Future-Proofing Your Firm Starts with Getting the AI Basics Right

At a recent industry gathering with peers and operations leaders, the animated discussion reflected the tremendous amount of change happening now in the investment and wealth management industry.

Digital assets are reshaping operational rails and requirements. With the SEC’s proposed Regulation E-Delivery, firms are supportive but figuring out how best to adopt digital communications as the new normal. Market expectations are moving toward 24/7 trading and faster everything. Each of these is a project on its own—together, they are a test of whether a firm can keep adapting without breaking what already works.

The conversation kept returning to one idea: AI is no longer a side experiment

As attendees discussed how to best navigate this wave of change, the conversation kept returning to one idea: AI is no longer a side experiment. It is now a deciding factor for every firm’s future success. To address these new demands (and others that are sure to emerge in the future) within the budgets and timelines they already have, it is critically important for firms to get AI right.

Based on what we heard, we’re going to dive into where firms are today, the pitfalls to watch for, and what separates the firms pulling ahead from those still experimenting.

AI as a Springboard

Designed and deployed the right way, AI can unlock an easier, faster path through the change and complexity ahead. Some of it AI can address directly, by automating document-heavy work or giving teams faster access to what they need. Some of it AI addresses indirectly, by freeing people and budget to take on the new work elsewhere. Either way, the firms that build this capability well will have room to maneuver, and the firms that don't will be absorbing new complexity with the same manual effort as before.

Where Firms Are Today

Despite the flood of AI offerings on the market, most firms are still early in their adoption journey. Several mentioned that six months ago they had nothing in place, and many are now putting AI tools on employees' desktops and watching what happens. We call it the "big toe" phase: people are in the water, but not yet swimming.

Most firms are still early in their adoption journey

The desktop tool most firms have chosen is telling. It is the office solution that sits inside the environment they already trust, with data protections and controls already in place, inherited from the same pre-vetted foundation. The comfort and popularity of this approach validate the need for visibility, auditability, and trust to start with the data foundation underlying AI—a key principle behind our own DataXChange platform.

Many firms might be adopting this and other desktop tools, but their impact today is mostly individual efficiency: drafting faster, summarizing faster, building a presentation in minutes. Those user-level desktop gains might save time day-to-day, but they won’t add up to meaningful enterprise value the way operations-centric AI can.

The production use cases we did hear about were narrow and sensible. Firms are extracting information from documents and procedures, and building internal assistants that answer questions from the people who serve clients, so advisors can get answers from the back office in minutes. These are valuable, but they also mostly surface information rather than take action, again limiting the real impact of AI.

What to Be Wary Of

Alongside those early successes, the conversations surfaced lessons that are just as useful—the pitfalls that can keep AI from moving beyond individual productivity.

• Mistaking individual productivity for transformation. Faster slide decks are a tangible gain, but they don't materially change how a firm operates.

• Bolt-on solutions. Without connection throughout the whole stack, firms end up with disparate tools that don't talk to each other or to the systems of record.

• A ceiling set by infrastructure. There is a limit to how far and how fast AI can go if the underlying data and systems don't lead the way first. A modern, unified data foundation is step one.

• Waiting for regulatory clarity. Expectations for AI governance are still taking shape. Firms don't have to wait to do the obvious things: document how AI is used, keep humans accountable, and maintain audit trails. Those moves will hold up however the rules land.

• Unclear ownership. Perhaps the most consequential question: Who is accountable for AI, technology or the business? The firms we heard from leaned toward the business, because accountability should follow outcomes. Whichever way a firm lands, it needs to be decided, not assumed.

What Leaders Are Doing Differently

We see a clear gap emerging between firms that are experimenting and firms that are pulling ahead. It has less to do with which tool they bought than with how they organized around it.

The last row of that table may be the most important. As AI access becomes universal, it will level the playing field: everyone will have it. What will separate leading firms is how meaningfully and how safely they can apply it to their own content. Your data, your procedures, and your business rules are the things nobody else has—which is why your AI strategy must leverage your business’s unique context, not just general-purpose intelligence.

Why the Foundation Matters

Each of those differences traces back to the foundation—and that principle informed the design of our DataXChange platform, where data and AI work as one. When the underlying data is accurate, well-structured, and governed, with humans in the loop continuously rather than only at the end, AI has something dependable to work from. When user permissions and entitlements seamlessly carry over from your data layer to your AI applications, you can avoid accidental data leakages and cumbersome re-provisioning. And when that AI is powered by SME-curated Domain Intelligence, you can feel confident that the AI understands your business, not just your words.

It is an achievable future, where AI’s value can progress from opportunistic desktop efficiencies to enterprise-wide impact

That is the kind of future operations leaders told us they want: transformative capabilities, with control intact. It is an achievable future, where AI’s value can progress from opportunistic desktop efficiencies to enterprise-wide impact—the kind of real ROI we’ve designed DataXChange to deliver.

The Takeaway

The pace of change in our industry is not going to slow down. Getting AI right is how firms make room for everything else: new asset classes, new regulatory obligations, new expectations for speed. Those who lead the way will start with the data, put leadership behind it, and build for control from day one. And they will start now, because the firms that do will have more options when the next wave of change arrives.

To read more about how your data and AI strategy can deliver tangible value, be sure to follow our Real ROI content series. You can read Chapter 1, Built-In vs. Bolted-On, here.