Asset managers need to start having honest discussions with themselves about AI and trialling different solutions in order to ensure survival beyond the near term, notes Stuart Breyer, CEO of mallowstreet.

There is no doubt in my mind that Artificial Intelligence (AI) has arrived and, crucially, isn’t going anywhere – except to become ever more ingrained and entwined in our lives. What continues to blow my mind is not just the power of AI, and how it will transform the way we work and interact with clients, but the sheer speed of AI innovation. It is unlike anything I’ve ever seen before in my professional life, and that includes the smartphone, internet, or personal computer.

How asset managers are responding to Artificial Intelligence

I recall a conversation I had with one asset manager about a year ago. I was telling them about our AI tool SOFI, which is designed to analyse what happens in a meeting, surface the gold dust of a conversation, save time, check messaging, improve delivery, monitor performance and capture questions. They loved it, but informed me that their organisation had put a total ban on using AI. While sad not to onboard a new client, the question on my mind was ‘I wonder where this asset manager will be two years?’ The asset manager must have had the same thought, because nine months later, they called me and said they had started a few AI pilots, and would love to meet up again.

I put the phone down and said, ‘this manager is going to make it’. While they may be slightly behind in the adoption and implementation of AI, their shift in mindset and decision to start testing and experimenting was ultimately going to help them drive innovation and identify how AI can be applied to their business.

There isn’t a ‘silver bullet’

Asset managers I speak to broadly fall into two camps: those who want to test everything right away and those who are running pilots; slowly dipping their toe in the water.

My biggest learning has been the realisation that one AI solution isn’t going to solve every problem. When looking at how to implement and integrate AI into a business process, the key question to start with is ‘what is the actual problem I am trying to solve?’ – or put another way, what is the ‘use case’ I want to explore and develop.

When we built SOFI, we specifically wanted to improve the quality of the output from a meeting, understand how we could create efficiencies, and compare performance to help drive incremental improvements across an organisation. As our clients started to use SOFI, they went on to identify dozens of different ‘use cases’.

We realised SOFI has applications beyond client meetings. It could be used to practice and prepare for presentations, used in all internal meetings or at any event, and analysis could be shared in real-time to help support knowledge sharing across our business – and therefore, across all of our clients’ businesses. We try and focus on solving one or two use cases at a time – it is an iterative process that requires continued investment and refinement.

Using AI in your business

When it comes to your business, don’t spend time and money on trying to reinvent tools that are already out there – just make them available to your team to start experimenting. Evaluate the output and make sure it is giving you results that actually help you, and isn’t simply a box ticking exercise that says ‘yes, I’m using AI’.

Every business and organisation is structured differently, and will have different needs. Start thinking today about where you want to be in three or five years’ time. Have open and honest conversations internally about what you are trying to achieve. It is through these conversations that you will identify the AI you need to invest in to customise and tailor to your needs. The intel your business gains from starting to use existing AI tools and applications today will go a long way to helping support broader business objectives. Those adopting a growth mindset, trialling new technologies, and thinking long-term will ultimately shape the winners in the coming decade.