The Core Value of AI: Restructuring Collaboration Rather Than Simply Enabling Automation

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In the current era of rapid advancements in artificial intelligence, perceptions of its value often center on the efficiency gains brought about by automation, while frequently overlooking a more fundamental dimension—the reshaping of collaborative models by AI. In reality, the greatest value of AI does not lie in simply replacing human labor to complete repetitive tasks, but rather in breaking down collaboration barriers within fragmented industries by radically reducing coordination costs. This enables diverse teams, systems, and tools to interact efficiently without the need for extensive prior consensus. This transformation not only restructures workflows but also fundamentally alters the underlying logic of value creation and industry competition, simultaneously presenting new opportunities and challenges for enterprise development.

Collaboration costs represent a long-overlooked, invisible friction in economic development. Here, collaboration is not merely about simple coordination or cooperation; it is the continuous process of transforming outputs from different entities into information that is mutually understandable and connectable. Its costs manifest in the mental effort expended on data conversion and output alignment, as well as the time wasted on coordinating meetings and repeated rework. In traditional models, efficient collaboration presupposes that all parties agree on standards, processes, and interfaces. However, in reality, the vast differences between systems make such alignment difficult to achieve, ultimately leading to collaboration that is highly dependent on human intervention and that incurs persistently high costs. Whether it is the differing tools used by architects, engineers, and contractors in the construction industry, or the disconnected systems of insurance companies, repair shops, and assessors in auto claims processing, this fragmented reality makes cross-organizational collaboration a common pain point across many sectors.

The emergence of AI liberates collaboration from its dependence on consensus, enabling low-cost, generalized cooperation. It overcomes barriers through two core capabilities: first, extracting usable frameworks from unstructured information, such as mining effective data from disparate sources like BIM software, site photos, emails, and annotated PDFs; and second, utilizing structured information to drive task completion, integrating previously isolated data into a unified view. This allows complementary elements to connect efficiently without the need for enforced standardization. In the construction industry, for example, AI no longer requires all parties to use a single platform. Instead, it constructs a panoramic view of the project. When an architect adjusts the position of a staircase, AI can automatically identify the impact on structural beams and instantly notify the engineer, acting as an invisible collaboration layer. This compresses work that previously required multiple coordination meetings into a matter of moments. The practices of companies like Trunk Tools and Procore validate this value; by using AI to integrate information from various tools, they provide all parties with a unified source of truth, significantly reducing the costs of verification and rework.

This AI-driven, consensus-free collaboration is profoundly reshaping the competitive landscape of industries. In the past, many companies established industry barriers and dominated markets by creating unified standards and interfaces. CCC Intelligent Solutions, a major player in the U.S. auto claims sector, exemplifies this. Its standardized damage codes and workflows became the common language of the industry, making it difficult for competitors to challenge its position due to high switching costs. However, the startup Tractable achieved a breakthrough using AI. Bypassing the battle over standards, it directly uses AI to interpret photos of vehicle damage, generate repair estimates, and seamlessly integrate with insurers' existing processes. By 2023, it was processing nearly $7 billion in claims annually, demonstrating how traditional barriers crumble under the disruptive force of AI. This shift signifies that the core of industry competition has moved from the struggle for standard-setting power to the pursuit of efficient collaboration by leveraging AI to overcome systemic barriers.

Facing the industry transformation brought by AI collaboration, enterprises need to choose development strategies aligned with their positioning to find a foothold in the new competitive landscape. The first strategy is to become a collaboration layer, acknowledging the limitations of proprietary standards and building a panoramic view of the ecosystem. For instance, the logistics platform project44 provides a unified, full visibility of shipments without requiring carriers to change their standards, becoming an indispensable "translation hub" within the ecosystem. The second strategy is to strengthen responsibility assumption, opting not to compete in neutral collaboration platforms but to focus on guaranteeing final outcomes. Maersk is a prime example; it has transformed into an integrated logistics service provider, offering end-to-end, full-chain services and building competitiveness through the reliable assurance brought by integrated control. The third strategy is to control collaboration and enable layered empowerment, constructing a unified view internally while conditionally opening certain capabilities to partners. FedEx's significant investment in AI route systems to form a core operational view and its subsequent consolidation of its hub position by sharing key data for a fee exemplifies this strategy.

Of course, AI-driven collaboration presents not only opportunities but also numerous challenges throughout its development that require collective responses from the industry and enterprises. In the short term, AI collaboration may trigger a power shift within industries. Companies that are the first to master AI collaboration capabilities and break through systemic barriers will gain a first-mover advantage. However, in the long run, technological collaboration alone is insufficient. As project scales expand and the cost of failure rises, issues concerning responsibility definition, trust building, and ownership delineation will become increasingly prominent. This necessitates that the industry, while leveraging AI to reduce collaboration costs, consciously build robust governance mechanisms, contractual norms, and systems of rights and responsibilities. Furthermore, the development of AI collaboration also faces issues such as data security, algorithmic ethics, and the fragmentation of rules. The lack of unified global governance rules increases compliance costs for multinational corporations and impedes the global advancement of collaboration.

AI's restructuring of collaborative models represents a transformation of production relations driven by technological development, with value extending far beyond the realm of automation. It liberates fragmented industries from the constraints of traditional collaboration, achieving substantial improvements in speed, cost, and innovation. It also shifts the logic of enterprise competition from resource monopolization and standard control towards capability innovation and ecosystem co-creation. For enterprises, only by recognizing the core value of AI and choosing adaptive development strategies based on their own realities can they seize opportunities amidst the transformation. For the industry as a whole, it is necessary to embrace technological progress while simultaneously building governance systems and regulatory frameworks suited to AI-driven collaboration, transforming rapid coordination into a sustainable and trustworthy ecosystem.

In the future, the advancement of artificial intelligence will continue to deepen its reshaping of collaborative models. The realization of true value will lie not in the sophistication of the technology itself, but in the ability to deeply integrate technology with industry practices, making collaboration the core driver of value creation. In the AI era, only by breaking down barriers and co-building ecosystems can the full potential of technological dividends be unleashed, propelling industries towards higher-quality development.