When people talk about Artificial Intelligence, what rarely gets singled out is the phenomenal speed at which AI has arrived on the world stage. Some context is important: it took Netflix about 9.5 years to reach 100 million users; Instagram did it in about 2.5 years; ChatGPT did it in about 60 days. Never have we seen such widespread uptake, so quickly. But that’s public-facing AI – what about more targeted business solutions?
Before a business plunges headfirst into AI, it is important to identify the use case – the exact problem you are trying to solve. In my experience, it is vital to be as specific as you can. At Mallowstreet, our use case was the meeting.
We all have lots of meetings – too many. So, the Mallowstreet team started by asking ourselves the simple question: how can we help make meetings, and the necessary follow-up, more efficient and effective? As a result, we built our own AI tool SOFI to analyse a meeting.
We used our industry-specific knowledge to train SOFI to fluently summarise financial meetings and discussions, surfacing key actions and takeaways in the process. We also built the capability to analyse a meeting through critical lenses – a dashboard of how the meeting deals with risk, return, liquidity profile, investment time horizon, growth versus matching investment characteristics, and ESG.
The result was amazing. As a meeting attendee, you could now actively listen and truly pay attention – feeling 100% present, knowing that SOFI is in the background ensuring no discussion points are missed, and instantly see if the salient lines of enquiry are being attended to.
Expanding the use case
Then came our second round of development – again leaning on the use case approach. This time, we asked: can we apply SOFI analysis to a whole series of related meetings to highlight key themes, identify discrepancies and surface common questions across sessions? This was the genesis of Multi-Vertical Analysis.
Multi-Vertical Analysis unlocks our trend analysis tools, paving the way for the development of ‘SOFI scores’ which quantify vital meeting dynamics. For example, how much time does each participant speak (soapbox score) or how many ‘ums’ and ‘ahs’ do you say (disfluency score)? With this added layer, SOFI has also become a pitch/ presentation practice tool – providing feedback that is objective, consistent, and transparent.
It’s clear that, when implemented in a thoughtful and structured way, AI does deliver on specific use cases. But what I’ve found more interesting is the concept of the ‘impact coefficient’: how an individual can achieve uplift in delivery of their core role.
Very few people go into a job to become, for example, a financial adviser who says: ‘wow, I really cannot wait to write up the reports for my clients, fill in the KYC forms, and update the CRM with all of the required points to satisfy an increasingly more regulated world’.
On the contrary, people become financial advisers because they want to help people on their journey. They want to make a difference, to spend time with their clients and get to know them, so they can provide the best advice to allow each person to achieve their respective goals.
I repeatedly hear the above tension of how people want to spend time vs where time actually gets spent by wealth advisers and financial advisers. And this is where the impact coefficient comes into its own. If an adviser is freed from the responsibility of capturing and analysing all the information from a conversation, they can spend their time focusing on asking the right questions and really getting to the heart of what a client needs. They can pay far more attention to body language and tone, helping to understand the ultimate driver of a client’s concerns or decision making.
Allowing the adviser to be truly present in a conversation with their clients allows them to help provide better advice, allowing the client to make better decisions, and ultimately having a fundamental positive impact on their long-term trajectory.
Imagine a world where all the advice being offered was elevated in this way. The long-term impact on the UK wouldn’t just be significant – it could be transformational.
A tool, not a replacement
People often ask me if AI is going to ultimately make a huge amount of the work force redundant. The honest answer is I don’t think anyone knows. But what I am sure of is this: the people integrating AI into their daily workflow are not only becoming more efficient, but also more effective. Those who are leveraging the impact coefficient are gaining ground on all of us.
Stepping back, when you are approaching AI, it is incredibly important to nurture curiosity – ask the challenging questions, and push for how things can be done differently and, even, better?
You aren’t looking for a quantum leap. Think about the Tour de France. Better handwashing to avoid germs, improved pillows for better sleep, tiny aerodynamic redesigns for faster results. Alone these tweaks are helpful – together they are transformational.
And make sure you have a growth mindset – the belief that abilities, intelligence, and talents can be developed over time through dedication, hard work, and learning. This contrasts with a fixed mindset, where someone believes their abilities and intelligence are static and cannot change.
What excites me is the fact that we now have a new set of tools which can transform the way we work together and leverage the impact coefficient. Be specific in the issues you’re trying to solve – find your use case – and automate the tasks that can be.
Because the more time we can save, the more intellectual firepower that can be deployed to help ensure we all achieve our respective goals. By putting financial advisers and the financial services at the forefront of innovation and driving adoption of AI to benefit from its advantages, we can achieve great things.
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