{"id":5783,"date":"2026-10-29T07:02:00","date_gmt":"2026-10-29T05:02:00","guid":{"rendered":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/?p=5783"},"modified":"2026-10-28T09:09:24","modified_gmt":"2026-10-28T07:09:24","slug":"artificial-intelligence-for-sustainability-transition-opportunities-barriers-and-managerial-implications","status":"publish","type":"post","link":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/2026\/10\/29\/artificial-intelligence-for-sustainability-transition-opportunities-barriers-and-managerial-implications\/","title":{"rendered":"Artificial Intelligence for Sustainability Transition: Opportunities, Barriers, and Managerial Implications"},"content":{"rendered":"\n

Download PDF<\/a><\/p>\n\n\n\n

Sanna-Mari Renfors, PhD, Adjunct Professor, Principal Lecturer, Responsibility in Business and Services, Lapland University of Applied Sciences <\/em><\/p>\n\n\n\n

Anzelika Krastina, M.Ed., MBA, Senior Lecturer, Responsibility in Business and Services, Lapland University of Applied Sciences<\/em><\/p>\n\n\n\n

\"Erasmus+<\/figure>\n\n\n\n

Keywords: sustainable business, business transformation, AI integration<\/p>\n\n\n\n

Sustainability is no longer just a compliance issue or a corporate responsibility initiative. Sustainability is increasingly recognised as a strategic organisational capability that strengthens resilience, enhances adaptive capacity under conditions of uncertainty, and supports the creation of long-term ecological, social, and economic value (Florez-Jimenez et al., 2025). The reason behind this is that the contemporary world is characterised by the simultaneous occurrence and interconnection of multiple global crises, including climate change, geopolitical tensions, economic disruptions, societal polarisation, and rising inequality.<\/p>\n\n\n\n

New approaches are therefore essential, not only for addressing global sustainability challenges, but also for strengthening long-term resilience at the local level as well as in companies. Recently, artificial intelligence (AI) has emerged as an increasingly crucial tool to enable sustainable development. AI includes an array of computational technologies that enable machines to acquire knowledge, process information, and solve problems in ways that resemble human cognitive processes (Tripathi et al., 2024). It is viewed as a transformative force that supports the shift towards sustainable business (Gand\u00eda et al., 2025).  As sustainability challenges become more complex, it is even seen that without AI, companies may struggle to achieve their sustainability goals (Shahidi Hademani et al., 2025).  Indeed, when AI is embedded within sustainability efforts, companies can create value while contributing to solutions for pressing environmental and societal issues.<\/p>\n\n\n\n

For these reasons, Lapland University of Applied Sciences in collaboration with international partners launched the project titled \u201cBusiness Model Innovation with Artificial Intelligence for Urgent Green Agenda\u201d or in short \u201cAI4GreenBus\u201d, funded by Erasmus+ programme. The project aims to strengthen competencies for developing AI-enabled sustainable and circular business models across industries within the European Union, leveraging digitalisation as a catalyst of sustainable business transformation. The project’s primary objective is to design an innovative methodology and a micro-credential programme centred on AI-driven Sustainable Business Model Innovation.<\/p>\n\n\n\n

This article examines how AI can help companies create sustainability value and address environmental, social, and economic challenges. It also explores the key barriers hindering the adoption of AI for sustainability purposes and discusses managerial implications for companies. The article draws on insights generated during a project workshop titled \u201cWhat Businesses Really Need from Future AI Skills\u201c held at the AI4GreenBus consortium meeting in Riga, Latvia, in August 2026, and complements these insights with findings from the relevant academic literature.  The workshop organised by the project partner Latvian Chamber of Commerce and Industry (LCCI) gathered more than 50 participants representing all relevant project stakeholders. In the workshop representatives from academia, vocational education and training, businesses and public organisations participated in an open discussion on how organisations are preparing their workforce for AI, current business needs and cooperation opportunities, as well as practical AI solutions and lessons learned. Interactive roundtable discussions (Photo 1) explored the use of AI in everyday business operations, AI for sustainability and the green transition, AI-based products and solutions, emerging AI skills, and education needs.<\/p>\n\n\n\n

\"Results<\/figure>\n\n\n\n

Photo 1<\/strong>. Workshop discussion canvas (Anzelika Krastina)<\/p>\n\n\n\n

AI Addressing Sustainability Challenges<\/h2>\n\n\n\n

It is imperative to acknowledge AI has both the direct and indirect impacts on sustainability (Schwaeke et al., 2025). Direct impacts primarily relate to improving resource efficiency and reducing environmental burdens while generating cost savings and improved operational performance. In particular, the strong connection between AI and the circular economy is highlighted, emphasising AI\u2019s ability to support more efficient management of resources, materials, and waste streams. As highlighted also by the workshop participants, real-time monitoring and predictive analytics enable organisations to optimise resource utilisation. They help minimise waste generation and reduce environmental footprints while advancing sustainability objectives. For example, AI can help identify opportunities for material reuse and recycling across supply chains, thereby supporting more sustainable business practices. AI has a significant role in advancing the transition to a circular economy as it enables more effective approaches to extending material use reducing resource consumption, and improving recycling processes (Sj\u00f6din et al., 2023).<\/p>\n\n\n\n

Beyond its direct impacts, AI can contribute to sustainability indirectly by supporting informed and strategic decision-making, stimulating innovation, and fostering behavioural change (Bahaw et al., 2025; Gand\u00eda et al., 2025; Schwaeke et al., 2025). As was pointed out in the workshop, automated ESG reporting and sustainability measurement improves collection, accuracy, consistency, and reliability of sustainability data. AI-enabled innovation allows companies to generate new ideas and develop products that align with consumer expectations while minimising environmental impacts. This is possible as AI can process and analyse large volumes of market and consumer data to uncover patterns, trends, and preferences related to sustainability. Such insights allow to design more sustainable products and services, while enhancing a company’s market position, competitiveness, and customer satisfaction. Furthermore, AI can play an important role in building a sustainable brand identity by enabling the creation and the dissemination of compelling sustainability-related information that increases awareness and promotes more sustainable behaviours.<\/p>\n\n\n\n

In addition, AI can support social sustainability (Toniolo et al., 2020). However, the workshop participants raised concerns that the social aspect is too often forgotten. AI has the potential to enhance employee well-being by automating repetitive and time-consuming tasks such as scheduling, reporting, and document processing. This enables employees to focus their skills and expertise on more meaningful, creative, and value-adding activities, thereby reducing workload, alleviating work-related stress, and increasing job satisfaction. In some industries, AI can also improve workplace safety by monitoring risks, predicting potential hazards, and supporting preventive actions that enhance working conditions. Furthermore, AI tools can support a range of human resource management (HRM) practices, including recruitment, employee development, and training.<\/p>\n\n\n\n

Barriers Hindering the Adoption of AI<\/h2>\n\n\n\n

As the adoption of AI in companies is still at a relatively early stage, its implementation continues to be hindered by several barriers. Many companies remain reluctant to adopt AI-enabled solutions due to a limited understanding of their potential benefits and implications (Shahidi Hademani et al., 2025). Lack of knowledge and understanding about the use of AI was also considered as a biggest barrier hindering its adoption by the project workshop participants. In addition, companies face challenges related to ethical, privacy, and security concerns, as AI applications often rely on the processing of sensitive data and may unintentionally perpetuate existing biases embedded in datasets. Issues related to data availability, quality, and accessibility can constrain the effective use of AI. Therefore, fostering AI literacy and addressing the ethical, organisational, and technical challenges related to AI are critical prerequisites for its successful adoption and integration within companies.<\/p>\n\n\n\n

Managerial Implications for Companies<\/h2>\n\n\n\n

A key conclusion drawn from the project workshop was that AI and sustainability should be integrated more closely. As also highlighted in recent research (Kar et al., 2022; Schwaeke et al., 2025), companies should take a strategic approach to AI and integrate it into sustainability strategies rather than treating it merely as a technological tool. This requires establishing clear sustainability objectives and aligning AI initiatives with these goals. In this sense, AI should be viewed as a strategic asset and enabler that supports long-term sustainability efforts. Moreover, companies should recognise that AI does not operate as a standalone technology but forms part of a broader digital ecosystem and infrastructure that supports business transformation and sustainability outcomes (Gand\u00eda et al., 2025; Tripathi et al., 2024).<\/p>\n\n\n\n

It is also necessary for managers to acknowledge the ongoing paradigm shift from sustainability towards circular and regenerative economies (cf. Gibbons, 2020; Hellstr\u00f6m, 2023). This shift is driven by the growing recognition that conventional sustainability approaches alone are insufficient to address today’s interconnected environmental, social, and economic challenges. Merely reducing negative impacts is no longer considered adequate. Instead, companies are increasingly seeking to generate net-positive value for both society and the natural environment. In this context, AI supports the transition towards circular and regenerative business models by helping companies address the systemic sustainability challenges (Tripathi et al., 2024). Through its potential to analyse large and complex datasets, identify patterns, and model relationships across systems, AI can provide insights into leverage points for change and enable more informed decision-making. As a result, companies can identify opportunities for systemic interventions that generate broader environmental, social, and economic benefits.<\/p>\n\n\n\n

Moreover, several leadership implications emerge. First, leadership commitment is a key determinant of AI adoption, as it enables companies to systematically integrate AI into sustainability efforts, operational processes, and strategic decision-making. Second, an organisational culture that supports innovation, continuous learning, and technological progress is required to employ AI as a tool for sustainability transformation. Leaders should also foster a culture of continuous learning and adaptability to equip employees with the capabilities needed to leverage AI for sustainable value creation. Third, investment in digital infrastructure and employee upskilling is a critical prerequisite for effectively utilising AI to accelerate progress towards sustainability objectives (Schwaeke et al., 2025; Tripathi et al., 2024). Finally, leaders should ensure that AI adoption is in line with ethical principles and company\u2019s long-term sustainability goals to maximise its positive impact on sustainable development.<\/p>\n\n\n\n

Final Remarks<\/h2>\n\n\n\n

In summary, the most critical factor in leveraging AI as a driver of sustainable business transformation is the integration of AI into the company’s sustainability strategy and objectives (Figure 1). Achieving this transformation requires careful consideration of both the prerequisites that support implementation and the barriers that may hinder progress. Given the wide range of direct and indirect impacts associated with AI, companies should prioritise those that are most closely aligned with their strategic sustainability objectives. By doing so, AI can advance sustainability performance, increase resilience, and create long-term value for business, society, and the planet.<\/p>\n\n\n\n

\"Figure<\/figure>\n\n\n\n

Figure 1<\/strong>. AI for sustainability transition.<\/p>\n\n\n\n

Learn more about the AIGreenBus project by visiting its website https:\/\/ai4greenbus.eu\/. <\/p>\n\n\n\n

References<\/h2>\n\n\n\n

Bahaw, P., Forgenie, D., Sadiq, G., & Sookhai, S. (2025). Generative AI for business sustainability: Examining usability, usefulness, and triple bottom line impacts in small and medium enterprises. Sustainable Futures, 10, 100815. https:\/\/doi.org\/10.1016\/j.sftr.2025.100815<\/p>\n\n\n\n

Florez-Jimenez, M. P., Lleo, A., Ruiz-Palomino, P., & Mu\u00f1oz-Villamizar, A. F. (2025). Corporate sustainability, organizational resilience, and corporate purpose: a review of the academic traditions connecting them. Review of Managerial Science, 19(1), 67\u2013104. https:\/\/doi.org\/10.1007\/s11846-024-00735-3<\/p>\n\n\n\n

Gandia, J. A. G., de Lucas Ancillo, A., & del Val N\u00fa\u00f1ez, M. T. (2025). The role of artificial intelligence and knowledge in enhancing corporate sustainability. Journal of Innovation & Knowledge, 10(5), 100792. https:\/\/doi.org\/10.1016\/j.jik.2025.100792<\/p>\n\n\n\n

Gibbons, L. V. (2020). Regenerative\u2014The new sustainable. Sustainability, 12(13), 5483. https:\/\/doi.org\/10.3390\/su12135483<\/p>\n\n\n\n

Hellstr\u00f6m, E. (2023). Kohti uusintavaa taloutta. Tavoitteena luonnon ja ihmisten elinvoima. SITRA<\/p>\n\n\n\n

Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 376, 134120. https:\/\/doi.org\/10.1016\/j.jclepro.2022.134120<\/p>\n\n\n\n

Schwaeke, J., Gerlich, C., Nguyen, H. L., Kanbach, D. K., & Gast, J. (2025). Artificial intelligence (AI) for good? Enabling organizational change towards sustainability. Review of Managerial Science, 19(10), 3013\u20133038.  https:\/\/doi.org\/10.1007\/s11846-025-00840-x<\/p>\n\n\n\n

Shahidi Hamedani, S., Aslam, S., & Shahidi Hamedani, S. (2025). AI in business operations: driving urban growth and societal sustainability. Frontiers in Artificial Intelligence, 8, 1568210. https:\/\/doi.org\/10.3389\/frai.2025.1568210<\/p>\n\n\n\n

Sj\u00f6din, D., Parida, V., & Kohtam\u00e4ki, M. (2023). Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects. Technological Forecasting and Social Change, 197, 122903. https:\/\/doi.org\/10.1016\/j.techfore.2023.122903<\/p>\n\n\n\n

Toniolo, K., Masiero, E., Massaro, M., & Bagnoli, C. (2020). Sustainable business models and artificial intelligence: Opportunities and challenges. In Matos, F., Vairinhos, V., Salavisa, I., Edvinsson, L., & Massaro, M. (Eds.). Knowledge, people, and digital transformation: Approaches for a sustainable future, 103\u2013117. Springer.<\/p>\n\n\n\n

Tripathi, S., Bachmann, N., Brunner, M., Rizk, Z., & Jodlbauer, H. (2024). Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges. Journal of Big Data, 11(1), 65. https:\/\/doi.org\/10.1186\/s40537-024-00912-x<\/p>\n","protected":false},"excerpt":{"rendered":"

Sustainability is no longer just a compliance issue or a corporate responsibility initiative. Recently, artificial intelligence (AI) has emerged as an increasingly crucial tool to enable sustainable development. <\/p>\n

This article examines how AI can help companies create sustainability value and address environmental, social, and economic challenges. <\/p>\n","protected":false},"author":1658,"featured_media":5763,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[798270,798245,110187],"tags":[7402,43350,798242,17802],"class_list":["post-5783","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-3-2026","category-inenglish","category-teema-artikkeli","tag-johtaminen","tag-kestava-kehitys","tag-tekoaly","tag-yrittajyys"],"jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p7X4tP-1vh","jetpack_featured_media_url":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/files\/2026\/10\/Renfors_Krastina_theme-article_Lumen_3_2026.jpg","_links":{"self":[{"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/posts\/5783","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/users\/1658"}],"replies":[{"embeddable":true,"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/comments?post=5783"}],"version-history":[{"count":2,"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/posts\/5783\/revisions"}],"predecessor-version":[{"id":5904,"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/posts\/5783\/revisions\/5904"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/media\/5763"}],"wp:attachment":[{"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/media?parent=5783"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/categories?post=5783"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogi.eoppimispalvelut.fi\/lumenlehti\/wp-json\/wp\/v2\/tags?post=5783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}