Working on the AI Strategy Lab project was challenging, but it was also a highly insightful learning experience for me. During my previous studies in Communication Science, my exposure to AI was largely limited to using generative AI tools, such as ChatGPT, to support my academic work. I had rarely explored AI beyond the user perspective, particularly in terms of how AI systems create value, reshape business processes, and become integrated into organizational strategy. Therefore, the project became my first experience of thinking about AI not only as a technology, but also as part of a business process, operating model, and strategic decision. Our team worked on COR, an Argentine SaaS company, and developed an AI-supported customer support workflow for existing customers with product-support inquiries.
Personal Role and Contribution
My contribution covered several parts of the project. At the early stage, I participated in the discussion of possible problem cases and contributed to the idea of the prototype. Throughout the project, one of my main responsibilities was developing the business model section, focusing on value creation, value delivery, and value capture. Besides, I worked on governance and failure analysis by identifying potential risks, limitations, and privacy issues. In addition, I contributed to the future-market test, where we reconsidered the proposed strategy under a scenario in which the market and competitors also use capable AI agents. Finally, I worked on the final recommendations about what COR should implement, avoid, and test next.
Project Development: Key Decisions, Challenges, and Lessons Learned
One of the most important things I learned throughout the process was the importance of narrowing the problem. At the beginning, our idea was relatively broad: using AI to improve customer support at COR. Initially, I believed that a broader AI assistant that is capable to handle various types of customer questions would create more value as it reduces time and effort human employees spend on answering repetitive questions. However, through feedback and discussion, we gradually narrowed the scope toward product-support inquiries for existing customers. This made the project much clearer and more feasible from a practical perspective. Once the scope became more specific, we could better define what information the AI agent needs, what tasks it could realistically automate, what should remain with human employees, and how the prototype should be evaluated. Therefore, this process taught me that a strong AI strategy is often not about maximizing the number of things AI can do, but more about identifying a specific problem where AI can create meaningful and measurable value.
My contribution on the business model and future-market test significantly changed how I think about the strategic value of AI. Initially, I mainly saw the benefits of our solution in terms of faster responses and reduced employee workload, and I tended to view the AI agent itself as the main innovation. However, distinguishing between value creation, value delivery, and value capture made me realize that simply saying “We have AI” and “AI makes the process faster” is not enough to explain its business value. A practical business model requires a deeper understanding of how the technology creates, delivers, and captures value, rather than relying on a superficial assessment of its benefits.
The future-market test extended this thinking further. When we considered a scenario in which COR’s competitors also had access to capable AI agents, it became clear that the AI model itself would become less distinctive over time. Instead, a more sustainable competitive advantage would come from COR’s proprietary knowledge and COR-specific learning loop that continuously improves the agent based on existing customer interactions and verified human feedback. This helped me understand that AI should not simply be treated as an innovative feature. Its strategic value depends on how effectively it is integrated into the business and supported by complementary resources that are difficult for competitors to replicate.
Lesson Learned from Collaboration
The collaboration and the communication within the group was mostly positive and smooth throughout the process. Every group member contributed from the early stage of identifying possible problem cases until the finalization of the project. And everyone was proactively taking responsibility for sections in which they had stronger skills and experiences. This was especially helpful and insightful for me as I could learn from my group members who had more experience with AI and prototype development.
Nevertheless, I also realized that our approach to project management could have been more effective. At the beginning of the project, we did not clearly define individual responsibilities or establish firm internal deadlines. As a result, there were moments when it was unclear who was responsible for particular tasks or when certain sections were expected to be completed. Although our communication helped us overcome this confusion, I learned that effective communication alone cannot necessarily replace a clear structure of task division. In future group projects, I would maintain the same collaborative and flexible working style while establishing clearer task ownership and internal milestones.
New Understanding of AI and Business Strategy
