At SIFMA Ops 2026, I had the opportunity to join the panel AI & Emerging Technologies in Wealth to talk about where our industry is today and what it will take to move forward.
There’s no question that artificial intelligence (AI) is already reshaping wealth management. Most firms are experimenting, and many are starting to see real benefits. But from my vantage point, the biggest challenge isn’t getting started. It’s scaling what works.
For me, it comes down to one key enabler: data.
If we want AI to deliver meaningful, lasting impact, we need to ensure our data is ready. That means it must be easy to find, clean, structured and trusted. It also needs to be flexible enough to support not just today’s use cases, but the ones we haven’t thought of yet. Without that foundation, it becomes very difficult to move beyond isolated pilots.
I’m encouraged by the progress we’re seeing. Across the industry, firms are beginning to focus more on outcomes and how AI fits into the full workflow. As others on the panel noted, simply layering AI on top of existing processes only takes you so far. The real opportunity comes when you look end-to-end and rethink how work gets done.
We are seeing that play out at DTCC as well. AI is helping us accelerate our own modernization efforts, particularly in how we build and deliver technology. We are already using AI in parts of the software development lifecycle (SDLC), including to streamline how requirements are documented and translated into development tasks, as well as to support more automated and consistent testing processes, helping teams identify issues earlier and reduce rework. We’re also excited about the opportunity to extend that work more broadly across the end-to-end software development lifecycle. While that broader use case is one of our AI big ideas and not yet in production, it reflects the kind of lifecycle-wide thinking that can help teams move faster and with greater consistency. But again, the value comes from connecting those steps across the full lifecycle, not just optimizing one part.
Another important point we discussed is that AI is not just a technology story. If we want AI to succeed, we need to invest in our people. That means helping teams understand how to use these tools, where they add value and where human judgment is still critical. At DTCC, we have focused on building awareness and confidence through targeted training, internal forums to share use cases and lessons learned, and encouraging responsible experimentation within clear governance frameworks. AI can improve speed and efficiency, but it doesn’t replace accountability.
That’s especially important in a highly regulated industry like ours. As we introduce AI into processes, we need the same level of control and oversight we’ve always had and in many cases, even more. That oversight may come from humans or additional safeguards built into the system. Either way, building trust is essential.
One lesson that stood out to me is that creating AI capabilities is only half the battle. Adoption matters just as much. If teams don’t use the tools or don’t trust them, value won;t be realized. Understanding what drives adoption, and what gets in the way, is critical to making AI part of everyday work.
As we look ahead, my advice is to focus on the fundamentals. Invest in data and people and think about how to scale the use cases that are already delivering results. AI is moving quickly, and the landscape will continue to evolve. But if we get the foundation right, we’ll be in a much stronger position to take advantage of what comes next.