​​Stop starving your AI: Update your infrastructure in banking and finance​

Many banks and financial institutions are struggling to get past the starting line when it comes to turning AI pilot projects into implementation, and that’s because they don’t have the infrastructure to run AI at full strength.

Their legacy systems can’t deliver the power and functionality they need, which means they’re missing out on the full benefits of enterprise AI.

AI agents, in particular, are changing what’s possible in just about every business area, from risk detection to personalized marketing and customer service. Agents can handle repetitive work, streamline workflows and deliver real-time insights that used to require a week’s worth of manual analysis. They also keep costs in check and contribute to increasing revenue.

All these benefits will remain out of reach if your infrastructure can’t carry the load — but what’s the best way to overhaul your systems efficiently so you can close the gap between what’s available and what’s needed?

ALSO READ ->AI in financial services: Where value is already emerging

6 factors that determine AI performance

For any AI project to deliver results and ultimately transform your business, your organization needs the following capabilities:

1. Connectivity: Movement of data is crucial for AI systems, which need up-to-date information to function swiftly and accurately. You need secure pathways between systems, clouds and edge environments.
2. Responsiveness: The speedy delivery of real-time insights is one of the main benefits of using AI tools. You can respond in the moment and stay ahead of the market.
3. Scalability: Your infrastructure should be able to grow with your business and your AI ecosystem. Rigid architecture creates a time-consuming and costly bottleneck.
4. Security: In financial services in particular, technological developments must always go hand in hand with security implementations to eliminate weak spots. The free movement of data shouldn’t make the data more vulnerable to attacks.
5. Power: The advanced functionality of AI programs requires considerable computing power — much more than most organizations possess. Even a well-trained algorithm can’t do what it was designed to do without the power to support it.
6. Compliance: NTT DATA research shows that cross-geography data privacy and sovereignty are now top governance concerns for banking and financial services leaders, making infrastructure decisions inseparable from compliance strategies.

AI factories for more efficient AI infrastructure

Now that you know why you need to update your infrastructure, where do you start?

We’ve developed a service that allows you to work backward from your AI goals to configure the infrastructure you need to support it.

Our enterprise AI factories are based on expertly designed blueprints for innovation, depending on the kind of AI you’re looking to implement and the industry-specific needs in play. First, we get a clear picture of your business needs, then we recommend the technology setup that will produce the best performance, with cost benefits and a faster time to market.

For example, if you want to use AI to provide real-time information to human agents in your contact center, your digital transformation will require a significant CPU-based or GPU-based architecture. If you plan to use AI to gain insights from large datasets, your most pressing need will relate to memory or storage, and the supporting architecture will have to match.

Because we have already created the blueprints for these different needs, you can quickly identify a path forward and skip straight to deploying a streamlined technology stack.

ALSO READ -> Your bank is now an application — and that changes everything

Solve the data center conundrum with NTT DATA’s help

Whatever your technology configuration, you’ll need to host it in a data center, with access to the infrastructure and power you’ll need to support your AI capabilities. This in itself is becoming a challenge because data centers can’t keep up with the rapid growth of AI.

The hardware has come a long way — a single GPU now does what 15 CPUs once couldn’t — but raw power still comes with a price. AI infrastructure consumes a phenomenal amount of energy and generates the type of heat that demands expensive, sophisticated cooling. Building and running a data center capable of handling it all is a serious undertaking, which is why most large organizations are choosing not to.

As one of the largest data center providers in the world, NTT DATA supports leading-edge AI infrastructure. As our data centers evolve, including through ongoing innovation in cooling — using water, air and, more recently, immersion techniques — we make these advances available to our clients.

In the few regions where we don’t have data centers, we have strong relationships with other providers and can advise on how best to house your AI infrastructure.

AI-led progress has many moving parts: hardware, operating systems and network providers —all before you even start thinking about the AI platforms and applications themselves. Let us help you find the right providers and make the most cost-effective, industry-specific decisions to supercharge your AI innovation from end to end.

ALSO READ ->AI in action: Saving energy, cutting costs and keeping data centers cool

WHAT TO DO NEXT

Read more about "Why banks need to rethink functionality now and not later" to see how we help you deploy AI ecosystems.