This week, we had the privilege of hosting our Future of Wealth Management executive roundtable discussions here in Toronto, with a Canadian audience this time (see the insights from the one in NYC). It was a half-day event aimed at discussing the evolution of the advisor technology landscape, with a focus on customer engagement and relationship management.
We had a great turnout, with leadership from RBC, Investors Group, CIBC, Fidelity, and others. So, I want to start by thanking everyone that attended – we had a lot of really engaged discussions. Will Trout from Celent opened things up with a presentation of his recent and very insightful research on the Canadian wealth management industry. He covered trends and his outlook, with everything from the automation of advice, to AI, to block-chain and the exciting potential for that in wealth management. One of the areas that stood out for me was the discussion on making a more “cognitive advisor” – a term I’m going to start using more. With the automation of advice and advancements in AI, I think we’re seeing a clear convergence on providing augmented intelligence for the advisor as a way of helping them personalize service at scale.
The idea of enabling that personalization and scale is fundamental to our approach with intelligent customer management and our vision for adaptive intelligence for customer engagement. It starts with leveraging our industry-leading, integration-first technology platform to aggregate data. Then using business and domain modelling to really understand the data and give it meaning so we can quickly provision it for analytics, data sciences, and machine learning. Eventually, operationalizing the outputs with decision models and automation through bots and cross-system workflows and presenting it to users across all integrated front-office applications.
The second half of the morning was an open discussion. With the room was represented heavily by the heads of advisor platforms, it was an interesting discussion on priorities and focus to start with. So many firms still struggle with “table stakes” of just getting that integrated front-office experience or automating key tasks. But for those who are beyond that, there were differing approaches to AI. Some were more focused on Robotic Process Automation (RPA) than AI. Most, however, were still trying to rationalize and integrate all their data and capabilities through data lakes and micro-services. Some had already built these vast data lakes, but struggled to realize benefits. In the end, we seemed to agree that the best approach is one that heavily involves the front-office but takes an evolutionary, data-first approach to introducing AI into the business. So while you should start with semantically normalizing data, don’t focus on getting all the data together – start with a target business use case – get the data you need, normalize it, do the analytics on it, and pump the results back to the user-facing applications. Prove out the stack and landscape rather than focus on a monolithic data project.
What are some of your priorities and objectives for advisor technologies as they relate to customer engagement and relationship management? Feel free to comment below or get in touch with me to continue the discussion.
