The AI Revolution: How to Prepare Your IT Infrastructure

Today, AI’s capabilities are already beyond what we ever imagined and increasing exponentially. You can use AI to run your entire inventory, manage and fulfill orders, and process shipments. It can also provide order information, give real-time shipping updates, and send customers a gift card to apologize for the delay.

With these advanced capabilities, business owners need the right digital infrastructure to take full advantage of AI’s potential. If you want to successfully leverage AI and machine learning, you need to prepare your machine learning infrastructure first.

Is Your Business Ready

At Cox Business, we have AI infrastructure experts ready to guide you today. Reach out to learn how to prep your organization for next-generation computing.

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How AI Puts Infrastructure to the Test

AI infrastructure isn’t “different” than traditional IT, but it’s faster and more efficient to transmit data to the applications that need it. For example, machine learning consumes enormous chunks of information. It then sends it back to a computer, which sends it back to an endpoint. Each of those transmissions has to happen extremely fast, or else the system fails. With a faster infrastructure, you make quick data transmissions possible.

Below are a few important considerations

Processing Power

Most of the time, AI needs graphics processing units (GPUs) to run thousands of computational processes simultaneously. This provides the engine for many AI operations.
Some chips are also designed to run AI processes. Their physical infrastructure reduces the time it takes to complete computations and reduces the energy needed to power AI and ML processes.

Data Storage

Training is everything for AI and machine learning since it requires massive datasets. This requires scalable, high-performance storage systems that deliver real-time data to AI-powered apps. This way, AI can accurately imitate human-like behaviors like recognizing images and making split-second decisions.

Network Bandwidth

Your network’s bandwidth is crucial for any AI implementation because when you increase bandwidth, you decrease the chances of data bottlenecks slowing processes down.
With high-speed networking, fyou can have dozens of AI-powered processes running on different computers in your office. As each process sends and receives data to and from a server, it has enough bandwidth to manage the task. You could still use AI without the correct bandwidth, but only on one or a few machines.
You can also optimize data pipelines in a high-speed, high-bandwidth network, which makes it possible to prioritize data for specific apps, ensuring they function as needed.

Security Considerations

Powerful AI security is crucial because it prevents hackers from taking over your accounts or corrupting data on which your system depends.

Business should use:

Multi-factor authentication to prevent unauthorized access to training and stored ML data
Role-based access to only those who need to work with your systems to do their jobs have access.
Firewalls to prevent malware from penetrating your network and infecting computers that perform AI tasks

Additional Steps to Prepare Your IT Infrastructure

Whether you use cloud computing for AI or an on-premise system, the first step is to set up your infrastructure. This involves:

Assess your current infrastructure. For example, you may need a network with more bandwidth or to transition from copper to fiber optic connections.
Develop an AI infrastructure strategy. Your roadmap may include bandwidth considerations, identifying cloud providers that can scale quickly, or setting up an on-premise server with enough throughput and processing power to run your AI systems.
Choose the right technologies. Networking, data storage, security, and processing must be slightly different for business-critical AI processes. Networking, processing, and data storage must be faster, and your security may need more segmentation.
Consider cloud solutions. With cloud computing as part of your IT strategy, you can scale far faster and enjoy advanced security, thanks to your cloud provider’s services.
Prioritize scalability and flexibility. Your infrastructure needs to be flexible enough to enable new AI processes without slowing down existing ones. It also needs to integrate with apps and processes AI may need to access.

Best Practices for Managing AI Infrastructure

Even though managing an AI infrastructure may seem like demanding, you can develop a smooth, dependable system. Some best practices to follow are:

Monitoring and optimization. Monitor the speed and accuracy of AI tools, the performance of databases, apps that run on AI, and computers handling the workloads.
Data governance and security. Your governance policies should control who is responsible for sensitive data, how it’s stored, the encryption protocols you use, and how to align these with compliance concerns.
Collaboration and integration. AI apps often have to pull data from other apps in your ecosystem—whether in the cloud or on-premise—and humans can collaborate with each other and AI systems. But you need to make sure you can integrate ahead of time.
Talent development. Whether your AI adoption is to enable a deep digital transformation or you just want to speed up a single niche process, those involved will need training. It’s better to err on the side of caution, giving them enough time to learn the new system before holding them accountable for how well they use it.

Is Your IT Infrastructure Ready for AI?

A powerful and adaptable IT infrastructure lays the foundation for a robust AI implementation. At the top of your infrastructure improvement checklist should be enabling adequate throughput. Data is the lifeblood of any AI system, and you will make your AI and ML solutions thrive by ensuring they flow smoothly and quickly.

It’s essential to assess your current infrastructure from the perspective of the integrations, processing power, and storage capabilities you’ll need a few years down the road. Identifying a scalable cloud provider makes it easier to future-proof your setup, preventing unexpected issues in the coming months and years.

As an IT leader, you should proactively plan and invest to bolster your infrastructure so it’s ready to handle AI-powered solutions. By taking steps now, you make it easier to adopt AI if the competitive landscape requires a shift.