Wealth management and financial services firms are accelerating AI spending at an exponential rate, yet the industry has largely failed to establish formal methods for measuring whether those investments actually deliver business value. A new national survey of 40 leading registered investment advisors (RIAs), wealth management firms, and broker-dealers representing $8.6 trillion in assets found a striking disconnect: spending on AI technology has surged over the past three years, but measurable returns remain elusive.

The research, conducted by F2 Strategy, a wealth management industry consultancy, surveyed firms representing $31 trillion in assets under management (AUM). The findings paint a picture of an industry moving fast but not necessarily moving smart. "We're seeing a very loose correlation in 2026 between firms' spend on both AI technology and its tokens and a meaningful measurable value in a classic sense to the business," explained Doug Fritz, co-founder and executive chairman of F2 Strategy.

What's Blocking Wealth Firms From Measuring AI Success?

The barriers to measuring AI ROI (return on investment) are structural, not just cultural. According to the F2 Strategy survey, 64 percent of wealth management firms and 83 percent of bank and trust respondents lack a unified data layer, which is essential infrastructure for making AI projects function effectively. Without that foundation, tracking whether an AI initiative actually improved efficiency or client outcomes becomes nearly impossible.

This measurement gap is particularly troubling because the stakes are high. For many firms, especially those backed by private equity, the future viability of the business now hinges on whether AI initiatives deliver measurable returns. Yet most organizations have not established formal measurement frameworks to answer that question.

The broader enterprise AI landscape tells a similar story. According to McKinsey research cited in recent analysis, 88 percent of organizations now use AI in at least one business function, but only about 6 percent qualify as "AI high performers" generating more than 5 percent EBIT (earnings before interest and taxes) impact. This gap between adoption and measurable value has become the defining challenge of enterprise AI in 2026.

How Are AI Leaders Different From Everyone Else?

Among the wealth management firms that do measure their AI investments, the results are encouraging. Sixty-eight percent of those firms reported gaining 25 percent more efficiency in targeted workflows. This suggests that measurement itself may be a marker of success, not just a way to track it.

The research identifies a growing divide between "AI leaders" assembling agentic technology stacks (AI systems that can plan and execute tasks with some autonomy) and firms lagging behind by as much as 12 to 24 months. The difference between these two groups comes down to more than just technology choices. McKinsey's analysis of high-performing organizations found stark behavioral differences:

• Workflow Redesign: High performers are nearly three times more likely than others to have fundamentally redesigned their workflows around AI, rather than simply bolting new tools onto existing processes.
• Leadership Ownership: High performers are three times more likely to report senior leaders demonstrating true ownership of AI initiatives, including actively role-modeling AI use within their organizations.
• Investment Intensity: High performers commit more than 20 percent of their digital budgets to AI roughly five times more often than other organizations.
• Human-in-the-Loop Discipline: High performers are substantially more likely to have defined processes determining when AI model outputs require human validation before deployment.

The implication is clear: success in enterprise AI is not primarily a technology problem. It's an organizational and governance problem.

Why Are Trading Desks Hiring More, Not Less?

One of the most counterintuitive findings in recent financial services research challenges the widespread assumption that AI will eliminate jobs. A July 2026 study by Crisil Coalition Greenwich found that more than half of US brokers surveyed expect to increase headcount across multiple roles, even as AI adoption accelerates.

Specifically, 52 percent of brokers plan to increase desk coverage, 48 percent expect to hire more on-desk trade assistants, and 45 percent plan to expand algorithmic sales teams. These hiring plans coincide with near-record US equity trading volumes and expectations of further growth, driven in part by a pipeline of high-profile initial public offerings.

"The human element is actually becoming more central, not less," explained Jesse Forster, senior analyst in Market Structure and Technology at Crisil Coalition Greenwich. "As automation handles routine tasks, brokers see rising value in judgment, client relationships, exception management, and the ability to explain and defend decisions, skills machines still cannot replicate".

AI is being deployed across trading workflows, with approximately one-third of brokers currently using it for real-time algorithm optimization (32 percent), venue selection (29 percent), and market data analysis (29 percent). Compliance and surveillance adoption remains low at 12 percent, though 44 percent of brokers plan to implement it in the near term.

How Are Banks and Advisory Platforms Deploying AI in Client Service?

In the retail and wealth advisory space, concrete examples of AI deployment are emerging. Bank of America announced enhancements this week to EricaAssist, its generative AI-powered tool that supports more than 18,000 customer service representatives during client calls. The updated system delivers contextual guidance in under three seconds, helping employees summarize client needs, surface relevant information, and recommend next steps without interrupting the flow of conversation.

Bank of America reports that EricaAssist already reduces average call times by nearly one minute per interaction. The Charlotte, North Carolina-based bank spends $14 billion annually on technology, of which more than $4 billion is directed toward new initiatives including AI. The bank plans to expand EricaAssist to additional servicing scenarios and business lines later in 2026.

"EricaAssist reflects our high-tech, high-touch approach. By combining human judgment with real-time AI guidance, we're helping employees navigate complex topics more easily and serve clients more effectively in the moments that matter most," said Ashley Ross, head of consumer client experience and business transformation at Bank of America.

Ashley Ross, Head of Consumer Client Experience and Business Transformation at Bank of America

For independent advisors, a new partnership between Vise Technologies, a New York-based registered investment advisor and technology platform, and Alpha Architect, a quantitative asset manager, is bringing AI-powered, tax-optimized custom model portfolios to RIAs regardless of firm size. The integration allows advisors to deploy research-driven, rules-based investment strategies and tailor each one to individual clients, accounting for concentrated positions, values-based exclusions, and tax circumstances, all managed within a single workflow.

As of July 2026, the Vise platform holds more than $100 billion in platform assets across more than 100 advisory firms and 135,000 accounts.

Steps to Move Beyond AI Experimentation Toward Measurable Impact

• Establish Formal Measurement Frameworks: Define clear metrics for AI success before deployment, including efficiency gains, cost reductions, and revenue impact. Organizations without measurement systems cannot identify which initiatives are working and which are wasting resources.
• Build Unified Data Infrastructure: Invest in the foundational data architecture that makes AI projects function effectively. Without a unified data layer, tracking AI performance across the organization becomes nearly impossible.
• Redesign Workflows Around AI, Not Alongside It: Rather than adding AI tools to unchanged business processes, fundamentally rethink how work gets done. High performers are nearly three times more likely to rebuild workflows from the ground up.
• Secure Senior Leadership Ownership: Ensure that C-suite executives actively champion and use AI tools themselves. Leadership ownership is a stronger predictor of success than technology choice or budget size.
• Define Human-Validation Processes: Establish clear governance rules for when AI outputs require human review before deployment, particularly in regulated industries like financial services.

The broader context is one of rapid adoption outpacing measurable value. Consulting firms are racing to help organizations close this gap. Sia, a global management consulting firm backed by Blackstone, was recently promoted to Advanced Partner status in OpenAI's Partner Network, a designation recognizing its expertise in helping organizations move beyond AI pilots toward enterprise-wide transformation. Sia has deployed its 1,000th AI agent to support both client engagements and internal operations.

"The biggest challenge in enterprise AI is no longer the technology, it is adoption," said David Martineau, Chief AI Officer of Sia. "Our Forward Deployed Success model embeds our teams alongside clients to accelerate deployment, drive lasting behavioral change and turn ChatGPT and Codex into everyday business capabilities".

David Martineau, Chief AI Officer of Sia

For Anthropic, the frontier AI company, the strategy centers on positioning safety and governance as drivers of enterprise ROI, not just compliance costs. Dario Amodei, Anthropic's CEO, has framed the company's Responsible Scaling Policy as essential infrastructure for enterprise buyers navigating increasingly complex AI governance requirements. As AI systems begin planning and executing real-world tasks, auditability, interpretability, permission controls, and constrained autonomy become essential enterprise infrastructure.

The wealth management industry's experience offers a cautionary tale for all enterprises: spending on AI without establishing measurement frameworks, governance structures, and workflow redesign is unlikely to deliver the returns that justify the investment. The firms capturing durable value are those treating AI not as a technology purchase but as an organizational transformation requiring new measurement disciplines, leadership commitment, and fundamental changes to how work gets done.