Over the last few years, I have watched our wealth management industry chase one transformational promise after another. Microservices would replace rigid monolithic architectures. Cloud migration would unlock agility and scale. Digital platforms would democratise access and reshape client expectations.
Yet at a Swiss WealthTech conference in 2022, a senior private banker from one of the oldest private banks in Switzerland offered a sobering confession: "I've worked in this industry for 10 years, and nothing has fundamentally changed." Now, in the past three years since ChatGPT's launch, artificial intelligence (AI) and more specifically, generative AI (GenAI) has emerged as the new transformational theme.
Will AI suffer the same fate of being extensively talked about on conference stages and in thought leadership articles, yet never meaningfully adopted in practice?
Let’s look at how advisers and firms are actually adopting AI in their operations. To do so, I have examined public case studies and vendor client testimonials in the ‘Adviser Tools’ category of The Wealth Mosaic's latest AI WealthTech Market Map. I can categorise my findings into three levels:
- Level One: GenAI for content creation and note-taking
Many client testimonials I have seen simply use GenAI solutions for routine tasks: producing periodic investment commentary, drafting client emails, or summarising virtual client meeting notes for upload to customer relationship management (CRM) systems. While helpful, these are wildly underutilising what AI is capable of.
- Level Two: Agent AI powering specific end-to-end workflows
Some wealth management firms adopt agents for single-objective, bounded workflows: creating investment analyses, generating advice proposals, automating client onboarding, etc. These agents deliver efficiency within their lane, but they operate in isolation. Each workflow begins and ends within its own silo, and any transition to the next step still relies heavily on humans stitching the process together.
- Level Three: Multi-agent orchestration across multiple workflows
This is where the promise becomes truly transformative, with specialised agents completing their stage and seamlessly handing off to the next. Imagine: once onboarding finishes, an agent automatically generates the investment proposal and submits it to the client. Once approved, another agent executes the implementation.
For this to work in practice, agents need a standardised way to access data across disparate systems and pass context between workflows – a challenge that protocols like Model Context Protocol (MCP) are beginning to address. A fully linked chain like this would minimise human touchpoints across onboarding, advice creation, and execution – fundamentally reshaping the operating model rather than simply accelerating it.
Most firms sit somewhere between Level One and Level Two. For now, Level Three still feels largely conceptual. Perhaps some are building it quietly or simply have not yet found a way to make it work.
The scepticism among advisers
I have also been speaking with wealth advisers about whether agentic AI could actually scale their practices. The business case is compelling: advice is service-intensive, offering attractive recurring revenue, but is also plagued by low margins and long conversion cycles. If AI could expand adviser capacity without sacrificing personalisation, the value proposition would be enormous.
But when I have raised this possibility, I have found scepticism more than enthusiasm. Common concerns advisers raise include the following:
- Regulated advice requires clear auditability, yet most agentic systems don't produce a transparent reasoning trail.
- When an output differs from the model-calculated answer, there's often no way to pinpoint where the reasoning broke.
- And even with the same inputs, the system does not always generate consistent outputs.
These concerns are valid, but they do not invalidate the potential for agentic AI to scale wealth advice while retaining personalisation. Most of these concerns are largely agent-design problems rather than fundamental technological limitations. With the right architecture, clear constraints, and domain-specific guardrails, many of these gaps can be reduced.
Interested in reading more about Mark Yeo Tee Shen’s article? You can read and download the report online here.
The real bottleneck: a talent problem
Adoption of new technologies in wealth management has a track record of being relatively slow compared to other industries. While AI is still in its early stages, there is a risk that operational AI in wealth management firms will be adopted far later than any other industry would have already implemented it.
One could blame the regulatory environment of wealth management. But from my personal experience working in both investment and non-investment functions across London, India, and Singapore, both for established wealth management firms and for highly innovative WealthTech scale-ups, I have formed another view: the more fundamental reason for slow technology adoption is our industry's critical lack of cross-domain talent possessing both investment finance and software engineering expertise.
I suspect that the individuals who do have this blend of skills (i.e. the quant developers) often gravitate toward quant-driven hedge funds that are better structured and compensated. In these firms, quant developers' research, models, and implementation directly influence the bottom line, just as software engineers drive revenue in SaaS companies.
By contrast, in wealth management career pathways, incentives, and internal recognition still seem to favour sales and relationship management over technical talent. The result is a structural disadvantage in attracting and retaining the very people needed to operationalise AI effectively in the wealth management industry.
Conclusion
To whichever wealth management firm that can blend operational AI capabilities with deep domain expertise: Carpe Diem! While operational AI will eventually be commoditised, proprietary domain expertise in investment portfolio construction and the ability to deliver superior investment outcomes remain difficult to replicate.
Whoever can close this chasm between technological capability and financial expertise will win significant market share among modern customers who expect seamless user experiences while also enjoying differentiated investment outcomes. This is the moment when competitive advantage can be built.
And, in rebuttal to that Swiss banker's lament, perhaps in the near future wealth management can look fundamentally different.
Discover the themes and trends shaping technology in wealth management today. Read the report here.
Deepen the conversation at WealthTech 2026: US edition
This report sets the foundation for our WealthTech 2026: US edition live event, taking place on 29 April 2026 at the New York headquarters of report contributor EY. Brought to you in partnership with EY, the US event will bring together stakeholders across the US wealth management ecosystem to deepen the report’s insights and explore the latest WealthTech trends.
Attendees will include WealthTech vendors and wealth managers representing banks, broker-dealers, credit unions, registered investment advisers, and family officers. To find out more about WealthTech 2026: US edition, read our full preview article here.
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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.
For solution providers and vendors, the sell side of our marketplace, The Wealth Mosaic exists to support the positioning, exposure and business development needs of these firms in a more complex and demanding market.
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