The best way to use AI as a Financial Adviser

There’s no shortage of noise about AI in financial advice. But for most advisers, the real question is simple: how do you actually use it to make your practice better?

It may sound counterintuitive, but to understand the best way to use AI as a financial adviser, it’s worth first looking at what not to do.

When AI is adopted for the wrong reasons

One of the biggest risks comes when advisers or firms adopt AI for the wrong reasons. Don’t get it twisted - saving time and reducing costs are perfectly valid goals, and AI can absolutely deliver both. But they should never be the only goals.

In my view, there are three core objectives when adopting AI in a financial-advice business:

1. Save time - freeing advisers and paraplanners from repetitive admin.
   
   
2. Reduce costs - allowing firms to operate more efficiently.
   
   
3. Serve clients better - improving the quality, clarity, and consistency of advice.

The third point is the cornerstone. Remove client outcomes from your strategy, and you risk scaling efficiency at the expense of quality, advice that’s quicker to generate but thinner in context, weaker in relationship, and shallower in substance.

There’s constant talk, and pressure, on advisers to serve more clients and help close the so-called “advice gap.” But the best way to achieve that isn’t by simply expanding capacity; it’s by serving the clients you already have with deeper context and better insight, and leveraging AI to help you do it.

Scaling the wrong thing

Take Bob, for example. He currently serves around 100 clients.

He adopts a few AI tools that promise efficiency, they churn out notes and reports quickly, but the outputs are generic and lack depth. On paper, his capacity jumps to 150 or even 200 clients, but in reality, the quality of what he delivers starts to suffer.

Let’s map that against the three objectives above:

1. Save time? Absolutely. Bob now has hours back in his day.
   
   
2. Reduce costs? Most likely. Less time on admin means lower paraplanning and operational overheads.
   
   
3. Serve clients better? Not necessarily.

While Bob’s output has increased, his ability to serve those additional clients hasn’t. The time saved isn’t automatically being reinvested into deeper conversations, proactive reviews, or better understanding of client context.

The result? Bob doesn’t actually end up serving more clients in any meaningful way. He risks weaker relationships, fewer referrals (still the lifeblood of most advice businesses) and potentially higher client churn.

So yes, the big win: we’ve given Bob four hours of his day back…

AI that serves advisers - not the other way around

This is where priorities can become misplaced. The challenge isn’t whether advisers adopt AI, but which kind of tools they choose, and whether those tools are built to improve client outcomes, not just efficiency.

The right solution, in my view, should do two things:

1. Complement how you already work, not force you to conform to how the tool operates and
   
   
2. Strengthen the advice process, not just speed it up. 

That’s the principle we’ve built into Marloo. Our focus isn’t on replacing people but on building tools that amplify advisers, giving back time and reducing costs while helping them provide advice that’s more relevant, insightful, and genuinely client-centred.

Meeting Notes: from transcription to insight

Let’s start with the area where most advisers first encounter AI - meeting notes.

We’ve all seen the rise of generic note-takers that record a call and produce a summary. They’re useful, but they rarely go beyond surface-level transcription. The output often reads like a checklist rather than a reflection of a meaningful client conversation.

Every note we produce is fully customisable, written in your tone and structure, not a generic template. More importantly, our notes don’t just capture what was said; they capture why it matters - client motivations, values, concerns, and opportunities. The details that actually drive good advice.

Once that insight is captured, it becomes usable. With Ask Marloo, advisers can query their notes instantly, from recalling a client’s biggest retirement concern to drafting a thoughtful follow-up email.

This achieves all three objectives:

1. It saves time, because notes and follow-ups are produced instantly.
   
   
2. It reduces costs, by removing repetitive paraplanning and admin work.
   
   
3. It helps advisers serve clients better, by building a connected understanding of each relationship.

Each interaction adds value, turning every meeting into part of a structured record of client understanding, something you can actually use and not just store.

From one meeting to the whole relationship

Now, imagine expanding that same intelligence across an entire client file. Every meeting, suitability report, fact-find, review letter, and email all digested, understood, and linked together.

What was once simply a “compliance record” becomes an instantly searchable knowledge base, giving advisers immediate access to the full context behind every recommendation and relationship.

Connecting context across the advice process also changes how documents are produced. Reports and letters become a reflection of genuine understanding and not just an admin exercise.

That’s what quality at scale should look like: clarity, consistency, and advice that clients can actually engage with.

So, what’s the best way to use AI as a Financial Adviser?

Don’t stop at implementing a strategy that simply saves time and cost. The real opportunity lies in using AI to improve client outcomes at scale, because that’s what will deliver the greatest long-term value.

Efficiency matters, but it’s only the beginning. The advisers who thrive will be the ones who use AI to enhance the quality, consistency, and impact of the advice they deliver, turning technology into a genuine driver of better outcomes for their clients.