Demystifying the Initial Deployment Challenges of Artificial Intelligence Used Cases

Artificial Intelligence (AI) has been a buzzword for quite some time, and it is undeniable that it is real and will play a significant role in various aspects of life. However, the way it is positioned, and the reality of its implementation can be quite different. Organizations are beginning to grasp the significance of this for their operations and the necessary investments they must make. Many organizations struggle with the challenges of developing a clear roadmap, making informed investments, and navigating legal requirements when adopting this technology. Despite the fact that AI is a reality and requires the attention of every organization, there appears to be some prejudice against organizations claiming to be implementing AI and having an AI strategy. The development of AI implementation use cases and industry-specific models is still in progress. Conversely, the hardware required to support AI implementations is beset by problems of supply and demand as well as affordability concerns.

In an effort to partially debunk it, I am outlining some of the obstacles that must be surmounted in the present ecosystem to ensure accessibility, affordability, and long-term viability.

1. Use Cases:

In their transition to cloud computing, organizations have made substantial investments in redesigning their architectures over the past decade; consequently, nearly everything in the technology sector is now cloudified. Understanding the use cases for AI implementation and how it is beneficial for their organization is crucial. At the enterprise level, architecture is required to understand the roadmap and return on investment, along with addressing challenges such as data privacy and security concerns when implementing AI solutions. The growing volume of data being collected and utilized in AI algorithms gives rise to significant ethical and legal concerns that require attention.

Incorporating AI into current systems can be a challenging task that demands substantial resources and specialized knowledge, posing a potential obstacle. In order to achieve accessibility, affordability, and long-term viability, organizations need to ensure that their AI implementations are in line with their organizational goals, values, and budgets.?

2. Models:

The use cases for AI play a crucial role in defining the objectives, return on investment (ROI), subjective aspects, and the roadmap for its implementation. It is essential to gain a deep understanding of them and the associated facts before proceeding with AI adoption. They serve as a fundamental component in determining the specific learning model that is needed for different organizations.

A substantial degree of reuse is required throughout the process of implementing and utilizing these models. Moreover, a considerable amount of effort is devoted to the evolutionary phase, during which models from various industries are being developed. Achieving industry-wide applicability of these models and methodologies requires further fine-tuning. However, the framework for standard businesses to utilize them is still unclear. Without a clear framework, organizations may struggle to effectively implement and utilize them in their own industries. The lack of standardization can lead to confusion and inefficiency in the learning process. Hence, it is crucial for the industry to establish a common framework that outlines best practices and guidelines. This will ensure that organizations can confidently adopt and adapt these models to suit their specific needs, ultimately driving better learning outcomes and success in their respective industries.

3. Accessibility, affordability, and supply-demand issues:

There is a significant amount of supply and demand scarcity for different types of GPUs that are required. It needs to be understood why such a significant gap exists. The demand for AI hardware is increasing, but the supply is not keeping up. This scarcity is driving prices significantly higher than they need to be and is causing affordability issues for most organizations, especially in the mid-to-startup segment and for those looking to implement AI solutions, potentially preventing them from fully realizing its benefits.??

Unlike cloud technology, which was deployed more than a decade ago and was accessible and affordable to most startups and mid-size companies, the current trend of GPU’s pricing is making the technology prohibitively expensive. At an industry level, this needs to be studied to figure out how prices can be driven down so that it is practical for companies to implement AI. Any new technology is useless unless it is accessible to all enterprises in their own capacities. In order to fully leverage the potential of AI and achieve the best results in their industries, organizations must address the demand and supply issues related to GPUs.??

This might involve establishing industry partnerships with GPU manufacturers to secure a reliable hardware supply. In an effort to address issues of demand, supply, and affordability, research and development organizations must investigate new startups that offer AI-based alternatives to conventional GPU models. By effectively addressing these challenges, organizations can ensure they have the necessary tools to fully leverage the power of AI and stay ahead in the market.

In conclusion, organizations may face numerous challenges when it comes to implementing Artificial Intelligence (AI). Developing a clear roadmap and understanding the use cases for AI implementation can be a significant challenge. Organizations must also tackle challenges like data privacy and security concerns when implementing AI solutions, demonstrating a professional approach. The absence of uniformity in AI models and methodologies can result in confusion and inefficiency during the learning process.

The supply and demand challenges associated with the hardware needed to support AI implementations can create affordability concerns for many organizations. To successfully address these challenges, organizations need to skillfully navigate the obstacles and ensure that their AI implementations align with their organizational goals, values, and budgets.

#AI #ArtificialIntelligence #GPU #Cloud #datacenter National Informatics Centre, MeitY nasscom ASSOCHAM (The Associated Chambers of Commerce and Industry of India) CII Infrastructure