At a recent Hubbis roundtable luncheon in Manila, hosted in partnership with Backbase, senior wealth management leaders gathered to discuss one of the industry’s most consequential questions: how to harness AI to transform the frontline advisory model without eroding the human relationships that define private banking in this market. The intimate, off-the-record session revealed an industry at a genuine inflection point – ambition running ahead of infrastructure, and the promise of AI being tempered by the practical realities of fragmented data, legacy systems, and the enduring primacy of trust.

Key Takeaways


Early-stage but accelerating: Most Philippine wealth institutions are still in the exploratory phase of AI adoption, focused on productivity tools, but several have established dedicated AI governance councils, training programmes, and budgeted AI desks.
Data readiness as the critical foundation: Fragmented data environments and legacy systems remain the most significant barriers to AI deployment, with clean, integrated data across banking platforms a prerequisite for meaningful progress.
Productivity as the immediate prize: With Relationship Managers (RMs) spending 50 to 70 percent of their time on administration, AI-driven automation of account opening, trade execution, and portfolio management offers the clearest path to shifting RMs toward client-facing activity.
Investment management: Augmentation over replacement: AI is being used for research and financial modelling, but participants stressed the need for human oversight, particularly given conflicting outputs across platforms. Junior roles are being redefined, while frontline advisory positions remain resistant to displacement.
Talent and cultural change: Successful AI adoption depends on retraining, retooling, and behavioural change across all levels of seniority, with cultural acceptance as important as technical capability.
The intergenerational challenge: AI is complicating intergenerational wealth transfer, as younger family members favour informal digital communication over formal governance structures, creating friction with established family constitutions.
Backbase’s AI-Native Banking OS: Backbase’s AI-native Banking OS addresses the fragmented operational layer where roughly 50 percent of a bank’s work resides – the coordination whitespace between core systems that no single platform owns – delivering a 76 percent increase in assets under management per advisor in a private banking deployment.

 

An Industry at the Starting Line

What emerged most clearly from the discussion was a striking degree of honesty about where the Philippines’ wealth management sector stands on its AI journey. Across more than a dozen institutions represented at the table, the consensus was remarkably consistent: most are still in the early or exploratory stages of AI adoption, and the majority of current applications centre on productivity enhancement rather than transformative business model change.

Several participants described their organisations as being in the initial phase of AI adoption, primarily through embedded capabilities within existing software platforms. Common applications include using AI-powered tools such as Microsoft Copilot for email drafting, scheduling, and presentation preparation. A number of institutions have begun using AI for market research consolidation, enabling teams to aggregate and summarise daily market commentary without manual effort, distributing it to both internal stakeholders and external clients.

One participant noted that their organisation had formed a dedicated AI governance council alongside a data governance framework, and had begun training its entire workforce on generative AI, starting with the board and senior management before rolling out transformation labs focused on redesigning workflows from advisory generation through to trade order management.

Another institution described establishing a dedicated AI desk with its own budget, mandates, and accountability structures, reflecting a growing recognition that AI adoption requires organisational commitment rather than ad hoc experimentation. Yet even among the more advanced adopters, the prevailing sentiment was one of cautious optimism: the potential is enormous, but the foundations must be laid properly first.

The Data Readiness Challenge

A recurring theme throughout the discussion was the fundamental importance of data quality and system integration as prerequisites for meaningful AI deployment. Multiple participants pointed to fragmented data environments and legacy systems as the most significant barriers to progress.

The challenge is a practical one: in many Philippine banks, card systems, wealth platforms, deposit records, and Customer Relationship Management (CRM) tools operate in isolation. Without interoperability, AI tools cannot deliver the holistic client view required for intelligent advice or personalised engagement. The aspiration to understand client behaviour at a granular level is undermined when payment data, savings data, and investment data sit in separate, non-communicating systems.

One institution described its approach to solving this problem by designing its wealth platform with integration at its core, connecting its investment platform with its CRM system and back-end infrastructure to create a unified view across brokerage, trust, and treasury products. Only once the data foundation is sound can AI be layered on top to drive genuine improvements in productivity, advice quality, and client engagement.

Several participants emphasised the importance of cleaning and structuring data before venturing into AI implementation, with one institution noting that it was focused on strengthening its foundation through cleaner data, proper segmentation, and organisational frameworks to ensure AI adoption is both intentional and purposeful.

Productivity: The Most Immediate Use Case

If there was one area of near-universal agreement, it was that AI’s most immediate and tangible impact in the Philippine wealth management sector lies in productivity enhancement, particularly in reducing the administrative burden on Relationship Managers (RMs).

The scale of the problem is well understood. Across markets, RMs are estimated to spend between 50 and 70 percent of their time on administrative tasks, many of which detract from client-facing activity. In the Philippine context, participants cited examples of manual statement generation that could take up to a month, paper-based account opening processes requiring courier services for client signatures, and cumbersome trade execution workflows demanding multiple rounds of documentation.

One institution shared its experience of deploying an AI-powered wealth management tool that retrieves client information across multiple business units, consolidating investment holdings, market outlooks, and economic commentary into a single interface for RMs. The result has been a measurable improvement in both productivity and cross-selling effectiveness, with RMs no longer defaulting to a narrow range of favoured products but instead being guided toward the most appropriate solutions for each client.

The broader ambition is to invert the current productivity ratio, shifting RMs from spending the majority of their time on administration to spending the majority of their time with clients. Participants saw AI-driven automation of account opening, investment transactions, and portfolio management processes as the clearest pathway to achieving this, with the added benefit of enabling higher client-to-RM ratios without compromising service quality.

Investment Management: Scaling Up, Not Replacing

On the investment side, the discussion revealed a nuanced picture. AI is already being used for research augmentation, financial modelling, and the generation of daily talking points for RMs. One participant noted that tasks which previously required a junior analyst a full week to complete, such as building a five-year financial model, can now be accomplished in minutes using AI tools.

This has led some institutions to rethink their talent structures, effectively scaling up entry-level requirements and bypassing certain junior analytical roles that AI can now perform more efficiently. However, participants were careful to distinguish between efficiency gains and wholesale replacement. The consensus was that AI is best deployed as a complement to human judgement, particularly in a wealth management context where client trust and relationship depth remain paramount.

One participant described a disciplined approach to validating AI outputs, cross-referencing results across multiple platforms and noting that conflicting recommendations remain a real concern. In a business where clients’ serious capital is at stake, the need for human oversight and validation was seen as non-negotiable, at least at the current stage of AI maturity.

Talent: Threat and Opportunity

The question of how AI affects talent was addressed with a blend of pragmatism and caution. Several participants acknowledged that AI is already reshaping workforce requirements, with back-office and middle-office roles most immediately affected. The ability to automate routine analytical tasks, generate research summaries, and streamline compliance workflows means that certain roles, particularly at the junior level, are being redefined or absorbed.

Yet the frontline advisory role was viewed as far more resistant to displacement. Participants consistently emphasised that the soft skills, experiential judgement, and relationship depth that define effective private banking cannot be replicated by algorithms. The challenge, rather, is one of retraining and retooling existing staff to work effectively alongside AI, ensuring that adoption is embraced rather than resisted.

One institution described a comprehensive approach, developing special graduate programmes that recruit top engineering and management talent into rigorous one-year training programmes designed to build the hybrid skillsets required in an AI-augmented wealth environment.

The behavioural dimension of AI adoption was also highlighted as a critical factor. Technology implementation alone is insufficient; success depends on ensuring that users across all levels of seniority understand, appreciate, and actively engage with the new tools. Without that cultural shift, even the most sophisticated AI capabilities risk remaining underutilised.

Looking Ahead: From Productivity to Transformation

While the Philippine wealth management industry is largely in the early stages of AI adoption, the trajectory is clear. Institutions are moving from initial experimentation with productivity tools toward more ambitious applications spanning client advisory, portfolio analysis, campaign management, and process redesign.

The challenges are significant but well understood: data fragmentation, legacy system integration, regulatory alignment with the Bangko Sentral ng Pilipinas’ evolving digital finance frameworks, and the cultural change required to embed AI into daily workflows. Yet the opportunity is equally compelling: a rapidly growing high-net-worth population, significant diaspora-driven wealth flows, and one of Asia’s most mobile-first societies create fertile ground for digitally enhanced wealth management.

The institutions that will lead this transformation are those that invest not only in technology but in the foundational work of data readiness, system integration, talent development, and organisational alignment. AI will not replace the relationship manager, but it will fundamentally reshape what that role looks like, and the institutions that prepare their people and platforms accordingly will be best positioned to capture the Philippines’ wealth management opportunity in the years ahead.

Backbase’s Vision for an AI-Native Wealth Frontline

The Philippine banking sector has made significant strides in digital transformation, yet a critical gap remains in how wealth management institutions equip their frontline advisors. According to Aileen Sanchez, Country Sales Director at Backbase, the real AI opportunity for wealth managers extends well beyond chatbots and client-facing applications. It lies in transforming the operational middle layer, what she described as a white space that most banks have yet to address, between core banking systems and the client relationship.

Backbase, headquartered in Amsterdam and founded in 2003, has built a growing footprint in the Philippines, working with several of the country’s leading financial institutions on their digital transformation journeys. The company invests between 50 and 60 percent of its revenue into research and development, reflecting a conviction that AI will become an integral part of how banks operate and engage with clients.

Sanchez framed the central question for wealth managers as one of dual ambition. “Where does the AI opportunity truly lie? Is it doing things faster or doing things better?” she asked. In the Philippine context, where high-net-worth (HNW) clients remain highly relationship-driven while a growing affluent and mass affluent segment demands scale, she argued that AI must deliver on both fronts simultaneously, enabling Relationship Managers (RMs) to serve a broader client base without sacrificing the quality of engagement that wealthier clients expect.

The key challenge, Sanchez explained, is that most banks continue to operate with what she characterised as a fragmented frontline. Across the typical wealth management operation, RMs navigate dozens of disconnected systems, from core banking and Customer Relationship Management (CRM) platforms to portfolio management systems and compliance tools, relying on manual coordination, spreadsheets, and email to piece together a client view. “These manual coordinations actually make each RM’s life a bit of a mess,” Sanchez observed.

By Backbase’s estimate, roughly 50 percent of a bank’s operational work resides in this fragmented coordination layer, a figure that aligns closely with the roundtable participants’ own observations that RMs spend between 50 and 70 percent of their time on administrative rather than client-facing tasks.

Backbase’s answer to this challenge is the AI-native Banking OS – a control plane that sits above existing core systems and coordinates execution across customer applications, employee workspaces, and AI agents through a single, shared operating model. The Banking OS is designed to unify data and workflows across customer applications, employee workspaces, and AI agents into a single, shared operating model. “This AI-native banking OS sits on top of your core systems and it is below your customer experience,” Sanchez explained. “This is the white space that I think most banks should really look at.”

Critically, the Banking OS is designed to complement rather than compete with existing core banking investments. Sanchez was explicit that Backbase does not replace systems of record but coordinates execution across them, creating a single source of truth that clients, advisors, and AI agents can all access. The architecture supports what Backbase terms an adopt-and-build model: banks can deploy out-of-the-box capabilities for speed, then build custom journeys and integrations on top to differentiate their offering from competitors serving the same market.

To illustrate the potential impact, Sanchez presented a case study from an international private banking client. After implementing the Banking OS, client onboarding time fell from 18 days to five. The cumulative operational effect was a 76 percent increase in AUM per advisor – rising from USD 151 million to USD 266 million.

“AI will not replace people. We are all irreplaceable,” Sanchez concluded. “AI is something that we could partner with in order to improve our lives.”

The Next Generation Question: AI, Dependency, and the Intergenerational Challenge

The roundtable’s final discussion moved beyond operational efficiency into more personal territory: what does the rise of AI mean for the next generation, both as future clients and as the children of the industry’s current leaders?

The conversation revealed a shared unease among participants about the extent to which young people are becoming dependent on AI tools. Several noted that while AI literacy is essential for the future workforce, there is a growing risk that over-reliance on technology erodes the foundational skills, including critical thinking, interpersonal communication, and independent problem-solving, that remain central to professional success. One participant observed that young people who produce flawless written work with the help of AI tools often struggle to articulate the same ideas in person, raising questions about how much genuine learning is taking place beneath the polished output.

The concern extends beyond academic settings. In wealth management, participants noted that the same dynamic applies to RMs: if advisors become too reliant on AI-generated insights and talking points, they risk becoming interchangeable with the tools themselves. The consensus was that clients who can access the same AI platforms would have little reason to work through an advisor who adds no independent judgement or relationship depth on top of the technology. The balancing act, several participants agreed, is to equip advisors with AI while ensuring they retain the ability to think critically, exercise judgement, and engage clients on a genuinely personal level.

The discussion also touched on the challenge of intergenerational wealth transfer, a perennial concern for the Philippine wealth management industry given the country’s deeply rooted family business culture. Participants noted that the third generation of wealthy families often operates in fundamentally different ways from their predecessors, preferring informal digital communication channels over formal board meetings and relying on AI tools for decision-making support. This cultural shift creates friction when families attempt to align younger members with established governance structures such as family constitutions and family assemblies.

One participant described the practical difficulty of working with a multi-branch family office undergoing a generational transition, where the third generation’s preference for messaging platforms and AI-generated analysis clashed with the governance expectations of the founding generation. The challenge is not merely technological but deeply behavioural: how to preserve the discipline and documentation that formal governance requires while accommodating the communication norms of a generation raised on digital tools.

For wealth managers, this generational divide presents both a risk and an opportunity. Institutions that fail to engage younger family members on their terms risk losing relationships when wealth transfers across generations, a pattern that is well documented across Asian markets. Yet those that invest in digital engagement models, combining AI-powered tools with genuine advisory relationships, may be better positioned to retain and grow these multigenerational client relationships over time.

The roundtable’s closing reflections underscored a theme that ran through the entire discussion: AI is neither a threat nor a panacea, but a tool whose value depends entirely on how thoughtfully it is implemented, whether in a bank’s operations, an advisor’s workflow, or a young person’s education. The institutions and individuals that approach AI with both ambition and discipline, embracing its capabilities while preserving the irreplaceable human skills that underpin trust, will be best placed to thrive in the years ahead.