Why Wealth Managers Are Not Seeing Expected AI Returns
In recent years, wealth management firms have increased their artificial intelligence spending exponentially. Firms are buying AI tools, training staff on them, and announcing to employees and clients alike that they are now an “AI-forward organization.”
The results of this upsurge in investment, while tangible, are somewhat narrow. Meetings get prepped faster, client emails go out cleaner and prospect research takes less time. If you step back, however, it’s clear that AI investments affect only a thin slice of the operations pie.
And as AI budgets have grown, so has the scrutiny. CEOs, CFOs and private equity investors want to know what they’re getting for skyrocketing AI spending. For most firms, the honest ROI answer is that it’s less than expected.
A few data points reveal where AI investments are falling short of the mark:
The 2026 WealthStack Study found that 87% of firms use or pilot AI. Deloitte found that only 6% use agentic tools to complete tasks inside workflows rather than assist with them. While most firms have bought into AI, few have gotten past the front door.
F2 Strategy’s Q2 2026 Trend Report, which surveyed 40 leading RIAs, wealth management firms, and broker-dealers representing $8.6 trillion in assets, found a very weak correlation between firms’ spending on AI technology and meaningful, measurable value to the business.
Kitces Research found that top-performing advisors spend roughly 30% to 35% of their time meeting directly with clients. Most advisors fall well short of even that. The reason: Advisors need to dedicate most of the day to dealing with operational and administrative work—work that AI investments have yet to reclaim meaningfully.
AI has yet to deliver the sweeping operational changes that proponents have predicted for years because of one crucial issue: Most firms have bought AI that sits alongside the work rather than inside it.
Think of AI as a six-lane highway. The technology can move enormous amounts of work quickly and efficiently. The problem is that most wealth management firms are stuck on the on-ramp, blocked by a traffic jam of unreachable data.
Often, an advisor’s legacy technology consists of disconnected layers of systems that resemble a Rube Goldberg contraption—an overly complicated machine designed to perform simple tasks in convoluted, indirect ways.
In these environments, data typically doesn’t move between CRM, portfolio reporting, financial planning and other layers of the tech stack without a person doing the heavy lifting. That is because every workflow that crosses from one system to another requires human handoffs at every step.
The bottleneck arises because each handoff is a lever that only a person can pull. No amount of horsepower can automate this hand-operated crank. When you add AI, it operates within the same limitations, and firms can’t unlock the value it promises.
McKinsey’s “Seizing the Agentic AI Advantage” called this the “gen AI paradox.” Nearly eight in 10 companies have deployed generative AI, and roughly the same percentage report no material impact on earnings. McKinsey found that firms have tended to invest more in horizontal tools—productivity assistants that generate meeting minutes, write emails and summarize, or secured access to AI chatbots enriched with company-specific data. They have underinvested in vertical use cases—those embedded into specific business functions and processes that materially change how a business operates. A lot of that is due to the bottleneck caused by disconnected tech stacks.
Advisors who are moving past that bottleneck share one characteristic. They build AI-native operating systems from the ground up. Historically, advisors used single-purpose tools. As advisors’ businesses evolved, they continually layered on new software to meet emerging investment, operational and client needs. Bottleneck-free systems operate natively within a unified environment where data, decisions, and actions coexist.
Deloitte found productivity gains of roughly 103% at the AI-native stage, when automation runs multistep, end-to-end workflows and wealth managers operate with a team of digital agents, supervising AI systems that handle preparation, monitoring, and routine servicing. The WealthStack Study found that firms seeing transformational results are distinguished not by which AI products they’ve purchased, but by how fully they integrate technology into their operations.
At Ridgeline, 100% of our customers are using AI within the platform. Over the past year, they’ve run more than 1 million workflow automations, with many customers using in-platform AI agents to take on meaningful work across reconciliation, compliance, and relationship management.
Eighty-seven percent of wealth managers are using AI. The question is whether the operating system underneath it can actually put it to work. For most firms, it can’t. That’s the bottleneck. It won’t move until the foundation does.
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