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Jay
Jay

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AI is Everywhere Around Us

AI has been around since 1950, and over the years, it has grown with significant investment and development. However, when ChatGPT was released on November 30, 2022, it truly sparked a wave of excitement. Since then, businesses have been eager to explore AI, investing in it to accelerate their processes, innovate, and boost their revenue.

I'm sure that due to AI innovations and growth, the way we work will definitely change, depending on the industry.

1. Do I need AI for my business?

The answer is depends on the size of your business, and it's important to carefully assess how AI can benefit you without straining your running budget. I recall when cloud computing first emerged, particularly with the launch of Amazon Elastic Compute Cloud (EC2) in August 2006. At that time, everyone rushed to host their services on EC2, even small businesses migrating their WordPress sites. For example, many people ended up paying around $300 a month for EC2, compared to just $400 annually for traditional hosting.

We don’t need to host a small business WordPress site on an Amazon EC2 instance unless it’s handling significant traffic.

Therefore, we need to take a thoughtful approach—conducting a thorough investigation to determine whether AI is the right fit for your business at this moment. It’s not about jumping on the AI bandwagon just because everyone else is doing it; we need to evaluate if and when it makes sense for us to implement AI.

2. If AI is going to accelerate my business activities, what are the factors should I consider?

Cost – Each AI model comes with its own costs, especially when connecting to different systems, managing inbound and outbound calls, and handling tokens. It’s crucial to monitor real-time AI costs to avoid overspending. Otherwise, we risk paying more than the actual revenue generated, which could lead to business losses and potentially force us to close if we can’t recover the costs.

So therefore, we need to have a proper checklist that includes cost management and other critical factors before implementing AI into your business activities.

Caching Options – We might explore caching strategies, especially if users are asking the same questions repeatedly. Caching can help reduce costs by retrieving data from the model more efficiently. However, if the business involves CRM applications, caching won’t be feasible as each customer is unique, and their responses will vary. Caching may only be applicable to general AI applications.

Security, Governance, and Risk Management – It’s essential to consider the security and governance of AI systems, as well as the risks associated with implementing AI. There should be a checklist of items to tick off to ensure compliance, protect data, and minimize potential threats.

3. How can I reduce my AI spending costs?

Reducing AI costs requires careful planning and analysis. As we know, AI costs are typically based on tokens, as well as inbound and outbound calls to connect to various systems. One way to reduce costs is by hosting your model in your own Docker containers, which can help lower expenses.

Note : Please visit the Hugging Face website at https://huggingface.co to explore various AI models and see how to host them in your own environment.

However, at a high level, it’s important to undertake a comprehensive exercise to evaluate all potential cost-saving strategies and determine if this approach will be effective in reducing your AI spending.

4. There are many AI service hosting providers in the marketplace. so which one is the most cost-effective and best for your needs?

The answer depends on your business use cases and the specific features you require. It’s essential to perform a proof of concept (POC) to determine which provider is the best fit for your business while considering factors such as cost and functionalities which requires for your AI application.

Additionally, keep in mind that once your AI application is hosted on a particular cloud provider, migrating it to another can be a complex and costly process. Depending on your application implementation, the operational costs might be low, but migration could involve redevelopment and fine-tuning of AI application, which can increase both time and other expenses.

Finally, considering the points mentioned above, these are just a few of the factors to take into account when integrating AI into your business. In addition to these, there will be a range of other exercises and operational policies that need to be carefully planned and considered based on your industry.

For instance, you will need to evaluate how AI will fit into your existing workflows, what kind of data governance practices are required, and how AI will interact with other systems. It's also important to establish policies around the management of AI systems, such as who will oversee its performance and how it will be updated over time.

This means that adopting AI isn't just about selecting a provider or model; it involves a comprehensive approach to ensure that the technology aligns with your business goals, can be implemented effectively, and is managed properly throughout its lifecycle.

5. What are the crucial key factors(in terms of sustainability) we should consider when using AI?

While we often focus on the impressive capabilities and potential of AI, it’s equally important to consider other crucial factors that come with its use. One of the most significant concerns is the sustainability of AI systems. AI technologies, especially those that rely on large-scale data processing, require substantial amounts of energy to function. As the demand for AI services continues to rise, so does the energy consumption associated with it. Many companies that offer AI services operate extensive data centers, which are energy-hungry and require vast amounts of electricity to keep them running smoothly.

However, the environmental impact of these data centers goes beyond just electricity consumption. They also use large amounts of water for cooling purposes and other environmental resources to maintain their operations. The extraction and usage of these utilities can strain local supplies, especially in areas where water or other resources are already scarce.

This high demand for both energy and environmental resources can have a long-term impact on local ecosystems and communities. Over time, the environmental effects of this increased consumption could contribute to higher carbon emissions, water shortages, and other resource-related challenges.

Given the rapid pace of AI development, it’s crucial that energy efficiency and sustainability are prioritized in how these systems are designed, implemented, and maintained. This includes not only reducing energy consumption but also minimizing the use of water and other environmental resources. Whether it's using renewable energy sources, improving the efficiency of hardware and software, or optimizing the operation of data centers, there are several ways to make AI more sustainable.

Ultimately, we all have a responsibility to ensure that AI is used in a way that minimizes its environmental impact. As we continue to advance and harness the power of AI, it’s essential to do so with a mindset that balances innovation with thoughtful, sustainable practices that protect our planet and its resources for future generations.

Thank you for taking the time to read my post and have a wonderful day ahead!.

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