Artificial intelligence driven platforms in the construction industry: implications for companies’ business models Open Access

The global construction industry faces persistent challenges, including fragmentation, low productivity growth, cost overruns and tight profit margins (Barbosa et al., 2017; Mischke et al., 2024). Technological advances, such as artificial intelligence (AI) and platform solutions, offer the potential to revolutionize traditional business models and foster new ecosystems (Ji et al., 2024).

Digital platforms facilitate stronger data-driven operations through novel ways of organizing stakeholders around the platform ecosystems (Antai et al., 2025). Such solutions offer potential positive impacts on the construction sector, which has traditionally been characterized by fragmented operations and linear value chains (Prebanić and Vukomanović, 2021).

While recent advancements highlight the transformative potential of digital platforms across various other sectors (Fu et al., 2021), construction has remained notably slow in technology adoption compared to e-commerce (Li et al., 2023), hospitality (Hall et al., 2022) and social media (Kraus et al., 2022), for example, where digital platforms and AI have significantly reshaped prior business practices (Kavadias et al., 2016; Şimşek et al., 2022).

The aforementioned examples have demonstrated how technological innovations can impact business operations. Yet, despite these promising examples, the construction industry’s uptake remains occasional and often limited to isolated pioneering attempts rather than widespread systemic transformation (Uusitalo et al., 2024; Antai et al., 2025).

Integrating AI with platforms potentially suits the construction industry’s nature, fostering adaptive ecosystems and novel services (Ancillai et al., 2023; Ji et al., 2024). Realizing this potential emphasizes the value of studying business model innovation driven by AI; empirical research exploring this specific intersection remains limited within the construction industry companies’ context (Gharaibeh et al., 2024; Antai et al., 2025).

Thus, the three research gaps remain: First, a lack of in-depth empirical studies examining AI-platform integration and business model innovation in construction. Second, existing literature often focuses broadly on technological benefits or barriers, with studies frequently adopting reductionist approaches, overlooking the complex social and organizational contexts involved in technological adoption and lacking specificity and insights into the organizational, cultural and strategic implications required for successful platform adoption (Wafai and Aouad, 2023). And third, the exploration of challenges related to technical and ethical dimensions of data management is introduced in literature (e.g. Oladinrin et al., 2023) but remains limited in scope. There is a need for investigating additional practical platform development cases where challenges related to technical interoperability, data standardization and ethical and privacy issues are factored in.

This study addresses the above research gaps by empirically examining how three construction industry companies (alpha, beta and gamma) are transforming their business models through AI-driven digital platforms.

The overarching research question is: how are AI-driven platforms impacting the business models of companies in the construction industry? Specifically, this research investigates how these platforms impact the four main business model components:

1. value propositions;

2. market and customers;

3. value creation; and

4. value capture mechanisms of the companies.

Based on empirical research, this study aims to:

• Identify and analyze the specific strengths, weaknesses and changes associated with business models driven by AI platforms within construction industry companies.

• Investigate the barriers that construction industry companies encounter when developing, implementing and scaling these platforms and propose implications to overcome them.

The paper is structured into five main sections. The first section covers existing literature, highlighting digital solutions and current industry business models, with an emphasis on creating a literature synthesis on strengths and weaknesses related to digital platforms and summary on knowledge gaps. The second part outlines the research approach, presenting the chosen qualitative multiple-case study methodology, that follows a three phase research process:

1. background and preparations;

2. data gathering and creation of case specific business models; and

3. data analysis and synthesis of results.

The third section reports the findings through both within-case and across-cases. The fourth section discusses the results, integrating them with existing theoretical insights, highlighting managerial and practical implications, addressing the limitations of the study, and proposing directions for future research. Finally, the fifth section provides a conclusion.

This section has been structured to cover a contextual literature background on existing research on the role of AI-driven digital platforms in transforming existing business models in the construction industry. The section has been divided into three main parts. It begins by introducing AI-driven digital platforms, including the strengths and weaknesses of the platforms, as synthesized from the literature (see also full synthesis in  Appendix 1).

The second section shifts to business model and ecosystem development, outlining how AI-driven platforms are seen to reshape value propositions, markets and customers, value creation and value capture, including an overview of the role of building information modeling (BIM). Finally, the third section presents a summary of knowledge gaps from the literature (Table 1).

Digital platforms are technological solutions that enable the development and emergence of new business ecosystems, focusing on interactions and connectivity among people, products, services, data and capital (Jacobides et al., 2018; Reuver et al., 2018; Adner, 2021).

Typically, platforms evolve from transactional platforms that facilitate spot-market matching of buyers and suppliers to platforms that support the development of complementary products and services (Gawer, 2022). The platforms enable scalable business models and network effects, facilitate the integration of diverse stakeholders and harness lock-in effects for a long-term competitive edge (Stampfl et al., 2013; Chang et al., 2019; Gawer, 2022).

Literature shows that digital platforms have led to new business models in various industries, exemplified by the increased use of transactional platforms in the restaurant industry (Azzoni et al., 2025). Digital platforms have revolutionized the way businesses operate by facilitating e-commerce and improving supply chain management. Also, companies like Amazon and Alibaba are leveraging digital platforms to connect buyers and sellers globally, streamline logistics and leverage big data for personalized marketing, creating new and efficient business models (Bagnoli et al., 2022).

In the construction industry, platform-based business models similarly offer opportunities but have not yet made a proper breakthrough (Laine et al., 2017; Antai et al., 2025). Especially empirical research on digital platforms and related business models in the construction industry has remained scarce (Gharaibeh et al., 2024).

A review of relevant previous studies exploring platform-based businesses, digital transformation and related subtopics in the construction-related literature reveals an interesting variety of recurring strengths and weaknesses, as summarized in  Appendix 1.

 Appendix 1 synthesizes the strengths of digital platforms by categorizing key advantages such as enhanced collaboration, integration, network effects, scalability and innovation. It highlights how digital tools – ranging from BIM and Internet of Things (IoT) to advanced analytics – boost operational efficiency, improve project management and foster stakeholder trust and transparency. This structured overview underscores the role of technology in streamlining workflows and creating dynamic service networks in the construction industry.

The Appendix also details weaknesses identified in the literature. It emphasizes challenges related to complex implementation, high initial investments, skills gaps and resistance to change. Issues with data security, interoperability and regulatory complexities, as well as dependency on external technology providers, are noted.

While the existing literature outlines 20 strengths and 26 weaknesses of digital platforms (see summary in  Appendix 1), gaps persist. Notably, in-depth, practically oriented empirical results of AI-driven platforms from construction industry companies are scarce.

A business model outlines how a company creates, delivers and captures value through its core activities, including its value proposition, customer segments, revenue streams and cost structure (Osterwalder et al., 2005; Zott and Amit, 2013; Madanaguli et al., 2023). In contrast, a business ecosystem is a network of organizations – suppliers, partners, customers and competitors – that work together to create and exchange value (Li et al., 2022; Smania et al., 2024). Together, these frameworks help explain both a company’s internal operations and its interactions within the broader market (Ancillai et al., 2023).

The business model canvas, a widely used tool, deconstructs a business into nine interconnected blocks to analyze how an company creates, delivers and captures value (Osterwalder and Pigneur, 2010). This framework is particularly well-suited for studying the effects of digital transformation, as AI and platforms often simultaneously reconfigure multiple business model components, such as introducing data-driven value propositions and new recurring revenue streams. The canvas is therefore a suitable tool for examining how AI-driven platforms are reshaping a company’s core logic.

Despite its utility, studies on the strategic shifts of digital platforms in the construction industry from a business model perspective remain limited. For instance, research on business model adaptation in the construction sector is not sufficiently developed, with Abeynayake et al. (2022) calling it a “novel concept” that “has not been sufficiently studied”. While some studies, like one by Das et al. (2021), have attempted to create a transformation canvas for the industry, they lack empirical results on AI-driven platforms, partly because they were published before the mainstream emergence of generative AI, such as ChatGPT in late 2022. Even more recent work, such as that by Synott and Aksenova (2025), has explored business model innovations in the AI era but has provided limited insights into platform-economics. Consequently, although existing research highlights the value of business modeling, there is a significant lack of empirical findings on the specific impacts of AI-driven platform economics on companies within the construction industry.

Traditional project-to-project construction business models – including contracting methods like design-bid-build, design-build and construction management at risk, as well as consulting, manufacturing and software services (e.g. licensing for scheduling and design software) to support the contracting-oriented businesses – have long dominated the industry (Pekuri, 2015). These models can be characterized by fragmented, sequential processes with clearly defined roles and limited collaboration, often resulting in inefficiencies, poor communication, low transparency and poor value-based competition (Pekuri, 2015; Assaad and El-adaway, 2020).

In contrast, emerging models such as platform-oriented contracting (Laine et al., 2017), data-driven project delivery and integrated digital ecosystems (Statsenko et al., 2021) extend across multiple service areas. These new approaches emphasize stakeholder integration, information flow and AI analytics to boost efficiency and innovation. They also enable the creation of novel minimum viable ecosystem structures (Adner, 2021).

BIM has played a big role in shaping construction project data platforms, especially by facilitating the flow and enrichment of project data that also supports other emerging technologies (Hardin and McCool, 2015). Unfortunately, BIM use has often been confined to geometric representations, omitting valuable product details and despite its potential benefits for companies (Sompolgrunk et al., 2023), its widespread use as a platform solution is not yet the industry standard (Hu and Dossick, 2023). Ongoing initiatives aim to advance comprehensive BIM usage (Farea et al., 2024), potentially bridging these gaps and enhancing data integration across construction projects.

Essentially, AI is contributing to changes in construction business models. For example, machine learning (ML) supports predictive maintenance, cost reduction and influences pricing strategies (Alshboul et al., 2024; Gao, 2024; Dikmen et al., 2025). Generative AI can assist with project administration and environmental analysis (Nyqvist et al., 2025). Computer vision improves project monitoring and quality control, while BIM serves as a foundation for many digital operations. Collectively, these technologies pose an impact on the companies’ business models, as well as evolve industry ecosystems, but despite apparent potential in conjunction with platform business models peer-reviewed publications on this topic are largely absent.

Next, Table 1 summarizes key knowledge gaps identified in the literature on AI-driven platform integration in construction. It highlights three primary areas:

1. the need for empirical insights into AI-platform integration and business model innovation;

2. a deeper understanding of organizational, cultural and strategic implications; and

3. the technical and ethical challenges of data management.

The research adopts an interpretivist approach to capture the complexity of organizational contexts and human interactions in AI-driven platforms, enabling a deep understanding of stakeholder perceptions and the influence of AI on business model innovation (Walsham, 2006). An interpretivist stance was chosen because AI’s impact on business models is socio-technical, not purely technical. As Walsham (2006) suggests, this approach allows for understanding through participant perspectives. Thus, a qualitative methodology was selected for in-depth exploration of how and why companies perceive, adapt to and shape AI-driven platform transformations.

To examine the development and impact of AI-based digital platform business models in the construction industry, the study uses a three-phase qualitative multiple-case study approach (Yin, 2009), as outlined in Figure 1. The figure below lists the outcomes of each phase. Each phase was structured to provide the prerequisites for the next. The exact details, including the approaches to data gathering and analysis, are then provided in a phase-oriented sequence.

A qualitative multiple-case study design was used, as articulated by Yin (2009), because it is effective for analyzing contemporary phenomena within their real-life contexts, especially when the boundaries between phenomena – such as the nuanced impacts of AI platforms on business models – and their contexts are indistinct. Allowing for contextualized insights into how different companies are navigating the integration of AI platforms.

Furthermore, the multiple-case study design is justified as it enables comparisons across diverse organizational settings (i.e. with case companies alpha, beta and gamma). This approach strengthens the generalizability and depth of the research findings by identifying variations within cases and common patterns across cases of AI-driven platforms (Eisenhardt and Graebner, 2007).

In Phase 1 (see Figure 1), existing studies were compiled from literature searches in databases such as ScienceDirect and Google Scholar. The authors emphasized the appropriateness and timeliness of the literature. Appropriateness was ensured by focusing on peer-reviewed journal articles and conference proceedings related to the construction industry, digital transformation and the platform economy. Topicality was ensured by emphasizing recent publications, as opposed to studies that predate major global platform-based companies. The study synthesized an overview of strengths and weaknesses related to digital platforms, resulting in an outcome presented in  Appendix 1 and a summary of knowledge gaps in the literature (see Table 1). These outcomes paved the way for Phases 2 and 3.

To gain novel empirical insights, three case companies were selected for the multi-year study spanning from 2022 to 2024. Table 2 below presents the cases alpha, beta and gamma, and after the table the three selection criteria (1) diversity, (2) representativeness and (3) relevance to the overarching research objective are detailed.

The selection criteria emphasized three key aspects. First, diversity: These cases capture key phases of the construction value chain, from design and planning to manufacturing, supply chain optimization and project execution. Also, representing companies from a small spin-off (i.e. gamma) to a European-wide (i.e. Beta) and global scale of operation (i.e. alpha).

Second, the potential for digital transformation was a priority, with companies demonstrating a commitment to AI-driven innovation. The companies were in the active development phase of digital platforms as part of their business and demonstrated a commitment to opening their development to research, and had made tangible progress in implementing AI solutions, thus ensuring representativeness to the overall study objective.

Third, relevance to AI-driven platforms was considered through international market ambitions of the companies, which broadened the scope of the research findings’ transferability as the results represent companies with global aspirations and exposure to diverse industry challenges.

Furthermore, individual research participants from the companies and their ecosystems were selected based on their expertise and roles in the construction industry, targeting stakeholders such as project managers, technology innovators and business leaders (see  Appendix 2).

During Phase 2 (data gathering and creation of case specific business models), multiple data sources were used to ensure sufficient data coverage. Data gathering included case company documentation, including development plans, internal and external presentations, five workshops, two post-workshops, nine group meetings and 18 semi-structured interviews. A complete overview of data gathered can be seen also in  Appendix 2.

As detailed in  Appendix 2, the research started with gathering data from the case companies through company documentation, interviews and group meetings. Semi-structured interviews were chosen for their flexibility, enabling exploration of emergent themes while ensuring consistent coverage of predefined topics like business model components and strategic implications. This method is good for gathering narratives and individual perspectives on complex processes (Cassell et al., 2018; Flick, 2022) and allows follow-up questions to uncover nuances of AI platform impact often missed by more rigid approaches.

From the data gathered in Phase 2, and insights from Phase 1, the authors created initial case-specific business model descriptions using the Osterwalder business model canvas (Osterwalder and Pigneur, 2010). These business model descriptions were then refined in workshops, and post-workshops with the individual companies representatives, enabling the gathering of relevant novel insights from the companies. Each workshop was held with relative company representatives, spanning 45–75 min. Due to competitive sensitivities, the detailed business model canvases of the case companies are not included in this publication.

The business model canvas was selected for its ability to visually represent core business aspects and facilitate structured analysis. This tool enabled a systematic exploration of how digital platforms reshape business operations and strategies within the case studies. As supported by Şimşek et al. (2022) and Veile et al. (2022), the canvas effectively identifies strategic alignment between digital innovations and business goals, providing a holistic view of platform impact.

To ensure methodological rigor and ethical compliance, research preparations Phase 2 included drafting detailed guidelines and obtaining consent forms. Remote interviews and workshop participation were conducted via videoconferencing software, allowing for broader participation and accessibility.

In Phase 3 (i.e. data analysis and synthesis of results), to ensure results and their transferability, data from the individual cases was integrated through a cross-case synthesis combining outcomes and data gathered in Phase 2. Thematic analysis was chosen to systematically yet flexibly identify, analyze and report patterns within the qualitative data (Easterby-Smith et al., 2015). This method allowed for distilling key insights from company documentation, interviews, group meetings and workshop data on AI-driven platforms’ multifaceted impacts and challenges. It facilitated an interpretation of recurrent strengths, weaknesses, changes and barriers, enabling an analytical understanding of how AI platforms shape business models in construction, moving beyond mere description.

The insights from thematic analysis were structured around a concise version of the Osterwalders business model canvas, using four primary components:

1. value proposition;

2. market and customer segments;

3. value creation; and

4. value capture, which are also briefly described below.

First, the value proposition component highlights the unique products or services designed to meet customer needs, showing how digital platforms enhance these offerings. Second, market and customer segments focus on specific customer groups, emphasizing how digital platforms personalize engagement. Third, value creation examines how digital platforms streamline operations, boost efficiency and foster innovation. Fourth, value capture explores how businesses monetize offerings, detailing how digital platforms create or optimize revenue streams to enhance profitability and sustainability. (Osterwalder and Pigneur, 2010)

To conduct the thematic analysis, the authors first immersed themselves in the qualitative data from all sources (see  Appendix 2), identifying key concepts related to platform impacts. These were then grouped into emergent themes that reflected recurring strengths, weaknesses and changes from previous models. In the final step, these empirically-derived themes were mapped onto the four components of the business model canvas to provide a structured analysis of the platforms’ influence.

For instance, initial codes identified specific revenue sources, such as Gamma’s “Subscription fees” and “Consulting services”. These were thematically grouped with similar concepts from other cases, like Alpha’s potential for new revenue streams from data, creating a broader theme of a shift toward recurring and service-based revenue. This theme was then directly mapped to the C4: Value capture component in Table 3, resulting in the identified strength “+ Recurring revenue”. Similarly, coded concerns about cannibalizing existing business informed the weakness “– Revenue uncertainty”.

The findings from both the individual case studies and the combined synthesis could then be contextualized with literature by reflecting on Phase 1 outcomes (e.g. see  Appendix 1 and Table 1), enabling a comparison and insights from and with the existing research.

Transferability and validity were strengthened through peer review with case companies and member checking with external participants, ensuring the synthesis reflected diverse stakeholder perspectives. Ethical rigor was maintained throughout the research by ensuring participant confidentiality and pseudonymity. Informed consent, detailing the study’s purpose, methods and participant rights, was obtained from all participants. Data was securely stored and restricted to the research team. Participants were informed of their right to withdraw and findings were reported pseudonymously.

This analysis presents the findings in two sections. First, individual case studies of Alpha, Beta and Gamma are detailed. Each case explores the company’s development initiatives and evaluates the strengths and weaknesses of their respective AI-driven platforms.

Second, a synthesis of the case studies is presented, including a comparative analysis of platform-based business model strengths and weaknesses across the four key components defined in the research methods. This section also details the transition from previous business models to the new platform-based models.

Alpha is a global technology company specializing in connecting the physical and digital world with industrial technologies, such as AI-supported structural design software. In the construction industry, Alpha’s digital platform is designed to transform traditional structural design processes by integrating AI functionalities. The platform leverages ML to enable continuous learning and automated design adjustments based on data from design libraries, resulting in dynamic, data-driven and personalized design solutions.

Specifically, alpha’s system automatically adjusts structural designs the user is making based on both historical data and real-time feedback. This not only reduces human error and streamlines repetitive tasks but also allows the platform to continuously refine and tailor design outputs to meet individual project needs. As a result, stakeholders, such as structural engineers and clients, benefit from immediate quality checks and productivity boosts to enhance the prior design processes.

Empirical insights gathered indicate that the key strength of alpha’s solution is its ability to enhance design quality and productivity. For instance, the system’s dynamic suggestions accelerate project delivery by reducing the manual rework typically required in traditional design processes, e.g. by cross-referencing the designs with prior high-quality designs. Highlighting discrepancies between structural designs in a cloud environment can also enhance communication and transparency. This collaborative environment ensures that all parties are well-informed and aligned, leading to improved design outcomes.

However, despite its strengths, alpha’s digital platform faces several weaknesses. The implementation of such an AI system demands high upfront investments from technology acquisition, integration and reengineering of existing processes, a barrier that can be especially problematic for smaller firms. Moreover, integrating the new AI systems with legacy workflows has proven technically complex, often requiring substantial training and customization to align with current practices. In addition, due to the sensitive nature of data, concerns about data security, privacy and intellectual property (IP) rights persist, underscoring the need for robust data governance frameworks and clear ownership agreements.

Overall, while alpha’s AI-supported structural design software demonstrates the potential to enhance design practices through enhanced efficiency, design quality and improved stakeholder communication, it also highlights the challenges of cost, integration complexity and data management that must be addressed for widespread adoption.

Beta is a European multinational concrete rebar manufacturer in the construction industry that provides materials for structural works. This case study examines how beta’s smart value chain platform aims to transform rebar production by leveraging AI-powered analytics to optimize logistics, inventory management, sustainability outcomes and extend the scope of product offerings. Its primary objective is to enhance the production lifecycle through data-driven operations.

Beta’s smart value chain platform is designed to optimize the entire lifecycle of concrete rebar production and distribution. Key features include AI-powered analytics for manufacturability and CO₂ emission tracking, automated workflows for production scheduling and logistics coordination and inventory optimization. In addition, the system supports precise, product-specific pricing based on real-time data inputs, setting it apart from traditional manual methods.

Empirical evidence from interviews and workshops indicates that beta’s platform can enhance internal resource management, leading to reduced waste and overall lower operational costs. Its predictive analytics enable proactive adjustments to production and logistics, thereby improving efficiency and reducing production facilities’ downtime. Moreover, comprehensive CO₂ tracking promotes sustainability by providing clients with low-carbon offerings. The platform enables detailed manufacturability assessments and customized pricing models, which can be integrated across platform ecosystems. For example, manufacturability analysis can be linked with Alpha’s structural design software and Gamma’s production systems, creating a network effect between the platforms.

Despite the benefits, beta’s digital platform faces several weaknesses. The initial implementation costs and technical complexities associated with integrating AI capabilities – especially for CO₂ tracking and inventory management – are substantial. Extensive training and process reengineering are required to ensure effective adoption among stakeholders, including Beta’s customers. Furthermore, variability in design software, country-specific standards and plant-specific procedures add layers of complexity to system integration and replicability.

In addition, rigid client practices hinder the full utilization of the platform’s capabilities. For example, while the betas system can process BIM data to provide more accurate pricing, productivity analysis and CO₂ tracking for clients, many of them underuse this critical input, limiting the benefits of BIM data-analysis capabilities. Low-carbon products and life-cycle analysis calculation capabilities are also available, but clients have made a poor transition from incumbent solutions to using these services and sustainable products.

Overall, beta’s digital platform demonstrates the potential to transform the concrete rebar value chain. Its value proposition is rooted in improved efficiency, reduced costs and enhanced sustainability, making beta’s participatory position stronger in its competitive ecosystem. The case highlights the need to invest in employee training, process changes and robust data governance to address challenges related to system integration and client adoption.

Gamma is a spin-off company that aims to offer a cloud-based digital production platform designed to revolutionize construction management and production processes. Positioned as a disruptor, Gamma’s system hopes to leverage real-time data and AI-driven tools to enhance project coordination, resource allocation and stakeholder collaboration.

Gamma’s scalable production system is built on a cloud infrastructure that integrates real-time project updates, data analytics and modular production mechanisms. Key features include dynamic resource optimization and collaboration tools that allow users to swiftly adapt to changing project demands. By providing continuous, centralized, real-time updates on project status, product availability and workflow progress, the platform not only aims to streamline traditional project management but also to foster trust and accountability among stakeholders. Data from the case study indicates that these features could enhance communication and decision-making, leading to a more coordinated and effective project execution.

Despite its promising capabilities, gamma’s digital platform faces several weaknesses. The construction industry’s entrenched resistance to change and established cultural practices can hinder the adoption of such solutions. Moreover, the high initial investment required for implementing and integrating a sophisticated cloud-based system, along with the need for extensive training and customization, poses a substantial barrier, especially given the diverse maturity levels of production systems among potential clients. This variability means that a one-size-fits-all solution is not feasible; gamma must tailor its offerings and often supplement its platform with outsourced consulting services to meet the distinct needs of different ecosystem stakeholders. These challenges highlight the need for strategic planning toward stakeholder engagement to facilitate a smooth transition to gamma’s solutions.

Overall, Gamma’s platform demonstrates the potential to transform traditional project management practices by improving operational efficiency, enhancing stakeholder collaboration and optimizing resource management. The case underscores the importance of strategic planning, targeted training and stakeholder engagement to overcome implementation challenges and realize the benefits of digital integration in the construction industry.

By examining the cases of alpha, beta and gamma, key strengths and weaknesses across four core dimensions: C1: value proposition, C2: market and customer dynamics, C3: value creation mechanisms and C4: value capture strategies, were synthesized, as seen in Table 3, which also provides an overview of business model change.

Going through the findings in a sequence. First, the transition to AI-driven digital platforms has redefined the value proposition in construction business models. Traditional approaches, characterized by manual, one-off processes and limited real-time feedback, are being replaced by dynamic systems that enable continuous learning and automated design adjustments. This shift facilitates the development of data-driven solutions that offer personalized and adaptive design capabilities, transforming the way companies approach project delivery and stakeholder collaboration.

Empirical evidence from the case studies illustrates that AI-driven design adjustments can enhance efficiency, as seen with alpha’s structural design solution, while real-time data supports customized offerings, seen by beta. In addition, continuous feedback loops, such as gamma’s updates, foster transparency and trust among stakeholders. However, these advancements come at the cost of high upfront investments (e.g. operations reengineering expenses) and complex integration challenges with legacy systems.

Traditionally, construction industry companies have offered product-based solutions with limited customer interaction (Pekuri, 2015). However, the shift toward service-oriented and relationship-driven models has transformed the market landscape. Digital platforms now enable continuous engagement, real-time sustainability tracking and tailored support, fostering long-term collaboration and stronger customer relationships.

Empirical findings indicate that AI-enhanced customer support – illustrated by Alpha’s continuous design improvement feedback – can boost engagement. In addition, Beta’s data-driven CO₂ tracking can appeal to eco-conscious clients. Yet, challenges with varying practices and limited digital literacy, as seen in Gamma’s client base, lead to fragmented adoption.

The integration of AI has transformed resource management from a reactive, manual approach into a proactive, data-centric process. Companies can use predictive analytics and real-time data monitoring to optimize operations and streamline processes. This enhances operations, as demonstrated by beta’s optimized inventory management and the data-oriented feedback in both alpha and gamma.

These advances introduce notable challenges. The handling of sensitive design and production data raises issues concerning privacy, security and IP, as illustrated by alpha’s system training on client design libraries. In beta’s case, the separation of rebar procurement from design causes a reduced feedback loop, limiting beta’s ability to influence clients’ BIM data due to contractual constraints between clients, designers and project owners.

Revenue models in construction have been largely project-based and transactional. However, digital platforms are shifting focus toward recurring revenue streams through subscriptions, consulting and data monetization. For example, gamma’s subscription model provides predictable income, while alpha’s data library sales reveal data monetization opportunities. However, challenges persist in AI-driven features, which may cannibalize existing revenue streams (e.g. automating previously monetized services) and create legal and compliance issues regarding data use and ownership restricting scalability and profitability.

Beyond the specific components, the analysis of the cases reveals a broader impact of digital platforms in construction. These platforms underscore distinct roles where some focus on external customer engagement through multi-sided ecosystems (e.g. alpha and gamma) and others integrate stakeholder data to streamline production systems (e.g. beta), reinforcing network effects and customer lock-in.

Simultaneously, the findings highlight common barriers to adoption. High upfront investments, complex technology integration and extensive process reengineering create hurdles, particularly for small and medium-sized enterprises (SMEs). In addition, pervasive concerns regarding data security, privacy and IP, combined with cultural resistance and a gap between technological innovation and strategic business model planning, underscore the industry’s hesitancy to fully embrace digital transformation. These challenges demand actions discussed in the next section.

The discussion section reflects on the findings to address how AI-driven platforms impact business models, drawing insights from both research on the construction industry and comparable research in other sectors. It responds to the study’s aims of analyzing platform-related changes and challenges within construction industry companies, recognizing the transferable nature of these insights. The section is structured to first explore theoretical contributions, then to move on to practical and managerial implications and finally to follow with research limitations and suggestions for future research.

Business model research in the construction industry context is still developing (Abeynayake et al., 2022; Das et al., 2023; Gledson et al., 2024); as stated by Abeynayake et al. (2022) business model adaptation is a “novel concept” that “has not been sufficiently studied”, indicating a need for more studies. Especially combined with rapidly developing AI (Maslej et al., 2024), this study contributes to the limited literature on AI-platform–driven business models in construction by offering empirically grounded, company-oriented insights. The research presents three core contributions.

This study offers three core empirically grounded, company-oriented contributions to how AI-platform–driven business models unfold in the construction industry. First, this study provides specific empirical evidence demonstrating how AI-driven platforms actively transform core business model components’ in the construction context. Second, findings deepen understanding of transformation challenges, highlighting how varying levels of stakeholder maturity impede platform scaling and integration. Third, AI platforms in construction are not all generic technologies (e.g. multi-purpose data-sharing platforms) but also contain specialization targeting inefficiencies like low productivity and fragmentation (Barbosa et al., 2017; Mischke et al., 2024). AI’s uniqueness in this sector lies in its application as a corrective tool for systemic problems. The cases exemplify this. Alpha targets design productivity, beta tackles supply chain improvements and Gamma addresses poor project coordination, with each platform providing a direct solution to an industry-specific problem and synthesizes findings across multiple cases.

The findings highlight changes from traditional business models, often manual and project-specific, toward models with AI platforms. The findings show that AI-driven feedback loops, a concept also seen in DevOps (Lokiny, 2023), are crucially challenged by construction’s fragmented ecosystem (Ji et al., 2024). Construction’s contractual and operational silos can obstruct information flow between stakeholders, a key barrier identified in cases alpha and beta. This reveals that a primary barrier is not technological but organizational. Successful adoption requires reengineering business models to break these systemic barriers. Also, the study underscores the importance of AI assimilation for realizing performance gains, complementing findings by Luo et al. (2025).

However, achieving these benefits is hindered by the “digitalization paradox” – significant investments yielding limited results – due to integration complexities, reflecting Bajpai and Misra’s (2024) emphasis on compatibility and interoperability challenges. The studied cases show that this paradox is amplified in construction by integration complexities with legacy systems and fragmented data (reflecting Bajpai and Misra’s, 2024 emphasis on compatibility). High upfront investments and tight profit margins exacerbate this, requiring innovative business models and financial planning.

Historically, the construction industry has offered products with limited customer interaction. However, the value creation of AI platforms in construction is shaped by the sector’s digital immaturity, which is a contrast to general data ecosystems (Toorajipour et al., 2024). While these platforms support the transition to digital servitization, they often cannot succeed as standalone products. As demonstrated by case Gamma, they must be bundled with consulting and training services to address client skill deficiencies. This necessity poses a significant, industry-specific challenge to developing scalable business models, resonating with Ancillai et al.’s (2023) review and the work of Toorajipour et al. (2024) on value creation in general data ecosystems via continuous interaction outside of the construction industry.

The identified challenges surrounding digital literacy and maturity gaps, consistent with findings by Das et al. (2023), Kissi et al. (2021) and Aghimien et al. (2024), emphasized the need for capability building and suggest the transferability of insights. The studied cases point to a need for robust governance frameworks to manage stakeholder interactions, ensure data security and uphold ethical standards. This resonates with Oladinrin et al. (2023), who argued that the ethical dimensions of construction innovation are often overlooked and call for updated ethical frameworks and codes of conduct, particularly regarding cybersecurity and the implementation of new technologies. Such governance is also crucial in addressing platform governance challenges within multi-stakeholder environments, as noted by Antai et al. (2025).

Regarding value creation, conventional reactive resource management is being augmented by AI analytics and real-time monitoring. Beta and Gamma illustrate enhanced operational efficiency and decision-making, extending earlier findings (Laine et al., 2017; Kovacic et al., 2020). Ancillai et al. (2023) identified infrastructure management as heavily impacted by digital technologies, affecting efficiency and requiring new skills and partnerships, themes reflected in the cases. Persistent challenges around data security and skills gaps, noted by Kissi et al. (2021), underscore the need for specific governance frameworks like Bagheri’s (2023).

In value capture, the shift from project-based transactions toward recurring revenue (e.g. subscriptions and data monetization) is evident. This aligns with Veile et al.’s (2022) research and provides construction-specific empirical context for platform frameworks like Bartels and Gordijn’s (2021). However, uncertainties around revenue streams and the challenges of monetizing AI features (e.g. Alpha’s cannibalization concerns), noted also by Huang and Mithas (2024), remain, reflecting broader difficulties in quantifying technology return on investment, as seen in the BIM study by Sompolgrunk et al. (2023).

Furthermore, success requires more than technology; it demands cultural shifts, ongoing training and proactive stakeholder engagement, supported by Prebanić and Vukomanović (2021) and Kissi et al. (2021). From an ecosystem perspective, findings align with Adner’s (2021) concept of minimum viable ecosystems and Gawer’s (2022) platform framework. They help to illustrate how AI platforms strive to build multi-sided markets with network effects, a central concept discussed by Ancillai et al. (2023) and specifically within the construction context by Antai et al. (2025).

In summary, this research contributes to theory by (1) detailing specific mechanisms of AI-platform-driven business model transformation in construction, (2) identifying customer maturity as a critical, context-specific barrier to scaling and (3) providing an empirically grounded synthesis of strengths, weaknesses and strategic considerations for AI-driven business model innovation. These contributions address the fragmentation noted by e.g. Ancillai et al. (2023) and Antai et al. (2025) by offering a more construction industry context.

Structured around four business model components, the research offered insights and related implications targeted for construction industry companies seeking to leverage digital platforms in their businesses.

For the value proposition, AI-driven digital platforms shift static, manual processes to adaptive, data-driven offerings that enhance efficiency and personalization. Key insights stress that fostering a platform-supportive organizational culture and digital literacy is essential. Companies should invest in comprehensive training and change management initiatives to build digital skills and drive cultural alignment while also launching phased pilot projects with select partners to test specific AI features. The experience of Case Gamma, which required supplemental consulting to bridge client skill gaps, suggests an initial offering should blend technology with hands-on support, tailoring solutions to customers of varying maturity. This creates a “service-to-product” pathway that builds client capabilities and demonstrates return on investment while establishing the early, continuous feedback loops between users and developers crucial for platform refinement (e.g. as seen outside the construction industry by Lokiny, 2023).

For the market and customer component, proactive co-creation is key to overcoming the cultural resistance and fragmented adoption identified in the study. Rather than presenting a finished platform, companies should engage key clients in co-development workshops to design and validate artefact features. For instance, a tool like Beta’s CO₂ tracking could be refined with users to ensure it meets their workflow and reporting needs. This approach not only builds better products but also cultivates early adopters who can champion the platform internally, driving broader acceptance and building long-term relationships. Moreover, proactive engagement with policymakers helps shape regulatory frameworks that ensure fair data governance and enhance transparency.

For value creation, AI integration affects internal operations by transitioning toward proactive, data-centric workflows. Key insights recommend a phased integration, beginning with critical functions such as scheduling (e.g. Gamma) and inventory management (e.g. Beta), accompanied by workflow redesign that harmonizes human expertise with AI-driven predictive analytics (e.g. integration of Alpha’s solutions with the existing structural design practices). Collaborative development with platform experts ensures that secure, scalable solutions meet construction-specific needs, while upgraded IT infrastructure and targeted cross-functional training foster data governance and data-driven decision-making.

For value capture, digital platforms enable new revenue streams through subscription-based consulting models and data monetization. Key insights stress the importance of establishing flexible pricing structures and clear contractual frameworks that define IP rights, data ownership and liability. These insights also call for flexible pricing strategies and supportive legal frameworks, as also suggested by Chen et al. (2022). For example, a value capture model could combine a base platform fee with a “gain-sharing” component tied to documented efficiency improvements.

In addition, fostering robust ecosystem partnerships is critical to overcoming the industry fragmentation identified as a key barrier in this study. On one hand, business model transformations in established companies are rare (Şimşek et al., 2022) and often involve complex dynamics during platform transitions (Vuori and Tushman, 2023), while on the other hand, SMEs are often seen as lacking the influence and resources needed to drive significant transformation (Han et al., 2024). This makes a partnership-centered approach a potential strategy for enabling shared innovation and building necessary network effects.

A specific “keystone partnership” can make this tangible. For instance, an alliance between a design platform like Alpha and production system operators like Gamma could create a 'design-to-fabrication’ ecosystem. Such an integrated offering provides a compelling value proposition for customers, as the design platform would combine relevant information from across its ecosystem actors. This partnership-centered approach helps secure stable, recurring revenue streams and is more effective at promoting customer loyalty because similar access to information can only be gained through hard-to-replicate ecosystem structures.

To address the cultural misalignment hindering technology adoption, managers must tackle the specific anxieties of expert users. For instance, for a structural engineer using Alpha’s software, the fear may not be just unemployment, but the loss of professional autonomy to a 'black box’ AI. To counter this, companies should adopt a “human-in-the-loop” communication strategy. This strategy frames AI not as a replacement, but as an “intelligent co-pilot” that augments human expertise. It frees professionals to focus on more complex problem-solving. The strategy prioritizes employee education that focuses on the practical impacts of AI and digital platforms. It also builds a culture that addresses change-related anxieties (e.g. fear of unemployment).

Finally, the research highlights the necessity of tailoring digital solutions to a client’s operational maturity. As illustrated by case Gamma, where a one-size-fits-all approach was impractical, companies should adopt a modular implementation strategy. This involves offering a core platform with essential features first, then introducing more advanced AI modules as the client’s own workflows mature. This incremental approach ensures early wins and a smoother transition by blending new technology with existing human expertise.

This research has several limitations that may affect the transferability of the findings. First, potential participant bias and skewed perspectives, as it primarily involved stakeholders with vested interests in digital platforms’ success and limited diversity.

Second, partly virtual data collection may have constrained engagement depth, limiting qualitative data richness. Third, the regional focus on companies operating in Finland may not fully capture global industry experiences and challenges due to varying regulatory environments, market conditions and technological adoption rates across regions.

Finally, while providing valuable insights into the adoption and impact of digital platform-based business models, future research could have included a more diverse sample to validate and refine the findings further and a more extensive longitudinal study to provide a comprehensive global understanding and enhance the applicability of the findings.

This study’s findings highlight several avenues for future investigation. The challenge of varying client maturity, a key barrier to scalability noted in case Gamma, calls for the development of a framework to assess a potential client’s maturity. Such a framework would enable platform providers to tailor implementation strategies and pricing models, thereby mitigating significant adoption risks.

Furthermore, the data security and IP concerns identified as major barriers in the study warrant investigation. Research should explore the technical and commercial feasibility of federated learning models and blockchains within construction ecosystems. This would determine if AI systems can be trained effectively on sensitive, decentralized client data – such as design libraries – without compromising privacy or intellectual property, thus resolving a critical trust and adoption barrier.

The study also found that platforms often require a supplemental consulting component to succeed. Future research could conduct a longitudinal study to quantify the return on investment of these hybrid “product-service” models. An investigation of this nature would help determine the optimal service-to-product revenue ratio and identify when a client has developed sufficient maturity to transition to a pure AI-as-a-service model.

The difficulty in aligning stakeholders and sharing data, as seen in case Beta’s fragmented feedback loop, points to a need for new commercial structures. Future research should investigate novel multi-party contractual frameworks, such as integrated project delivery for digital platforms. The goal would be to identify models that can effectively incentivize data sharing and collaborative value creation among designers, contractors and suppliers operating within a shared platform ecosystem.

Future studies should also broaden the scope to analyze the scalability and long-term impact of these platform models across diverse organizations. Comparative analyses across sectors and regions would be valuable to understand how local regulations, markets and cultures influence adoption. This work would identify transferable best practices and context-specific challenges, providing a richer understanding of the platform economy in construction.

Finally, a deeper exploration of organizational culture’s role in digital transformation is crucial. Building on the need for readiness assessments, future research should investigate the specific cultural attributes and strategic alignment practices that enable successful AI platform integration. Understanding these human and organizational factors is essential for creating effective, sustainable change management strategies and ensuring long-term adoption.

Together these future studies could contribute to a comprehensive construction industry landscape understanding, guiding effective AI-driven platform strategies.

This research, through an analysis of AI-driven platforms developed at Alpha, Beta and Gamma, demonstrates the potential of these innovations within the construction industry. From an empirical business model perspective, the study highlights that AI-driven platforms offer strengths, including the delivery of personalized solutions that enhance productivity, improved collaboration, increased customer retention, notable scalability and an emerging sustainability appeal. Importantly, these platforms do not merely introduce new technology; they also enable and require new business models that address long-standing industry challenges.

The study also highlights critical weaknesses hindering successful platform implementation. These include high upfront investment costs, data security and privacy concerns, the complexities of integrating new systems with (or replacing) existing operations, an ingrained cultural resistance to change and uncertainties surrounding revenue generation with novel business models. The construction industry’s project-based, often adversarial, operating environment can exacerbate these challenges. This necessitates tailored strategies, both within companies and across the broader ecosystem, including fostering digital maturity development among all stakeholders.

Beyond mere technological adoption, this research underscores the need to align organizational cultures with digital strategies. Successfully navigating challenges such as data security and integration complexities (as identified in the findings) requires collaboration with platform developers and ecosystem partners for secure, industry-specific solutions. Furthermore, addressing the noted regulatory constraints and data risks necessitates engagement with policymakers to develop supportive frameworks for data ownership, privacy and liability.

This study offers construction industry companies a strategic framework for evaluating and capitalizing on AI-driven platform opportunities. By leveraging identified strengths and addressing weaknesses, companies can enhance project outcomes, unlock novel revenue streams (such as subscription models and data monetization) and build a more robust competitive advantage. Crucially, achieving these benefits hinges not merely on technological adoption but on the fundamental reengineering of their business models.

For widespread, sector-level transformation, the research indicates that efforts must extend beyond individual companies to a multi-faceted, systemic approach. To overcome common adoption barriers and support the platforms’ potential, this involves aligning policy and funding, establishing clear data governance guidelines, fostering public-private collaboration, and significantly investing in skills development. Such measures are essential for fostering viable ecosystems that can nurture platform-based innovation.

In conclusion, this research advances the understanding of digital transformation in construction by bridging key knowledge gaps with actionable insights derived from empirical data. The industry’s future trajectory will be increasingly shaped by the strategic embrace of digital platforms, necessitating a sustained commitment to ongoing research and adaptation in response to the ever-evolving technological and industry landscape.

The authors thank the companies involved in this research for their valuable feedback and cooperation with the research.