Getting your approach to AI right: Business Advice for Financial Services Organisations

I have never seen quite as much interest and engagement at boardroom level in a new technology, as we are seeing with AI and more recently, generative AI, which refers to a subset of artificial intelligence and deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on.

The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

Yet for all the well-founded excitement and the transformative potential of this technology, it is important financial sector leaders stay focused on genuine business outputs: How can generative AI help their organisation improve productivity, enhance the customer experience, automate key processes, and accelerate innovation?

To answer these questions, it’s worth considering some key strategic principles, learnings from early adopters, and successful use cases from leading financial brands that are already using AI well.

The right approach for financial organisations

There is a huge breadth and variety of potential use cases for AI, so rather than considering everything the technology can do, leaders should focus first on what their business needs most. Gen AI, rolled out effectively, should help financial organisations do even more of what they’re great at - investment firms will want to spend more time advising their high value clients, banks may want to focus on providing more mortgages. The strategic value of AI lies in creating more capacity to concentrate on what matters most.

It's also important not to confuse the concept of an AI-powered co-pilot with autopilot, because people using AI, rather than AI itself, are where the biggest benefits are to be found. Also, the financial services industry is highly regulated and keeping ‘humans in the loop’ is a key element of taking a responsible approach. We are learning alongside our customers that to unlock sustainable long-term value from AI, financial organisations should focus on sound governance and security – which is the case for all significant new technologies, so in these respects AI is no different. A helpful place to start is Microsoft’s own principles for responsible AI.

The broad adoption of AI should not come at the expense of sustainability commitments. I am hearing directly from financial services organisations how keen they are to work with AI partners who are investing in R&D to make the technology more energy efficient. Microsoft, for example, powers its cloud computing data centres with 100 per cent carbon-free energy where currently possible, with a roadmap in place achieve this across all our data centres by 2030. So, every time a workload is migrated from an on-premises environment to Azure cloud, this can represent a greener choice with a significant positive impact on a customer's CO2 footprint.

Lastly, financial services organisations should see collaboration with government and regulators about AI as a valuable opportunity to create a safer and more secure operating environment for everyone. Engaging positively and constructively will create the best outcomes for the finance sector.

Lessons from early adopters

The customers I speak to often report that treating AI as business-led, transformation project, as opposed to just an IT project, massively boosts its positive impact on their organisation, in terms of productivity, customer satisfaction and efficiency. Business leaders have the authority, remit, and resources to help AI projects move more quickly from planning to action. Evaluating use cases is important - but it's easy to spend too much time scenario planning, when there’s more value in leadership encouraging a culture of rapid experimentation that empowers staff to test solutions in practice. So, once business objectives and responsible AI principles have been agreed, focus on getting projects from experiment, to pilot, to production quicker, as this is where the magic really happens.

Technical debt, such as fragmented information or slow cloud adoption is now holding back some companies’ generative AI adoption efforts, because AI is only as good as the data it can access. Completing earlier generations of digital transformation projects to put the right IT and analytics infrastructure in place, will save AI initiatives capex, opex and time, later on.

At Microsoft, we have been measuring the feedback from early adopters of Copilot for M365, the AI companion that’s integrated into the productivity apps, like Excel, PowerPoint and Teams – that financial sector staff use every day. The productivity gains are impressive, 70 per cent of Copilot users said they were more productive, 73 per cent said they could complete tasks faster and 85 per cent said Copilot helps them get to a good first draft faster. It’s important to look beyond just productivity though, and track whether people feel like they have more time and energy. This sense of reduced strain and having more creative capacity can only benefit a positive company culture.

What AI success can look like

The results we are seeing also show that major, well-established financial brands are among those moving fastest to use AI. Hargreaves Lansdown (HL) is using Copilot and Teams to boost productivity, by automatically creating meeting notes and summaries for financial advisers, which they review or edit before sending on. This has reduced what used to be a four-hour task to just one hour, and 96 per cent of HL employees using Copilot already find the tool useful, with most expecting it to become even more so.

HSBC is using Dynamics 365 to give staff a single – yet deeper and wider view of the customer, in a way that supports frontline teams by bringing customer data together more effectively, efficiently, and automatically. Sharing the right information with the right customer-facing staff at the right time frees up a huge amount of time and energy, so they can focus on providing a great client experience.

Rabobank is using Power Virtual Agents – which are text and voice capable chatbots, to enhance the customer journey and handle up to 80,000 calls each month, efficiently routing incoming queries to the most suitable human customer support agent. These advanced chatbots are automatically answering easier queries and directly connecting customers to channels where they can self-serve, too.

Business outputs like these make it clear that AI has already become an indispensable tool for any organisation looking to build future-ready financial services.

If you’d like to learn more about how AI can unlock value for your organisation, download a copy of ‘Building a Foundation for AI Success: A Leader’s Guide’, or read AI Use Cases for Business Leaders: Realise Value with AI. You can also discover more about how AI and the cloud are being combined to support sustainable growth in financial services, here.