Challenges and risks
While the opportunity is substantial, firms must navigate several challenges and risks as they seek to implement AI in various areas of their operations:
- Legacy systems, integration pain, data quality issues
Many wealth management firms have grown through acquisition, hold decades of legacy systems, and are burdened by disparate technology stacks. Integrating these systems to achieve real-time, high-quality data is non-trivial. Poor data quality, or inconsistent data, undermine analytics investment and adviser trust.
- Shortage of AI/data-science talent
Building, deploying, and maintaining advanced analytics and AI models requires specialist skills — data scientists, ML engineers, data engineers, and model-governance experts. Many firms struggle to recruit and retain this talent, or to embed it effectively in the business (versus a central ‘tech’ team disconnected from business needs).
- Regulatory scrutiny and the importance of explainable AI
The regulatory environment for wealth management is increasingly demanding: data governance, model risk, fair treatment of clients, and transparency are all under scrutiny. AI/ML models must be explainable, auditable, and compliant. For example, LSEG has published on how it is building data-trust through governance and lineage.
- Cultural resistance: advisers and clients wary of machine-driven insights
Even with the best technology, adoption can falter if advisers mistrust analytics or feel threatened by automation. Furthermore, clients may mistrust ‘machine-only’ advice. LSEG research has found that while investors are open to AI, the adviser’s role remains critical: 45percent of current adviser-users and 51 percent of non-users believe the adviser’s value lies in providing trusted investment advice over the next three years.
Therefore, the human-plus-machine model (rather than machine-only) is essential.
The road ahead: strategic priorities for leaders
For wealth management firms determined to lead the shift from data to value, the following priorities are essential.
- Start with the ‘why’: AI must solve specific business problems
Rather than launching AI initiatives because ‘everyone is doing it’, firms should identify specific business problems (e.g., adviser time spent on proposals, client churn in certain segments, or poor client-portal engagement) and define measurable outcomes. This purpose-first approach ensures technology becomes an enabler, not a distraction.
- Invest in data discipline: clean, centralised, and accessible
Before advanced analytics can deliver, the data foundation must be solid, built on data that is clean, consistent, and accessible. Building a ‘data catalogue’, tracking lineage, ensuring governance. and establishing a centralised data lake or warehouse, are essential. For example, LSEG’s data-trust programme emphasises data lineage as foundational to trusted analytics.
- Balance human and machine: AI empowers, but advisers remain critical
Wealth management is a human-centric business. AI should enhance the adviser-client relationship, not replace it. The hybrid advisory model — where AI tools support advisers in generating insights, and the adviser adds personal judgement, empathy and client-specific nuance — is emerging as the industry norm.
- Reskill the workforce: advisers fluent in data, not threatened by it
Advisers and back office staff must be upskilled to work with data and insights. Rather than viewing analytics as a threat, advisers should see it as a productivity multiplier and relationship-enhancer. Training programmes, user-friendly tools, and change management are key to this shift.
- Adopt modular, flexible architecture: avoid monolithic re-platforming
Many firms fall into the trap of large-scale ‘rip-and-replace’ projects. Instead, a modular, API-first architecture facilitates agile deployments, incremental change, and faster time-to-value. It also allows firms to plug in best-of-breed data/analytics vendors (for example LSEG’s wealth-data and widget capabilities) rather than building everything in-house.
- Cybersecurity and trust: safeguarding sensitive wealth data is non-negotiable
The sensitivity of wealth management client data means that cybersecurity, data-privacy controls, and operational resilience must underpin any analytics or AI deployment. Failure in these domains undermines client trust, regulatory compliance, and the foundation upon which data-driven value is built.
Conclusion: building the wealth firm of the future
The winners in wealth management over the next decade will be those firms that treat technology not as an overhead, but as a growth engine. They will shift from dashboards to decision-making, from passive reporting to proactive engagement. They will harness real-time data, analytics, and AI to empower advisers, personalise client experiences, and drive measurable business outcomes.
With the industry moving fast, 62 percent of wealth firms see AI as a key growth driver, while rising client expectations make technology maturity a critical differentiator.
Now is the time for decisive action. Wealth management firms must prioritise the ‘why’, invest in data foundations, balance human expertise with machine intelligence, reskill their workforce, adopt flexible architectures, and embed trust and security across their platforms.
In short: the journey from data to value is underway — but the firms that lead it will be those who act with clarity, speed and purpose. The future belongs to those who not only keep pace but define what ‘digital-first, client-centric wealth management’ will look like.
Define your problem, clean your data, deploy a pilot AI use-case, measure outcomes, and scale.
The time to act is now.
Interested in reading From data to value — AI and analytics in wealth management? You can read the full white paper online here.
About the WealthTech Insight Series (WTIS)
This research-led white paper is part of The Wealth Mosaic’s WealthTech Insight Series (WTIS), an ongoing research series focused exclusively on technology in the wealth management sector across the world.
Rather than a one-off research process, the WTIS will seek to build an ongoing program of research among wealth managers of different types across the world on a broad range of technology and related topics, building up an aggregated knowledge base of both qualitative views and perspectives as well as quantitative data points.
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About LSEG Data & Analytics
LSEG Data & Analytics is one of the world’s largest providers of financial markets data and infrastructure. With over 40,000 customers and 400,000 end users across approximately 170 markets, it is an essential partner to the global financial community and is redefining the future of data in financial services.
LSEG enables customers to draw crucial insights through data, feeds, analytics, AI, and workflow solutions. With its unique insights seamlessly integrated into customers workflow, it can help them to identify opportunity and seize competitive advantage.
LSEG’s solutions empower wealth managers to improve adviser productivity, streamline digital onboarding, provide timely advice, and improve the overall experience for end investors. It creates valuable and personalised experiences with a suite of solutions tailored for wealth managers.
About The Wealth Mosaic
The Wealth Mosaic is a UK-headquartered online solution provider directory and knowledge resource, focused specifically on the wealth management industry.
For wealth managers, the buy side of our marketplace, The Wealth Mosaic is designed to enable discovery of key solutions, solution providers and knowledge resources by specific business needs.
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