How Financial Firms Can Get AI-Ready

Editor’s Note: This blog was originally published in January 2025. It was updated in February 2026 to include the latest information.

When it comes to AI in financial services, we can divide things into two generations:

• The present (classical AI)
• The future (generative and agentic AI)

Today, classical AI is widely used across the industry. In fact, banks have been using it for decades now. In the early days, they called it by different names, including “machine learning” and “business analytics.” Over the years, they’ve gotten very good at using predictive models for use cases like fraud detection and risk modelling.

As the next generation of GPUs is launched, classical AI will evolve fast, and models will be able to process much larger datasets much faster. This results in banks being able to perform Monte Carlo simulations, common for predictive modelling, on a greater scale to provide a clearer, faster picture of risk.

The next generation of AI in financial services will involve using large language models (LLMs) to support employees and customers and agentic AI workflows to automate the delivery of services. In some cases, this means firms will evolve their products and services to be better and faster, with the aim of improving customer satisfaction and increasing revenue and profitability. Embedding LLMs and agents into the customer-facing side of the bank is inevitable and one of the most exciting possibilities for AI.

Three AI use cases for financial services that continue to gain momentum

In addition to evolving existing use cases, advanced AI technology will unlock use cases that are entirely new. It’s not a question of whether firms will adopt these use cases; it’s only a matter of when and how they’ll do it. Let’s take a quick look at a few of these use cases, and what firms need to do to prepare.

Algorithmic trading

Electronic trading is transitioning to an inference-first architecture. This means that high-frequency trading (HFT) firms are using specialized hardware such as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) for on-chip AI inference. This allows them to achieve sub-microsecond execution within the trading stack.

This ultra-low latency is complemented by agentic AI, which has moved from pilot programs to a mission-critical operational layer across the front, middle and back offices:

• In the front office, agents autonomously integrate multimodal alternative data for real-time liquidity discovery.
• In the middle and back offices, agents orchestrate complex workflows like real-time risk assessment, proactive fraud detection, and bot-to-bot settlement negotiations.

Firms are prioritizing hybrid infrastructure that balances high-performance GPU clusters with edge-native processing. This is one reason that inference workloads are expected to drive roughly two-thirds of all AI compute in 2026, up from only one-third in 2023 and half in 2025.[1] With the right inference hardware in the right locations, firms are empowered to ensure data sovereignty, rigorous governance and the rapid response times that today’s market dynamics demand.

Customer engagement

With generative AI, banks can provide a human interface that enables better, more personalized customer service. This could include collecting information about a particular customer and then suggesting customized financial planning for that customer. Chatbots could also improve the user experience by helping customers quickly gather the information they need to make informed decisions.

Operational efficiency

It’s an old business adage that anything that can be automated, will be automated. The financial services industry is no exception to this rule. Banks still perform many processes that are ripe for automation, and new agentic AI capabilities are emerging that can help them do that. This could help drive greater efficiency and customer satisfaction.

AI agents enable automation by performing different tasks in pursuit of a common goal. For instance, if the goal is to respond quickly to loan applications, then one agent might take in data from the applications, while another performs compliance checks and a third helps suggest the loan rate.

We’re also seeing the emergence of agentic AI in commerce. This means that agents can now buy and sell from one another on behalf of human users. This is a promising use case, but there’s a lot that must happen before it can go mainstream. For instance, the industry needs to establish standards for how agents will interact with one another and how payments will be processed.

What’s stopping financial firms from adopting advanced AI?

In some ways, identifying use cases is the easy part. In fact, most firms are already testing these use cases internally. However, there’s a big difference between testing AI models and moving them into production.

For one thing, firms need to have complete confidence in the accuracy of their models, and that alone is a tall order. Also, the industry is heavily regulated, which means models need to be both accurate and compliant. Even if a firm creates a model that meets both criteria, it would still need to implement that model into its business workflows, which is not as simple as flipping a switch.

Let’s consider banks planning to use chatbots in their loan approvals process. First, they need to clearly articulate the business value they expect to gain from doing so; it can’t just be a matter of buying into the hype. Then, to enable a trusted approach to AI, they need transparency into why their chatbots return the results they do. This includes ensuring that there’s no hidden bias impacting the results. Then they’ll need to change the way they lend money to incorporate the new automated processes, which could involve completely rewiring a massive global operation.

It’s easy to see why firms are taking a cautious, measured approach to advanced AI use cases. This transformation will take years to fully play out.

How banks and trading firms can start getting AI-ready

Despite the challenges ahead, there are steps that firms can take today to ensure they’re ready to implement advanced AI use cases when the time comes. Like everything else in AI, it all comes back to the data. Companies need to know they have the right data from the right sources at the right time, curated and ready to be ingested by their AI models. Perhaps most importantly for the performance of the application and the customer experience, their future-proof AI data strategy needs to define where the data will be stored. It’s essential to get this right today, because the future AI inference models will need to be deployed in proximity to the data.

Data readiness is a major bottleneck that could stand in the way of implementing AI inference at scale. Many firms have focused on getting the right models, only to realize they don’t have the right data to feed into those models. In some cases, they do have the data, but it’s inaccessible because it’s spread across different locations, structures and types, and environments. This problem is known as data sprawl. Firms need to start implementing data management policies to ensure that data can move between workloads quickly. This is one reason that low-latency connectivity is an essential part of any AI infrastructure strategy.

Today, most financial firms use public cloud environments to host their AI testing. This may have seemed like the logical choice back when they first planned the testing of their designs. They likely saw it as a quick and convenient way to access the AI resources they needed. However, when it comes time for them to transition from testing to production, the public cloud may no longer be the best option. For one thing, using cloud native storage can limit the mobility of AI datasets, contributing to data sprawl.

On the other hand, deploying AI inference in their own data centers may not be the answer either. Many firms’ owned-and-operated data centers have constraints around space, power, cooling and specialist skills, meaning that they probably can’t support the latest generation of GPU hardware. Building inside on-premises data centers also limits infrastructure flexibility. When the need inevitably arises to move models and data, doing so will be difficult and expensive.

Firms will need to deploy their AI inference close to all their data sources, including mainframes, trading platforms, payments and fraud data. This need for proximity will drive many of the decisions about where the data needs to reside. As they place their current datasets, firms need to plan ahead for the space and power requirements of any AI GPUs they might deploy in the future.

It’s clear that public cloud and on-premises data centers aren’t appropriate for all scenarios. So then, what option does that leave for financial firms looking to get AI-ready? Deploying inside a high-performance colocation data center could be the answer.

Colocation at Equinix enables an AI-ready data strategy

Leading colocation providers like Equinix are always investing in AI-ready technology, including the additional power, advanced air-cooling and liquid-cooling capabilities, and operational skills needed to keep GPUs running.

Colocation at Equinix can also simplify the process of implementing a distributed AI infrastructure strategy. This is when firms host different AI workloads in environments that are ideally suited to support them: training workloads in locations with ample compute capacity, inference workloads in proximity to data sources, and sensitive processing in environments that were built with data privacy and sovereignty in mind.

With the help of Equinix Distributed AI™, you can deploy the right AI workloads in the right places, whether that means a colocation environment that you own and control or a cloud environment accessed via our industry-leading portfolio of cloud on-ramps. You can also find the networking tools you need to ensure the uninterrupted flow of data across your distributed AI infrastructure. This includes Equinix Fabric®, our global network for on-demand virtual connections, and Equinix Network Edge for virtual networking devices from top vendors.

AI-readiness is just one example of what separates high-performance data centers from the commodity facilities that firms have typically relied on in the past. Learn how your current data center may be holding you back, and what you can do about it: Get your copy of High-Performance Data Centers For Dummies today.

[1] Deloitte Global, Why AI’s Next Phase Will Likely Demand More Computing Power—Not Less, The Wall Street Journal, January 22, 2026.