Generative AI: Catalyzing the Next Wave of Corporate Unbundling

In 2000, McKinsey's seminal article "???????????????????? ?????? ??????????????????????" highlighted how declining interaction costs—expenses related to exchanging goods, services, or ideas—were reshaping industries. ?????????????????????? ??????????, as defined in the original article, are "the costs associated with coordinating, communicating, and decision-making between individuals or units within a company or between companies." In late 70s and early 80s advancements in technology, particularly the rise of personal computers, enabled companies to specialize, leading to the fragmentation of traditional, vertically integrated corporations.

The story of the computer industry illustrates the crucial role that interaction costs play in shaping industries and companies. These costs represent the money and time expended whenever people and companies exchange goods, services, or ideas. The exchanges can occur within a company, among companies, or between a company and a customer, and they can take many everyday forms, including management meetings, conferences, phone conversations, sales calls, reports, and memos. In a real sense, interaction costs are the friction in the economy. Taken together, they determine the way companies organize themselves and form relationships with other parties. All else being equal, a company will organize in whatever way minimizes overall interaction costs.

Fast forward to today, and we're witnessing a similar paradigm shift with the advent of Generative AI (GenAI) and AI agents. These technologies are further reducing interaction costs, prompting businesses to reevaluate and restructure their operations.

The Role of Generative AI in Reducing Interaction Costs

• Automation of Routine Tasks: GenAI can handle tasks such as customer service inquiries, content creation, and data analysis, which traditionally required significant human effort. This automation not only accelerates processes but also reduces labor costs.
• Enhanced Decision-Making: AI agents can process vast datasets in real-time, providing insights that facilitate quicker and more informed decision-making, thereby minimizing delays associated with information gathering and analysis.

Implications for Business Structures

• Increased Specialization: As interaction costs decline, companies can afford to specialize further. This specialization allows businesses to focus on their core competencies while outsourcing or automating peripheral activities.
• Emergence of New Business Models: The capabilities of GenAI open avenues for innovative business models, particularly those that leverage AI-driven services to meet niche market demands.

Economic Impact

McKinsey estimates that GenAI could contribute an additional $2.6 trillion to $4.4 trillion annually across various industries by enhancing productivity and efficiency.

Challenges and Considerations

• Implementation Costs: Developing and deploying GenAI systems require substantial investment, with training large models incurring significant expenses depending on complexity.
• Data Requirements: Effective AI deployment necessitates access to large volumes of high-quality data, which can be a barrier for some organizations.
• Ethical and Compliance Issues: The integration of AI raises concerns regarding data privacy, security, and ethical use, necessitating robust governance frameworks.

The rise of Generative AI and AI agents is driving a new wave of unbundling in the corporate world. If these technologies can significantly decrease interaction costs for businesses, it would enable businesses to specialize and innovate, leading to more agile and efficient organizational structures.

However, to fully realize these benefits, companies must navigate the associated challenges thoughtfully.

References:

• Hagel III, J., & Singer, M. (2000). Unbundling the Corporation. McKinsey Quarterly.
• Chui, M., & Manyika, J. (2023). The economic potential of generative AI: The next productivity frontier. McKinsey & Company.
• Dencik, J. (2023). The hidden costs of AI: How generative models are reshaping the economics of artificial intelligence. IBM.