AI Concepts and Applications for Business Leaders – AI Strategic & Challenges

Question 1

Evaluate the effectiveness of TWO (2) initiatives powered by Generative AI that have been implemented by organizations or governments in Asia. Provide well-supported judgments on their suitability and impact, using specific examples and relevant data to substantiate your analysis.

Question 2

Based on the Generative AI initiative(s) you highlighted in Q1, examine a total of TWO (2) prevalent challenges that such organization(s) or government(s) face when implementing these initiatives. Then, formulate a comprehensive solution to effectively alleviate the impact of each of these challenges.

Question 3

Think of a small-to-medium sized organization and propose FIVE (5) strategic actions that organizational leaders can take to successfully implement Artificial Intelligence (AI) initiatives. Your response should address leadership practices, resource alignment, and cultural transformation. Please provide rationale for the effectiveness and relevance of these actions in driving sustainable AI adoption and competitive advantage.

Question 4

Evaluate how a multi-national corporation can design and implement a comprehensive ethical governance framework to ensure responsible AI adoption. Your response should address key dimensions such as algorithmic fairness, data privacy, regulatory compliance, and leadership accountability. Explain how these elements contribute to cultivating trust and long-term sustainability in AI-driven initiatives.

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Expert Answers on Above Questions on AI Strategic Challenges

Evaluation of two generative AI initiatives in Asia

The two generative AI initiatives selected for the purpose of analysis is aI chat assistants in public services as utilised by Singapore government, and secondly AI powered customer and risk systems utilised by an insurance company Ping in China. The Singapore government has implemented an AI chat bot with the objective of automating the queries of its citizens. It is quite effective in terms of achieving reduction in the response time from days to minutes, improved overall service accessibility, help in reducing cost reduction as the manpower cost reduced significantly, and improved the overall image of Singapore as an AI ready government at the global level. It is quite suitable because of strong digital infrastructure across the country, high level of digital literacy among people and clear regulatory frameworks. The overall impact of this initiative is improved efficiency, better satisfaction among people and operational productivity.
With respect to the initiative by China’s Insurance Company of I powered customer and risk systems, it is also quite effective in supporting millions of AI driven customer interactions, enhances the claim processing process and also assists with fraud detection accuracy. It is suitable because of having a large data ecosystem and strong AI investment capability further supported it. The impact is better operational efficiency and customer experience but it also raises issues in terms of data governance and ethical risk.

Two key challenges and solutions

The most significant is data privacy and regulatory risk because generative AI makes use of large databases which increases the risk of data breach, and loss of public trust. The solution can be to implement a strong data governance framework, develop an AI ethics committee, and perform regular audits. The second challenge is the resistance from the workforce because of the skills gap, and this particular problem can be solved by upskilling and organising AI literacy programs, positioning AI as augmentation and encouraging innovative participation through incentives.

Five strategic actions for SMEs

Small and medium enterprise leaders should consider developing a clear vision, initiate a high impact pilot project, perform investment in Ai skills and partnerships, build data infrastructure and positively support cultural change.

Ethical governance Framework for multinational companies

The Framework for multinational companies with respect to Ai governance should be based on algorithmic fairness, data privacy, regulatory compliance and leadership accountability. It will help in building trust, reducing operation risk, avoid any legal penalties and ensure equitable outcomes.

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