AI is reshaping industries by generating content, predicting patterns, and automating tasks. However, many organizations are just beginning to explore what is possible and are realizing that the greatest value of AI lies in combining it with their own data. This process requires sufficient amounts of high-quality data, along with the right tools and skillsets, to unlock its potential.
But here's the challenge. Enterprise data is highly distributed, with significant amounts residing within on-premises environments, not to mention growing data at the edge. Gartner predicts that 75% of the data enterprises generate is created and processed outside of traditional data centers or the cloud. The public cloud model has been a boon for organizations in terms of flexibility and scalability, and many organizations may find running their AI workloads in the public cloud to be a logical choice. However, given the complexity of data governance and security in the AI ​​era, as well as the need for greater resource and cost efficiency, many organizations are rethinking their environments in favor of the cloud. Organizations are beginning to realize real benefits by moving AI models closer to where the data is processed. This significantly reduces latency and increases time to insight. More importantly, it improves data security by reducing the risk of sending sensitive information to the public cloud.
As organizations embark on an AI journey, they first identify their needs and long-term goals, then deploy these technologies in a way that supports efficiency and drives innovation while keeping the business secure. You need to build a roadmap that will help you. Here are three reasons why organizations should bring AI to their data.
1. Maintain complete control of your data
In the age of AI, data is more important than ever. According to a recent Dell study, 73% of organizations say their data and intellectual property is too valuable to put into AI tools that third parties can access. This reflects a firm belief in the value of data and the potential risks of mishandling it.
When both your AI models and your data reside securely in your environment, you have full control and can better protect against unauthorized access and leaks. This allows you to confidently apply your own data to your model, whether you're implementing search enhancement generation, retraining the model with your own data, or building a model from scratch. Additionally, you can set guardrails and retrain with high-quality data to reduce reputational risk.
2. Improved cost efficiency
Operating AI technology in your own environment can also help limit the potential escalating costs associated with public cloud services. Not all use cases require the largest model on a large infrastructure. By customizing AI to your company's specific needs, you can control costs while adopting a rational OpEx or CapEx model.
In fact, running a small (7B parameter) LLM model on-premises is 38% to 48% more cost-effective, and a larger 70B parameter LLM model is 69% to 75% more cost-effective. The data shows that it does. Public cloud options. This also means avoiding other public cloud pitfalls, such as data transfer fees and inflated storage costs. By controlling your own infrastructure, you can also control costs.
3. Maximize energy and resource efficiency
Right-sizing AI is a critical step in achieving efficiency in energy consumption. This includes choosing the right model size, such as a domain-specific model tailored to an industrial use case, or a small language model that injects a very specific knowledge base and fits it into an appropriately sized infrastructure. requires a careful eye. Balance computing and energy consumption. These types of configurations can be done on-premises or even on-device, which can significantly reduce infrastructure and energy requirements for generative AI compared to these large-scale monolithic models. Masu.
To realize the full potential of generative AI, it's important to integrate it in a way that aligns with your organization's values, operational needs, and long-term goals. In this way, organizations can harness the full power of AI to drive innovation, productivity, and growth.
Learn more about how Dell technologies can help you bring AI to your data.
This post was created by Dell Technologies. Insider Studio.