AI Won’t Save the SDGs Alone, But It Could Build the Workforce That Can

The global sustainable-development agenda is running short of time, but the key problem may be deeper than inadequate investment or slow technology adoption. Progress across the Sustainable Development Goals (SDGs) remains uneven because many of the systems designed to advance them, industrial modernization, digital transformation, green growth, innovation and social development, still operate largely in parallel rather than as a coordinated whole.

A new study, "Artificial Intelligence as a Synergistic Driver of Contemporary Strategies for Accelerating Sustainable Development Processes: A Human Resource Perspective," published in Sustainability by Oksana Kiseleva, Anna Firsova and Alla Vavilina, proposes a different route. Instead of presenting artificial intelligence as a direct solution to the SDGs, the authors argue that AI could strengthen the human capabilities needed to connect several major development paradigms and make them work more effectively together.

The study is based on a PRISMA-based review of 89 studies selected from an initial pool of 998 publications identified through Google Scholar.

The SDG problem is increasingly one of fragmentation, not simply insufficient effort

Based on the 2025 Sustainable Development Goals Report, the authors note that only 17% of targets are on track, while 18% have regressed relative to their 2015 baseline. Progress is concentrated in a limited number of areas, while many social, institutional, environmental and infrastructure-related targets continue to advance too slowly.

The paper traces this uneven performance to overlapping constraints rather than a single policy failure. Financial shortages, climate disruption, insufficient coordination, gaps in education, weak research capacity and limited data availability all slow implementation. Interdependence among the goals compounds the problem because failure in one area can obstruct progress elsewhere.

Against this backdrop, the study examines several influential development frameworks that already shape national and corporate policy. Industry 4.0 emphasizes digitalization, automation and production efficiency. Industry 5.0 moves toward human-centred industrial systems. Society 5.0 extends digital transformation across society, while green and circular economy strategies focus heavily on environmental efficiency and resource use. Open innovation accelerates the movement of ideas, knowledge and technologies across institutions.

Each framework contributes to sustainable development, but the authors argue that each also tends to privilege particular dimensions. Digital industrialization may improve productivity and resource efficiency without fully resolving social concerns. Green and circular strategies address environmental pressures but do not automatically deliver broader economic or social inclusion. Society 5.0 strengthens the human-centred dimension but depends heavily on technological capability.

The resulting policy challenge is not a lack of development models; it's the absence of a sufficiently integrated mechanism capable of linking them.

Human capital becomes the bridge between green, digital and innovation agendas

Innovation may generate new solutions and digital technologies may provide the tools to deploy them, but people ultimately determine how effectively technologies are designed, adopted, governed and adapted to economic, environmental and social objectives.

Current training systems often reproduce the same fragmentation found in development policy. Industrial strategies prioritize digital and technical skills. Green transitions emphasize environmental competencies. Innovation systems focus on creativity, research and collaboration. Sustainable-development education may promote social and ecological awareness without being deeply integrated into technological or industrial training.

The authors propose a workforce model in which these competencies become complementary rather than separate. Professionals would combine digital literacy with environmental knowledge, critical thinking, innovation capacity, interdisciplinary reasoning, adaptability and an understanding of social consequences. Such capabilities, they argue, could make it easier to design projects that pursue technological modernization and sustainability simultaneously.

Education, hence, becomes a critical implementation arena. Universities, vocational institutions and workplace training systems would need to move beyond narrow digital upskilling and incorporate environmental and social dimensions into programmes aligned with Industry 4.0 and Industry 5.0. Sustainability training, meanwhile, would need stronger links to technology, innovation and changing labour-market requirements.

For developing economies, this approach carries particular significance. Countries often face simultaneous pressure to industrialize, digitalize, create employment, improve productivity and reduce environmental damage. Treating each objective as a separate policy portfolio can stretch already limited institutional and financial capacity. Building workers capable of navigating several agendas at once could offer a more integrated development pathway.

AI's strongest contribution may be developing people rather than replacing them

Artificial intelligence enters the study as an enabling mechanism for human-resource development. The authors identify applications ranging from personalized training and competency-gap analysis to recruitment, talent management, predictive analytics, employee engagement, performance assessment and decision support.

Education is especially important in the proposed model. AI systems can generate individualized learning pathways, adjust materials to a learner's progress, identify missing competencies and combine content from different fields. The paper envisages educational platforms capable of bringing together digitalization, sustainability, open innovation and green-economy knowledge instead of teaching them as disconnected subjects.

Organizations could use similar tools to identify candidates with combinations of digital and environmental skills, forecast future competency needs and design development pathways for existing employees. AI-supported systems could also help create cross-functional teams and accelerate collaboration between technical specialists, sustainability professionals and innovation managers.

Such applications reposition AI within the sustainability debate. The technology is not portrayed primarily as an autonomous engine capable of solving poverty, climate, infrastructure or governance problems. Its strategic value lies in enhancing the capabilities of people who must manage those problems across increasingly complex systems.

The study also proposes that stronger human capability could generate synergies between development paradigms. Industry 4.0 combined with Industry 5.0 could align efficiency with worker well-being. Society 5.0 combined with green and circular models could connect digitalization with environmental awareness and resource efficiency. Open innovation could accelerate the diffusion of technologies and practices generated by those systems.

These interactions remain theoretical. The paper does not measure whether combining multiple paradigms actually produces faster SDG progress, nor does it quantify the additional benefits of AI-enabled workforce development.

A sustainability strategy built around AI must confront AI's own costs

The study does not treat artificial intelligence as an uncomplicated sustainability asset. Its expansion carries significant environmental, social and governance risks that could undermine the very objectives it is expected to support.

AI infrastructure consumes large amounts of energy, water and materials. The paper highlights the carbon and water footprints associated with data centres, demand for non-renewable resources used in hardware production and growing volumes of electronic waste. It also notes that generative AI could contribute substantially to future e-waste if equipment cycles continue to shorten.

Human-resource risks are equally significant. Automation can displace workers and create mismatches between existing skills and new technological requirements. Unequal access to AI capabilities may widen income and productivity gaps between workers, firms and regions. Heavy dependence on automated systems may also weaken critical thinking, individual agency and independent decision-making.

Algorithmic bias, cybersecurity threats, privacy concerns and excessive profiling create additional governance challenges. Developing countries could face an especially difficult trade-off if the benefits of AI concentrate in economies with stronger infrastructure, better data systems and deeper pools of technical talent, leaving weaker economies further behind.

The authors argue for human-centred deployment. AI should assist, integrate and support decision-making rather than replace human judgement. Upskilling, reskilling, ethical standards, more transparent models, diverse datasets, cybersecurity safeguards and more efficient computing infrastructure become necessary conditions for responsible adoption.

The study's own limitations reinforce the need for caution. Its evidence base is restricted to English-language publications indexed through Google Scholar, while much of the literature reviewed is conceptual rather than empirical. The authors acknowledge that broader database coverage and future work involving more concrete solutions, including educational platforms, would strengthen the proposed framework.

The larger policy proposition remains compelling even without empirical confirmation. Sustainable development is increasingly shaped by the interaction of technologies, institutions, labour markets, environmental pressures and innovation systems. Managing those interactions requires people who can operate across traditional disciplinary and policy boundaries.