Challenges in Commercial Deployment of AI IBM Watson Quy Huy Timo Vuori Tero Ojanpera Lisa S Duke 2023 Case Study Solution

Challenges in Commercial Deployment of AI IBM Watson Quy Huy Timo Vuori Tero Ojanpera Lisa S Duke 2023

Alternatives

In the early days, it was mostly a niche industry; today, it’s an essential part of many businesses. This essay will discuss three challenges that IBM Watson, a superb and revolutionary AI product from IBM, can be faced during commercial deployment. First, the cost issue: It’s been predicted that the cost of AI and Big Data tools will decrease significantly in the future. This reduction in the cost of acquiring these tools, in particular IBM Watson, will open a plethora of opportunities. As more and more organizations adopt A

Case Study Analysis

Challenges in Commercial Deployment of AI IBM Watson Artificial Intelligence (AI) has become an integral part of our lives, and it is widely used by various organizations. In recent years, it has also become a driving force for commercial growth, with large organizations investing in it. IBM Watson, a cognitive system developed by IBM, has been deployed in various industries as a service to assist businesses with various tasks such as natural language processing, machine learning, and predictive analytics. In this paper, I will discuss the

Case Study Solution

Artificial Intelligence (AI) IBM Watson is becoming increasingly essential in many industries, but challenges still exist when it comes to its deployment in commercial settings. In this case study, we’ll examine the challenges that companies have encountered while utilizing IBM Watson to analyze large volumes of data. We’ll also examine the potential benefits of AI in enhancing their operations and how this may impact customer relationships. The first challenge I faced in utilizing IBM Watson was the lack of familiarity in a lot of stakeholders. When it came to

VRIO Analysis

1. Technological Limitations IBM Watson uses a wide range of advanced technologies, including natural language processing (NLP), machine learning, and robotics. However, the scalability and efficiency of these technologies still require significant investment and ongoing development. IBM Watson’s current platform is primarily focused on large-scale datasets and a small subset of businesses, with no easy integration of AI into smaller or niche markets. 2. Security and Privacy Concerns AI systems can be exploited by cybercriminals for

Marketing Plan

Artificial intelligence (AI) is undoubtedly a game-changer in various fields of human activities. browse around here It is rapidly advancing, and its impact on different domains is significant. However, commercializing AI solutions for everyday life remains a challenge. This section addresses a few key challenges in commercializing AI solutions, such as: 1. Funding: There is a significant gap between research and commercialization, and raising adequate funds is a huge hurdle in the development of AI applications. 2. Data: Data is a

Porters Five Forces Analysis

I do not have experience of implementing, and managing such AI technology, but I can provide you with practical insights, and best practices. As far as challenges, I can say this: 1. High Cost of AI technology implementation (the most significant challenge) Artificial intelligence has been in development for a decade or more, and the technology’s capabilities are quite impressive. However, the initial cost of purchasing the software, hardware, and infrastructure, and its implementation to ensure maximum utilization is often quite high. Many companies invest

Evaluation of Alternatives

A commercial deployment of AI IBM Watson is an important step for businesses looking to implement the technology. However, while the potential benefits of AI are widely recognized, challenges remain in making it a reality. Some of the main issues that enterprises face include: 1. High Cost: The initial costs of implementing AI are often high, especially for smaller enterprises. The expense of setting up and maintaining a robust infrastructure can be prohibitive for small businesses, making it more difficult to justify the cost of AI compared to other technology solutions.