Artificial intelligence is quickly becoming the defining battleground for wealth management. From predictive portfolio analysis to automated client engagement, firms across the industry are racing to integrate AI into the way they deliver advice and manage investments.
But beneath the excitement surrounding artificial intelligence lies a quieter reality. Many wealth managers are discovering that their existing technology infrastructure is not yet capable of supporting the scale of transformation they are aiming for.
As part of FinTech Global’s prestigious WealthTech100, Harry Slade sat down with Adam Kasraoui, Regional Sales Manager at ERI, to explore the growing gap between AI ambition and the realities of legacy infrastructure within the wealth management industry.
Across the sector, institutions are investing heavily in AI capabilities. Yet for many firms, the real challenge is not building intelligent systems. It is fixing the fragmented data environments that sit beneath them.
The infrastructure gap beneath the AI boom
Artificial intelligence has rapidly moved from experimental technology to strategic priority within wealth management.
Industry research from PwC suggests that more than 70% of wealth and asset management firms now identify AI as a strategic priority, with firms investing in everything from generative AI to predictive analytics and automation tools.
Yet turning that ambition into operational reality has proven more difficult.
“The biggest gap sits at the infrastructure and data architecture layer,” Kasraoui explained. “Across the industry, AI investment is accelerating rapidly, yet many institutions still struggle to move beyond experimentation.”
The core problem lies in how financial institutions manage their data. AI systems depend on large volumes of accurate and consistent information in order to generate meaningful insights. But in many organisations, that data is fragmented across multiple systems built over decades.
“The reason is simple,” Kasraoui said. “AI cannot deliver reliable insights if it sits on top of fragmented systems or inconsistent data.”
In wealth management firms, critical information such as client profiles, portfolio positions and transaction histories often sits across separate platforms that do not communicate easily with one another.
“When client, portfolio and transactional information is spread across multiple platforms, the intelligence generated by AI becomes unreliable,” he said.
For many institutions, this means the real work of AI transformation lies not in adopting new tools, but in modernising the infrastructure that supports them.
Architecture as the new competitive advantage
The conversation around technology in wealth management has evolved significantly over the past decade.
Earlier digital initiatives focused heavily on client-facing applications. Firms invested in mobile platforms, online dashboards and digital onboarding tools designed to improve accessibility and user experience.
Those capabilities are now largely standard across the industry. Increasingly, the real differentiator lies beneath the surface.
“The competitive advantage in wealth management is increasingly shifting to the infrastructure layer that connects data, AI and advisory workflows,” Kasraoui said.
Many wealth managers now operate complex technology stacks made up of dozens of different systems. While each platform may serve a specific operational function, together they often create fragmented environments that slow innovation.
Modern architecture aims to address this by creating unified data models and real-time integration across front, middle and back-office functions.
“Digital interfaces and mobile access have become standard,” Kasraoui said. “What differentiates institutions now is their ability to integrate data, automate workflows and generate insights in real time.”
As a result, technology modernisation is increasingly being viewed not simply as an IT project, but as a strategic business priority.
“That is why architecture modernisation has become a strategic business priority rather than simply an IT upgrade,” he added.
The hidden cost of legacy systems
Legacy infrastructure remains one of the biggest structural barriers to innovation in financial services.
Over the past decade, many institutions improved digital client experiences without replacing their core systems. New tools were layered on top of existing platforms, creating environments that are increasingly difficult to maintain.
“Legacy platforms typically create data silos, operational complexity and limited interoperability,” Kasraoui explained. “That significantly slows innovation.”
The financial impact of maintaining these systems is also considerable.
Research suggests that financial institutions can spend up to 70% of their technology budgets maintaining legacy infrastructure, leaving relatively little capacity for innovation or new product development.
Without modern infrastructure capable of supporting real-time data integration, open APIs and automated workflows, scaling AI becomes extremely difficult.
“Without the ability to support real-time data, open APIs and integrated workflows, it becomes difficult to deploy AI-driven processes or introduce new services at scale,” Kasraoui said.
Data consistency and the trust challenge
Data architecture is not just an operational issue. It is also central to maintaining client trust.
Wealth managers hold vast volumes of sensitive information about their clients, including financial goals, investment histories and long-term planning strategies. If that data becomes fragmented or inconsistent across systems, its value quickly diminishes.
“Data consistency is fundamental for both AI effectiveness and client trust,” Kasraoui said.
For AI systems to produce meaningful recommendations, they must be able to draw on accurate information across the entire client lifecycle.
At the same time, wealth management operates within a highly regulated environment where transparency is essential.
“When intelligent systems generate recommendations, both clients and regulators must be able to understand the rationale behind them,” he explained.
This makes robust data governance a critical component of responsible AI deployment.
“Consistent and well-governed data is the foundation for scaling AI responsibly and delivering personalised advice with confidence.”
Personalisation in the age of generational wealth transfer
The pressure to modernise infrastructure is likely to intensify over the coming decade.
Wealth management is entering one of the largest generational wealth transfers in history. According to analysts from the World Economic Forum, $70tn in wealth will pass from older generations to younger investors over the next decade.
These clients often expect far more personalised, digitally enabled financial services.
Unified data architecture will play a key role in enabling wealth managers to meet those expectations.
“Unified data architecture allows institutions to bring together client profiles, portfolio information and transaction data within a single environment,” Kasraoui said.
With that foundation in place, AI systems can analyse large volumes of information to identify trends, simulate market scenarios and generate personalised investment insights.
However, Kasraoui emphasised that technology will complement rather than replace human advisers.
“AI and unified data models will allow firms to deliver that level of personalisation while maintaining the human expertise that remains central to advisory relationships.”
Building the foundations for the next decade
As wealth managers continue their digital transformation journeys, the industry is beginning to recognise that AI success depends on structural readiness.
The firms that succeed will be those that invest in modern infrastructure capable of supporting intelligent systems at scale.
“The key priority is structural readiness,” Kasraoui said.
That includes building unified data architecture across the wealth lifecycle, supporting real-time data flows and embedding governance frameworks directly into operational systems.
“Firms that invest in these foundations will be far better positioned to scale personalisation, deploy AI responsibly and adapt to evolving client expectations over the next decade,” he said.
Ultimately, the transformation of wealth management will not be driven by artificial intelligence alone.
It will depend on the quieter but far more fundamental work of modernising the systems that make intelligent technology possible.
Only once those foundations are in place will the industry be able to move beyond AI experimentation and unlock its full potential.
ERI was recently named in this year’s WealthTech100, which identifies the companies that every leader in the wealth and asset management industries needs to know about in 2026. The full WealthTech100, including profiles on each company, can be found here.
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