The study was carried out by Tomáš Mandičák, Katarína Krajníková and Peter Mésároš of the Faculty of Civil Engineering at the Technical University of Košice in Slovakia, together with Ivan Marović of the Faculty of Civil Engineering at the University of Rijeka in Croatia. It addresses a long-standing blind spot in the literature on construction digitization. While wealthy markets in Western Europe and North America have been studied extensively, comparatively little is known about how firms in smaller Central European economies—and especially small and medium-sized enterprises, which dominate the regional construction sector—are progressing along the digital pipeline from computer-aided drafting, through BIM, toward AI.

The researchers posed three questions. First, how widely is BIM used across the different stages of construction projects? Second, does that use actually show up in measurable performance outcomes such as cost, waste and sustainability? And third, what stands in the way of firms moving from BIM to artificial intelligence? To answer them, the team ran two surveys grounded in official statistical records. One covered construction firms in Slovakia, Croatia and Slovenia, three neighboring markets at visibly different stages of digital development. The other focused specifically on Slovak firms’ experience with AI and the barriers they encounter.

The picture that emerges on BIM maturity is strikingly uneven. On a five-point scale of BIM maturity, the mean score was 1.33 in Slovakia, 2.49 in Croatia and 4.33 in Slovenia—a spread so large that the differences are statistically unambiguous. The researchers tested the country differences with the Kruskal-Wallis test, a non-parametric method suited to comparing more than two groups when the underlying distributions cannot be assumed to be normal. The result, a test statistic of H = 86.77 with a p-value below 0.001, confirms that the gaps between the three national samples are not a product of sampling noise. Slovenia’s firms, in other words, operate at a level of BIM integration that their Slovak counterparts have yet to approach, with Croatia occupying a middle ground.

That unevenness matters because BIM maturity turned out to be strongly associated with real-world performance. Using Spearman rank correlation analysis, the team found that firms reporting heavier and more sophisticated BIM use also reported better outcomes across cost, material waste and sustainability indicators. The correlations are remarkably strong by the standards of survey research: up to 0.93 for material cost performance and 0.92 for emission reduction. A Spearman coefficient close to 1 would indicate a nearly perfect monotonic relationship, so values in the low 0.9s suggest that, within this sample, the link between BIM depth and reported performance is about as tight as such links ever get. Firms that model buildings as coordinated, data-rich digital objects appear to waste less material, spend less and cut emissions more effectively than firms that do not.

The sustainability finding was subjected to an additional check. The researchers cross-validated the BIM–sustainability relationship against a convergent model, a technique used to confirm that the correlation observed in the survey data is not an artifact of a single analytical specification. That the relationship survived this test strengthens the case that BIM is not merely correlated with green performance by coincidence—that the structured, information-rich workflows that BIM enforces genuinely feed into how efficiently materials are used and how much carbon a project embodies. This matters because construction remains one of the world’s largest sources of waste and emissions, and tools that measurably shrink a project’s material and carbon footprint carry enormous aggregate value.

Against this backdrop, the state of AI adoption is sobering. Genuine, operational use of artificial intelligence among the surveyed firms remains rare. This is not for lack of conceivable applications; the research literature describes machine-learning systems that generate construction schedules from BIM models, computer-vision platforms that monitor site safety in real time, digital twins that fuse sensor data with building models to optimize facility operations, and explainable AI methods designed to make algorithmic decisions legible to engineers and clients. The gap, it appears, lies between what the technology can do and what the typical Central European firm is equipped to absorb.

The most consequential finding of the study concerns what separates AI-ready firms from the rest. The researchers examined which firm characteristics correlated with AI readiness and found that digital maturity—not company size—was the decisive factor. The correlation between AI readiness and the use of digital tools was 0.49, and between AI readiness and broader digitalization 0.37, both meaningful associations. Company size, by contrast, showed a correlation of just 0.01, which is statistically indistinguishable from zero. In plain terms, a small firm with a deep digital foundation is a far more plausible AI adopter than a large firm still working with disconnected documents and 2D drawings. The implication for policy and industry strategy is that simply scaling up or subsidizing large players will not accelerate AI diffusion; building the digital base across the many small and medium-sized enterprises will.

When firms were asked what actually blocks AI adoption, the answers clustered overwhelmingly around human and organizational factors rather than technical ones. Shortages of qualified staff, the cost and difficulty of training an existing workforce, uncertainty about the return on investment, and managerial unfamiliarity with what AI can realistically deliver were cited far more often than problems with the technology itself. This pattern echoes a long line of technology-adoption research, from the classic Technology Acceptance Model onward, which has repeatedly shown that perceived usefulness and perceived ease of use—both fundamentally human judgments—govern whether organizations embrace new tools. It also aligns with the Technology–Organization–Environment framework, in which the technological context is only one of three forces shaping adoption, alongside organizational readiness and the external environment.

The authors frame their results as a dose of realism for an industry often swept up in AI hype. The findings, they write, point to a solid digital and BIM base as AI’s realistic starting point. Firms hoping to deploy machine learning for schedule optimization or cost prediction first need the structured digital data that BIM produces; without it, there is nothing for the algorithms to learn from. The study’s regional focus also serves as a reminder that digital transformation is not uniform even within a single corner of Europe. A construction firm in Ljubljana and one in Bratislava may face the same global technology frontier, but they start from very different places on the maturity curve.

For Central European policymakers, the study arrives with concrete signals. The strong BIM–performance link suggests that continuing investment in BIM adoption pays off not only in productivity but in the sustainability outcomes that EU decarbonization policy increasingly demands. And the finding that barriers are concentrated in people and skills implies that the highest-leverage interventions may be education and workforce development—curricula that combine civil engineering with data science, targeted training programs for SMEs, and support for managers to understand what AI can and cannot do—rather than hardware subsidies or software licenses. The project was supported by the Slovak Research and Development Agency under contracts APVV-22-0576 and APVV-17-0549, both aimed at researching digital technologies for sustainable construction.

The wider lesson extends beyond the three countries studied. As the construction industry worldwide grapples with stagnant productivity, chronic waste and mounting pressure to decarbonize, the temptation to leapfrog straight to AI is understandable. But this study suggests that there are no shortcuts: the firms that will benefit from artificial intelligence are the ones that have already done the slower, less glamorous work of digitizing their models, their data and their workflows. In construction, it seems, the future arrives one maturity level at a time.

In the last decade, Building Information Modelling has emerged as a pivotal digital innovation transforming the architecture, engineering, and construction sectors. By enabling detailed 3D modeling, data integration, and real-time collaboration, BIM has the potential to reduce cost overruns, minimize errors, and enhance project transparency. Yet, experts acknowledge a paradox: while BIM’s technological capabilities are mature and continuously evolving, its practical deployment often falters. According to the new study, this dissonance results from a lack of adaptive leadership capable of guiding organizations through the transformational complexities inherent in BIM adoption.

The study places particular emphasis on Latin America, a region where the BIM adoption landscape presents unique challenges compared to North America and Europe. Cultural nuances, distinct institutional frameworks, and significant resource limitations converge to create a demanding environment for BIM implementation. Unlike in regions with more robust governmental mandates and industry-wide standards, Latin American organizations frequently confront fragmented support systems and inconsistent stakeholder engagement, complicating leadership’s role in fostering BIM utilization.

Dr. Schery and his colleagues identify a critical gap in understanding which specific leadership behaviors are necessary at each stage of a BIM implementation journey. They argue that leadership during BIM adoption is not a monolithic process but rather an evolving paradigm that must adjust to changing organizational needs across preparatory, implementation, and operational phases. Thus, effective BIM leadership cannot be reduced to promoting technological uptake alone; it requires nuanced behavioral strategies that consider organizational culture, workforce capabilities, and stakeholder dynamics.

One key insight from the research is the recognition that leadership in BIM initiatives must transcend conventional project management roles. Leaders must act as change agents, cultivating an environment that embraces learning, collaboration, and resilience against setbacks. This challenging mandate demands skills in communication, conflict resolution, and cultural sensitivity – particularly in Latin American contexts, where hierarchical organizational norms and power distances can inhibit transparent dialogue and adaptive change.

Moreover, Schery et al. highlight that BIM implementation is not purely a technical endeavor but a socio-technical transformation. Leadership styles that emphasize participative decision-making and knowledge sharing tend to outperform more authoritarian approaches in facilitating adoption. These findings resonate with broader organizational change theories but are tailored to meet BIM’s specific intricacies, which include integrating multidisciplinary teams, aligning diverse software platforms, and managing fragmented supply chains.

The study also delves into the importance of setting realistic expectations aligned with organizational capabilities. Overambitious BIM deployment strategies, often driven by a desire to leapfrog stages, can lead to frustration and disengagement. Leadership must, therefore, calibrate BIM goals according to contextual factors such as institutional support, technological infrastructure, and workforce skill levels. This staged approach not only manages risk but also builds momentum through incremental successes.

Significantly, the research underscores leadership’s role in fostering an innovation culture that encourages experimentation without fear of failure. In many Latin American construction firms, risk-averse cultures hinder BIM pioneers from pushing boundaries. The study documents cases where leaders who championed iterative learning and tolerated early setbacks achieved more effective BIM integration than those demanding immediate perfection.

By focusing on leadership, the study challenges a widespread narrative that inadequate BIM outcomes derive solely from technical or financial constraints. While these factors cannot be dismissed, Dr. Schery and his team establish that leadership behavior is the linchpin that can either unlock or stall BIM’s transformative potential. Their findings advocate for leadership development programs explicitly designed to equip organizational leaders with competencies tailored to BIM’s evolving demands.

This research carries profound implications for policymakers, industry associations, and academic institutions invested in BIM proliferation throughout Latin America. Developing region-specific leadership frameworks and training modules could address the leadership deficit identified by Schery et al., shifting the BIM failure rate closer to success benchmarks seen in European and North American settings. Furthermore, integrating insights about cultural sensitivities and institutional realities ensures that leadership approaches are not only effective but sustainable.

As BIM continues to cement its role in the digitalization of construction, this study is a timely reminder that technology adoption is ultimately a human endeavor. The sophisticated software tools at the heart of BIM do little good if the people driving change lack guidance on how to navigate complexities and resistance. By reframing BIM failures through a leadership lens, the work of Dr. Carlos Alejandro Diaz Schery, Flávia de Souza Costa Neves Cavazotte, and Rodrigo Goyannes Gusmão Caiado expands the conversation from technological optimism to pragmatic organizational capacity-building.

In conclusion, this pioneering research provides a crucial roadmap for transforming BIM deployment outcomes in Latin America by emphasizing the indispensable role of leadership. It advocates for a strategic shift from viewing BIM as merely software implementation to perceiving it as a staged organizational transformation requiring dynamic leadership behaviors tailored to cultural and institutional contexts. If these lessons are heeded, the perennial gap between BIM promises and project realities can begin to close, ushering in a new era of efficient, resilient, and transparent construction practices well suited to Latin America’s evolving landscape.

Subject of Research: Leadership behaviors in Building Information Modelling (BIM) adoption and implementation in Latin America

Article Title: Leadership as the Missing Link in Latin American BIM Adoption: Insights from The Open Construction & Building Technology Journal

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Keywords: Building Information Modelling, BIM adoption, leadership behavior, organizational change, construction technology, Latin America, digital transformation, construction management, BIM failure rates, socio-technical systems, cultural dynamics, project management