Laying the groundwork for AI – why data is everything in asset management
In the rapidly evolving world of asset management, artificial intelligence (AI) has become essential for firms seeking to optimise portfolio insights, personalise client experiences, and streamline operations. However, the true potential of AI can only be unlocked by establishing a robust data foundation that overcomes the current landscape of fragmented and inconsistent information.
Everyone wants AI. Few are ready for It.
Artificial Intelligence has moved from buzzword to boardroom priority in asset management. Firms want to use it for portfolio insights, client personalisation, automated reporting, and more. But here’s the inconvenient truth: AI is only as good as the data that powers it. And right now, too many asset managers are trying to build machine learning models on a foundation of fragmented, inconsistent, manual data.
That’s like installing a Formula 1 engine in a 1980s hatchback—it won’t get you very far.
Data isn’t just a resource. It’s infrastructure.
Today’s investment firms are drowning in data but starving for insight.
Why?
Because the data lives in silos—spread across legacy systems, spreadsheets, and vendor platforms that don’t talk to each other. This creates five core challenges:
- No single source of truth
- Inconsistent formatting and validation rules
- Manual intervention needed to reconcile outputs
- Slow time-to-decision
- Regulatory risk due to data errors or outdated inputs
Firms want to use AI to scale reporting, enhance investment decisions, or streamline operations—but without unified, accurate, accessible data, AI becomes a high-cost science project.
The strategic shift: From "Data Exhaust" to "Data Asset"
Leaders at future-ready asset managers are starting to reframe the role of data:
- Not just a byproduct of operations—but a strategic asset to be governed, standardised, and activated.
- Not a job for IT alone—but a board-level focus area for resilience, growth, and innovation.
- Not an overhead cost—but the foundation for scalable, intelligent automation and analytics.
In fact, a recent BNY Mellon survey found that data integration is the single biggest hurdle firms face in applying AI across the fund lifecycle. More than two-thirds of respondents said their data architecture limits their ability to automate regulatory reporting, generate insights, or personalise client communications.
Clean, connected, AI-ready: What a modern data stack looks like
What does a future-proof data strategy look like in practice?
- One master source of truth for all fund data (from objectives to holdings to fees)
- Real-time validation and enrichment so that data is accurate before it’s published
- Integrated systems that distribute that data everywhere it’s needed—websites, documents, regulators, client portals—automatically
- Auditability so that every number, fact, or disclosure is traceable and defensible
- AI-ready architecture, such as cloud-based platforms with open APIs, where data can flow freely to power analytics or automation
This is exactly what platforms like Nexus by FE fundinfo enable—helping firms move from a patchwork of legacy systems to a connected data ecosystem.
"Garbage in, garbage out" is now a business risk
If your firm can’t trust its own data, how can clients? Or regulators?
Bad data leads to:
- Incorrect investor documents
- Misleading factsheets
- Slower fund launches
- Compliance breaches
- Lost trust and reputation damage
As one industry CTO put it, “Data isn’t just operational anymore—it’s existential. If you can’t manage it at scale, you’re already losing.” The highest-performing asset managers understand this. They’re building governance frameworks, investing in enterprise data platforms, and staffing dedicated data teams—not because it’s trendy, but because it drives performance and protects against risk.
Laying the groundwork for AI begins with fixing the data first
Everyone wants to use AI to generate insights, write reports, detect anomalies, or predict client behaviour. But AI only works when fed clean, consistent, structured data.
The reality?
- 80% of data science time in most firms is spent cleaning and structuring data—not analysing it.
- 45% of asset managers plan to develop predictive analytics and AI capabilities within three years—but cite poor data integration as the #1 obstacle.
- AI without data discipline leads to half-baked outcomes and high implementation costs.
Firms that fix their data foundation now will leapfrog those that don't—because they’ll be ready to scale AI, not pilot it endlessly.
The leadership mandate: Govern your data like a strategic asset
If you're a COO, CTO, or Head of Ops, here's the uncomfortable question: If your firm wanted to launch an AI-driven tool tomorrow, how long would it take just to prepare the data?
If the answer is measured in weeks or months, the time to act is now. Build the strategy. Appoint a data owner. Invest in the platform. Choose partners that understand not just fund data, but asset management’s unique complexity. Because in the future, your data quality will determine your operational agility, compliance posture, and ability to compete.
The takeaway: AI Is the destination. But data Is the road.
The asset managers that succeed in 2025 and beyond won’t be those with the fanciest AI demos. They’ll be the ones who can trust every number, every report, and every regulatory output—because their data is clean, governed, and connected.
Want to learn how firms are building AI-ready operations?
Download our latest whitepaper, Navigating Uncertainty, to see how data-driven leaders are consolidating systems, automating workflows, and setting the foundation for next-gen technology in asset management.