What Infrastructure and Analytics Capabilities Do Companies Need to Support AI?

Businesses are quickly adopting artificial intelligence (AI) programs to improve productivity and efficiency. According to Harvard Business Review, 55% of companies reported they accelerated their AI strategy in 2020 due to the COVID-19 pandemic, and 67% expected to further accelerate their AI strategy in 2021.

However, for these programs to run smoothly and effectively, businesses need the right infrastructure in place. This includes big data storage, robust analytics, AI networking infrastructure, and powerful computing resources.

Here’s what you need to know about the technologies and infrastructure you need to support AI in your organization.

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Data Storage

Big data is a critical component of artificial intelligence (AI). AI programs rely on large amounts of data to “learn” and make predictions. To support these programs, businesses need robust big data storage capabilities.

Fortunately, there are multiple ways businesses can obtain this storage. They can do so internally, through service providers and MSPs, or via the cloud.

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Internal vs External vs Cloud-Based

When it comes to big data storage for artificial intelligence (AI), businesses have three primary options: internal storage, external storage, and cloud-based storage. Each option has its own set of benefits and drawbacks that businesses should weigh before making a decision.

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Internal Storage:?This is when companies store all their data within their infrastructure. This can be done through on-premises servers or by collocating proprietary services in third-party data centers.

The main benefit of internal storage is that businesses have complete control over their data, its security, and at least some of its infrastructure. They can access it whenever they want, manage it however they want, and use it however they want.

The downside to internal storage is that it is expensive. Adding internal storage costs on top of an AI project can lead to considerable overhead.

External Storage:?This is when companies store their data with an external provider, such as an MSP. The main benefit of external storage is that businesses don’t have to worry about managing the data themselves.

Additionally, external storage can be more secure than internal or cloud-based storage since providers typically have robust security protocols in place.

Of course, the traditional dynamics of external data storage have changed significantly.

Companies can’t simply rely on an external repository to keep their data safe. They need constant access to their data from any location, especially when using their data to support AI. This is what created the impetus for alternative data storage models, including cloud-based storage and even edge computing.

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Cloud-Based Storage:?This is when companies store their data externally but can access it remotely via the cloud. The main benefit of cloud-based storage is that businesses can scale their storage capacity up or down as needed. Additionally, cloud-based storage is typically more reliable than internal or external storage.

Analytics

Data analytics is a critical component of artificial intelligence (AI). AI programs rely on large amounts of data to “learn” and make predictions. To support these programs, businesses need robust data analytics capabilities.

Analytics solutions produce usable insights from data. However, they are also critical in producing usable data that can be applied in various contexts across the enterprise.

AI programs tend to have specific applications. For example, many companies use AI to predict changes in the supply chain or to make forecasts about future sales activity. These predictions can’t be accurate without an abundance of data points.

The more data that is available, the better AI programs can perform. This is why businesses need robust data analytics capabilities to support their AI initiatives.

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Networking Infrastructure

Networking infrastructure refers to the physical and logical systems that enable communication among devices on a network. Networking infrastructure is essential for businesses, as it allows employees to share data and communicate with each other.

There are several components of networking infrastructure. The most important are the switches, routers, and firewalls that enable communication among devices.

To support AI programs, businesses need a networking infrastructure that can handle the large amount of data that is generated by these programs. They also need an infrastructure that is scalable and can be easily expanded as the needs of their AI programs change.

That means implementing networking infrastructure, which requires a significant amount of physical hardware, is not usually cost-effective when conducted internally. Although most major companies need some level of proprietary infrastructure in place, Infrastructure-as-a-Service (IaaS) arrangements are much more affordable and scalable.

IaaS enables organizations of all sizes to support ambitious AI programs.

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Compute

At the most basic level, compute is the process of executing a set of instructions in a specific order. Computers use compute to run programs and applications.

But in recent years, a company’s access to computing capabilities relates specifically to its ability to run advanced programs, perform analytics, and support advanced applications like artificial intelligence and machine learning.

Artificial intelligence is a process of programming a computer to make decisions for itself. This can be done through several methods, including but not limited to: rule-based systems, decision trees, genetic algorithms, artificial neural networks, and fuzzy logic systems. The goal of artificial intelligence is to create a system that can learn and improve on its own through experience.

Machine learning is a subset of artificial intelligence that focuses on the ability of machines to learn from data and improve their performance over time. Machine learning algorithms are used to automatically detect patterns in data and then use those patterns to make predictions or recommendations.

As you can imagine, the computing capabilities one needs to run AI programs are massive compared to traditional programs. Most organizations can’t deploy a large enough computing capability internally to support their own AI applications, which is why they turn to providers like AWS and Azure.

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Innovating with AI

As artificial intelligence (AI) becomes more prevalent in businesses, the need for robust infrastructure and analytics capabilities increases. AI requires a lot of data storage, powerful analytics tools to make sense of that data, an AI-friendly networking infrastructure, and plenty of computing power.

Companies can build these technologies themselves, but it’s often more affordable to outsource them through cloud-based services or an MSP. If you need additional infrastructure in place to support your company’s AI initiatives, let us help you find the right technology solutions so you can focus more on innovating and less on management.