The state retirement agencies are focused on modernizing their legacy technology platforms on both the administration and investments sides of the business. They want to achieve a total portfolio view, understand and use their data better, and implement AI. What we’ve seen a lot lately is agencies working to update these platforms concurrently, which reduces disruption time. In the past, they would complete one side and then start the other, prolonging business disruption. We think it’s smart to do the work concurrently, but it does require a lot of careful planning and evaluation of the current state and mapping that to the desired future state.

We help clients implement the Ridgeline platform, so it’s been interesting to closely follow its evolution over the past several years. One thing that stood out at this year’s event was a noticeable acceleration in their platform’s growth. We can clearly see they’ve listened to their customers and incorporated their feedback and needs into the implementation process. Many of the organizations they serve are eager to accelerate their adoption of cutting-edge tools like AI, but they need to ensure they’ve established a strong data foundation first. Once that foundation is in place, platforms that keep this pace, like Ridgeline, pay back the investment more quickly. That foundation work is worth confirming before implementation starts, not after.

At Advyzon's inaugural Consultant Experience, Advyzon made the case that its all-in-one platform is ready for enterprise wealth firms, not just independent RIAs. They showed how firms can adopt the platform in modules alongside their own build strategy, use it to bring acquired firms and multi-firm structures onto one platform, and connect it to the rest of their stack through open APIs. Underneath all of it is a unified data model, which is what makes Advyzon AI's agentic capabilities possible. That holds whether advisors work inside Advyzon's native AI experience or through outside tools like Claude or ChatGPT. Our perspective is that the platform conversation has to include the operating conversation. As AI moves from answering questions to acting, firms must establish how those actions will be reviewed, governed, and supported when the platform is live. They should also evaluate implementation and ongoing service as closely as they evaluate features.

During the meeting, SimCorp debuted its new performance measurement engine, called IAP Simcorp Americas brought together front office personnel, accounting experts, performance teams, and others from its asset manager, asset owner, and insurance firm clients and prospects. We led a session highlighting the unique capabilities of Simcorp's new performance measurement engine IAP (Investment Analytics Platform), which will offer on-demand performance calculations. We see this tool as a game changer in the performance measurement space. It moves performance calculations from occurring in an overnight batch to in real-time. Firms no longer have to predict every question in advance, and it frees an immense amount of storage because this is cloud-based. As asset owners migrate to IAP, we want to ensure that they build the business transformation that’s required around it in the right way.

Future Proof brought to life the staggering pace at which new AI tools that aim to support wealth managers are being developed. The fragmented landscape of vendors and solutions can lead advisors to become distracted and overwhelmed. Staying on the sidelines is not an option. We advise firms to prevent paralysis by starting at the beginning: take operational steps to solidify their data strategy. From there, you can design the Art of the Possible and choose tools that inform a deliberate AI strategy rather than experimentation.
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To go deeper with our insights from Future Proof, watch The Data Layer Nobody Talks About: The Plumbing Under Your Tech Stack

We found many of the sessions during this year’s Summit focused on how data is the essential element for moving firms to a future state. As AI forces the asset management industry to redesign the data operating model, data architecture, governance model, talent model, and definition of accountability simultaneously, firms must remember their goal of using AI. For some, that’s generating more revenue; for others, it’s increasing efficiency or reducing risk. Often, they lack the person in the middle who would connect these business goals to the engineers implementing the technology. Firms need to consider all elements of their data, including the people behind it, and how the roles of Chief Data Officers are changing. There is a lot at stake for firms to get their data right, because none of today’s technology can operate effectively on fragmented data. And a big shout-out to Brian Buzzeli, who was honored with the USA Data Management Industry Professional of the Year in the Data Management Insight Awards USA during the event this year. Congratulations!