Banks focus on efficiency with AI
Explore the perspectives of 300+ banking professionals on the potential and value of artificial intelligence from the Avanade AI Readiness Report.
AI readiness: banks need to invest in technology and people
Major data and analytics investment and staff training is required
There is a strong focus on automation, data and analytics platforms to scale AI across the business. But this will not necessarily lead to headcount reduction. Rather, AI will change the nature of the tasks performed in the workplace. Almost a quarter of bankers (24%) thought that AI will help them take more intelligent actions with assistance from generative AI tools, such as Microsoft 365 Copilot.
However, over half of the banks interviewed (53%) reckon they will need significant support to train staff to use generative AI tools. “The data most companies have gathered wasn't for machine learning or AI,” commented one European banker. “That data may not be useful. Risk and compliance is huge. KYC, credit risk, all of it is data-rich.”
Technology understanding is vital
The top investment priorities for banks in 2024 in order to scale up AI within the business are automation, data and analytics platforms (57-62%), such as Microsoft Fabric or Power Platform.
Just over half (51%) of thought leaders understand generative AI and its governance needs today. One US banker commented: “The operating committee and the board level are not folks who traditionally understand technology. Leadership is excited about the potential of AI, but doesn't necessarily understand what it means to be AI-ready. Many are still struggling with their journey to cloud. Many haven't even completely transitioned to cloud yet, let alone using tech that is cloud-native. (We’re) using applications which still need to be modernised to be cloud-ready, let alone have them feed off of generative AI capability."
Next steps: AI-ready data, modernisation and cybersecurity
Clearly banks are carefully targeting the right use cases and ensuring they mitigate risk and comply with regulation. However certain challenges persist: data needs to be AI-ready and managed in a cloud environment, application portfolios and core banking systems need to be modernised, operations need to be cyber-secure and staff need considerable training and development to use new generative AI tools effectively.
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