, AI systems are shown to augment human decision-making by expanding cognitive horizons, surfacing non-obvious correlations, and enabling scenario-based reasoning under uncertainty. This reframing contributes to an emerging body of scholarship that positions AI as a form of decision intelligence rather than a deterministic decision-maker.

At the experiential level, the paper argues that AI-enabled personalisation represents a qualitative transformation in how tourism experiences are conceptualised and delivered. Personalisation is not treated as surface-level customisation of content or offers, but as a deeper alignment between individual meaning-making processes and system-level responsiveness. Through the continuous integration of behavioural signals, AI systems enable tourism providers to design experiences that are temporally, emotionally, and contextually attuned to the traveller. This perspective situates AI-driven personalisation within broader debates on experience economy theory, co-creation of value, and relational service design. Crucially, the paper also addresses the methodological and ethical implications of AI-driven personalisation. It interrogates the risks of algorithmic opacity, data bias, and reductive modelling of human experience, arguing that over-reliance on optimisation metrics may inadvertently narrow experiential diversity and reinforce existing inequalities. To address these concerns, the paper introduces a human-centred AI lens that emphasises transparency, interpretability, and reflexive governance in data-driven tourism systems. This approach aligns with contemporary discussions on responsible innovation and underscores the necessity of embedding ethical reasoning within the design and deployment of AI infrastructures. From a methodological standpoint, the paper adopts a conceptual-analytical approach supported by applied insights from contemporary AI-enabled tourism practices. Rather than presenting a single empirical case, it synthesises cross-domain operational patterns to construct an integrative analytical model illustrating how data flows, AI mechanisms, and human decision agents interact within tourism ecosystems. This model serves as a heuristic device for future empirical research, offering a structured way to examine AI’s role across different tourism contexts, organisational scales, and cultural environments. The academic significance of the paper lies in its  contribution to bridging fragmented strands of research on digital tourism, data analytics, and experience design. By foregrounding decision-making as the pivotal mediating process between data and experience, the paper advances a unifying conceptual framework that speaks to scholars across disciplines. It invites tourism researchers to engage more deeply with theories of decision intelligence and socio-technical systems, while encouraging data science scholars to incorporate experiential and organisational dimensions often overlooked in technical models.For the wider academic community, the paper provides a timely and theoretically grounded response to ongoing debates surrounding AI’s role in service industries. It challenges deterministic narratives that portray AI either as a disruptive threat or a neutral efficiency enhancer, proposing instead a nuanced view that recognises AI as a transformative but inherently relational technology. In doing so, it contributes to a more mature understanding of digital transformation—one that acknowledges complexity, uncertainty, and the irreducible centrality of human experience. In conclusion, this paper argues that the future of tourism lies not merely in the accumulation of data or the sophistication of algorithms, but in the intelligent orchestration of decision-making processes that align organisational intelligence with human meaning. By conceptualising AI as a bridge from data to experience, the study offers a theoretically robust and practically relevant contribution to the evolving discourse on AI-driven tourism ecosystems, positioning itself as a valuable resource for scholars seeking to understand and critically engage with the next phase of digital transformation in tourism.