How a Fractured AI Stack Is Holding Advisory Firms Back

By Erin Colledge, EVP, Platform Unification & AI Strategy | Orion
August 11, 2026

Most advisory firms have too many AI tools and not enough connection between them. Here’s what a better approach actually looks like.

Something I keep hearing from technology leaders at advisory firms sounds almost like a complaint about success: “We’ve adopted AI for certain solutions but our technology still isn’t connecting the dots.”

It makes sense. The last two years produced a wave of useful AI tools for advisors. Note-taking. Meeting summaries. Client communication drafts. Proposal generation. Each solves a real problem. But somewhere around the fourth or fifth point solution, something breaks down – not in the tools themselves, but in the overhead of managing them. Security reviews, vendor contracts, siloed data, workflows that never connect. Firms end up with a dozen excellent tools that don’t talk to each other, and an operations team stretched thin keeping all of them running.

That’s the problem an AI native platform is built to solve.

The Swivel Chair Is Still Spinning

Before we can talk about what makes a platform truly AI native, it’s worth naming what we’re actually trying to fix. For years, advisors and their teams have lived with what I call the swivel chair: moving from system to system – CRM, portfolio management, financial planning, risk – to piece together a complete picture of a client. The information existed. It just wasn’t connected.

That workflow failure is exactly what prevents AI from delivering on its potential. If the data is fragmented across systems, an AI tool that plugs into only one of them will produce fragmented answers. A note-taking solution can transcribe a meeting beautifully. But if it can’t see a client’s portfolio exposure or a recent compliance flag, the summary lacks the context that makes it actionable.

Real AI value starts with connected data – organized around the client, the advisor, and the workflow they’re trying to complete. Not data sitting in a warehouse somewhere. Data in context.

What “AI Native” Actually Means

An AI native platform isn’t one that added a chatbot to an existing product. It’s one built from the ground up with the assumption that intelligence isn’t a feature – it’s the operating layer. The data architecture, the user experience, and the workflow logic are all designed around a single question: what does the advisor need to know right now, and how do we get it to them without friction?

In practice, that looks like this. A client calls, upset about recent market volatility. Before getting on the phone, an advisor types a simple question – “What’s happening with this client?” – and gets back a complete picture: portfolio exposure, recent account activity, outstanding service requests, notes from the last meeting, upcoming financial planning milestones. No toggling between tabs. One answer, drawn from connected systems.

Or consider a book-of-business scenario. When a market event creates concentration risk, an advisor asks which clients have excess exposure and gets a targeted call list in seconds. That query used to require pulling data from multiple systems, running Excel lookups, and hoping nothing got out of sync. Now it’s a conversation.

The Question Every Firm Should Ask AI Vendors

If an AI vendor can’t clearly answer “what data does this have access to?” – that should give you pause.

It sounds basic, but it’s surprisingly hard to get a direct answer to in vendor evaluations. Every data source connected to a well-built system should be visible, with firms opting in at the data point level. If a firm’s administrator restricts access to a specific data source, the AI shouldn’t just be unable to use it – it shouldn’t be able to see that it exists. That distinction matters.

The same philosophy applies to explainability. When AI reaches a conclusion – “these twelve clients have excess Tesla exposure” – there needs to be a traceable audit trail: what data it queried, what logic it applied. For RIAs and broker-dealers, that isn’t just good practice. It’s the foundation of defensible AI use.

Fewer Vendors, Deeper Value

My honest take? Two or three B+ solutions that are deeply integrated are worth more than twelve A+ solutions that each solve one problem in isolation.

The vendor management burden alone is significant. But more importantly, integration is where the value actually lives. An AI that connects insight to action – not just surfacing information but routing it into a workflow in your CRM, your planning tool, your compliance system – is categorically more useful than a collection of individually excellent tools that never talk to each other.

That’s the direction the industry is heading. Not away from AI, but toward fewer, more connected AI experiences. Platforms that meet advisors where they work, whether that’s a desktop prompt, a mobile interface, or eventually voice. Platforms that earn the right to call themselves AI native because the intelligence is woven into the workflow, not bolted on top of it.

The firms that figure this out first won’t just reduce operational overhead. They’ll create the kind of advisor experience where people actually want to do their jobs – and the kind of client experience that earns lasting loyalty.

Originally published by Orion Advisor Solutions at https://orion.com/blog/how-fractured-ai-stack-holding-advisory-firms-back. Compliance tracking #: 1847-U-26168. Photo Credit: Shutterstock, ID 628562552, by Nenad Aksic

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