Expert: Elemi Atigolo, Managing Partner at Consult Venture Partners Facilitator: Caroline Burkhart, Director of Wealth Management Consulting, Alpha FMC
Overview:
AI and its use in improving adviser efficiency, supporting better use of client data, and capturing the content of meetings and other conversations is under scrutiny by many, if not most, firms.
But no matter how powerful AI could be, it relies on the data that feeds it, and any AI system needs to be fed data of sufficiently high quality to make sure the end output is fit for purpose. ‘Garbage in, garbage out’ is a well-known phrase for a reason. Other issues include regulatory constraints, technology adoption challenges, privacy concerns, and communication strategies.
Participants all recognised the need for experimentation with AI and talked about how they had done this within their own firm. They all cited data quality, tightly-controlled usage, narrow application in the test phase, and specialist skill requirements as barriers to broader implantation at this point in time. The difficulty in improving data and systems while lacking AI expertise and project clarity was also raised.
Clients' reactions when it comes to recording conversations and other case uses for AI ithin their interactions with advisers were everything from complete indifference to concerns over privacy. Participants thought that clear communication of benefits, like better service quality, would allay concerns. Indeed, adviser efficiency improves when AI tools are used to help with real-time interactions versus post-meeting administrative work.
Participants were interested in looking at other sectors, such as health and consumer, where more advanced AI applications are already in situ and enabled by fewer regulations.
Ultimately, participants thought that successful AI and technology adoption requires focusing on specific use cases and outcomes, not just capability demonstrations. Communication, training and careful change management contribute by enabling incremental improvements balancing benefits and risks. Relevant metrics to measure adviser time savings from streamlined processes, increased client face time from technology assistance, data accuracy improvements from AI validation would also be valuable.
Key findings:
- AI and its use are under scrutiny
- AI relies on the data that feeds it, and any AI system needs to be fed data that is of sufficiently high quality to make sure the end output is fit for purpose
- Clients' reactions when it comes to recording conversations and other case uses for AI within their interactions with advisers were everything from complete indifference to concerns over privacy
- Participants were interested in looking at other sectors, such as health and consumer, where more advanced AI application is already in situ, enabled by fewer regulations
- Relevant metrics to measure adviser time savings from streamlined processes, increased client face time from technology assistance, and data accuracy improvements from AI validation would also be valuable
