Nikolai Braiden, an early adopter of blockchain, is our resident FinTech expert. He strongly advocates for financial technology’s transformative potential in reshaping digital payment and lending systems and has extensive experience advising startups on leveraging technology to drive innovation and advancement within the industry. Today, we sit down with him to explore the critical priorities facing wealth management firms.
Our conversation delves into the urgent need for firms to evolve beyond AI pilots toward true “Agentic AI” that can execute transactions, a shift that hinges on first fixing broken data foundations. We also explore how to create genuinely seamless client journeys that meet modern expectations, the practicalities of migrating away from decades-old legacy systems, and the strategies for closing the significant “advice gap” for the mass-affluent segment. Finally, we’ll discuss the cultural changes required to stop funding projects without clear returns and automate workflows to free up advisors for what they do best: building relationships.
The conversation around AI is shifting from pilots to “Agentic AI” that can transact for clients. What are the biggest hurdles firms face when transforming end-to-end processes to support this, and what practical steps should they take now to fix their underlying data foundations first?
That’s the core challenge for 2026. The shift from chatbots that just answer questions to “Agentic AI” that can meaningfully plan and transact on a client’s behalf is a monumental leap. The biggest hurdle isn’t the AI model itself; it’s that the entire organizational process, from the front-end interface straight through to the back-office execution, isn’t built for it. You can’t just install an AI agent on top of a fragmented system and expect it to work miracles. The first, non-negotiable step is to fix the underlying data problem. Client documents, emails, suitability evidence—this is the firm’s lifeblood, yet it’s often scattered across countless disconnected repositories. Firms won’t unlock the exciting value of AI until they first solve this fragmentation and build a trusted, context-rich information foundation. Data is the rocket fuel for AI, and without it, the rocket never leaves the launchpad.
Clients now judge financial firms against the best digital experiences they’ve had anywhere. How can wealth managers realistically create seamless, end-to-end journeys that bridge the gap between impressive front-end demos and actual back-office execution? Please share some key performance indicators for success.
This is a critical point because clients truly don’t judge you against other banks anymore; they judge you against the best digital experience they had this week. The key is to stop thinking in terms of “fancy demos” for the front office. A truly seamless journey is one where the beautiful interface is deeply connected to the core systems that execute transactions. Personalization and transparency are impossible if the front-end AI can’t talk to the core system to pull real-time data or execute a trade. Realistically, this means focusing on the entire lifecycle: digital onboarding with eID and KYC reuse, frictionless funding, instant portfolio previews, and proactive nudges for contribution reminders or life-event prompts. As for success metrics, firms must abandon vanity metrics like page views. Instead, they should measure what truly matters: time-to-value for the client, journey completion rates, advice explainability usage, and ultimately, client retention and advice consistency across different segments.
Many firms are still running on legacy software that is 15-20 years old. Could you walk me through what a successful, phased migration to a cloud-native, API-first infrastructure looks like and how firms can ensure they remain resilient and future-ready throughout the process?
It’s a daunting task, but clinging to 15- or 20-year-old on-premise, monolithic systems is no longer an option. A successful migration is about embracing flexibility. It doesn’t have to be a single, terrifying “Big Bang” event. The priority is adopting a cloud-native infrastructure built on API-first microservices. This interoperable software design allows you to migrate gradually. You can start by replacing one component at a time, like onboarding or reporting, and have it communicate seamlessly with the old system via APIs. This phased approach minimizes disruption while progressively building a modern, resilient foundation. The goal is to create an ecosystem where systems can adapt instantly under stress, not after the fact. The ability to scale and remain stable in volatile conditions is no longer just an operational concern; it’s a fundamental competitive advantage that makes you future-ready.
There is a significant “advice gap” for mass-affluent clients. How can firms effectively scale personalized advice and “private banking light” services for this segment, and what technologies are most critical for balancing the high cost of customization with the need for profitability?
The “advice gap” for the mass affluent is one of the biggest opportunities in the industry. The key to serving this segment holistically is to blend human expertise with explainable automation. It’s not about replacing advisors with black boxes. Instead, it’s about empowering them with tools that can scale personalization. The technology needs to go beyond simple risk scores to encompass a client’s goals, sustainability preferences, tax situation, and specific constraints. This allows for “private banking light” services, like Lombard lending or specialized tax wrappers, to be offered profitably to a broader audience. The most critical technology here is explainable AI, which can generate recommendations and then clearly articulate the “why” behind them. This builds trust and empowers clients. The demand is there; a recent McKinsey study found almost 80% of affluent households would pay a premium of 50 basis points or more for human advice over a basic digital service.
Advisors often lose valuable time to manual administrative tasks and searching for information across disconnected systems. What are the most impactful automated workflows a firm can implement to reduce this friction, and how does that directly translate into deeper client relationships and higher lifetime value?
This friction is a huge drain on productivity and morale. Advisors spend far too much of their day just searching for files or re-keying the same information into multiple systems. The most impactful automated workflows are those that tackle the most document-heavy processes: client onboarding, suitability reviews, and periodic updates. By moving these tasks from email and spreadsheets to case-managed, API-driven workflows, you eliminate the source of the friction. This directly translates into deeper client relationships because it frees up the advisor’s time. Instead of chasing paperwork, they can focus on providing strategic advice, having meaningful conversations, and building better financial plans. More time with clients means higher lifetime value—it’s that simple. The performance advantage comes from shifting that wasted effort into high-value, relationship-building activities.
Firms are often told to stop patching legacy systems and funding projects without clear returns. What is the best way to shift an organization’s culture away from a “sunk cost” fallacy, and how can leaders better evaluate which initiatives truly deliver demonstrable, measurable value?
Shifting away from the “sunk cost” fallacy requires a profound cultural change driven by leadership discipline. The first step is to stop treating regulatory work as discretionary. Regulatory delivery is the non-negotiable baseload of the organization; everything else comes after. Once that is secured, every single non-regulatory initiative must compete for capacity based on a clearly defined, measurable return on investment. Too many firms are overflowing with projects that are easy to start but hard to stop, not because they deliver value, but because of inertia. Leaders must force a strategic reassessment of aging platforms that require constant patching. The question must shift from “How do we extend this system again?” to “Should this system be replaced, outsourced, or retired entirely?” This discipline is crucial: regulatory delivery first, followed only by business initiatives where the return is explicit, measurable, and defensible.
What is your forecast for wealth management?
My forecast is for an industry that will be defined by radical transparency and intelligent automation. The winners will be the firms that stop layering technology on top of legacy platforms and instead embed data, AI, and automation at the very heart of their operating model. We’ll see a move away from one-size-fits-all servicing, with firms leveraging technology to deliver hyper-personalized, “private banking light” experiences to the mass-affluent segment, closing the advice gap. Success will no longer be measured by assets under management alone but by the quality of the client journey, the depth of the advisor-client relationship, and the operational resilience of the firm. The most competitive organizations will be those that have the discipline to retire their tech debt, invest only in initiatives with measurable returns, and build a culture where compliance and data governance are treated as foundational product capabilities, not afterthoughts.