Challenges in Commercial Deployment of AI IBM Watson Quy Huy Timo Vuori Tero Ojanpera Lisa S Duke 2023
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AI has been the biggest opportunity in the data-driven enterprise space over the last few years. IBM Watson and IBM Cloud AI offer several ways to bring AI to bear for any problem. However, with so many AI vendors and AI use cases, it’s hard for businesses to navigate the maze. That’s why my colleagues and I developed an online guide for businesses to help them decide between the AI offerings from IBM Watson and AI providers like Microsoft, Amazon, and Google. Based on the passage above, Could you
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AI has become a critical technology that enables many companies to gain a competitive advantage by optimizing their operations, improving their bottom line, and providing new opportunities for growth. However, in recent years, the commercial deployment of AI has faced challenges. These include lack of understanding, misplaced priorities, lack of standardization, difficulty in integrating with existing systems, lack of qualified personnel, and the need for significant budget investments. I experienced these challenges during my work as an IBM Watson Business Transformation Consultant in the retail sector. In
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The deployment of AI in business is the buzzword of the moment. resource However, deploying AI has been a significant challenge across all industries. However, AI-based solutions have been implemented in various industries, including financial, healthcare, and retail sectors. However, AI has brought challenges that have delayed its commercialization. However, businesses should address these challenges to ensure the smooth deployment of AI. 1. Cost-Effective Deployment: The cost of AI tools has been a major challenge in commercializing it
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“Commercial deployment of AI IBM Watson is a great achievement for companies. However, challenges have been identified in the commercial deployment of AI. Here are some of the common challenges identified: 1. Cost of AI System The cost of AI system is a major challenge for companies. The cost of AI systems depends on several factors, including the complexity of the system, the amount of data, the technology used, and the number of personnel required to operate the system. Companies must carefully evaluate the costs involved before purchasing the AI
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– Difficult to collect and interpret vast quantities of human-generated data for machine learning. – Limited technical capability, especially for developing applications that work on large-scale data. – Overlapping requirements of multiple users and stakeholders. – Lack of integration with the infrastructure and business models of current systems. – Complex business models and governance issues associated with large scale deployment. – Regulatory and legal concerns around data privacy and access. – Limited access to and support from human expertise.
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A few months ago, I started working with IBM Watson, and the first challenge I faced was to explain to my colleagues why it was important. They didn’t quite get the significance. more To my surprise, even IBM Watson didn’t know what AI was. My boss was delighted when I finally made a breakthrough: “Why does this matter? I’ve got an AI program in place, and it’s doing great work. How does Watson stand out from this?” I could have easily just said, “Sure! It has