Leading the AI and Data Product Transformation in Financial Services

We all hear it everyday, and may I say every waking moment nowadays? Artificial intelligence continues to present extraordinary opportunities for financial services firms to innovate, optimize operations, and deepen client relationships. Yet, after years of ambitious AI efforts, many organizations still struggle to scale impactful use cases at speed. My assertion is that the root cause often lies in how data is managed across the enterprise.

From my experience advising leading financial firms, the most profound shift enabling AI at scale is their transition to a federated data product model. I can count the leaders who are taking a ‘prioritize and clean data domains’ approach instead of fragmented, project-driven data efforts or overly centralized data lakes struggling to meet business-specific needs. The takeaway is - data must be treated as a product that serves clearly defined domains, use cases, and needs of the enterprise.

The most significant shift I’ve seen driving AI maturity is the move to a federated data productmodel. Leading firms are prioritizing critical data domains—rather than scattered projects or centralized data lakes that often fail to meet dynamic business needs—with an explicit focus on treating data as a product.

But what does this mean in practical terms for wealth managers?

• Domain ownership and accountability: Cross-functional teams comprised of portfolio managers, client service professionals, data scientists, and technologists own the end-to-end lifecycle of curated datasets. For example, a “Client Profile” data product enables advisors to access clean, enriched client risk profiles, preferences, and holdings—all essential for personalized portfolio recommendations powered by AI.

• Data as a living product: These data products are continuously refined and versioned based on changing regulatory requirements or evolving client expectations. A fixed data extract is replaced by dynamic, enriched datasets—for instance, ESG scoring data continuously updated and embedded into client dashboards to support sustainable investing mandates.

• Federated governance with platform enablement: Central platform teams provide the backbone—cloud infrastructure, data engineering tools, and compliance guardrails—while domain teams accelerate value delivery without sacrificing controls. For instance, secure access to transactional data enables real-time fraud detection using AI models embedded into operational workflows.

But how do Data Products accelerate AI adoption?

• Faster delivery of trusted, domain-specific data: AI models can be trained and deployed rapidly on datasets like aggregated risk exposure across multi-asset portfolios, enabling near real-time analytics and personalized client insights.

• Risk mitigation through embedded quality and compliance: Pre-built validations in products like trade blotter data reduce errors and ease compliance with regulatory reporting (e.g., SEC Form PF submissions).

• Enhanced collaboration: Bridging gaps between quants, technologists, and relationship managers improves model accuracy and adoption, for example, in AI-enabled client sentiment analysis.

• Agility to respond to evolving needs: Dedicated teams can independently evolve products like “Market Intelligence Data” to support new AI-driven product allocation or proposal automation initiatives.

Well, what are actionable steps for wealth management leaders:

1. Map your enterprise data landscape to identify silos and prioritize high-impact domains such as Client Insight, Investment Research, and Regulatory Data.

2. Launch a federated data product initiative with clear roles, business outcomes, and KPIs tied to AI use cases—such as improving onboarding experience with AI-powered suitability assessments.

3. Invest in robust platforms and catalogs that empower fast, governed access to curated datasets used by portfolio managers, compliance officers, and AI models alike.

4. Form agile, multidisciplinary teams owning data products aligned to evolving wealth management goals—model risk, compliance, digital client engagement.

5. Use rigorous metrics around data freshness, quality scores, and end-user satisfaction to continually optimize data products and AI success.

In my view, wealth managers who embrace this federated data product model are uniquely positioned to unlock AI’s exponential value—delivering personalized, regulatory-compliant solutions with agility and integrity.

I’m proud to help several leading wealth and asset management clients shape this transformation—where data isn’t just managed but engineered as a trusted, strategic product fueling AI-led growth.

But really, why data products and why now - when I have a burning platform on my hands?

As the wealth and asset management landscape continues to evolve, I notice the ways we think about and manage data must keep pace.

When your organization is grappling with urgent challenges—a burning platform of regulatory pressure, mounting operational costs, or slow innovation pipelines—it’s natural to question why you should invest energy in fundamentally rethinking data management.

The answer lies in the compounding costs and risks that come from continuing with fragmented or centralized “big bang” data approaches. These legacy models often fuel silos, create bottlenecks, and limit agility, making rapid AI adoption nearly impossible. This perpetuates the very pain points leaders seek to resolve: missed business opportunities, compliance risks, and client dissatisfaction.

I look forward to hearing how others are navigating this journey—what successes, challenges, or emerging ideas are shaping your approach to data and AI enablement. Sharing perspectives helps us all accelerate toward more agile, trusted, and impactful outcomes.