In my last blog we looked at the role that people will play in the future of AI, based on Avanade’s latest AI banking research. This time I want to look at data within banks and how AI-ready it is for use by new generative AI tools.

Here’s what we found:

• There is an appetite for investment. When asked how banks will drive the most business value from AI by the end of 2024, banks ranked investments in data and technology platforms first (40%), closely followed by better strategy and actions informed by the collaborative intelligence of humans and AI (39%).
• Banks think their IT departments are strong in this area. 52% said that they were very confident in the banks’ IT capability and resources to scale generative AI across the whole enterprise.
• The focus is on data, analytics and automation. Specifically, investment will be targeted at data and analytics platforms (62%), closely followed by automation platforms (57%) and cybersecurity (56%). However, as one European banker observed: “The data most companies have gathered wasn't for machine learning or AI. Risk and compliance were huge. KYC, credit risk, all of it is data rich.”
• But banks are bottom of all sectors when it comes to AI investment. Banks were the lowest of all sectors (87%) in putting digital technology investment into generative AI.
• Banks find it difficult to quickly access data to assess AI impact. Only 28% of banks were able to assess the impact of AI on their business in less than three months – the lowest of all sectors. 45% reckoned 4-6 months was more likely.

One of the top challenges when it comes to AI is how to manage data and prepare it for AI usage. To achieve AI at scale, banks need to unlock the value of all their data, finding every insight, efficiency and opportunity to put their data to work. But first it needs to be in a state for banks to use with new generative AI tools. AI needs clean, governed data that is accessible regardless of its cloud location.

Banks are large, heavily regulated organizations with complex data structures, usually due to a history of multiple acquisitions. Data is kept in a variety of formats and repositories across the business, typically in silos and disconnected clouds. Data is often duplicated across a bank. Loading in new data is complex and time consuming. Product groups within banks typically keep data within their divisions. This is one of the reasons why creating an integrated single customer view has proved difficult.

Banks still have a fragmented and costly architecture as data needs to be moved through layers and systems. They tell us they want a simplified and unified architecture across data engineering, data science and business intelligence workloads.

This means a renewed focus on data platforms and cloud migration. You may be aware that Microsoft have recently launched Fabric. It offers a simplified SaaS-based platform that unifies data from different sources via a single, intuitive user interface for the whole organization (no silos). Avanade has also developed a Cloud Impact platform that drives greater ROI from cloud, based on work with 100 clients. Our research found that they could save, on average, 22% from Microsoft Azure. When combined with standards, governance and automation recommendations, savings could reach up to 50%. Find out more in our latest report ‘Banks: are you AI-ready?’

Let us help you scale your business analytics and AI vision by building on your existing investments. Register for a Data Platform in 30 days workshop, where we’ll help you create a data migration plan for your bank.

Imagine what you will do with AI. Are you ready?