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AI is the latest buzzword, but should it be part of a short-term roadmap for building societies just yet? From enhancing customer service to improving risk management and operational efficiency, the benefits are undeniable, but are building societies ready to truly embrace such a change?

It’s clear AI is here to stay, and it stands out from previous tech trends because it can learn, adapt, and perform tasks without needing detailed programming. It can analyse massive amounts of data, identify patterns, and make decisions, continuously improving over time. Some find that immensely impressive – others may say terrifying. So, I wanted to spend some time thinking about how the building society sector  could best leverage this latest technology trend taking the financial sector by storm.

Building societies have always been the heart of their communities, offering essential financial services and building strong relationships with their members. They play a vital role in supporting local economies, providing personalised advice, and making sure members feel valued and understood. With AI in the mix, building societies can (and arguably should) take these high-value interactions to the next level. AI can handle the routine tasks and crunch the data, freeing up staff to focus on the meaningful engagements that really matter. This means more personalised and timely support, which strengthens member loyalty and trust; helping building societies stay true to their community-focused roots while embracing innovation and efficiency at the same time.

We’ve all seen many tech trends come and go, each promising transformation but often facing adoption hurdles. 

Here are some common challenges societies have faced adopting new tech in the past:

• Legacy systems: Moving away from traditional systems is time-consuming, complex and therefore costly. The fear of disrupting established processes often slowing adoption.
• Upfront costs: New tech comes with costs and risk, and the uncertainty of return on investment can lead to hesitation. 
• Data concerns: Handling sensitive information means building societies are cautious about data security and privacy.
• Change management: Technology change is never easy, but shifting mindsets and aligning priorities across departments and stakeholders is often even harder.

These challenges apply to AI too. We know the potential is there, but where do you start? 

Getting Data and Infrastructure in Order

Before building societies (or any lender) should even start to think about AI, they need to get their data and infrastructure in order. Rushing to adopt AI without proper planning can create long-term issues and without a solid foundation, the benefits of AI can't be fully realised. 

Here’s what needs to be done:

• Streamline your Data: Establish a fully aligned approach to data management. This includes data governance, quality, and integration. A unified data strategy ensures that AI can leverage accurate and comprehensive data for better decision-making.  
• Upgrade Legacy Tech: Invest in modern infrastructure that supports AI capabilities. This means replacing or updating legacy systems so you can ensure scalability and integrate new technologies seamlessly. Robust infrastructure is crucial for handling the demands of AI applications.
• Stay Compliant: Implement strong data protection and security measures. Building societies must ensure that sensitive information is safeguarded, and compliance with regulations is maintained.
• Get your Teams Ready: Equip teams with the necessary skills to manage and utilise AI effectively. Training and development programs can help staff understand and leverage AI tools.

AI has the potential to revolutionise operational efficiency, automating routine tasks like document verification and property valuations, reducing errors and speeding up approvals. This could streamline processes and redefine customer interactions, ushering in a new era for the industry. 

AI is certainly here to stay, but should building societies jump on the trend? A patient, incremental approach is crucial. Start with a robust data strategy, define clear use cases, and align your stakeholders.

A key point here is to focus on solving targeted business problems rather than adopting AI for the sake of it. Invest in training, collaborate across business lines, and play the long game to ensure successful adoption and long-term success.

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