Is Implementing AI Complex? What Enterprise Leaders Should Know

For most enterprises, the question isn’t whether to use AI—it’s where and how. For customers, AI enables more personalized experiences and faster response times. Employees can achieve higher productivity with more intuitive ways of working. Businesses can open new revenue streams and build competitive differentiation while strengthening security. The potential benefits are many—but realizing them isn’t a trivial matter.

Key Areas to Consider for AI Implementation

Implementing AI in the enterprise is often costly, time-consuming, and resource intensive. To maximize your return on that investment, it’s important to have a clear understanding of what’s involved and how to make the right choices for your organization. In this blog series, we’ll explore key areas to consider for your AI implementation, including:

• Choosing a deployment model
• Anticipating and overcoming implementation challenges
• Meeting security and threat mitigation requirements
• Ensuring performance and latency

In this blog, we’ll look at the main deployment models for enterprise AI—on-premises, cloud, and hybrid—and their pros and cons in areas such as security, cost, and scalability.

On-Premises AI Deployment

In this model, AI applications and infrastructure run within your own data center on new or existing hardware. This approach offers several advantages:

• Security and compliance – On-prem deployment lets you maintain full control over your AI infrastructure. This greatly simplifies compliance requirements around data sovereignty and data privacy, making this model especially popular in highly regulated enterprises like healthcare and finance.
• Cost – Running compute-intensive AI and machine learning workloads on owned hardware can be cheaper than paying for high-performance cloud instances. With no recurring cloud fees, higher up-front costs can be offset by lower long-term costs. If most data is already stored on-prem, this also lets you avoid costly data transfers.
• Performance and latency – Keeping processing close to the data source is ideal for real-time processing, especially when you optimize your network infrastructure for AI workloads. You’re also unaffected by internet connectivity issues or cloud provider outages.

While on-prem AI can be a great choice for many organizations, there are also a few drawbacks to keep in mind. AI requires a CAPEX-intensive environment, so depending on your existing resources, you may have to spend quite a bit on costly GPUs and networking upgrades. You’ll also need a sizable team for maintenance, management, and optimization. And of course, scalability of any kind is more time-consuming and expensive on-prem than in a cloud environment.

Cloud AI Deployment

Here, AI applications and services run in cloud environments managed by cloud providers like Amazon Web Services (AWS), Google Cloud (GCP), and Microsoft Azure. The pluses of this model include:

• Scalability – With unlimited resources at the tip of your fingers, you can quickly launch and expand new AI projects without significant upfront investment. It’s also easy to adapt to fluctuating workloads.
• Rapid deployment – Cloud services provide a wide array of pre-trained models and tools, making this a good option for companies looking to quickly test new ideas or enter the market.
• Cost – There’s no need to invest heavily in new hardware, though you may see higher long-term costs due to ongoing service fees.

The simplicity of the cloud comes with a few downsides. If the AI-enabled applications are deployed on-premises, data transfers to and from the cloud can cause latency and performance issues, as well as complicating security and regulatory compliance. The deployment of AI Inference should follow where the apps are located.

Hybrid Cloud AI Deployment

A hybrid cloud AI strategy lets you tap into elements of both cloud and on-prem infrastructure. This can offer benefits in terms of:

• Flexibility – By making selective use of both types of environments, you can tailor your AI infrastructure strategy any way you like—for example, running production on-prem while doing experiments, development, and testing in the cloud.
• Security and compliance – Sensitive data can remain on-prem while non-sensitive workloads are processed in the cloud, allowing you to comply with regulatory requirements while still taking advantage of the scalability and compute power of the cloud.
• Cost management – By balancing workloads between on-prem and cloud environments, you can optimize costs by using each platform’s strengths where they’re most needed.

Hybrid cloud AI can give you the best of both worlds—but you’ll have to work a bit to get it. Integrating on-prem and cloud systems is a complex matter requiring specialized skills. Balancing and optimizing expenses across cloud and on-prem environments can be complicated as well. And of course, maintaining uniform security policies across different environments is both critical and potentially challenging.

Making the Right Choice for Your Organization

With these factors in mind, the next step is to evaluate the priorities and capabilities of your organization. Your decision-making process should focus on questions such as:

• What are our requirements around security and compliance, performance, and scalability?
• Are we more concerned about up-front or long-term costs?
• Do we have internal hardware resources available to devote to AI, or would we need to make entirely new investments?
• Do we have the internal talent needed to manage an extensive and complex new AI infrastructure?

In our next blog, we’ll explore the challenges of implementing AI in your infrastructure.

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AI Cloud & Networking Cyber Security

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November 26, 2024