In the ever-evolving landscape of wealth management, the integration of Artificial Intelligence (AI) is not just a technological advancement but a strategic imperative for independent firms in Singapore. The Hubbis Independent Wealth Management Forum - Singapore 2026 shed light on how AI is revolutionizing the independent wealth management model, touching upon productivity, client engagement, and operational efficiency. The discussion, led by industry luminaries like Andrew Hendry, CEO Asia and Senior Managing Director at Janus Henderson Investors, revealed that AI is not merely a cost-saving tool but a catalyst for enhancing client relationships and scaling advisory businesses.
AI: A Relationship-Centric Transformation
One of the key insights from the forum was that independent wealth management remains fundamentally a people and relationship business. AI, however, is becoming a critical tool for improving Relationship Manager (RM) productivity and client service. The panellists, including Alexander Kearns, CEO & Co-Founder of DataDasher, and Hrishikesh Unni, Managing Director at Taurus Wealth Advisors, emphasized that AI should not be adopted for the sake of fashion but because it helps firms deliver on their client promises. The focus should be on how AI can support personalized advice and close alignment with clients, rather than being an abstract exercise in technology adoption.
From Experimentation to Execution
The sector is moving from experimentation to execution, with a growing emphasis on enterprise AI, application layers, and operational systems. The panellists noted that many independent wealth firms are still in the early stages of AI adoption, often testing off-the-shelf tools and assessing what works. However, the conversation made clear that experimentation is no longer enough. Firms are being pushed to convert pilot projects into practical execution, marking a shift from individual experimentation to institutional adoption.
AI Capability vs. Business Application
A recurring theme was that AI capability alone does not create value. Firms need an application layer that turns the technology into usable workflows, revenue impact, and operational improvement. The panellists argued that many AI tools are powerful in isolation but fail to translate into business outcomes because they are not properly embedded into the daily work of advisers, investment teams, operations, compliance, or management. The key is to define the business problem before selecting the technology.
RM Time: The Scarce Resource
The panel repeatedly highlighted the cost and productivity of Relationship Managers. In independent wealth management, senior adviser time is one of the most valuable and least scalable resources. Yet, RMs often spend a significant portion of their week on non-revenue-generating activities. This creates a clear use case for AI. Tools that reduce administrative work, improve preparation, streamline meeting follow-up, support portfolio reviews, or enhance client communication can help advisers spend more time on activities that drive trust, revenue, and retention.
AI Beyond Cost Reduction
While much of the AI discussion in financial services focuses on efficiency, the panel also highlighted its revenue potential. AI can support prospecting, client segmentation, engagement planning, service consistency, and share-of-wallet growth. The panellists pointed out that a meaningful proportion of Ultra High Net Worth (UHNW) clients are considering consolidating more assets with a primary provider. Independent firms can use AI to engage clients more intelligently and consistently, potentially capturing consolidation opportunities and protecting relationships.
Build, Buy, or Partner: Assessing Realistically
The panel addressed the build, buy, or partner decision. Building proprietary AI infrastructure from scratch is unlikely to be the practical route for most independent wealth managers. Development can become expensive quickly, and the cost does not stop at launch. The panellists warned that firms often underestimate the maintenance burden, implementation complexity, and speed of iteration required. Even examples that appear to be internal builds may involve significant external partnerships, making the partner route particularly relevant for independent wealth managers.
Technology Budgets: Reflecting Strategic Importance
When asked how much independent wealth firms should be spending on AI, the panellists avoided a single fixed number but suggested that firms should think seriously about increasing their technology budgets. The argument was not that every firm should spend aggressively without discipline. Rather, AI should be assessed in terms of what it replaces, enhances, or enables. If it saves meaningful adviser time, improves client engagement, or increases revenue opportunities, the budget should reflect that strategic value.
Outcomes Matter More Than Speed
The panel cautioned against implementing AI simply for the sake of being first. Wealth management clients do not typically evaluate an adviser based on technology adoption alone. They evaluate outcomes, trust, responsiveness, and the value they receive. This means firms need to balance speed with discipline. AI is moving quickly, and opportunity cost matters. But rushed adoption without clear use cases, controls, or integration can create operational and reputational risk.
Cybersecurity and Data Protection: Core to AI Adoption
Panellists also highlighted the importance of security. As firms build AI ecosystems, they must consider data confidentiality, cybersecurity, regulatory compliance, and the governance layer around AI usage. This is particularly sensitive in wealth management, where client data is confidential, cross-border considerations can be complex, and trust is central to the advisory relationship. Firms cannot treat AI tools as casual productivity applications without understanding where data goes, how it is processed, who has access, and how outputs are controlled.
Clients Are Using AI Too
The panel noted that clients themselves increasingly have access to the same broad AI tools as advisers. This is changing the client conversation. Some clients remain sceptical and prefer traditional human-led discussions. Others are already using AI tools heavily to inform their own investment decisions. A third group sits in the middle: they still value adviser judgement but cross-check recommendations using AI applications. This creates both pressure and opportunity for advisers to explain, contextualize, challenge, and refine AI-generated information.
Cultural Adoption: Leadership and Context
The final part of the discussion focused on cultural adoption. Panellists challenged the assumption that AI adoption is purely an age-related issue. While younger employees may be more comfortable experimenting with new tools, openness to AI ultimately depends on character, leadership, firm culture, and perceived usefulness. The challenge is not simply giving staff access to tools but creating a framework where those tools are used consistently and effectively across the firm.
The Next Phase: Institutional AI
In closing, the panel suggested that AI will become an increasingly important differentiator for independent wealth managers in Singapore. The opportunity is not simply to automate tasks but to reshape how firms support advisers, engage clients, manage workflows, and scale relationship-led advice. However, the benefits will not come automatically. Firms need to define their proposition, select focused use cases, manage security and compliance, invest appropriately, and build a culture of adoption. They must also avoid confusing individual experimentation with institutional capability.
The most successful independent firms are likely to be those that use AI to strengthen, rather than dilute, the relationship model. Technology can help advisers become more proactive, more consistent, and more scalable, but the client outcome remains the ultimate test. The firms that win will not be those that use AI most loudly but those that use it most deliberately.
As Singapore’s independent wealth management sector continues to mature, AI will increasingly sit at the centre of discussions around scale, productivity, client relevance, and operational resilience. The challenge is no longer whether firms should explore AI but whether they can turn exploration into disciplined execution and execution into measurable client value.
