The AI "Arms Race" In RIAs: Hype, Scale and the Quiet Middle

The RIA industry is being told it has entered an AI "arms race."

On one side, global banks and wirehouses are pouring billions into AI programs and tightly integrated tech stacks. On the other, many independent RIAs are still experimenting with generic tools and asking, "Where do we even start?"

Recent surveys highlight a growing gap. Captive advisors at large institutions report much higher adoption of AI tools than independent broker/dealers and RIAs, largely because the big firms can centralize budgets, data and decision-making. Meanwhile, most RIAs describe their AI usage as "experimentation": individual advisors using AI for marketing copy, meeting notes or light automation rather than firmwide transformation.

This is the context in which headlines about an "AI arms race" emerge. But for many independent RIAs, the real question is not, "How do we match a JP Morgan budget?" It is, "How do we turn AI from a vague pressure into practical, defensible advantage?"

What's Really Driving the "Arms Race"?

The largest banks and "meta-RIAs" are not just experimenting; they are building platforms.

Some firms are investing eight-figure sums each year to embed AI into their operating systems, client portals and advisor workflows, often partnering with AI vendors or building internal AI "operating systems." Others are piloting fully agentic AI assistants that sit on top of existing data and systems to orchestrate tasks like data gathering, summarization, and workflow execution.

From a seller's perspective, this shows up in M&A conversations. Large buyers increasingly differentiate themselves by demonstrating how their AI-enabled platforms can support acquired advisors: automated onboarding, AI assistants for advisors, and integrated analytics that small firms would struggle to build alone. Technology, and especially AI, is becoming a visible part of the "scale premium" in RIA consolidation.

For smaller RIAs, this dynamic can feel like a threat: "If I cannot keep up with AI, do I eventually have to sell?"

Yet the same experts who talk about AI-driven scale also point out that AI will make it easier for smaller firms to run their businesses more efficiently, if they choose their focus carefully.

The Real Risk: Passive Adoption, Not Smaller Budgets

There is a temptation for RIAs to respond to this arms race in two unhelpful ways:

• Treat AI as a marketing checkbox: adding "AI-enabled" language to websites without changing how decisions are made or how clients are served.
• Stay in perpetual "tinkering mode": allowing every advisor to use different tools without a coherent data, risk or governance framework.

The bigger risk is not having a smaller budget; it is having no intentional strategy.

A firm that never moves beyond individual experimentation risks fragmented data, inconsistent client communication and unmanaged operational risks, from hallucinated outputs to regulatory issues. Over time, this can erode trust, both internally and with clients.

On the other hand, firms that define a clear, narrow set of AI use cases and build repeatable workflows around them can unlock leverage without massive spending. Industry research increasingly points to operational and client experience gains, rather than "AI portfolios", as the first meaningful ROI for AI in wealth management.

A Pragmatic AI Playbook For Independent RIAs

Most RIAs do not need an AI "arms race" strategy. They need an AI "operational edge" strategy that can be executed with their current size, culture and resources.

A pragmatic playbook might look like this:

1. Anchor AI to 2-3 concrete outcomes

Instead of "AI everywhere," pick a small number of measurable goals: for example, reducing time spent on meeting prep, cutting onboarding cycle time, or improving the consistency of client follow-ups. Each AI initiative must tie to a specific process and KPI, not to a generic innovation narrative.

2. Standardize on a data foundation, not a single tool

Many AI projects fail because data is siloed across CRM, portfolio systems and document repositories. Even the most advanced agent cannot fix broken plumbing. For smaller RIAs, this means:

• Clarifying which system of record is authoritative for client, portfolio and document data.
• Working with existing vendors (custodians, CRMs, portfolio platforms) to understand their AI and integration roadmaps.
• Documenting minimal data standards so that AI tools can operate on clean, consistent information.

3. Design human-in-the-loop from day one

The most successful use cases in wealth management today augment advisors rather than replace them. Examples include:

• AI-generated call summaries that advisors review and correct.
• Draft client emails, reviews or portfolio commentaries that remain under human signature.
• Risk flags and alerts that prompt advisor judgment rather than automated actions.

This approach not only reduces risk but also builds advisor confidence and buy-in.

4. Start with "agentic" workflows you can explain to a client

As more vendors introduce agentic AI, systems that can operate autonomously across workflows, RIAs need to be able to explain in plain language how these agents work, what they can and cannot do, and how they are supervised. A simple rule: if you cannot explain a use case to a client in two minutes, it may be too complex for your current governance maturity.

5. Treat AI as part of culture and M&A, not just IT

AI is already influencing perceived scale advantages and RIA acquisition decisions. Firms that want to remain independent, or attractive as buyers or sellers, will need:

• An internal narrative about how AI supports their client promise.
• Basic training for advisors on responsible AI use.
• A view on how AI-enabled capabilities fit into their longer-term strategic positioning.

What This Means For RIA Leaders Today

The "AI arms race" language may grab headlines, but it can be misleading for RIAs. The industry is not simply racing to acquire the most AI; it is racing to translate AI into better-run firms, more prepared advisors and more trusted client relationships.

Independent RIAs have three advantages that can balance the scale of larger institutions:

• Proximity to clients: Shorter feedback loops on what actually improves meetings, reporting and communication.
• Cultural agility: Less bureaucracy when piloting new workflows or redefining roles.
• Vendor flexibility: Freedom to choose and combine best-of-breed tools, including emerging agentic AI platforms, rather than being locked into a single enterprise stack.

The firms that win will not be those with the biggest AI budget, but those that deliberately align AI with their operating model, their people and their promise to clients.

For RIA leaders, a practical next step is simple: pick one high-friction process, define what "better" looks like, and experiment with AI in a way that your advisors and clients can understand, and trust.

I work with financial institutions on technology integration and data aggregation (including API/SDK solutions at Collation.AI). Happy to connect and discuss your firm's technology strategy.