Abstract
Recent advances in artificial intelligence (AI) give rise to new opportunities for regional path creation in peripheral regions when local capabilities are combined with the transformative potential of emerging technologies. Drawing on literature on the role of narratives and the influence of agency on path-development processes, we examine how a peripheral region can harness AI to move toward more resilient regional path creation archetypes. The study employs a novel approach consisting of an in-depth case study of the Faroe Islands, based on multiple secondary sources and key informant engagement, combined with systematic morphological scenario analysis. We identify three regional trajectories involving institutional reinforcement to move from persistent stagnation toward Faroese AI excellence, involving industry specialization to move toward technological elite with support, or involving grassroots mobilization to move toward grassroots AI leadership. Our findings reveal that locally grounded narratives about AI technologies can widen opportunity spaces in peripheral regions by aligning political vision, societal legitimacy, and native language assets. Peripheral regions seeking transformations driven by AI technologies should actively leverage their existing strengths, such as unique cultural or natural resources, while addressing fundamental challenges of political commitment, societal acceptance, and local language integration.
Introduction
Regional development is shaped by actors embedded in institutional and geographic contexts. The rapid advance of artificial intelligence (AI), 1 recently accelerated by generative models, is transforming sectors including healthcare, education, and tourism, as well as innovation processes (Bickley et al., 2022; Guarascio et al., 2025; Huang et al., 2023; Lee et al., 2022; Roberts and Candi, 2024).
Research on AI-driven regional development tends to focus on urban ecosystems where dense networks accelerate adoption (Doloreux and Turkina, 2021; Gherhes et al., 2021; Hautala, 2024; Herath and Mittal, 2022; Lundvall and Rikap, 2022). Peripheral regions receive less attention, even as disparities in AI readiness risk reinforcing structural inequalities (Guarascio et al., 2025; World Economic Forum, 2025). Indeed, Guarascio et al. (2025) highlight how diffusion and adoption patterns reflect pre-existing regional asymmetries in technological capabilities, infrastructure, and institutions.
Agglomeration economies and the spatial concentration of knowledge fuel urban innovation (Pedraza-Rodríguez et al., 2023; Rodríguez-Pose and Wilkie, 2019), whereas peripheral regions often face socio-economic lock-ins and isolation that constrain renewal (Roessler et al., 2025; Trippl et al., 2018). Efforts to intensify local interaction can foster lock-in and inhibit knowledge renewal, whereas deliberate extra-regional linkages are more likely to enable interactive learning and innovation (Trippl et al., 2018). Urban-centric perspectives tend to overlook the unique dynamics and challenges of peripheral regions (Nilsen, 2017; Nilsen et al., 2022; Pugh and Dubois, 2021).
Although peripheral regions may frequently struggle to overcome their “left-behindness” (Bråten et al., 2025), even under limited structural preconditions, such regions may discover untapped opportunities when local capabilities are combined with the transformative potential of emerging technologies (Calignano et al., 2024; Nilsen et al., 2022). Our perspective builds on research on narratives and opportunity spaces (Grillitsch and Sotarauta, 2020; Roessler et al., 2025) and future-oriented regional development (Gong 2024; Hauge et al., 2025; Kitagawa and Vidmar, 2023).
Research on emerging technology ecosystems (e.g., Beltagui et al., 2020) shows that innovation capacities can develop through bottom-up processes where local actors collectively articulate and maintain “innovation commons” to support shared experimentation and capability-building. We empirically investigate how the Faroe Islands—a natural resource-based micro-economy in the North Atlantic, which serves as a representative peripheral region—could harness AI to build new regional paths. Our investigation starts with the following question:
How can peripheral regions harness AI to redefine their opportunities and transition from being natural resource-based to more resilient regional archetypes?
Positioning the Faroe Islands within Nilsen et al.’s (2022) typology, we analyze how AI may help overcome geographic isolation and dependency on natural resources. This enables us to assess conditions under which peripheral regions can utilize AI to support diversification and regional path development. By exploring the dynamics of embedding AI technologies within regional economies that have been traditionally neglected by research, we increase our understanding of the conditions under which peripheral regions can effectively utilize these technologies to foster novel pathways of regional development.
By analyzing locally grounded narratives, we apply a place-based approach to the periphery to examine how engagement across actors can unlock new pathways for growth and resilience in the periphery region (Syssner and Erlingsson, 2024). We also highlight the importance of native language resources in shaping the usability and societal impact of AI.
Background
Dynamic views of peripheral regions
The concept of “periphery” has attracted scholarly attention (Eder and Trippl, 2019; Glückler et al., 2023; Pugh and Dubois, 2021). Peripheral status is relational rather than strictly geographic or economic, shaped by inter-regional power and function. Nilsen et al. (2022) identify four types of peripheral regions (Figure 1). Nilsen et al.’s (2022) taxonomy of types of peripheral regions.
According to Nilsen et al. (2022), Type I regions are resilient service centers with diversified structures and balanced power relations. Type II regions are specialized, with strong competencies but skewed power dynamics dominated by key industries. Type III rural regions have undifferentiated economic structures and limited governance capacity. Type IV regions are resource-based, characterized by skewed power relations and vulnerability to external shocks (Nilsen et al., 2022).
In addition to the understanding that not all peripheral regions are the same, Calignano et al. (2024) argue that peripheral regions are rarely static. Peripheral regions evolve in response to technological and socio-economic change, with historical local knowledge shaping future trajectories (Doloreux and Turkina, 2021; Hassink et al., 2019; Nilsen, 2017).
Miörner (2020: p. 593) distinguishes two types of new path creation. “Path importation” refers to the “establishment of an industry that is new to the region and unrelated to existing industries,” driven by the inflow of actors and resources to the region (see also, Grillitsch et al., 2018; Fredin et al., 2019; Trippl et al., 2018). Meanwhile, “path transformation” indicates “substantial innovation-based renewal processes of established industrial paths, based on the development of disruptive technological, organizational, or market innovations, leading to a new industrial path which is substantially different from the initial one” (see also Baumgartinger-Seiringer et al., 2020; Miörner and Trippl, 2019). Peripheral regions can possess advantages, including an absence of negative lock-ins and access to natural resources, which may facilitate path importation. They can also possess disadvantages, such as a lack of endogenous innovation capabilities and poor resource endowments for developing high-growth economic activities, which may constrain path transformation.
Grillitsch and Sotarauta (2020) propose the concept of the “trinity of change agency,” which they define as the combined interplay of innovative entrepreneurship, institutional entrepreneurship, and place-based leadership as the core drivers of regional structural change. They highlight the interplay between path-dependent structural forces and the growth of new paths through change agents (Rekers and Stihl, 2021). It is alleged that the emergence of new regional development paths is possible only if an agent perceives new opportunities, and only if the agent has the “capabilities to set actions towards the realization of these opportunities” (Grillitsch and Sotarauta, 2020: p. 716). The transformation of regions is shaped by multi-level political and institutional dynamics (Bråten et al., 2025). Research has demonstrated that varied forms of “agency within the region” are needed, including the function of state and policy actors at multiple scales to create favorable conditions for path creation and/or intervention to support promising new industries or technologies (Gherhes et al., 2021).
Indeed, roles and types of agency are gaining scholarly attention to better understand change processes across a variety of places. In the context of peripheral regions or areas traditionally deemed “lagging” (Hansmeier et al., 2026), where endogenous innovation capacities are limited, the nature of innovation agency needs to include broad social infrastructure, including healthcare, education, public services, research institutions, and municipalities, to form the institutional conditions that enable the introduction and adoption of new technologies. Furthermore, recent research on regional narratives reveals that local leaders can reshape perceptions and mobilize resources to create “opportunity spaces” (Roessler et al., 2025), reinforcing the need to analyze how agency interacts with structural conditions in peripheral contexts.
AI in peripheral regions
Digital systems can be place-independent when supported by robust communication infrastructures. This means that peripheral regions can exploit AI without hosting local servers or large computational resources. This enables local businesses and individuals to leverage cutting-edge tools without substantial investments in IT infrastructure. For example, AI can assist businesses in analyzing customer data, improving supply chain logistics, or designing marketing campaigns without requiring in-house technical expertise. The democratization of AI tools enhances access by providing user-friendly interfaces that do not require advanced technical skills.
The democratization of AI presents seemingly tantalizing opportunities for peripheral regions. But, if peripheral regions primarily rely on external knowledge and technologies without fostering local expertise and innovation capabilities, they risk remaining passive users rather than active exploiters of AI technologies (Nilsen, 2017). The absence of local skill development and innovation ecosystems could reinforce dependencies on external platforms, thereby limiting the transformative potential of these technologies. Building local knowledge and capabilities and fostering innovation are critical for ensuring that peripheral regions can leverage digital systems sustainably and equitably (Pedraza-Rodríguez et al., 2023; Price et al., 2022; Russell and Norvig, 2010).
While cities may benefit from dense social networks and established innovation ecosystems (Herath and Mittal, 2022), peripheral regions often lack the institutional and technical capacities needed for rapid innovation (Eder and Trippl, 2019; Nilsen et al., 2022; Pedraza-Rodríguez et al., 2023). Meanwhile, regional policies need to target not so much the institutions that shape the unique character of any territory, but the institutional factors that pose barriers to the efficacy of other factors influencing economic development, such as education, training and skills, innovation, and infrastructure (Rodríguez-Pose, 2013).
Peripheral regions must invest in education, infrastructure, and local partnerships to increase organizational capabilities and move beyond simple adoption of technologies to active adaptation and innovation (Pedraza-Rodríguez et al., 2023; Price et al., 2022). Hence, by integrating AI into local contexts and tailoring solutions to regional needs and challenges, peripheral regions can not only benefit economically but also develop unique competitive advantages. Therefore, there is a pressing need to examine how AI technologies can be adapted and applied in the unique contexts of regions on the periphery.
Research method
Narrative methodology
Recent work in organizational and strategy research as well as regional and geographical studies increasingly acknowledges the importance of understanding the role of narratives in contexts of uncertainty, change, and transformation (Rindova and Martins, 2021; Roessler et al., 2025). In contexts of peripheral regions, Nilsen and Njøs (2022) focus on how narratives help de/legitimize the emergence of new industries. Lund and Vildåsen (2022) show that industrial decisions are shaped by dominant narratives. Roessler et al. (2025) argue that studying narratives enables the observation of the processes through which actors make sense of and perceive opportunities, as narratives are central to shaping collective identities, legitimizing strategic initiatives, and facilitating the mobilization of resources. Changing narratives about what is possible can influence regional path development, particularly in peripheral regions (Harrison, 2017; Kitagawa and Candi, 2025; Nilsen and Njøs, 2022; Roessler et al., 2025).
Against the backdrop of this recognition of the importance of narrative in regional development, we adopted a narrative methodology as the first step in our research method. This methodology treats stories not as illustrative anecdotes but as primary data for theorizing dynamic processes. Narratives connect events, actors, and consequences, and thereby make processes traceable (Langley et al., 2013). They enable moving from rich accounts to analytical chronologies that reveal shifts in actors, artifacts, and organizations over time.
Narratives blend empirical events with hopes, fears, and moral evaluations, providing the imaginative resources through which actors pursue risky experimentation or resist it (Gabriel, 2000). By collecting multiple, sometimes conflicting, versions of key episodes from policymakers, entrepreneurs, citizens, and media, we capture this polyphony and examine how certain plotlines gain dominance while others are silenced.
Building on Pentland’s (1999) distinction between “surface discourse” (the story as told) and “deep narrative structure” (recurring patterns that explain why events unfold as they do), we coded our primary and secondary data with an emphasis on both actions and actors and underlying motifs such as disruption, contestation, or path reorientation. This dual coding allowed us to theorize how locally crafted narratives about AI had the ability to mobilize the “trinity of change agency” (Grillitsch and Sotarauta, 2020) by tracing causal links between talk, collective sense-making, and subsequent moves.
Morphological scenario analysis
Scenario planning is a foresight method that involves exploring diverse future scenarios by considering various combinations of assumptions and events. These scenarios are not predictions or forecasts but instead represent alternative views of how the future might unfold. This method is well-suited for formulating and examining multiple possibilities, given a clear understanding of the current situation (Wenzel et al., 2020). Using the narrative developed as our foundation, we followed established scenario-planning protocols (Schoemaker, 1995; Schwartz, 1991). This involved first identifying predetermined drivers, which are structural factors that must hold across any plausible future and then formulating critical uncertainties whose resolution shapes divergent trajectories and whose combinations determine which futures can be imagined. Guided by general morphological analysis (Ritchey 2011; Zwicky 1969), we specified four critical uncertainties, each with two discrete states, and populated a 2 × 2 × 2 × 2 morphological field (16 permutations).
Research setting
The Faroe Islands, a group of 18 islands in the North Atlantic Ocean between Iceland, Norway, and the Shetland Islands (Scotland), form a self-governing overseas administrative division of the Kingdom of Denmark. The population is about 55,000, and the national language is Faroese. Faroese is a core carrier of national identity and cultural continuity in this small island society, and language preservation has long been a salient public concern. This makes linguistic inclusion a practical as well as symbolic issue for digitalization and AI adoption; solutions that do not function in Faroese risk limiting everyday usability and, ultimately, societal legitimacy. Dominated by fisheries and aquaculture, and heavy dependence on related natural resources, the Faroe Islands align with Nilsen et al.’s (2022) archetype of a “locked-in and vulnerable resource-based region” (Type IV). However, emerging AI technologies present opportunities to overcome these limitations and foster new regional paths towards innovation and diversification. Indeed, recent diversification efforts, such as investments in tourism and digital services, indicate a transition towards a more diversified economic structure. The potential transformation of the Faroe Islands from a peripheral region dependent on fishing and fish farming to a more diversified economic landscape provides a compelling context for this study. The Faroe Islands, with their small population, remote location, and historical reliance on fishing, can be taken as an archetype of peripheral regions seeking to harness digital technology for economic diversification and reduced brain drain.
In May 2018, the Ministers responsible for digital development in Denmark, Estonia, Finland, the Faroe Islands, Iceland, Latvia, Lithuania, Norway, Sweden, and the Åland Islands released a “Declaration on AI in the Nordic-Baltic Region.” 2 The countries agreed to collaborate to “develop and promote the use of artificial intelligence to serve humans.” In 2024, the Norwegian Consortium for Artificial Intelligence Research (NORA), along with its North Atlantic counterparts from the Faroe Islands, Greenland, and Iceland, drafted an AI policy document, 3 which identifies 12 recommendations on how AI research institutes can work with policymakers and industry on (i) strengthening language and culture in AI, (ii) improving AI literacy, (iii) building sustainable digital infrastructure, and (iv) introducing new industrial policy for AI and robotics.
The strategic focus on AI in the Faroe Islands aligns with global trends that emphasize knowledge-driven economic activities. For example, the region’s advanced connectivity infrastructure, led by key telecom players, and partnerships with international technology providers have created a foundation for digital innovation. This setting allows us to examine how AI technologies can contribute to regional development, knowledge capital, and regional path creation.
Data collection
Secondary data listed alphabetically by title.
To validate and enhance the narrative (Guba and Lincoln, 2005), nine key stakeholders 4 in the Faroe Islands, including academics, public-sector leaders, and business leaders, were contacted and invited to review the draft. Subsequent conversations with these informants provided additional insights, corrections, and suggestions for extensions, ensuring that the narrative accurately reflected local developments and perspectives. This iterative engagement process ensured the narrative’s richness and reliability as a foundation for analysis.
Findings
Narrative
The following is the final version of the narrative, developed initially based on our analysis of secondary sources and subsequently refined through iterative rounds of reviews and discussions with local stakeholders. The narrative covers both digitalization efforts in general and AI initiatives more specifically, as these two were inextricably linked.
Predetermined drivers
Based on our analysis, we identified two predetermined drivers (Schoemaker 1995; Schwartz 1991) of the Faroe Islands’ ability to leverage AI for regional path development. First, digital infrastructure and services. The Faroe Islands benefit from substantial investments in internet connectivity and telecommunications infrastructure, which are essential for digital innovation. The country is among global leaders in broadband access, creating an ideal foundation for digital ventures. Second, public and private investment in innovation and education. Recent policy initiatives have prioritized innovation through increased funding for education to build local expertise, particularly in technology-related fields.
Key uncertainties
Based on our examination of the data and conversations with stakeholders, we arrived at four key uncertainties faced by the Faroe Islands in relation to the adoption of AI: (1) political commitment to harnessing AI, (2) capacity to build and retain local expertise/knowledge in AI, (3) societal acceptance of technology and willingness to diversify economically, (4) availability of resources for localized AI applications, including Faroese large language models.
First, informants emphasized uncertainty regarding long-term political stability and sustained commitment to technology policies. There is a recognition that political priorities shift frequently, and consistency is needed to build meaningful momentum. As regards the second key uncertainty, concerns were raised about the capacity to retain skilled individuals due to limited local career opportunities. “Brain drain” is recognized as an ever-present threat in the Faroe Islands. Educational institutions, including the local university, train talent, but keeping talented individuals in the Faroe Islands remains a challenge. Informants indicated varying levels of societal trust and openness towards technology-driven change, reflecting potential cultural resistance, which constitutes the third key uncertainty. Without broad support and public acceptance, even promising innovations can struggle to gain traction. Finally, the limited availability of Faroese language resources restricts the broad usability of AI technologies and makes developing locally relevant AI solutions challenging.
Possible future scenarios
Results of morphological scenario analysis.
Futures A1 and B1 arguably represent the most desirable outcomes, as they combine strong societal acceptance with high language resource support and local knowledge capital. In Future A1, government commitment is high, whereas it is low in Future B1, thus representing a more grassroots-driven future.
Nilsen et al.’s (2022) Type I regions (Resilient Regional Service Centers) are characterized by differentiated actor composition, balanced power relations, medium levels of specialization and diversity, and a relatively broad opportunity space. This corresponds to Future A1: Faroese AI Excellence, which aligns well with Type I due to its diversification of the economy by fostering digital services, tourism, and public-sector digital platforms, all foregrounding the Faroese language. AI technologies become enablers of broader innovation across sectors, and a strong emphasis is placed on workforce training and attracting international talent to the Faroe Islands.
Nilsen et al.’s Type II regions (Locked-in Specialized Regions) are characterized by differentiated actor composition, skewed power relations, high specialization, and a narrower opportunity space. This corresponds to Future A3: Technological Elite with Support due to its emphasis on strong support for specific industries driven by a few dominant players. This future builds on the Faroe Islands’ existing strengths in fisheries and aquaculture. AI technologies are used for optimizing sustainability, logistics, and the value chain. Innovations focus on deepening specialization in maritime industries.
Future B1, Grassroots Leadership, might be seen to present a challenge to Nilsen et al.'s typology. While exhibiting differentiated actor composition due to active community engagement, the scenario’s power relations are balanced rather than skewed. This grassroots model, driven predominantly by community leaders rather than government intervention, suggests a variant of Type II, with a specialization not in traditional industry but in grassroots-driven innovation. Consequently, this might indicate a limitation of Nilsen et al.’s framework, which underemphasizes the transformative potential of community-driven innovation pathways emerging independently from strong governmental leadership.
Trajectories to desirable regional paths
Understanding a region’s current situation is important, but it need not limit reaching a desirable future, particularly when basic conditions, such as ample natural and financial resources, are in place. As outlined in Table 2, by amplifying political commitment, local knowledge capital, societal acceptance, and language resources, the Faroe Islands can evolve into a more desirable future through path transformation in which AI technologies are harnessed for the common good. The trajectories should leverage existing strengths, such as the Faroese commitment to language preservation and the budding coworking culture, while addressing key challenges, including the need for local knowledge and expertise, as well as public engagement.
In addition to futures A1 and B1, which were flagged as desirable above, future A3: Technological Elite with Support might also be viewed as desirable. Admittedly, while A3 delivers some benefits, it risks excluding wider community involvement, which is inconsistent with inclusive place-based innovation. However, although not as desirable as A1 and B1, A3 does encompass some positive aspects and could potentially serve as a stepping-stone to A1.
Proposed plausible regional paths.
Discussion and conclusion
This study provides empirical insights into how peripheral regions can leverage AI to initiate and sustain resilient, future-proof regional pathways. By identifying and analyzing multiple scenarios, we contribute to understanding how regions typically seen as disadvantaged can actively shape their futures through the strategic deployment of AI technologies. This extends research beyond urban-centric narratives to illuminate the role that digital technologies can play in transforming regions into more equitable and sustainable outcomes.
It is important to recognize that AI simultaneously functions as a scalable general-purpose technology and a context-dependent tool whose effectiveness relies on local adaptation. While AI can lower technical barriers and offers widespread accessibility, its transformative potential in peripheral regions hinges on the availability of culturally and linguistically relevant resources, such as native language datasets and localized user interfaces, which shape how inclusive and impactful its adoption can be.
As Bråten et al. (2025) demonstrate in the Norwegian context, transitions in peripheral regions are mediated by political orientations and institutional configurations. We similarly demonstrate that the case of the Faroe Islands exemplifies how peripheral regions can harness AI to overcome structural constraints and achieve sustainable development. Transitioning from a Type IV to a Type II or Type I archetype (Nilsen et al., 2022) requires strategic investments in technology, collaborative innovation networks, and policy frameworks that support economic diversification and regional agency.
This study contributes to the literature on AI’s impact on peripheral regions and offers a blueprint for similar territories.
Contribution to theory
We integrate insights from technology diffusion and technology ecosystems literature (Beltagui et al., 2020; Bickley et al., 2022; Guarascio et al., 2025) with theories of economic geography to address how AI technologies can reshape peripheral regions. Whereas much of the previous research on the influence of AI technologies has focused on urban centers, where dense networks, advanced infrastructures, and concentrated expertise drive innovation, our work extends these frameworks by demonstrating that peripheral regions can also serve as fertile grounds for technological advancement. In doing so, we refine the existing typologies of peripheral regions (Calignano et al., 2024; Nilsen et al., 2022) by showing that even areas with limited structural preconditions can generate new regional path trajectories when aided by AI.
We explore how narratives specifically centered on AI technologies can transform perceptions of innovation opportunities in peripheral regions. This contribution enriches the literature by highlighting AI technologies as a potent thematic focus for narrative-driven regional transformation, thereby expanding existing understandings of how peripheral regions might proactively leverage technology-centered narratives to overcome structural disadvantages. This perspective underscores that regional path creation is not just a function of exogenous resources but is also co-constructed through the perceptions and actions of local actors.
By foregrounding AI in place narratives, peripheral regions can enlarge their opportunity space and mobilize the mutually reinforcing roles of Schumpeterian innovative entrepreneurship, institutional entrepreneurship, and place-based leadership (Grillitsch and Sotarauta, 2020). Arguably, when this triad is activated, AI ceases to be seen as an external trend and becomes a locally steered resource, enabling actors to break free of inherited path dependencies, recalibrate institutions, and orchestrate collective experimentation that propels the region onto a new, higher-value regional path, where AI may warrant recognition as an agent.
Our findings underline the critical role of framing and reinforcing positive narratives about regional innovation opportunities in relation to emerging technologies. Effective local narratives not only shape perceptions but also actively mobilize resources, attract external investments, and galvanize community support.
Finally, we bridge the literature on digitalization with the specificity of regional path development by showing that the diffusion of AI must be understood in relation to local socio-cultural and institutional contexts. For example, the need for robust Faroese language resources was prominent in our conversations with stakeholders. AI’s transformative impact depends on culturally and linguistically grounded implementation, particularly in regions with distinct language identities such as Faroese.
Contribution to methodology, policy/practice
This study presents a novel methodological approach of foresight methods grounded in narrative place-specific data.
For policymakers and practitioners, key takeaways from this research include the necessity of proactive narrative construction around AI, the critical role of building local competencies, the strategic value of community involvement, and the importance of maintaining stable, long-term policy environments to support regional path development. The proposed trajectories, premised on local knowledge capital (trajectories 1 and 3), explicitly require knowledge retention strategies, whereas trajectory 2 risks a technological elite enclave wherein only a subset of the population benefits. Our findings underscore the World Economic Forum’s (2025) warning: without deliberate local capability building, even well-connected small economies risk slipping into the passive-user quadrant of the global AI hierarchy.
We highlight the importance of locally crafted narratives in reshaping perceptions of possibility and mobilizing support for innovation (Kitagawa and Candi, 2025; Roessler et al., 2025). It is also important to recognize that narratives alone are insufficient drivers of regional transformation. In peripheral contexts, where institutional capacity and technological readiness may be uneven (Eder and Trippl, 2019), discursive shifts must be accompanied by material investments in infrastructure, skill development, and governance. The activation of “opportunity spaces” (Grillitsch and Sotarauta, 2020) requires not only persuasive storytelling but also credible implementation pathways anchored in local language resources, technical expertise, and long-term political commitment, by aligning “strategic conversation” with future visions, intelligence and decision-making (Robinson et al., 2021). In this sense, narrative agency can catalyze alignment and attract resources, but its transformative potential depends on integration with structural capabilities and institutional reinforcement.
Our findings also contribute to ongoing debates on the emergence of technology ecosystems (Beltagui et al., 2020). We extend insights from studies on AI ecosystem development in urban contexts into a peripheral regional setting, demonstrating that similar dynamics can emerge even in contexts where institutional density, market scale, and absorptive capacity are limited. However, in contrast to large urban ecosystems, where innovation commons formation is enabled by existing organizational concentrations, in peripheral regions these dynamics depend more heavily on strategically brokered extra-regional linkages and selective participation in transnational knowledge networks. This suggests that innovation commons in peripheral regions are not simply smaller versions of their urban counterparts, but are instead relationally constituted across distance, combining localized cultural and linguistic assets with externally sourced capabilities.
Peripheral regions seeking transformations driven by AI technologies should actively leverage their existing strengths, such as unique cultural or natural resources, while addressing fundamental challenges of political commitment, societal acceptance, and local language integration. Policymakers and regional leaders must recognize the strategic importance of proactively constructing and communicating compelling narratives around AI and digital innovation, especially in peripheral contexts where skepticism or resistance might otherwise dominate.
Limitations and directions for future research
This study’s limitations include its single region focus, which may limit the generalizability of the findings. Future research should employ comparative methodologies across multiple peripheral contexts to investigate how variations in local narratives, institutional support, community agency, and local power relations impact specific regional innovation outcomes. Furthermore, while high-quality broadband connectivity characterizes the Faroe Islands, in many peripheral regions, digital infrastructure remains uneven, and a persistent digital divide may condition who can access, develop, and benefit from AI capabilities. Future comparative work should therefore contrast the Faroe Islands with peripheral regions facing weaker digital access to examine how connectivity constraints shape the feasibility and speed of AI-driven path development.
Additionally, longitudinal studies could reveal how narratives evolve over time and their long-term impacts on innovation trajectories. Such studies could provide deeper insights into the causal mechanisms and contextual factors influencing the success or failure of technology-driven innovation initiatives.
Footnotes
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.