AI's New Business Model: What ASEAN Factories Need to Know

The Changing Landscape of Enterprise Software in the AI Era \\[10pt] For decades, the process of purchasing and deploying enterprise software has followed a well-trodden path. Executives would choose the platform, procurement teams would negotiate contracts, finance departments would approve budgets, and employees would receive licenses. This model ensured predictable costs and straightforward management. However, the advent of artificial intelligence (AI) is disrupting this traditional approach. As data becomes more valuable, the way companies purchase and use software is evolving, and this shift has significant implications for factories in Thailand, Vietnam, Indonesia, and Malaysia. \\[10pt] ## Strategic Challenges in Adopting AI \\[10pt] According to industry experts, the success of AI adoption in enterprises will not be determined by how quickly they implement the technology, but by how well they govern it. A clear strategy for AI is essential, yet many organizations are still in the early stages of developing one. Research indicates that up to 80% of companies are either just beginning to think about their AI strategy or have not yet defined it with enough clarity. This lack of strategic direction extends beyond the technical aspects and includes organizational readiness. Factories in ASEAN need to assess whether their workforce has the necessary skills and if the company culture is prepared to embrace AI. \\[10pt] ## Infrastructure and Financial Implications \\[10pt] Another critical challenge is the readiness of the company's infrastructure. Traditional software costs were relatively fixed, but with AI, the cost structure is becoming more dynamic. Instead of paying for user licenses, companies now pay for activities such as AI inference, API calls, workloads, data access, and computational consumption. This change means that IT decisions now have direct financial consequences, requiring closer collaboration between CIOs, CTOs, and CFOs. For factories in ASEAN, this means that infrastructure choices can no longer be made in isolation; they must be aligned with financial planning and governance. \\[10pt] ## The Concept of Tollgating \\[10pt] One emerging concept in the AI landscape is tollgating, which focuses on who controls and pays for access to enterprise data. In the context of ASEAN factories, this means that every time an AI system accesses data, there may be a cost associated with it. This is different from tokenization, which is about optimizing the consumption of tokens. Both concepts are interlinked but distinct, and understanding the nuances is crucial for effective AI governance. \\[10pt] ## Organizational Structure and Collaboration \\[10pt] The organizational structure is another key factor in successful AI adoption. Many companies still treat AI as a departmental initiative rather than a company-wide transformation. For ASEAN factories, this means that the CIO, CTO, and CFO must collaborate closely to ensure that technical decisions align with financial and strategic goals. By bringing these perspectives together, organizations can better understand how infrastructure choices, contractual commitments, and governance policies interact. \\[10pt] ## Concrete Takeaway for Factory Buyers \\[10pt] For factory buyers in ASEAN, the key takeaway is to start with a clear and comprehensive AI strategy. This involves assessing organizational readiness, ensuring the workforce has the necessary skills, and aligning infrastructure decisions with financial planning. By doing so, factories can avoid unexpected costs and fully leverage the benefits of AI, driving efficiency and innovation in their operations.