Artificial intelligence is rapidly moving from theoretical discussion to operational reality across the financial services industry. For private banks and wealth managers – institutions traditionally defined by stability, regulation and relationship-driven advice – the implications are significant. Technologies once seen as experimental are beginning to reshape how advisers interact with clients, how information moves through organisations, and how decisions are made.
Yet despite the surge of interest following the arrival of generative AI, adoption remains uneven. Some large global institutions are investing heavily in dedicated AI teams and platforms, while many smaller and mid-sized wealth managers are still experimenting with early-stage applications and assessing how the technology fits within highly regulated environments. For Dana Ritter, Chief Product Officer at Unique, this divergence reflects the early stage of what is likely to become a much broader transformation.
Key Takeaways
- AI Adoption in Wealth Management Remains Early-Stage: Despite intense interest following the rise of generative AI, much of the private banking industry is still experimenting with basic use cases such as summarising documents, drafting communications and querying internal data.
- The Next Phase Is Embedded AI Across Core Workflows: The real transformation will occur when AI moves beyond standalone tools and becomes integrated across CRM, portfolio management and compliance systems, enabling advisers to access insights and complete tasks far more efficiently.
- A Two-Speed Industry Is Emerging: Large global banks are investing heavily in dedicated AI teams and enterprise platforms, while many mid-sized and boutique wealth managers are still focused on securely connecting AI to internal data and infrastructure.
- The “Agentic Relationship Manager” Could Redefine Advisory Models: Future advisory environments may feature an AI orchestration layer that coordinates multiple banking systems, allowing relationship managers to steer intelligent agents that handle operational and analytical work.
- Organisational Adoption Is the Biggest Barrier: Building AI prototypes is relatively straightforward, but embedding them into daily workflows is far harder. Many internal tools fail when employees treat them as experiments rather than core working systems.
- Standing Still Carries Strategic Risk: As AI improves client onboarding, advisory preparation and operational efficiency, institutions that delay adoption risk falling behind competitors and losing both clients and talent.
While many firms are still experimenting with basic generative AI capabilities, Ritter argues that the real shift will occur when banks begin integrating intelligent systems directly into the operational fabric of wealth management. That transition – from isolated tools to embedded, decision-supporting systems – has the potential to fundamentally change how relationship managers operate.
From Curiosity to Operational Use Cases
Ritter explains that the earliest engagement with generative AI within private banking was largely exploratory.
“When ChatGPT first appeared, most banks approached it as a technical curiosity,” he says. “You had IT teams experimenting with prototypes – often driven by a few enthusiastic engineers or researchers.”
During this initial phase, activity was largely confined to internal experimentation. Institutions explored potential applications but rarely embedded the technology deeply into business processes.
By 2024, however, banks began to move beyond experimentation and into practical implementation. The earliest use cases were relatively straightforward – summarising documents, generating client emails or extracting insights from large volumes of data.
These applications, while useful, represented only a small step toward the broader transformation that AI could enable.
“The first real moment of excitement came when banks started connecting AI securely to their internal data,” Ritter explains. “Suddenly the technology could query information, generate insights and support presentations or documentation automatically. It felt almost magical because it eliminated so much manual work.”
Even so, Ritter estimates that a significant proportion of the industry remains at this early stage of adoption.
“I would say around sixty percent of the market is still operating at that level. The industry is still very, very nascent.”
Two Speeds of AI Adoption
The pace of adoption varies sharply across the industry.
Large global institutions such as UBS and Standard Chartered have established dedicated teams focused on more advanced AI initiatives. These organisations are already exploring how artificial intelligence can reshape advisory processes, operational workflows and client interactions.
By contrast, many mid-sized private banks and boutique wealth managers remain at a much earlier stage.
“For smaller institutions, the first step is simply being able to interact with AI securely,” Ritter says. “If they can safely connect the technology to internal systems and data, that alone is already a major milestone.”
This divergence is creating what Ritter describes as two parallel worlds within wealth management – one pushing toward deeper automation and AI-driven workflows, while the other remains focused on basic experimentation.
The Rise of the Agentic Relationship Manager
Looking ahead, Ritter believes the next major step will be the emergence of what he describes as an “agentic relationship manager”.
In this model, artificial intelligence operates as an intelligent layer that connects and coordinates the multiple systems used by banks – from client relationship management tools to portfolio management platforms and compliance databases.
“The idea is that you connect all your existing systems to a kind of AI ‘brain’,” he explains. “The relationship manager then steers that brain while the system performs much of the operational work.”
Such an approach could dramatically increase both efficiency and analytical depth.
“It should significantly increase the speed at which work can be done, while also improving quality,” Ritter notes.
However, achieving this level of integration is far from simple. While many banks have already built internal AI prototypes, turning those experiments into production systems that employees actually use remains a major challenge.
The Hardest Problem: Adoption
Ritter believes one of the most underestimated obstacles in AI deployment is organisational adoption.
Many banks have launched internal AI tools developed by technology teams, only to find that employees treat them as experimental novelties rather than core working tools.
“People try them once, think they’re interesting, and then go back to their normal workflows,” he says. “At that point the project loses momentum and the value disappears.”
The difficulty lies not in building AI systems – which Ritter argues is now relatively straightforward – but in refining them to the point where they genuinely enhance daily work.
“It’s quite easy today to build a ninety-percent prototype and show something impressive,” he explains. “But hardening that system and driving real adoption across the organisation is much more difficult.”
For most financial institutions, the transition from experimentation to fully embedded AI workflows is likely to be a multi-year journey.
“To make this real across an entire organisation will take two to five years for most banks,” Ritter says.
A Product Perspective on AI Innovation
Ritter joined Unique in April 2025, bringing with him a career that spans both global technology and private banking.
Prior to joining the firm, he spent a decade at Google, where he served as lead product manager for the early deployment of the Gemini assistant on Android devices. During that period, the product’s user base expanded from roughly two million daily active users to approximately thirty-five million.
Earlier in his career, Ritter spent eight years at Credit Suisse, where he oversaw front-end technology systems in Southeast Asia and contributed to the development of the bank’s international client portal.
This combination of financial services experience and AI product development ultimately led him to Unique.
“The opportunity felt unusual because it brought together two very different worlds,” he says. “It required someone who understood both AI product development and the realities of wealth management.”
Unique’s Evolution: From Transcription to AI Platform
Unique itself began in a very different market segment.
The company was originally focused on sales acceleration tools for technology companies before pivoting during the pandemic to build software capable of transcribing video meetings on platforms such as Zoom and Teams – functionality that had previously been limited.
This proved particularly valuable for financial institutions, which needed secure methods of documenting conversations and maintaining compliance records.
Through this work, the firm developed deep expertise in operating within highly regulated banking environments.
When generative AI technology began to emerge, Unique was therefore well positioned to integrate large language models within secure banking infrastructures.
“That experience with security and governance meant we already understood what banks needed,” Ritter explains.
The company subsequently secured several major Swiss banking clients and has since expanded globally, with offices in Singapore, London, Switzerland and New York, serving approximately forty institutional clients.
From LLM Tools to Context Graphs
The next phase of development, Ritter argues, involves moving beyond simple generative AI tools.
Capabilities such as summarising documents or drafting emails have rapidly become commoditised – widely available through mainstream platforms such as Microsoft Copilot.
Unique’s focus is therefore shifting toward deeper contextual intelligence within wealth management organisations.
Central to this strategy is the creation of what Ritter describes as a “context graph”.
This framework seeks to connect all the fragmented signals generated within a financial institution – emails, meeting transcripts, CRM updates, compliance interactions and other operational data – into a unified intelligence layer.
“In any bank, huge amounts of valuable information are scattered across different systems,” Ritter explains. “Our goal is to connect those signals and allow AI agents to extract insights from that noise.”
A typical example might involve the client onboarding process. Over several months, relationship managers may exchange emails, hold meetings, engage with compliance teams and record various interactions across multiple platforms.
Individually, each data point offers limited insight. Combined, however, they can reveal important patterns and signals relevant to client relationships and decision-making.
Specialisation in Wealth Management Workflows
Rather than competing with general-purpose AI tools, Unique is increasingly focused on highly specialised workflows within private banking.
“We are not trying to compete with generic tools on basic tasks like writing emails,” Ritter says. “Where we add value is in wealth management-specific interactions.”
This includes designing AI agents capable of supporting relationship managers, compliance officers and client advisers in highly specialised processes.
Such domain-specific capabilities, Ritter believes, will become increasingly important as the broader AI ecosystem continues to commoditise generic functionality.
The Strategic Risk of Standing Still
Despite the rapid pace of innovation, Ritter notes that some banks still question whether artificial intelligence is truly relevant to their business models.
His answer is direct. “The risk is simple – irrelevance.”
In wealth management, client experience and operational efficiency are becoming critical competitive differentiators. A bank that takes months to onboard a client may find itself competing with institutions capable of completing the process in a matter of days.
“If onboarding takes two months at one institution but one week at another, that absolutely influences where clients choose to place their assets,” Ritter explains.
Talent competition may prove equally decisive. Younger relationship managers entering the industry increasingly expect to work with advanced digital tools that allow them to manage client relationships more efficiently.
“If you cannot offer that kind of environment, you will lose in the war for talent,” he says.
Banking’s Structural Challenge
Yet the challenge facing the industry extends beyond technology alone.
Banks, by their nature, are structured around caution, regulatory oversight and operational stability – characteristics that can make rapid innovation difficult.
“The speed of change in AI right now is extraordinary,” Ritter observes. “New capabilities appear constantly, leapfrogging what existed just months earlier.”
Institutions able to adapt quickly may therefore gain a significant competitive advantage.
“The organisations that succeed will be the ones that can keep pace with the technology,” he says.
For the wealth management sector, the AI transformation may still be in its early stages – but the trajectory is already unmistakable.
