AI, Precision Agriculture and Tourism for Sustainable Regional Development: The Case of the Aegean Islands and Crete, Greece

Abstract

Artificial Intelligence plays an exponentially growing role in producing data-driven policy insights. In this policy-oriented case study, AI technology is examined as a necessary coordination node through evidence-based and data-enhanced policies, which can efficiently balance the processes of different and possibly competing sectors, such as agriculture and tourism. The focus is on the NUTS 1 region of the Aegean Islands and Crete (EL4) in Greece. The analysis aims to create a viable and resilient ecosystem of environmental, economic and social sustainability through innovation. Applying a “Growth Pole Theory” approach, key public administration frameworks like the European Interoperability Framework (EIF) and TAPIC (Transparency, Accountability, Participation, Integrity, Capacity) governance framework are discussed and analysed to structure the AI deployment and policy considerations for sustainable development. The paper argues in favour of AI’s transformative potential across both the agriculture and tourism sectors.

1. Introduction

The concept of sustainability has been a topic of extensive discussion over time. Initially, the term referred to agricultural and industrial technologies designed to minimise or prevent environmental harm typically associated with economic activities. Economists have defined sustainability from an economic perspective, emphasizing the capacity to maintain consistent consumption or productivity by substituting natural resources with human-made capital during production [1,2,3]. Human-made capital, according to the aforementioned authors, includes all outputs of human endeavour, encompassing physical assets (e.g., machinery, buildings) and intellectual assets (e.g., knowledge, information).

In contrast, the environmental perspective of sustainability [4,5] proposes that natural resources and human-made capital function as complementary inputs in production processes. Since natural resources function as the limiting factor, they stressed the necessity of preserving them. Furthermore, the United Nations broadened the definition of sustainability in 1972, describing it as the ability to meet the needs of the present without compromising the ability of future generations to meet their own needs [6].

In recent years, sustainability has evolved into a more comprehensive framework that integrates economic, environmental, technological and social considerations into development processes [7]. Applying this framework to agriculture, the American Society of Agronomy [8] defines sustainable agriculture as a system that, over the long term, improves environmental quality, preserves the resource base on which agriculture depends, fulfils basic human needs for food and fiber, remains economically viable, and enhances the quality of life for both farmers and society at large.

In Greece, where tourism accounted for 19.1% of the country’s GDP in 2021 [9], agriculture must coexist and evolve alongside the tourism sector. This interdependence adds complexity to achieving sustainability, as both industries must balance economic growth with environmental and social considerations. Especially when it comes to insular and remote regions such as the Greek Islands, which represent a distinct category of space due to their unique characteristics, which set them apart from other geographic areas. These features often contribute to geographical and socio-economic isolation with increased unemployment and poverty [10], as well as heightened inequalities at both local and trans-regional levels [11].

Artificial Intelligence (AI) is rapidly emerging as a transformative catalyst in both tourism and agriculture, offering substantial potential for innovation to tackle key challenges within these sectors [12], building upon past successful initiatives of data and infrastructure initiatives in the Greek Public Sector [13]. While the rate of AI adoption varies across different segments of tourism and agriculture, its current and prospective impacts are becoming increasingly evident. However, navigating the opportunities, risks, and challenges posed by AI requires careful consideration.

The extent of AI’s transformative influence on tourism and agriculture, with their distinctive reliance on high-touch, in-person interactions, remains uncertain. Nevertheless, it is an area of growing interest and debate. To advance the conversation, policymakers must first gain a comprehensive understanding of AI’s potential to drive innovation. This involves delving into the complexities of the digital ecosystems underpinning tourism and agriculture, grasping the fundamental components of AI systems, and aligning efforts with relevant national and international AI frameworks and principles.

Although sector-specific guidelines for implementing AI are expanding, a consistent and universal framework for application across industries is yet to be established, underscoring the need for coordinated and tailored approaches in these fields.

The growth of tourism often places it in direct competition with agriculture, creating a complex nexus of resource-based conflicts. These include, but are not limited to:

• Land Use: The conversion of arable land for tourism infrastructure (e.g., hotels, resorts).
• Water Resources: Divergent demands for water between agricultural irrigation and tourist consumption.
• Sustainability: The tension between the sustainable stewardship of resources and their potential depletion due to overtourism, which also pressures local energy and transportation infrastructure.

Mitigating these conflicts to balance economic development through tourism with the preservation of socio-cultural and environmental heritage demands policy interventions aimed at equitable and economically optimal resource distribution. This is a particularly pressing issue in remote and ecologically sensitive regions like islands, where resource scarcity intensifies these competitive interactions [14].

The present paper aims to shed light on the role of Artificial Intelligence as the necessary coordination tool and policy assistant for sustainable development. This is achieved through regulating the relevant processes of different and possible competing sectors like agriculture and tourism accordingly, through evidence-based decisions and data-enhanced policies, in order to create a viable and resilient ecosystem for environmental, economic and social sustainability.

Under the above epistemic lens, the Aegean Islands and Crete EL4 (designated as a great geographical area/NUTS 1) are examined as a case study with the following research questions:

How can AI and precision agriculture contribute to the sustainable development of the Aegean Islands and Crete?

What is the potential synergy between tourism and agriculture in creating a more sustainable economy?

What policy recommendations can be made to foster these innovations in the region?

The analysis proceeds by succinctly establishing the context and rationale, highlighting the evolving concept of sustainability and the persistent challenge of balancing agriculture and tourism in Greek island regions, an issue for which Artificial Intelligence (AI) is positioned as an emergent inclusive coordination [15] and optimization mechanism. Rather than providing a conventional sectoral review, the study’s primary contribution lies in:

(i)

Advancing a conceptual integration of AI, precision agriculture, and tourism specifically tailored to the conditions of insular regions, illustrating how these domains can be jointly leveraged to support ecological resilience and economic diversification.

The literature review subsequently consolidates current knowledge on AI applications in precision agriculture and tourism management, but its purpose is to underpin the second core contribution:

(ii)

The application of the European Interoperability Framework (EIF) and the TAPIC governance model to the context of AI-enabled sustainable development. By embedding these frameworks within the discussion of data use, ethical deployment, and institutional coordination, the paper introduces an originality that extends beyond purely technological accounts of AI’s potential.

A dedicated section on the Aegean Islands and Crete situates the analysis in the distinctive geographical, demographic, and economic conditions of the region. This leads to the third major contribution:

(iii)

The formulation of a Growth Pole-based regional development proposal that adapts Perroux’s theory to the contemporary digital and ecological challenges of island territories. The proposal outlines how AI-enhanced agriculture and tourism clusters may serve as catalysts for broader regional development, supported by national digital infrastructure investments and emerging EU-level initiatives such as common European data spaces and AI innovation hubs.

Subsequent sections elaborate on how AI-enabled synergies between agriculture and tourism, particularly through agritourism, operational optimization, and environmental monitoring, can contribute to sustainable development strategies. The concluding part synthesizes policy recommendations and identifies promising lines of future inquiry. In doing so, the paper underscores the novel analytical value of integrating AI governance frameworks, sectoral innovation pathways, and regional development theory for the specific case of the Aegean Islands and Crete.

2. Research Design

The nature of the present study is a conceptual/policy paper that mainly generates a framework and research agenda with an illustrative case study of the Aegean Islands and Crete. To this end, an extensive review of academic and policy literature has been accompanied by insightful data from national and international organizations such as ELSTAT, Eurostat, and the OECD. Methodologically, the study employs a framework-driven synthetic approach that combines elements of the European Interoperability Framework (EIF), the TAPIC model (Transparency, Accountability, Participation, Integrity, Capacity), and principles of Sustainable Operations Management (SOM). These perspectives are further contextualized through the lens of Growth Pole Theory. The convergence of these conceptual strands provides the basis for deriving policy-relevant propositions and identifying orientations for subsequent empirical and theoretical research.

Limitations and Scope

This study is conceptual and exploratory, drawing solely on secondary literature and policy documents. It does not include original data collection, modelling, cost–benefit analysis, or stakeholder research. The policy recommendations, such as regulatory sandboxes, regional growth poles, and AI innovation hubs, should therefore be seen as informed strategic options, not evaluated interventions. Further empirical work is needed to examine implementation pathways and to assess local digital readiness and governance capacities across different islands.

3. Literature Review

3.1. AI in Agriculture and Tourism

A systematic review [16] explores the transformative role of artificial intelligence (AI) in addressing critical challenges in agriculture, such as increasing food demand, climate variability, and supply chain disruptions. Applying the PRISMA methodology, the review analysed 176 selected studies from 906 initial results. Findings were categorized into three research questions focusing on influential contributions, AI technologies, and their benefits, challenges, and trends. The review highlights a significant increase in research output and citations over the past three years, signalling growing interest in AI-driven agricultural innovation. Prominent contributors include countries like India, China, and the USA, with leading publications in journals such as Computers and Electronics in Agriculture and Agricultural Systems. Institutions from the Netherlands, the USA, and China emerged as key players in advancing AI applications in agriculture.

Seven primary application domains were identified:

• Crop Management: Covering sowing, maintenance, harvesting, and distribution.
• Water Management: Optimizing irrigation and resource use.
• Soil Management: Enhancing plant nutrition.
• Fertigation: Integrating fertilizers into irrigation systems.
• Crop Prediction: Supporting logistical planning through yield forecasts.
• Crop Classification: Combining image processing and deep learning.
• Disease and Pest Management: Mitigating threats to crop yield and quality.

The review identified 24 AI techniques, with machine learning, convolutional neural networks (CNNs), Internet of Things (IoT), and robotics being the most prevalent. Emerging technologies like digital twins and the integration of big data and cloud computing are revolutionizing precision agriculture.

AI technologies can optimize agricultural processes, reduce input costs, and enhance food security. Notable benefits include improved irrigation efficiency through robotics and drones, enhanced pest and disease detection via CNN-based image analysis, and integration of IoT for real-time monitoring and decision-making.

However, challenges persist. The most important include:

• High costs of implementation, particularly for small and medium-scale farms.
• Data privacy, ownership, and real-time quality concerns.
• Limited accessibility to advanced technologies in developing regions.
• Labor skill gaps in adopting AI-driven systems.

Further research [17,18,19] underscores the potential of AI to transform agriculture through digitization and intelligent system design. However, achieving equitable technological adoption necessitates addressing affordability, workforce training, and policy support in food-producing regions.

Precision agriculture, often referred to as PA, is a modern farming practice that involves collecting, analysing, and using detailed data specific to location and time to optimize agricultural processes [20]. It aims to improve resource management by applying inputs, such as water, fertilizers, pesticides, and seeds, at precise amounts and locations, thereby increasing efficiency, yield, and sustainability while minimizing environmental impacts. Precision agriculture encompasses a suite of technologies, including GPS-guided equipment, variable rate technology (VRT), unmanned aerial vehicles (UAVs), remote and in-ground sensors, and advanced data analytics, often powered by artificial intelligence (AI).

In essence, it shifts agricultural decision-making from generalized practices to site-specific management based on real-time and predictive insights.

The authors provide a comprehensive review of how artificial intelligence (AI) integrated with unmanned aerial vehicle (UAV) sensing systems is revolutionizing precision agriculture (PA). This synthesis examines the interaction between AI technologies and UAV platforms in agricultural applications, highlighting their transformative potential and addressing related challenges.

Precision agriculture is increasingly essential due to global challenges such as rising food demand, shrinking arable land, and climate variability. UAVs, equipped with various sensing units (e.g., multispectral, hyperspectral, LiDAR), offer cost-effective, flexible, and high-resolution data collection capabilities, making them invaluable for monitoring crop health, soil conditions, and water stress. The UAV market is projected to grow substantially, reaching $70.91 billion by 2030 according to Allied Market Research (See: https://www.alliedmarketresearch.com/press-release/unmanned-aerial-vehicle-market.html) (accessed on 18 October 2025), with significant advancements in sensor miniaturization and modular design enabling broader adoption.

AI algorithms, particularly deep learning (DL), enhance UAV data analysis by automating complex tasks such as image classification, regression, and object detection. These methods analyse large-scale datasets with high computational efficiency, supported by advancements in cloud computing and GPU technology. Examples include convolutional neural network (CNN)-based models for disease detection [21,22] and regression techniques for yield prediction [23].

3.2. AI, Tourism, Precision Agriculture and Sustainability in Island Economies

AI and precision agriculture in island economies can promote agritourism as a form of alternative tourism, which can contribute to endogenous local development by diversifying rural economies and creating new connections with markets in island economies which present specific characteristics [24] such as insular space, fragile environment, spatial discontinuity and heterogeneity, making it difficult to implement uniform policies.

Agritourism is viewed as a broad concept that includes diverse activities in rural settings. It is defined [25,26,27] as any practice, activity, or service developed on a working farm to attract visitors, encompassing a wide range of options such as tours, overnight stays, special events, festivals, on-farm stores, fishing and hunting, bird-watching, hiking, horse-riding, and self-recreational harvesting. Essentially, agritourism combines any activity concerning agriculture and tourism.

Agritourism has evolved from small-scale agricultural activities into a broader tourism practice, providing an alternative income source for farm and island households, and creating opportunities for collaboration among various activities. It now includes a wide array of activities offered not only in continental rural areas but also beyond, such as in island landscapes.

Within this framework, agritourism is regarded as a desirable factor in diversifying local and regional economies by enhancing the provision of local services and contributing to social development, especially in marginalized and isolated regions such as islands. The success of agritourism depends on aligning the services that tourists desire with what the local community is willing to offer without overturning the environmental and social sustainability in the long term [27].

The benefits of agritourism are multi-faceted, including job creation, additional income opportunities, improved local cooperation, and the empowerment of women through new skills and income sources.

Furthermore, many AI-related applications have already been adopted in the tourism sector, with further scope for innovation to support sustainable and inclusive tourism development. Analysis of existing applications and potential of AI in tourism [28] shows the variety of implemented and planned AI applications in place, with the potential to:

• Enhance the visitor experience with more interactive, personalised experiences and seamless travel, while increasing responsiveness to demand with 24/7 and more personalised services.
• Improve accessibility and audience engagement with greater accessibility of content and a diversified offer of culture and recreation services.
• Enhance market intelligence and data use, as well as capacity for market segmentation and customer profiling.
• Optimise visitor flows and traffic management with real-time data and automated decision-making.
• Support price/cost optimisation, through predictive maintenance, resource use and procurement.
• Automate internal processes and basic customer services.

In another study [29], the authors observe that the concept of sustainability in tourism is complex, and there is no clear and operational definition, confusing what sustainable tourism means in practice [30]. Often, sustainable tourism is equated with alternative forms of tourism, especially ecotourism, but sustainable tourism is not a form of tourism; it is a state of tourist activity. Essentially, sustainable tourism aims to meet the needs of present tourists and host regions while protecting opportunities for the future, maintaining cultural integrity, ecological processes, biodiversity, and life support systems. Sustainable tourism can be achieved through three dimensions: improving the environmental performance of tourism businesses, developing special forms of tourism by utilizing the natural and cultural characteristics of the area, and developing forms of tourism that have a low environmental impact and contribute to the preservation of cultural heritage and economic activities in remote areas.

The approach of integrating AI in agriculture and tourism activities directly addresses the aim of sustainable tourism, which is defined as a state of activity that meets the present needs while safeguarding future opportunities, ecological processes, and cultural heritage. Sustainable tourism operates across three dimensions: improving environmental performance, leveraging local characteristics for special tourism forms, and developing low-impact tourism that supports remote economies.

However, the path to sustainable tourism in the Aegean islands is coping with systemic challenges. According to a special report of the Greek Ombudsman [31], tourist development is inextricably linked to the environment, both natural and man-made, and constitutes a basic resource for generating wealth. However, tourism exerts intense pressure on the environment and quality of life. A key objective of the state should be to shift towards sustainable tourism, which presupposes the protection of the assets that it utilizes. Sustainable tourism takes into account the economic, social, and environmental impacts and responds to the needs of visitors, the sector, and the communities.

Problems related to tourism development include:

• Lack of spatial planning: The failure to complete the spatial planning framework and exceeding the carrying capacity creates significant problems. The ambiguity of land uses and the lack of integrated spatial planning led to an overconcentration of tourist units in specific areas.
• Degradation of public space: Intense construction and the degradation of public space, such as the occupation of public spaces and the lack of cleanliness, are significant issues.
• Urban planning violations: There are urban planning and spatial violations and exploitation of the cultural product, with unauthorized constructions in areas of cultural interest.
• Traffic and safety: Increased tourist traffic exacerbates traffic problems and creates safety issues, especially in urban centres.
• Accessibility: There are accessibility problems for people with disabilities in public spaces and buildings.
• Noise pollution: The operation of health interest establishments (Katastimata Ygeionomikou Endiaferontos/KYE) and outdoor events creates noise pollution, burdening the quality of life.
• Water resources: Tourist development increases water consumption, leading to problems of water scarcity and salinization, especially on the islands. The degradation of water quality is due to the age of the networks and over-pumping.
• Waste management: The uncontrolled disposal of waste causes pollution and environmental degradation. The inadequate operation of wastewater treatment plants and the non-implementation of the planned projects exacerbate the problem.

4. The Aegean Islands Context

Aegean islands constitute a complex of islands in Greece that contains the Northern Aegean islands, the Southern Aegean islands and Crete as a great geographical area according to the Nomenclature of territorial units for statistics (NUTS 1).

The NUTS 1 territory of the Aegean Islands and Crete (EL4) consists of the following islands:

Lesvos, Chios, Ikaria, Lemnos, and Samos for Northern Aegean (Voreio Aigaio), Andros, Kalymnos, Karpathos, Kasos, Kea (Tzia), Kythnos, Kos, Milos, Mykonos, Naxos, Paros, Rhodes, Syros, Thira (Santorini), Tinos, for Southern Aegean (Notio Aigaio), Crete.

The following Figure 1 depicts the geographical position of the designated area.

4.1. Geographic and Economic Overview

The Aegean Archipelago in Greece, as defined by Eurostat, consists of 112 inhabited islands, accounting for 19% (24,739 km²) of the country’s total land area. This region features a diverse array of islands, each with unique characteristics. In the southern part, Crete stands out as one of the largest islands in the European Union. To the west, near the mainland, numerous islands are administratively linked to continental regions. The rest of the Aegean Archipelago, spanning nearly 480,000 km², is home to a multitude of medium and small islands, which collectively represent about 50% of the total island area and population [33]. Administratively, these islands are organized into five prefectures and two exclusively insular regions.

A historically significant feature of the Aegean islands is the fluctuation in their population levels. By the mid-19th century, the islands were home to 20% of Greece’s population, a figure that rose to 30% by the end of the century. During the first half of the 20th century, the population remained relatively stable, with minor increases primarily due to emigration. However, in the three decades following World War II (1945–1975), the islands saw a notable population decline of 13.4%.

In recent years, the population in the Northern Aegean region (

Table 1. Main demographics.

NUTS 2 Regions	Surface Area (km2)	Population Total (2021)	Population Total (2011)	Change (%) 2021/2011
Northern Aegean	3.854	194.943	199.231	−2.2
Southern Aegean	5.305	327.820	309.015	6.1
Crete	8.340	624.408	623.065	0.2

Source: Hellenic Statistical Authority (ELSTAT).

and

Table 2. Main macroeconomic data (GDP in constant prices 2021).

NUTS 2 Regions	Unemployment Rate (2011)	Unemployment Rate (2021)	Change (Percentage Points) 2021/2011	GDP per Capita (2011)	GDP per Capita (2021)	Change (Thousand €) 2021/2011
Northern Aegean	15.0	13.8	−1.2	14.500€	10.658€	−3.8
Southern Aegean	15.2	18.8	3.6	20.600€	16.639€	−4.0
Crete	15.8	16.3	0.5	16.000€	13.994€	−2.0

Source: Hellenic Statistical Authority (ELSTAT), Eurostat.

) has decreased by 2.2%, in the Southern Aegean has increased by 6.1% and in Crete has remained relatively stable, fluctuating by 0.2% according to the Hellenic Statistical Authority ([33]). The following Table 1 and Table 2 depict the main demographics and main macroeconomic data of the region studied in the present case study, spanning from 2011 to 2021.

4.2. Current Agricultural Practices and Challenges

Farming practices in the Aegean Islands and Crete, Greece, are deeply rooted in the region’s Mediterranean climate, topography, and cultural traditions. These practices are characterized by small-scale, family-owned farms, diversified crops, and a mix of traditional and modern techniques. A mapping on the stakeholders of Crete agroecological farming adoption [34] identifies three distinct viewpoints: “Interconnected ecological farms,” “Environment & ecosystem services,” and “Skills and labour.” This research highlights varied perspectives on how these practices might influence environmental, social, and economic aspects of the region. The findings underscore the importance of tailored agricultural support and policy development to facilitate a transition towards sustainable farming and inclusive coordination. The ‘Interconnected ecological farms’ viewpoint highlights a preference for collaboration and local value chains, which directly influences AI adoption by requiring interoperable digital platforms (EIF principle) to link small producers with tourism supply chains. This perspective benefits most from AI tools that support agri-tourism integration and streamline cooperative logistics. The ‘Environment & ecosystem services’ perspective mandates that policy implementation prioritizes AI applications for environmental monitoring and resource efficiency, such as precision irrigation to tackle water scarcity. This viewpoint strongly informs the Accountability domain of the TAPIC framework, requiring policymakers to integrate specific output and outcome indicators to track the environmental impacts of AI on water use, carbon footprint reduction, and biodiversity. Last but not least, the focus on ‘Skills and labour’ highlights critical implementation challenges, particularly limited accessibility to advanced technologies and high costs for small farms. Addressing this perspective necessitates a policy focused on the Capacity domain of TAPIC, requiring the development of specialised training programmes for farmers on AI literacy and the implementation of user-friendly, low-tech AI solutions (like Farmer.chat) to overcome skills and digital literacy barriers.

The Aegean Islands and Crete are known for their diverse agricultural output, including olives, grapes, citrus fruits, vegetables, and herbs. Olive cultivation has been a cornerstone of the local economy and culture since ancient times. Traditional practices, such as dry farming (relying on rainfall rather than irrigation) and terracing on hilly landscapes, are still widely used to conserve water and prevent soil erosion [35]. In recent years, there has been a growing shift toward organic farming in the region. Farmers are increasingly adopting sustainable practices to reduce chemical inputs and preserve the environment. For example, integrated pest management (IPM) and the use of organic fertilizers are becoming more common [36]. Crete, in particular, has seen a rise in organic olive oil production, driven by both environmental concerns and market demand for high-quality products.

Farmers in these regions face several challenges, including water scarcity, soil degradation, and the impacts of climate change [37]. To address these issues, many are adopting innovative practices such as drip irrigation, rainwater harvesting, and the cultivation of drought-resistant crops [38]. Additionally, the European Union’s Common Agricultural Policy (CAP) has provided financial support for modernization and sustainability initiatives.

Livestock farming, particularly sheep and goat rearing, is another important aspect of agriculture in the Aegean Islands and Crete. These animals are often raised in semi-free-range systems, where they graze on natural pastures and are supplemented with locally grown feed. This practice not only supports the production of meat and dairy products but also helps maintain the landscape and prevent wildfires [39]. Farming in the Aegean Islands and Crete is not just an economic activity but also a cultural heritage. Traditional festivals, local markets, and agrotourism initiatives underscore the significance of agriculture in the region’s cultural identity and heritage. These practices also contribute to the preservation of biodiversity and the maintenance of rural landscapes.

4.3. Tourism Landscape

The tourism sector in the Aegean Islands and Crete is a double-edged sword, serving as a vital economic driver while simultaneously exerting significant pressures on local communities and ecosystems. It is the primary economic activity in the Aegean Islands and Crete, contributing substantially to regional GDP. For instance, in islands like Mykonos and Santorini, tourism accounts for over 70% of GDP [40]. This revenue supports not only direct tourism-related businesses (hotels, restaurants, tour operators) but also indirectly benefits sectors such as agriculture, construction, and retail. Hence, the need for coordination of economic activities such as tourism and agriculture becomes critical for economic development and environmental preservation.

Tourism creates jobs, particularly for younger populations and those in rural areas where alternative employment options are limited. However, the quality of these jobs is often criticized due to their seasonal nature, low wages, and lack of job security [41]. This seasonal employment pattern leads to income instability and economic vulnerability for workers and their families. Due to the income created by tourism, investments have been made in infrastructure, including airports, ports, roads, and utilities. While these developments benefit tourists, they also improve the quality of life for local residents by enhancing accessibility and connectivity [42]. Furthermore, the tourism sector has enabled economic diversification in regions traditionally dependent on agriculture and fishing. For example, agrotourism has emerged as a niche market, allowing farmers to supplement their incomes by offering accommodation, food, and cultural experiences to tourists [35]. This diversification has helped mitigate the risks associated with reliance on a single industry.

However, tourism comes with certain pressures and challenges that must be taken into account and addressed. The highly seasonal nature of tourism (peaking between May and October) creates economic instability. Businesses often struggle to remain operational during the off-season, leading to layoffs and reduced incomes. This seasonality also discourages long-term investments in the sector, perpetuating a cycle of economic vulnerability.

The rapid expansion of tourism has placed immense pressure on the natural environment. Overdevelopment, particularly in coastal areas, has led to habitat destruction, loss of biodiversity, and landscape alteration. Water scarcity is a critical issue, as the high demand from tourists strains limited freshwater resources [43]. For example, islands like Mykonos and Santorini face severe water shortages during peak tourist seasons [44]. The influx of tourists generates significant waste, often exceeding the capacity of local waste management systems. This leads to pollution of land and marine environments, threatening ecosystems and public health. Coastal areas, in particular, suffer from plastic pollution and untreated sewage discharge. Popular destinations like Santorini and Crete often experience overcrowding, leading to strain on infrastructure and a decline in the quality of life for residents. Traffic congestion, overburdened public services, and overcrowded attractions are common issues. This not only diminishes the tourist experience but also creates resentment among local communities [42]. The commodification of local culture and traditions is a growing concern. In some cases, cultural practices are altered or commercialized to cater to tourist expectations, leading to a loss of authenticity. Additionally, the rise of short-term rental platforms (e.g., Airbnb) has driven up property prices, displacing local residents and altering the social fabric of communities [45].

5. AI and Precision Agriculture

5.1. AI Applications in Agriculture on the Aegean Islands

The Aegean Islands, with their unique geographical and climatic conditions, present both opportunities and challenges for agricultural development. Artificial Intelligence (AI) offers transformative potential to address these challenges, particularly for smallholder farmers. This section proposes specific AI-driven solutions for agriculture on the islands, discusses their scalability, and considers the unique constraints of island economies.

Water scarcity is a critical issue in the Aegean Islands, exacerbated by climate change and overuse. AI-powered precision irrigation systems can optimize water usage by analysing soil moisture levels, weather forecasts, and crop water requirements in real-time. These systems use sensors and IoT (Internet of Things) devices to deliver water precisely where and when it is needed, reducing waste and improving crop yields [46]. For example, presented AI algorithms could predict irrigation schedules based on evapotranspiration rates and soil conditions, ensuring efficient water management [37].

On another occasion, the authors propose that drones equipped with multispectral cameras and AI algorithms can monitor crop health, detect nutrient deficiencies, and identify pest infestations early. By capturing high-resolution images and analysing them using machine learning models, drones can provide actionable insights to farmers, enabling timely interventions [47]. This technology is particularly useful for smallholder farmers who lack access to traditional agricultural extension services, as in the case of the Aegean islands.

AI models trained on historical and real-time data are proposed to predict pest outbreaks and disease spread, allowing farmers to take preventive measures. For instance, machine learning algorithms can analyse weather patterns, crop types, and pest life cycles to forecast risks and recommend targeted interventions. This reduces reliance on chemical pesticides, promoting sustainable farming practices. AI-based decision support systems (DSS) can provide farmers with personalized recommendations on planting schedules, fertilizer application, and crop rotation. These systems integrate data from multiple sources, including satellite imagery, weather stations, and soil sensors, to generate actionable insights [48]. For example, an AI DSS could advise farmers on the optimal time to plant olive trees based on soil conditions and climate projections.

Many Aegean Islands face limited internet connectivity, which can hinder the deployment of AI technologies. To address this, offline AI solutions and edge computing can be implemented, which involve processing data closer to the source of generation (e.g., on local devices or edge servers) rather than relying on centralized cloud servers. This approach reduces latency, bandwidth usage, and dependency on internet connectivity, making it suitable for remote and resource-constrained environments. For example, AI models can be pre-trained and deployed on local devices, such as smartphones or drones, reducing the need for constant internet access [49].

Additionally, investments in broadband infrastructure are essential to enable real-time data transmission and cloud-based AI applications [32]. Indeed, three hyperscalers are already constructing large data centres in Greece, which could facilitate national and regional demand. Greece has already built a national supercomputer for researchers and scientists and has a national research and education network interconnecting and providing computing and cloud services to academic and research institutions, educational bodies at all levels, and agencies of the public, broader public and private Currently, Greece is investing in a new pre-exascale supercomputer, named “Daedalus,” [12] which is expected to be operational by 2025. Daedalus will offer the highest peak performance in the region and provide computational infrastructure for training and deploying AI models. The country is also enhancing its connectivity by building one of the largest fiber links between Europe and Asia, positioning itself as a key IT interconnection hub and gateway to the European Union. Furthermore, Greece has established regulatory sandboxes in various sectors, including financial technology, sustainable development, and the electricity sector, which can be utilized to test and refine AI systems in a controlled environment [4,50]. These initiatives collectively underscore Greece’s commitment to becoming a leader in AI and digital transformation. For AI-driven transformation to effectively take place in islands, a robust digital infrastructure is essential. To this end,

Table 3. Distinction between currently established national infrastructure (Enablers) and specific regional tools (Proposals).

Category	Item/Program/Application	Brief Details
Currently Implemented or Piloted (Enablers and Existing Programs)	Hyperscaler Data Centers	Three hyperscalers are currently constructing large data centers in Greece to facilitate national and regional digital demand.
	National Supercomputing Infrastructure	Greece has an existing national supercomputer and a national research and education network providing computing and cloud services to academic, research, and public institutions.
	“Daedalus” Supercomputer	Greece is investing in this new pre-exascale supercomputer, expected to be operational by 2025, providing necessary computational infrastructure for training and deploying advanced AI models.
	Pharos EU AI Factory	A strategic initiative anchored by the Daedalus supercomputer, designed to accelerate AI innovation, targeting startups and SMEs by providing access to high-quality datasets and model training capabilities.
	Regulatory Sandboxes	Established in various sectors (e.g., financial technology, sustainable development) to allow AI systems to be tested and refined in a controlled environment.
	Digital Platforms for Supply Chains (Piloted)	Platforms connecting island farmers with hotels and restaurants are currently being piloted under the GR-eco Islands program to reduce food miles and ensure fresher produce.
Proposed AI Applications (Recommendations for the Aegean Islands and Crete)	AI-powered Precision Irrigation Systems	Proposed to optimize water usage in agriculture by analyzing soil moisture, weather forecasts, and crop requirements in real-time, delivering water precisely where and when needed.
	Drones equipped with AI Algorithms	Proposed to monitor crop health, detect nutrient deficiencies, and identify pest infestations early by capturing and analyzing high-resolution images using machine learning.
	AI Models for Pest/Disease Prediction	Proposed to forecast outbreaks and disease spread by analyzing weather patterns and crop types, enabling farmers to take preventive measures and reduce reliance on chemical pesticides.
	AI-based Decision Support Systems (DSS)	Proposed to provide personalized agronomic recommendations (e.g., planting schedules, fertilizer application, crop rotation) by integrating data from satellite imagery, weather stations, and soil sensors.
	Offline AI Solutions/Edge Computing	Proposed as a strategy to overcome limited internet connectivity on the islands by processing AI models locally on devices like smartphones or drones.
	Low-tech AI Solutions (e.g., Farmer.chat analogy)	Proposed comparable AI-driven solutions to support olive growers in implementing precision irrigation and remotely monitoring greenhouse microclimates, helping to overcome digital literacy barriers.
	AI-driven Tourism Platforms	Proposed to provide personalized itineraries, optimize farm-based and cultural activities, and connect visitors with local producers.
	Visitor-flow Predictive Analytics (Santorini)	Proposed to optimize crowd management during peak tourism seasons, such as during the grape harvest.
	Blockchain-integrated AI systems (Crete)	Proposed to ensure product traceability, allowing tourists to follow agricultural products like olive oil or wine from the farm to the table.
	Heritage-focused Augmented Reality (Rhodes)	Proposed to provide a historical and cultural context of agricultural landscapes to tourists.
	Environmental Monitoring (Ikaria)	Proposed machine learning models and predictive scheduling to monitor and minimize the ecological footprint along herb trails and walking routes.

Source: Author’s contribution.

differentiates between already implemented or piloted AI projects that may serve as enabling mechanisms, and proposed AI applications that could amplify their effectiveness within the spatial context of the Aegean Islands and Crete.

5.2. Sustainability Impacts

Island ecosystems face disproportionate threats from climate change, including rising sea levels, saltwater intrusion, freshwater scarcity, and biodiversity loss. These vulnerabilities are compounded by limited land area, geographic isolation, and dependence on imported food and energy. Artificial Intelligence (AI) offers transformative solutions to these challenges by enabling precision agriculture, optimizing resource use, and enhancing climate adaptation strategies. Aegean islands can leverage AI to achieve environmental sustainability across three critical dimensions: water and energy efficiency, carbon footprint reduction, and biodiversity conservation through informed farming practices.

5.2.1. Water Efficiency

Isolated regions like islands frequently grapple with freshwater scarcity, making efficient water use in agriculture paramount. The vulnerability of small island freshwater lenses dictates careful assessment, vigilant monitoring, appropriate development, and astute management [43]. In the Aegean, where similar constraints of limited rainfall and reliance on fragile aquifers prevail, AI-driven precision irrigation becomes a critical tool. Irrigation systems that analyse real-time data from soil moisture sensors, weather forecasts, and evapotranspiration rates to deliver water only when and where crops need it are highly relevant. Companies like CropX have demonstrated that such systems can reduce water usage by 25–50% while maintaining or improving crop yields. For islands dependent on rainwater or energy-intensive desalination, these savings are particularly impactful [51].

AI algorithms processing data from distributed IoT sensors can identify leaks in agricultural water infrastructure, addressing a major source of water loss in island farming systems. Contemporary research on the subject shows that machine learning models can predict pipe failures before they occur [52,53], allowing preventative maintenance, something especially valuable for the Aegean, where aging and often fragmented water infrastructure is vulnerable to both gradual decay and extreme seasonal weather events. In the same direction, the authors [54] used an application software called Autoflow v3.1 that provides an intelligent platform to autonomously categorize residential water consumption data and generate management analysis reports providing an intelligent water resources management system that can extract the types of water usage, e.g., showering, toilet use and washing dishes, with an accuracy of about 90%. These systems, beyond detecting leaks, can go a step forward by offering recommendations directly to consumers for efficient water usage, demonstrating a pathway for the case of the Aegean islands and Crete.

The protection of the freshwater resource itself is equally critical. Coastal aquifers on islands are particularly susceptible to saltwater intrusion. AI models that integrate data on rainfall, tidal patterns, and groundwater extraction rates can predict intrusion risks and recommend sustainable withdrawal limits. The IBM Watson Decision Platform v.1.1 has successfully deployed such systems in coastal agricultural regions, helping farmers optimize irrigation while protecting freshwater resources [55]. Such a system could be instrumental in the Aegean by providing data-driven guidelines to help farmers and water authorities optimize irrigation while protecting the long-term viability of freshwater.

The Hellenic Court of Audit, examining the efficiency of desalination units in the islands of Siros, Nisiros, Thira (Santorini), Chios, Alonisos, Oinouses and Fournoi during the years 2019–2020, came up with the following conclusions [51]:

• Some of the audited entities do not apply reliable measurement systems and, as a result, lack a clear overview of the amounts of desalinated water generated by the units under their jurisdiction.
• Issues were identified with the power supply to the facilities, particularly during the summer months and on islands that are not yet connected to the Mainland High-Voltage Grid.
• Opportunities exist to reduce the environmental footprint of desalination plants, notably through assessing the feasibility of renewable energy integration and undertaking required studies to ensure brine discharge into an environmentally suitable recipient.
• While the expertise needed to operate and maintain the units is secured at the level of municipal water utilities, this is not the case for the municipalities themselves, which typically lack technical staff fully trained in desalination technology.
• No root-cause solution has been implemented to address the substantial water losses occurring in distribution networks, causing considerable waste of water.
• Although water quality parameters post-desalination are regularly checked, comprehensive monitoring systems to ensure the fitness-for-purpose of water reaching consumers remain incomplete.
• By boosting local water supplies in 2019–2020, these units proved that desalination can sustainably address water scarcity in vulnerable island communities.

5.2.2. Energy Efficiency

AI technology can significantly enhance the efficiency of renewable energy use in greenhouse agriculture, particularly on Crete, which dominates national production in greenhouse-cultivated vegetables-accounting for 72.4% of tomatoes, 56.7% of cucumbers, and 73.6% of eggplants in 2022. Machine learning algorithms can forecast solar and wind energy production and optimize the timing of energy-intensive activities such as irrigation and refrigeration [56]. Reanalysis of publicly available greenhouse datasets using advanced time-series techniques, including seasonality detection and noise reduction, demonstrates that AI can substantially reduce energy consumption, improving efficiency, though CO₂ reductions remain marginal.

This AI-driven efficiency aligns closely with the broader energy transition scenario proposed by Katsaprakakis et al. [57] for Crete. Their multidisciplinary plan integrates local renewable resources, including wind, solar, geothermal, and biomass, through centralized and decentralized power plants, smart grids, and pumped hydro storage systems. In this context, AI-enabled greenhouses can optimize the use of electricity and heat from renewable sources, synchronizing energy-intensive agricultural operations with periods of peak generation or stored energy availability. Such integration reinforces the feasibility of Crete’s 100% renewable energy target, improves the sustainability of its dominant greenhouse sector, and demonstrates the synergistic potential of combining AI-driven agricultural management with island-wide energy transition strategies.

5.3. Carbon Sequestration and Emission Reduction

AI-driven soil analysis enables precise fertilizer application, reducing nitrogen runoff and associated greenhouse gas emissions from agriculture [58]. This is critically important for island ecosystems where agricultural pollution threatens coastal waters and marine biodiversity [59]. In the Aegean, this challenge intersects directly with tourism, a major emission source. As far as the case of Greece is concerned, ref. [60] estimates carbon emissions in various sectors of tourism in Crete, including international and domestic transport, accommodation and other activities from data regarding tourist arrivals, modes of transport, overnight stays, carbon emissions in various modes of transport and in accommodation. Annual carbon emissions have been estimated at 488.77 kg CO₂ per visitor. International and domestic flights combined with arrivals by ships in Crete have the highest share of the total carbon emissions at 80.69%. Carbon emissions due to tourism, including international flights, have been estimated at 3.67 kg CO₂ per inhabitant in Crete, which is high compared with total carbon emissions on the island at 6.2 kg CO₂ per inhabitant.

To mitigate the forementioned risks and phenomena, policy focused on desired ecosystem outcomes, targeted regulatory approaches, up-scaling of watershed management, and long-term maintenance of scientifically robust monitoring programs linked with adaptive management are recommended [61]. For islands where agricultural runoff threatens marine ecosystems, such as the Aegean islands and Crete, these techniques offer considerable environmental benefits.

5.4. Biodiversity Enhancement Through AI-Informed Farming

The effective advancement of Artificial Intelligence (AI) and Machine Learning (ML) in agriculture is constrained by fundamental data management challenges [62]. Realizing their potential requires the validated integration, comparison, and visualization of large, multidimensional datasets from diverse scientific and operational sources. Key interconnected hurdles include managing heterogeneous data types, ensuring the quality and sufficient linkage between biological materials and digital records, and curating data adequate for AI training. Furthermore, progress is impeded by limited access to necessary software and expertise, underdeveloped open data infrastructures from public institutions, and the need for robust horizontal collaboration across multidisciplinary teams of data scientists, agronomists, and biologists.

5.5. Climate Adaptation: AI for Resilient Island Agriculture

AI processes climate projections, soil data, and market trends to recommend optimal crop rotations. Machine learning improves lead times for drought and flood predictions specific to island microclimates. Early warnings allow farmers to implement protective measures. Modern agriculture and food production systems are facing increasing pressures from climate change, land and water availability, and, more recently, a pandemic [63]. Furthermore, the application of artificial intelligence in precision agriculture of kiwifruit farming has been investigated [64], by analysing existing applications, current research and digital innovations. The research highlights a lack of open datasets, hindering benchmarking, comparability, and reproducibility of research, as well as leading to potential bias and limited generalization of AI models. Moreover, integration of multimodal data, such as images, sensory inputs, and chemical indicators, remains underexplored, while AI-based robotic automations and related research leading to commercial products provide space for further innovations.

5.6. Potential for Innovation

The Aegean islands and Crete face distinct agricultural challenges, including limited arable land, water scarcity, and fragmented supply chains. These regions rely heavily on traditional farming practices, yet climate change and market pressures demand innovative solutions. Artificial Intelligence offers transformative potential across the agricultural value chain, from precision farming to export optimization, while supporting small-scale farmers in achieving sustainable livelihoods.

Low-tech solutions like Farmer.chat (WhatsApp-based AI) (See: https://digitalgreen.org/farmer.chat/) (accessed on 5 July 2025) deliver agronomic advice in local languages, overcoming literacy barriers. In Crete, comparable AI-driven solutions could support olive growers in implementing precision irrigation systems, while also enabling remote monitoring and control of greenhouse microclimates, optimizing temperature and humidity conditions for enhanced crop productivity.

The GR-eco Islands initiative shows how sustainable farming practices can become tourist attractions themselves. On Rhodes, Lamar S.A.’s aquaculture operation offers snorkelling tours that educate visitors about sustainable fish farming while creating additional revenue streams. Similar models could and are already applied to vineyards in Santorini or herb farms in Kalymnos. Tilos’s zero-waste program [65] demonstrates how organic waste from tourism facilities can become compost for local farms, while agricultural byproducts can be repurposed for eco-friendly tourist amenities. The island of Chalki [66] has become the first municipality in the country where residents are actually producing the energy they consume. Platforms connecting island farmers with hotels and restaurants (like those piloted under the GR-eco Islands program) reduce food miles while ensuring fresher produce for tourists. Projects like “Paths of Greece” create hiking routes through agricultural landscapes, generating tourism income while maintaining firebreaks and biodiversity corridors.

As climate change intensifies, these integrated approaches offer pathways for island communities to thrive economically while preserving their environmental and cultural heritage. The challenge remains scaling pilot projects like those in Tilos and Halki across greater islands with diversified land and population density.

6. Governance and Policy Suggestions for AI-Driven Regional Development

Figure 2 encapsulates the content of the subsequent sections (Sustainable Operations Management, European Interoperability Framework, and TAPIC), which collectively provide the detailed frameworks and strategies for achieving AI-driven sustainable development. It depicts the conceptual space where the various academic disciplines and practical frameworks intersect to guide the governance and policymaking for sustainability initiatives that are empowered by Artificial Intelligence (AI). Setting the stage for understanding how integrated approaches can help regions like the Aegean Islands and Crete thrive economically while preserving their environmental and cultural heritage, especially as climate change intensifies.

6.1. Sustainable Operations Management

A novel framework on Sustainable Operations Management (SOM) [67] describes wastewater treatment as an integral component of Sustainable Operations Management (SOM), aiming to mitigate adverse environmental and social impacts while maximizing economic advantages and establishing the foundation for a sustainable circular economy, practices in Crete since antiquity. The European Union’s Urban Wastewater Treatment Directive [68,69] serves as a key regulatory framework, mandating the gathering and processing of sewage in urban areas with populations exceeding 2000 individuals, the application of secondary treatment, and enhanced measures for vulnerable water sources and sensitive areas, ultimately leading to substantial improvements in European water bodies. Modern approaches include Micellar-Enhanced Ultrafiltration (MEUF), a separation technology utilizing surfactants to form micelles that encapsulate or bind contaminants, allowing water to pass through an ultrafiltration membrane while retaining pollutants like heavy metals, dyes, and organic compounds. MEUF offers advantages such as superior selectivity, flexibility, and the potential for integration with other techniques, with ongoing research exploring biosurfactants as an eco-friendly alternative to chemical surfactants due to their biodegradability and reduced toxicity. In Greece, the Athens Water Supply & Sewerage Company (EYDAP S.A.) is actively undertaking initiatives to comply with UWWTD, including significant projects in East Attica for producing treated wastewater suitable for limitless irrigation and urban reuse, and plans for aquifer recharge in other agglomerations. These efforts are enhanced by the integration of innovative technological solutions like machine learning and artificial intelligence for predictive maintenance and optimization of treatment processes, alongside the use of public datasets. The paper also highlights the importance of public–private partnerships, community engagement, and the integration of green infrastructure like wetlands. Learning from Sweden’s extensive experience in water quality protection and phosphorus removal, Greece is adopting a holistic policy approach, integrating recycling and energy recovery, and utilizing economic instruments to incentivize sustainable practices, aiming to transform wastewater treatment plants into sites for energy production and resource recovery. Ultimately, SOM underscores wastewater treatment as a multifaceted and evolving domain, crucial for environmental preservation, resource efficiency, and achieving climate neutrality through the application of breakthrough technologies.

6.2. EIF—European Interoperability Framework

The European Interoperability Framework (EIF) [70] is a commonly agreed approach to the delivery of European public services in an interoperable manner. It defines basic interoperability guidelines through common principles, models, and recommendations. The EIF is designed to be a generic framework applicable to all public administrations within the European Union (EU), from local to EU levels, encompassing interactions between administrations (A2A), administrations and businesses (A2B), and administrations and citizens (A2C). The EIF’s purpose is to inspire European public administrations to design and deliver seamless, digital-by-default, cross-border-by-default, and open-by-default public services. It also provides guidance for Member States to develop and update their national interoperability frameworks (NIFs), policies, strategies, and guidelines, thus contributing to the establishment of the digital single market by fostering cross-border and cross-sectoral interoperability. The EIF addresses interoperability through a four-layer reference model targeting Legal, Organisational, Semantic and Technical Interoperability.

The EIF promotes “interoperability by design” as a standard approach for developing European public services, ensuring they are designed with interoperability and reusability requirements in mind [71]. It helps overcome challenges like fragmented ICT systems and the disconnect between organisational and technical actors.

6.3. Implementation in AI, Agriculture, and Tourism for Sustainable Development

While the EIF is a general framework, its principles are fundamental for achieving interoperability in diverse domains, including AI, agriculture, and tourism, particularly when aiming for sustainable development. The EIF’s emphasis on semantic and technical interoperability is critical for AI applications.

Similarly, the Common European Agricultural data space (CEADS) facilitates data sharing, processing, and analysis for agricultural data. CEADS is an initiative by the European Commission, part of the wider Common European Data Spaces, aimed at fostering the digital and green transformation of the agri-food sector [72]. Its primary goal is to facilitate the free flow of diverse agri-food data across the EU, providing clear benefits, especially for farmers. This involves the following:

• CEADS seeks to enhance the creation of data spaces where data can flow freely between individuals and organisations, ultimately creating an internal market for data within the EU, irrespective of physical storage location. It aims to integrate both open and protected data while maintaining their distinct regulatory frameworks.
• It is outlined in the European Strategy for Data and is supported by key EU legislation, including the General Data Protection Regulation (GDPR) for personal data, the Open Data Directive for public sector information, the Data Governance Act to improve sharing conditions, and the Data Act, which clarifies data access and use from connected devices. This aims to make complying with data sharing rules effortless for smaller companies, like most farms.
• CEADS is designed to contain a wide array of agri-food data, from farm structures and processes to administrative and communication data, sourced from Earth Observation, smart machinery, and transactions. A core principle is adherence to FAIR principles (Findable, Accessible, Interoperable, and Reusable), with interoperability being a significant challenge, especially for agriculture. The European Interoperability Framework (EIF) provides foundational guidelines for achieving this.
• Expected benefits include optimised data management, new data-related services, more efficient and less costly public controls, and reduced administrative burden for farmers through automated data feeding and supporting their decision-making. However, its establishment is complex, facing economic, legal, technical, motivational, and governance barriers. Key challenges include low farmer awareness of data’s value, limited involvement in tool development, lack of trust, data quality issues, and concerns about digital skills, privacy, and technology dependency.
• A crucial aspect is a farm-centred strategy, ensuring that agri-food data spaces maximise benefits for farmers, address their concerns, and provide clear incentives for data sharing. This involves encouraging, rather than forcing, data sharing to build trust.

Setting the foundation for implementing and developing AI strategies. The success of common data spaces, whether for health or agri-food sectors, which are closely related [73], hinges on interoperability and robust governance. The European Interoperability Framework (EIF) offers a foundational approach, emphasising legal, organisational, semantic, and technical interoperability. Similarly, the TAPIC framework (Transparency, Accountability, Participation, Integrity, Capacity) provides a structured way to address governance challenges. The success of AI strategies in critical sectors relies on intersectoral data exchange and collaboration, and a seamless integration of the different data spaces.

The concept of AI Factories in Europe, which brings together computing power, data, and talent to create cutting-edge AI models, also relies on an interoperable ecosystem. These factories, along with Testing and Experimentation Facilities and European Digital Innovation Hubs, are interconnected to boost AI innovation. This interconnectedness necessitates adherence to common standards and frameworks, echoing the EIF’s emphasis on structured data exchange and collaboration across diverse technologies and organisations. Digitalisation, broadly, acts as a catalyst for decarbonisation efforts by enabling advanced technologies like AI and IoT to improve energy efficiency, resource management, and greenhouse gas emission reduction across sectors. The EIF’s principles of openness, reusability, and user-centricity, coupled with its focus on legal, organisational, semantic, and technical interoperability, can significantly contribute to sustainable development in agriculture and tourism, especially in regions like the Aegean Islands and Crete.

Key advantages of EIF for precision agriculture are summarized below:

• Data-driven insights: AI and precision agriculture generate vast amounts of data (e.g., soil moisture, weather, crop health imagery). The EIF’s semantic interoperability ensures that this diverse data is consistently understood and exchanged between different systems and stakeholders (e.g., farmers, agronomists, AI models).
• Resource optimisation: AI-powered systems can optimise water usage, predict pest outbreaks, and recommend optimal crop rotations. This requires interoperability between sensors, IoT devices, and decision support systems to ensure seamless data flow and actionable insights. The EIF supports this by advocating for open specifications and data portability.
• Sustainability impacts: AI can enhance water and energy efficiency, reduce carbon footprint through precise fertiliser application, and support biodiversity. Interoperability facilitates the integration of various technologies and data sources to achieve these environmental benefits. For example, systems monitoring water desalination units and renewable energy production need to exchange data reliably.
• Addressing challenges: High implementation costs and data privacy concerns in agriculture can be mitigated by EIF principles. Reusability of solutions and open standards can reduce costs, and the EIF’s emphasis on security and privacy provides a framework for managing sensitive agricultural data responsibly.

Regarding agritourism and sustainable tourism:

• Economic diversification: Agritourism diversifies rural economies by connecting farming activities with tourism. Interoperability can link booking platforms, local farm produce inventories, and tourist information systems to create seamless experiences, enabling data exchange between different service providers (e.g., farmers, tour operators, hotels).
• Mitigating environmental pressures: Tourism in regions like the Aegean Islands can lead to water scarcity, waste management issues, and environmental degradation. AI applications can support sustainable tourism by optimising resource use and automating internal processes. The EIF’s focus on structured data exchange and common models can aid in monitoring and managing environmental impacts by integrating data from various sources related to consumption and waste.
• Policy and planning: Lack of spatial planning and uncontrolled waste disposal are problems in tourism development. The EIF, through its focus on organisational and legal interoperability, can help align different administrative entities and legal frameworks to implement integrated spatial planning and waste management strategies that support sustainable tourism [74].
• Promoting sustainable practices: Initiatives like the GR-eco Islands and “Paths of Greece” integrate sustainable farming and tourism. EIF supports this by fostering collaboration and sharing of best practices through common frameworks and platforms, ensuring that successful pilot projects can be scaled and replicated.

In essence, the EIF provides the architectural and conceptual bedrock upon which interoperable digital systems can be built, fostering the necessary data exchange, process alignment, and shared understanding crucial for leveraging AI in agriculture and tourism to achieve broader sustainable development goals.

6.4. TAPIC—Transparency, Accountability, Participation, Integrity, Capacity

The TAPIC [75] framework is a governance framework designed to strengthen decision-making and implementation by breaking down governance problems into five core domains: Transparency, Accountability, Participation, Integrity, and Capacity. It serves as a tool to identify, diagnose, and remedy governance failings and make future policy problems less likely. The framework stems from an extensive review of literature on governance, clustering common concepts into these five domains, and is rooted in the definition that governance is how societies make and implement decisions. While each domain has a positive connotation, the framework acknowledges that problems can arise from either too little or too much of a particular aspect, and it emphasizes the importance of adapting governance concepts to specific contexts rather than providing a rigid “cookbook” for “good governance”. The five key domains of the framework are applied:

• Transparency

The lack of clear information sharing about AI applications, agricultural practices, and tourism impacts can lead to low public understanding and potential resistance. Concerns also exist around data privacy and ownership in AI-driven agricultural systems. Governments and research institutions should prioritise making agricultural, tourism, and environmental data openly accessible using standards like DCAT-AP (Data Catalog Vocabulary Application) Profile for Data Portals in Europe (See: https://op.europa.eu/en/web/eu-vocabularies/dcat-ap) (accessed on 21 October 2025). This enables easier access for stakeholders and fosters innovation. By implementing consistent formats and guidelines for data collection and reporting across agricultural (e.g., yield, water usage, pest incidence), tourism (e.g., visitor flows, waste generation, resource consumption), and AI development initiatives like the EIF technical specifications. Furthermore, real-time reporting dashboards can provide up-to-date insights on sustainability metrics, facilitating monitoring and decision-making for policymakers and stakeholders. Through transparently framing and articulating the benefits and potential risks of AI applications in agriculture and tourism, and addressing public concerns and ensuring public trust, transparency is enhanced. This includes explaining dual-use aspects of emerging technologies where applicable. Finally, establishing or leveraging existing platforms (akin to the Interoperable Europe Portal) to act as a central hub for sharing knowledge, best practices, and data related to sustainable AI implementation in these sectors.

• Accountability

Ensuring that the development and deployment of AI in agriculture and tourism are conducted responsibly, that unintended consequences (e.g., environmental degradation, social displacement) are addressed, and that all actors (AI developers, farmers, tourism operators, local authorities) are held responsible for sustainability impacts. The establishment of clear and adaptable regulations and codes of conduct for AI use in agriculture (e.g., precision agriculture, drone application) and tourism (e.g., smart visitor management, sustainable accommodation) is also essential. These should mandate adherence to biosafety and biosecurity objectives, especially concerning potential environmental impacts from genetically modified organisms or engineered biological systems in agriculture. Moreover, the integration of Ethical, Legal, and Social Implications (ELSI) considerations early and iteratively into the technology development process requires developers to assess and mitigate risks proactively. This helps to anticipate and prevent potential dual-use applications of new technologies [61]. Policy and decision makers need to implement robust frameworks with specific output and outcome indicators to track the environmental, social, and economic impacts of AI in agriculture (e.g., water/energy efficiency, carbon footprint reduction) and tourism (e.g., waste management, water scarcity, noise pollution, impact on local communities) and ensure compliance with existing national and EU legal frameworks, such as the GDPR for data privacy and the AI Act for AI governance.

• Participation

The lack of public engagement and consultation can lead to resistance and hinder the adoption of new technologies and sustainable practices. The diverse interests among farmers, tourism stakeholders, and local communities require effective channels for input. It is the role of authorities to facilitate ongoing dialogues and collaboration among farmers, local communities, tourism businesses, AI developers, environmental NGOs, and government bodies through platforms that could also serve as a “collaboration hub” for sharing insights and best practices. Additionally, organising workshops to gather input on proposed AI applications and sustainable tourism initiatives, ensuring they meet local needs and address concerns. Training sessions for stakeholders can also enhance skills in utilising interoperability solutions and sustainable practices. In conclusion, establishing clear channels for citizens and affected parties to provide feedback and input without fear of retribution. This ensures that local knowledge and concerns are integrated into decision-making and implement schemes that incentivise and recognise contributions from stakeholders to sustainable development initiatives, fostering a sense of ownership and shared responsibility.

• Integrity

It is about ensuring that decision-making processes are clear, predictable, and free from undue influence or corruption, particularly concerning resource allocation (e.g., water, land use) and investments in AI infrastructure. Establish clear roles, responsibilities, and procedures for all public and private actors involved in AI development, agricultural management, and tourism operations utilizing tools such as RASCI matrices. To develop and enforce ethical guidelines and standards for AI applications in agriculture (e.g., data use in precision farming) and tourism (e.g., personalised services, visitor management) to prevent bias, ensure fairness, and protect privacy. By implementing comprehensive data protection policies (e.g., GDPR compliant) and information security measures to prevent unauthorised access, misuse, or “information hazards” arising from sensitive data (e.g., farm data, tourist demographics, personal health data used in related services). To ensure that public procurement processes for AI technologies or sustainable tourism infrastructure are transparent and regularised, reducing opportunities for corruption or politically motivated decisions. Finally, to conduct independent audits of AI systems and sustainability reporting to verify adherence to established standards and regulations, enhancing credibility and trust.

• Capacity

The need for sufficient expertise, resources, and institutional capability to develop, implement, and adapt effective policies in rapidly evolving fields like AI and sustainable development, particularly given the unique constraints of island economies. It requires developing and maintaining in-house expertise within government agencies (e.g., agricultural ministries, tourism boards, environmental protection agencies) to conduct research, analyse complex data (e.g., climate projections, market trends), and assess the feasibility and impacts of AI and sustainable development. Implement specialised training programmes for public servants, farmers, and tourism professionals on AI literacy, precision agriculture technologies, sustainable tourism practices, and interoperability skills. The “IOP Europe Academy” model could be adapted to provide certified training to build and maintain capacity. Also, to utilise and expand upon national supercomputing infrastructure (e.g., Greece’s Daedalus supercomputer) and European initiatives like AI Factories to provide the computational power and data storage necessary for advanced AI models in agriculture and tourism. The creation of controlled environments (regulatory sandboxes) where AI applications in agriculture and tourism can be tested and refined in real-world conditions under regulatory supervision. This fosters innovation while managing risks and by employing tools such as the Interoperability Maturity Assessment of Public Services (IMAPS) to evaluate the legal, organisational, semantic, and technical interoperability maturity of digital public services and infrastructure in these sectors, considering the EIF. This helps identify areas for improvement and guides investment in capacity building. Promotes collaboration between data scientists, agronomists, environmental specialists, tourism experts, and social scientists to integrate diverse expertise.

By systematically applying the TAPIC framework, policymakers in the Aegean Islands and Crete can address the multifaceted governance challenges associated with leveraging AI in agriculture and tourism for sustainable development. This structured approach helps ensure that policies are well-vetted, effectively implemented, and contribute to desired outcomes while mitigating potential risks and unintended consequences.

6.5. A Growth Pole Theory Proposal in the Aegean Context

The Growth Pole Theory (or Pole Regional Development Theory), originally developed by economist François Perroux in the 50s, offers a framework for understanding how economic development unfolds unevenly across regions. “Growth does not appear everywhere and all at once, it appears in points or development poles, with variable intensities, it spreads along diverse channels and with varying terminal effects to the whole of the economy” [76]. At its core, the theory argues that growth does not spread uniformly but instead concentrates around dynamic “poles”; typically, dominant industries or urban hubs that act as engines of economic activity. These poles generate powerful ripple effects through their networks of suppliers, customers, and supporting services.

A growth pole’s strength lies in its propulsive industries—innovative, high-productivity sectors like automotive manufacturing or technology that drive regional advancement. These industries create two critical types of linkages: backward linkages (stimulating demand for raw materials and components) and forward linkages (providing outputs that fuel other industries). For example, a thriving electronics sector might boost semiconductor production (backward linkage) while enabling growth in consumer tech manufacturing (forward linkage). Over time, this clustering of interrelated businesses creates agglomeration economies, where proximity reduces costs, fosters knowledge spillovers, and accelerates innovation.

The theory also highlights the spatial dynamics of development. Growth poles exert both centrifugal forces (spreading benefits like jobs and infrastructure to surrounding areas) and centripetal forces (concentrating resources and talent, which can sometimes drain neighbouring regions). This tension explains phenomena like the “Paris and the French Desert” effect [77], where a dominant capital city overshadows provincial development. Policymakers have adapted these ideas to design targeted regional development strategies, often investing in infrastructure, industrial zones, or innovation hubs to create artificial growth poles. However, the theory’s real-world application shows mixed results. While success stories like Silicon Valley demonstrate how tech-driven agglomeration can transform regions [78], other attempts [79,80] have faltered due to weak local linkages or inadequate institutional support.

This paper proposes a Growth Pole Theory solution for AI, precision agriculture and tourism for sustainable regional development in the Aegean islands’ context. Main poles as development clusters are considered the regions of Attica (Athens), Syros, Lesvos, Rhodes and Crete. The main development strategies for every cluster are depicted in the following

Table 4. Main development clusters in the Aegean.

Growth Pole	Key Industries	Backward Linkages	Forward Linkages	Unique Advantage
Athens	AI Tech, Finance, Strategic Policy	Policy planning, software development, and academic coordination	Tourism services, export logistics, and AI research	National transport and AI hub (AEGEAN HQ)
Crete	Agri-tourism, Renewable Energy	Organic farming inputs, solar panel production	Farm-to-table tourism, energy exports, and AI research	72.4% of Greece’s greenhouse tomatoes
Syros	Maritime, Public Administration	Shipbuilding, port infrastructure	Island-wide governance, cultural festivals	Capital of the Cyclades region
Lesvos	Olive Oil, Eco-tourism	Irrigation tech, packaging materials	Gastronomic tours, international exports	Proximity to the Turkish market
Rhodes	Cultural Tourism, Logistics	Heritage conservation, cruise ship services	Luxury resorts, medical tourism	UNESCO sites + 300 + sunshine days/year

Source: Authors’ contribution.

:

The aforementioned five poles were selected on the basis of the following criteria:

• size of resident population;
• GDP/employment share;
• presence of AI-relevant institutions (universities, R&D centres, data centres);
• tourism intensity;
• transport connectivity.

In practice, the undertaken Growth Pole approach seeks to improve regional development through AI-driven clusters. It is, though, inherently vulnerable to risks like unequal territorial benefits and centripetal forces that result in “Athens-centrism” or “backwash effects,” in which dominant hubs concentrate talent and resources, potentially draining peripheral islands. Furthermore, actual governance capacity limitations, such as the high costs of technology adoption for small farms and current skill gaps in digital literacy, make it difficult to achieve the proposed transformation. By utilizing the TAPIC (Transparency, Accountability, Participation, Integrity, Capacity) model and the European Interoperability Framework (EIF), the approach is structured to mitigate these downsides. Specifically, the Capacity domain of TAPIC requires the development of specialized training programs and the implementation of user-friendly, low-tech AI solutions to overcome digital barriers for smallholders, reducing cost and skill gaps. Participation domain ensures that all stakeholders, big or small, are actively participating in the decision-making process in order to communicate risks, results and suggestions, thus mitigating any displacement or distribution problems. Meanwhile, the EIF provides the essential architectural and conceptual foundation for interoperable digital systems, ensuring that diverse data (from small producers to tourism platforms) is consistently exchanged and understood, which is crucial for linking small producers with tourism supply chains and spreading economic benefits beyond the core poles. An exhaustive analysis of how TAPIC and EIF can be applied is presented in the relevant sections.

Table 5. Selection criteria analysis of main development clusters.

Pole (Region/Island)	Population (2021 Census)	Presence of AI-Relevant/Institutional Capacity	Tourism Intensity/Sectoral Specialization 1	Transport Connectivity (Airport/Port/Accessibility)
Attica (Athens)	Largest urban centre in Greece. (3,756.453)	National institutions, policy, and research centers concentrated in Athens (de facto central tech & governance hub)	Attica tops tourism revenues	National transport & connectivity hub: major airport, ports, infrastructure.
Crete	Largest-population Greek island (633.506)	Moderate: local economic structure includes agriculture, tourism, and fundamentals for development.	High tourism intensity: large share of hotel-bed capacity; second to national tourism receipts.	Strong connectivity: international airport(s), ports, and frequent transport links; substantial infrastructure for tourism and logistics.
Rhodes	Among the larger islands nationally (125.113)	Via tourism & services economy; potential to host supporting infrastructure (logistics, hospitality, heritage management)	Very high tourism intensity—part of a group of top tourist-destination islands in Greece, with strong historical/cultural tourism appeal.	Established transport connectivity—port infrastructure and airport, facilitating both cruise and air tourism.
Lesvos	Mid-sized island (83.755)	Some local economic activity; presence of agriculture (e.g., olive oil).	Potential for eco-tourism and agro-tourism, though tourism intensity appears lower than top-tier islands; recent efforts to boost charter arrivals.	Basic connectivity via port/regional transport; less intensive than major hubs but enough to support moderate tourism and trade.
Syros	Small island (21.124)	Administrative role (regional capital of Cyclades), local governance and port infrastructure may support development.	Cyclades islands show significant tourism demand, though some concentration in a few top-islands; Syros could leverage Cycladic tourism dynamics.	Port infrastructure and inter-island links (common for Cyclades) support connectivity, relevant for tourism and supplies.

Source: Authors’ contribution. Data: INSETE. ¹ https://insete.gr (accessed on 22 July 2025).

presents an analysis of the selection criteria applied to the main development clusters that are shown on Figure 3.

7. Policy Pathways for Sustainable Development of the Aegean and Crete

Artificial Intelligence (AI) holds significant potential to reshape local labour markets by enhancing productivity, generating or displacing employment opportunities, and altering the fundamental characteristics of certain occupations, including aspects of job quality. This chapter investigates the exposure of the region examined to automation technologies, analysing their potential implications for job creation and productivity growth. Furthermore, it explores how these effects are distributed across diverse communities, industries, and workforce segments.

7.1. Government Support and Investment

Pharos is a strategic initiative to establish an EU AI Factory in Greece (For further information on EU AI Factories see: https://digital-strategy.ec.europa.eu/en/policies/ai-factories (accessed on 27 June 2025)), anchored by the pre-exascale supercomputer Daedalus, designed to accelerate AI innovation and democratize access to advanced computational resources. Targeting startups and SMEs, the project will provide end-users with critical tools, including high-quality datasets, AI model training capabilities, and business innovation support, while addressing societal challenges in Tourism, Agriculture, Health, Culture, and Sustainability.

By integrating with Daedalus, Pharos will enable breakthroughs in high-impact domains:

• Tourism: Data harvesting for effective development policies.
• Agriculture: Algorithmic crop health monitoring, proposed AI-powered precision irrigation systems.
• Health: Predictive analytics for chronic diseases and personalized care.
• Culture & Language: Preservation and promotion of cultural heritage.
• Sustainability: Renewable energy optimization and resource management.

A core pillar of Pharos is its commitment to trustworthy AI, embedding principles of data privacy, security, and ethics across the AI lifecycle. As part of a broader EU network, it will foster collaboration with initiatives like EDIHs, Data Spaces, NCCs, DeployAI, and other AI Factories, strengthening Europe’s cross-border innovation ecosystem.

With a budget of €5.6 million (co-funded by the European Commission and NCSR), Pharos reflects Greece’s ambition to emerge as a leader in HPC-powered AI, aligning with EuroHPC’s goals to advance Europe’s digital sovereignty, ethical AI adoption, and sustainable innovation.

7.2. Tourism and Agriculture Integration

The Aegean Islands and Crete, with their extensive agricultural activities, present a significant opportunity for developing agri-tourism and eco-tourism. Visitors increasingly seek experiences that connect them with local food systems and sustainable practices, such as participating in grape harvests in Santorini, learning traditional cheesemaking in Naxos, or exploring herb trails in Ikaria. This demand for low-impact, authentic tourism aligns with the islands’ agricultural heritage and ecological characteristics.

Artificial Intelligence (AI) offers tools to enhance these experiences while supporting sustainable sector growth. AI-driven tourism platforms can provide personalized itineraries by analysing visitor preferences, seasonal availability, and weather patterns, thereby optimizing farm-based and cultural activities [81]. Digital interfaces can also connect visitors with local producers, while augmented reality (AR) applications can provide historical and agricultural context, and AI-powered language tools can facilitate real-time translation [14,81].

In addition to improving visitor experiences, AI can strengthen operational efficiency in the sector. Another suggestion is the utilization of predictive analytics that help smallholders anticipate tourism flows, optimize harvest schedules, and allocate staff effectively. Machine learning models can monitor the environmental footprint of agri-tourism activities [82], and blockchain-integrated AI systems can ensure product traceability, allowing tourists to follow the journey of olive oil, wine, or other agricultural products from farm to table.

The following

Table 6. Proposed AI applications to specific island contexts.

Island	AI Tool/Application	Purpose/Use Case
Santorini	Visitor-flow predictive analytics	Optimize crowd management during peak tourism and grape harvest periods
Naxos	AI-driven agritourism platform	Personalize farm-to-table experiences, cheesemaking workshops, and vineyard visits
Ikaria	Environmental monitoring & predictive scheduling	Minimize ecological footprint along herb trails and walking routes
Rhodes	Heritage-focused AR	Provide historical and cultural context of agricultural landscapes
Crete	Blockchain-integrated supply tracing	Ensure traceability of agricultural products from farm to table

Source: Authors’ contribution.

maps AI applications to specific island contexts.

7.3. Economic Diversification Through AI

The economies of the Aegean Islands and Crete have long been shaped by two dominant pillars: tourism and agriculture, each of which is vulnerable to distinct forms of disruption. While tourism revenues fluctuate with seasonality and global trends, agriculture faces mounting pressures from climate change, water scarcity, and shifting market demands. However, the strategic integration of AI across both sectors can unlock powerful synergies, foster economic diversification and build resilience against these systemic risks.

At the intersection of agriculture and tourism lies agri-tourism, a sector primed for AI-enabled transformation. Intelligent systems can analyse vast datasets, from soil health metrics and microclimate patterns to tourist booking trends and spending behaviours, to identify optimal moments for aligning farming activities with visitor experiences. For instance, AI could predict peak demand for organic wine tastings during harvest seasons [83,84], allowing vineyards to structure immersive tours that capitalize on both agricultural output and tourist interest. Similarly, during traditional olive harvests, farmers could leverage AI-driven marketing tools to attract visitors seeking hands-on participation [85], effectively monetizing what was once purely an agricultural activity.

Beyond direct agri-tourism applications, AI can mitigate the islands’ reliance on seasonal tourism by creating year-round revenue streams. Precision agriculture tools, powered by machine learning, enable farmers to diversify crops based on predictive climate models and emerging market opportunities. A vineyard might use AI to experiment with drought-resistant grape varieties, ensuring production stability while simultaneously offering “climate adaptation” workshops to off-season tourists interested in sustainability. Likewise, AI-optimized greenhouse operations could yield fresh produce year-round, supplying local restaurants even in winter and supporting a growing niche of gastronomic tourism beyond the summer months.

AI also strengthens the economic ecosystem by facilitating connections between small producers and broader markets. Smart platforms could match island farmers with hotels, restaurants, and speciality food retailers, both locally and internationally, based on real-time inventory and demand forecasts. This not only stabilizes agricultural incomes but also elevates the islands’ brand as a hub of authentic, farm-to-table experiences. During tourism downturns, these digital marketplaces provide alternative distribution channels, buffering against revenue losses.

Crucially, AI’s predictive capabilities help both sectors adapt to climate volatility. For agriculture, AI models can forecast drought risks or pest outbreaks, enabling pre-emptive adjustments to farming practices. For tourism, these same insights inform adaptive strategies, such as promoting agri-tourism activities less susceptible to weather disruptions (e.g., cheese aging cellars or herbal remedy workshops), during periods of extreme heat or rainfall.

The cumulative effect of these AI-driven synergies is a more circular and shock-resistant island economy. Farmers gain additional revenue through tourism-integrated activities, while tourism businesses diversify their offerings beyond sun-and-beach packages. Local supply chains become more agile, reducing dependency on imports. And as the islands cultivate a reputation for innovative, sustainable practices, they attract higher-value visitors, think eco-conscious travellers, culinary enthusiasts, and digital nomads, who contribute to economic stability across seasons.

The following Figure 4 summarizes how AI applications in agriculture and tourism interact with data interoperability and governance to produce sustainability outcomes.

Implementing this vision requires targeted investment in digital infrastructure, skills training for farmers and tourism operators, and policy frameworks that encourage cross-sector collaboration. By harnessing AI as a unifying force, the Aegean Islands can transform their vulnerabilities into interconnected strengths, ensuring that both the land and its people thrive in the face of an uncertain future. This is not merely economic adaptation; it is the cultivation of a new model for island resilience, one rooted in the intelligent integration of tradition and technology.

8. Conclusions

This paper develops an integrated conceptual framework linking AI, precision agriculture, and tourism to advance sustainable development in the Aegean Islands and Crete. By applying the European Interoperability Framework (EIF) and the TAPIC framework, it highlights how interoperability, accountability, and participatory governance can support ethical and effective AI adoption in insular contexts. The Growth Pole-motivated approach further illustrates how coordinated clusters of agricultural, tourism, and digital activities can enhance resilience, diversify local economies, and address structural challenges such as seasonality, geographic isolation, and climate risk.

Three policy priorities emerge. First, upgrading digital infrastructure, including broadband and edge computing, is essential for overcoming connectivity constraints. Second, strengthening human and institutional capacity through targeted training and public–private partnerships can accelerate the uptake of AI solutions in farming and tourism. Third, developing interoperable data ecosystems, aligned with the EIF and supported by regulatory sandboxes, can enable secure data sharing and responsible innovation. These measures form the foundation for leveraging AI to support sustainable, diversified island economies.

The role of AI as an inclusive coordination tool that brings together all stakeholders and facilitates initiative planning and interactions is paramount. AI cannot only promote data-driven insights but also suggest and devise strategies under the guidance and control of human policy-makers that adhere to the aforementioned analysis and the applied frameworks.

Future research should focus on empirical case studies of AI deployment in comparable island regions, assessments of socio-economic impacts on rural employment and local value chains, evaluations of AI-enabled tourism models in Mediterranean settings, and interdisciplinary analyses of AI’s contribution to climate resilience. Additional work is also needed to examine the scalability and long-term viability of AI solutions in resource-constrained island environments. Comparative studies could analyse governance approaches and policy frameworks that successfully balance economic, environmental, and social sustainability in island settings. Additionally, interdisciplinary work should investigate AI’s role in climate resilience, particularly in optimizing water and energy use, managing fragile ecosystems, and mitigating risks from extreme weather events. Finally, studies should assess the scalability of pilot projects and the long-term viability of AI solutions in resource-constrained island economies.