Table 1 AI-driven business model for... | Emerald Publishing

Table 1

AI-driven business model for general contractors with key operational focus on bidding, procurement and implementation

Business model component	Key finding(s)
C1: Customer segments	AI does not fundamentally change a general contractor's broad customer groups, but it enhances how they are served. As AI models become more capable of providing fine-grained customer analysis, they can identify niche segments (e.g. sustainability-focused clients) within existing customer groups. A key strategy is to segment customers based on their digital maturity and willingness to share data. This allows for the offering of tailored service levels and the use of data for purposes such as training AI systems or providing building life-cycle services. High customer digital maturity also creates the foundation for offering more AIaaS. When real estate developers have access to broader market analysis, it lowers the barrier to entry for new markets and increases customer diversity
C2: Value propositions	Enhancements to a contractor's core value proposition are happening: e.g., delivering higher-quality, cost-effective projects through data-driven predictability, improved coordination and knowledge transfer, resource optimization and verified environmental, social and governance outcomes. While value is primarily created during construction, it can be extended across the asset's lifecycle. Also, effective data use enables a shift to strategic partnerships. The contractor can offer clients early access to custom AI systems built on its data pool, helping them in their planning phase and securing the relationship much sooner
C3: Channels	Channels focus on two key areas. First, improving the sales process by using AI agents to research bid requests, which improves offer quality and allows for hyper-personalization; the use of AI for discovering potential clients and partners will also become more widespread. Second, creating a new channel for early engagement by giving clients direct access to the contractor's customized AI systems, which creates a stronger lock-in effect. During the project, these are complemented by platforms for collaboration, all integrated to support decision-making and to serve as an information source for learning across cases, enabling network effects. While interaction with the client may be limited during the bidding process, interaction with a contractor's AI may be seen as a novel, unrestricted channel
C4: Customer relationships	Some transformation from transactional to continuous, data-driven partnerships starts to occur. Using AI tools, the contractor can engage in active co-creation much earlier in the project phases. Contractor AI can answer client questions anytime, anywhere, between the designated meeting schedules or outside of contractual obligations. The role evolves from a mere executor to an advisor, using general contractors' data to exceed standard AI solutions' capabilities. These lower-barrier and extended relationships are built on trust, which is fostered through the transparent and ethical use of AI
C5: Revenue streams	An AI-driven model's primary near-future impact is not to revolutionize a contractor's earning logic, but to enhance the efficiency and profitability of the current project-based business. By improving productivity, AI boosts competitiveness in traditional bidding and increases profit margins on projects. Secondarily, this new capability and the data collected open the door to potential complementary revenue streams. These can include AIaaS consulting or subscription-based services, which support the core business
C6: Key resources	The existing key resources remain. When it comes to AI-driven solutions, the new resource is not just a single technology, but rather the organizational capability to manage change. This is built on a foundation of strategically-governed data, specialized and continuously trained personnel and custom AI solutions like integrated agents. At the very least, this data will be used for information retrieval, even if it is not used for model training. Critical intangible assets, including change management skills, an ethical framework for responsible AI, a trusted brand and partnerships to complement in-house expertise
C7: Key activities	An AI-driven model enhances, rather than replaces, a contractor's traditional activities like bidding and procurement. While the core activities remain, many tasks, such as gathering product data, can be automated or replaced by AI, freeing up professionals like site engineers. Success requires new, continuous processes. These include managing the AI model's lifecycle and company data, and integrating AI with core tools like building information modelling. This framework should be supported by functions like AI-enhanced research and development, along with critical enablers: continuous staff training, proactive cybersecurity and regular ethical audits. To transform these internal capabilities into AIaaS, these back-end functions need a new front-end interface, similar to what is used in application development
C8: Key partners	Partner network changes by both deepening existing relationships and requiring entirely new connections. Traditional partners like subcontractors and designers are enhanced through shared data and platforms. New partners include technology and data providers, programmers, innovation hubs like research labs and startups and specialists in ethical auditing and cybersecurity. Active participation in the wider industry ecosystem and co-development with clients are also seen as valuable
C9: Cost structure	Core cost structure remains largely unchanged, still dominated by labor, materials, subcontracting and central office operations expenses. However, the introduction of AI is expected to streamline or replace work processes, leading to potential savings in existing key costs. A new cost layer emerges for AI and data solutions. This includes initial investments in technology, specialist recruitment and integration (i.e. upfront costs), followed by ongoing costs for maintenance, data governance, continuous staff training and ethical oversight (i.e. maintenance costs)

Business model component	Key finding(s)
C1: Customer segments	AI does not fundamentally change a general contractor's broad customer groups, but it enhances how they are served. As AI models become more capable of providing fine-grained customer analysis, they can identify niche segments (e.g. sustainability-focused clients) within existing customer groups. A key strategy is to segment customers based on their digital maturity and willingness to share data. This allows for the offering of tailored service levels and the use of data for purposes such as training AI systems or providing building life-cycle services. High customer digital maturity also creates the foundation for offering more AIaaS. When real estate developers have access to broader market analysis, it lowers the barrier to entry for new markets and increases customer diversity
C2: Value propositions	Enhancements to a contractor's core value proposition are happening: e.g., delivering higher-quality, cost-effective projects through data-driven predictability, improved coordination and knowledge transfer, resource optimization and verified environmental, social and governance outcomes. While value is primarily created during construction, it can be extended across the asset's lifecycle. Also, effective data use enables a shift to strategic partnerships. The contractor can offer clients early access to custom AI systems built on its data pool, helping them in their planning phase and securing the relationship much sooner
C3: Channels	Channels focus on two key areas. First, improving the sales process by using AI agents to research bid requests, which improves offer quality and allows for hyper-personalization; the use of AI for discovering potential clients and partners will also become more widespread. Second, creating a new channel for early engagement by giving clients direct access to the contractor's customized AI systems, which creates a stronger lock-in effect. During the project, these are complemented by platforms for collaboration, all integrated to support decision-making and to serve as an information source for learning across cases, enabling network effects. While interaction with the client may be limited during the bidding process, interaction with a contractor's AI may be seen as a novel, unrestricted channel
C4: Customer relationships	Some transformation from transactional to continuous, data-driven partnerships starts to occur. Using AI tools, the contractor can engage in active co-creation much earlier in the project phases. Contractor AI can answer client questions anytime, anywhere, between the designated meeting schedules or outside of contractual obligations. The role evolves from a mere executor to an advisor, using general contractors' data to exceed standard AI solutions' capabilities. These lower-barrier and extended relationships are built on trust, which is fostered through the transparent and ethical use of AI
C5: Revenue streams	An AI-driven model's primary near-future impact is not to revolutionize a contractor's earning logic, but to enhance the efficiency and profitability of the current project-based business. By improving productivity, AI boosts competitiveness in traditional bidding and increases profit margins on projects. Secondarily, this new capability and the data collected open the door to potential complementary revenue streams. These can include AIaaS consulting or subscription-based services, which support the core business
C6: Key resources	The existing key resources remain. When it comes to AI-driven solutions, the new resource is not just a single technology, but rather the organizational capability to manage change. This is built on a foundation of strategically-governed data, specialized and continuously trained personnel and custom AI solutions like integrated agents. At the very least, this data will be used for information retrieval, even if it is not used for model training. Critical intangible assets, including change management skills, an ethical framework for responsible AI, a trusted brand and partnerships to complement in-house expertise
C7: Key activities	An AI-driven model enhances, rather than replaces, a contractor's traditional activities like bidding and procurement. While the core activities remain, many tasks, such as gathering product data, can be automated or replaced by AI, freeing up professionals like site engineers. Success requires new, continuous processes. These include managing the AI model's lifecycle and company data, and integrating AI with core tools like building information modelling. This framework should be supported by functions like AI-enhanced research and development, along with critical enablers: continuous staff training, proactive cybersecurity and regular ethical audits. To transform these internal capabilities into AIaaS, these back-end functions need a new front-end interface, similar to what is used in application development
C8: Key partners	Partner network changes by both deepening existing relationships and requiring entirely new connections. Traditional partners like subcontractors and designers are enhanced through shared data and platforms. New partners include technology and data providers, programmers, innovation hubs like research labs and startups and specialists in ethical auditing and cybersecurity. Active participation in the wider industry ecosystem and co-development with clients are also seen as valuable
C9: Cost structure	Core cost structure remains largely unchanged, still dominated by labor, materials, subcontracting and central office operations expenses. However, the introduction of AI is expected to streamline or replace work processes, leading to potential savings in existing key costs. A new cost layer emerges for AI and data solutions. This includes initial investments in technology, specialist recruitment and integration (i.e. upfront costs), followed by ongoing costs for maintenance, data governance, continuous staff training and ethical oversight (i.e. maintenance costs)