Unlocking your data: The essential AI asset you already own

Proprietary data: The essential AI asset

More than two-thirds of wealth management professionals are already using Generative AI, and users report a range of benefits.¹ Yet the impact of AI adoption in this industry has fallen short of expectations, with nearly half of firms reporting an ROI of less than 3%.²

One might assume the right adjustment is to search for a better AI tool, but the truth is that the next best tool alone isn’t enough to make a real impact. Buy the sharpest model on the market, aim it at flawed data, and you get flawed answers faster, delivered with more confidence. That’s why access to adequate data is the top technological challenge to AI adoption among wealth and asset managers.² And it’s why the World Economic Forum has called data readiness a strategic imperative for business.³

In the age of AI, when everyone has access to the same AI models, it’s a firm’s own data that enables them to apply those models to their business. And that makes AI-ready, propriety data a critical asset for today’s wealth managers.

Unlocked: What good data looks like

While many may already have the data they need to make the most of AI tools, the value of a firm’s data can become locked in the course of their natural evolution over time. As firms grow and transform, events such as mergers, acquisitions, system migrations, and operating model changes can introduce inconsistencies across data sources, making it increasingly difficult to maintain a unified and reliable data foundation. And where a person may understand that fields like “client type” and “household category” refer to the same data point, an AI model may read them as unrelated facts.

In contrast, good, unlocked data is:

1. Sound, in that it’s accurate, complete, up to date. This means having a single, trusted view of the client, with all systems—from CRMs and financial planning tools to portfolio management software—integrated into one source of truth.
2. Controlled, with clear ownership and usage within the rules. This requires buy-in at all levels of the organization—from firm leaders and advisors to support staff and technology teams—to embed governance in workflows and clear expectations.
3. Understood, by the business, including its origin. This could be as simple as a shared business glossary, accessible to users across systems, that consistently defines terms like “AUM”, “household”, and “held-away” for all.
4. Contextualized, so that an AI model reads meaning, not noise. This means that, in addition to confirming that the data itself is clean and accurate, firm leaders also need to ensure that it’s retrievable, accessible, and intelligible to a model.

It’s important to note that, for almost all firms, maintaining good data is a work in progress. The goal isn't perfection everywhere. It's about developing AI data readiness where your strategy needs it most, rather than trying to tackle everything at once.

Doing the work: How to unlock your firm’s data

Data readiness for AI is a process, a priority, and an ongoing discipline. Firms that are ready to roll up their sleeves and get to work on this initiative can think about the work to unlock their data across four phases: Define, Map, Sequence, and Evolve. The first two phases require no new technology spend; so, any firm can start this work now. And it all begins with a vision and an honest assessment.

Exhibit 1: A framework to pursue AI data readiness in four phases

1. Define how you’ll use AI and data to drive your business. In this phase, firms learn what AI can do and decide how to use it. This exercise starts with your business goals and strategies. From there, consider where AI tools can plug in. Will you use AI to grow your client base? To move upmarket and serve more complex households? To defend your margins with gains in scale and efficiency? That decision determines which data to ready first.
2. Map your current capabilities and the gaps to close. Before addressing any issues, firms need to take stock of their data. In this phase, firms identify gaps and opportunities, such as where inconsistent data may affect AI model performance or where better integrations are needed to deliver trusted data across AI-enabled functions. This helps firms determine where their current readiness stands compared with where it needs to be.
3. Sequence by pairing progress on your biggest constraint with an early win. As building begins, firms can solidify support for this work by starting with their biggest constraint and delivering an easy win that advisors feel within a quarter. Using the example of inconsistent client information, firms can kick-off foundational work on data governance, reconciliation, and integration, while delivering an early win with a unified client view that includes all client data on a single screen.
4. Evolve the way you work to prioritize ongoing data readiness. It’s easy to think of this work as a one-time cleanup with a clear endpoint. But after the strategy is defined, the gaps are mapped, and the initial execution is complete, there’s still work to do. Because change happens: Clients move, systems change, records go stale, and data drifts. So, this phase is for firms to forge new processes and practices—such as instituting recurring audits of client data—to build data readiness into their ongoing work.

Act now: Secure a lasting advantage

The AI advantage you’ve been looking for lives in data that you already own. Indeed, even the best AI engines can only take your business as far and fast as the data that fuels them.

This offers an opportunity for firms to secure a lasting, compounding advantage. As each new tool plugs into the same foundational data layer, every enhancement of that data elevates the full stack. This, in turn, raises the value of the data each tool generates.

The window to seize an advantage here is open. And though the benefits of this work will likely persist for many years to come, the chance to lead this rapid and profound industry transformation is fleeting.