Financial planner Ian Else did a deep dive to see how AI could save him time during the client review process. He found he could save 3 hours per client.
Once again, Timeline’s conference, this year ‘Adviser 3.0’ was a blast. Among the famous names like Chris Hoy and Tim Peake, a standout breakout session came from Heather Murray, presenting ‘AI for Non-Techies’ —in other words, people like me.
It’s becoming clear that AI will do for financial services what the internal combustion engine did for transport: it will be convenient, time-saving, and will probably end the world.
On the journey home, I started thinking: if a financial planner really committed, how could they leverage AI and technology to streamline their business? So, I took a deep dive into my own review process to find out.
Before anyone @s me, this was an experiment. I haven’t rolled it out to clients yet. I’ve completed full due diligence around data security, only using paid tools where client data is secure and have not used it to train large language models.
That totals around 7 to 9 hours per client. This is about what I expected when I started the business.
Assuming a working year of 219 days (365 days minus weekends, holidays, and leave), I have roughly 770 hours to spend on reviews — enough for about 100 clients if I dedicate half my time to this work.
Here’s what I came up with:
This was a straightforward automation. I already use a Google Sheet to track client review dates. I set up a Zapier automation to send a bespoke GPT-generated email with a Calendly link four weeks before a meeting is due.
Time saved: 12 minutes per client
Rather than reading last year’s meeting notes and suitability report, I now upload all relevant client documents into NoteBookLM (an AI-powered summarisation tool by Google). It produces an audio summary I can listen to while doing other tasks.
Time saved: 20 minutes per client
I’ve used Otter AI for five years, but I recently switched to industry-specific tools like Marloo and Saturn. With Zapier and ChatGPT, I can automatically update the fact find using transcribed meeting notes.
Time saved: 60 minutes per client
While I haven’t fully implemented this yet, the writing is on the wall: AI will soon be capable of generating compliant reports using client objectives, actions, performance data, and fees.
Estimated time saved: 60 minutes per client
This is another quick win. Using my review spreadsheet, I toggle one column from ‘true’ to ‘false’, triggering an email generated using meeting notes and a GPT trained to replicate my tone —whether formal, casual, or somewhere in between.
Time saved: 30 minutes per client
Altogether, I could save around 3 hours per client, bringing the total time down to 4–6 hours. That change boosts my potential capacity to 154 clients without adding admin support.
It’s not quite 200, but it’s a significant leap. And I believe there’s still more efficiency to unlock.
I’ve started exploring platforms like Notion to build customised workflows based on client-specific data. This led me to a bigger question: what is the future of traditional back-office systems?
Why should I manually upload documents and then search for them? Instead, I could create an AI-driven filing cabinet that locates any information in milliseconds. It’s not just about reducing hours — it’s about fundamentally changing how we work.
Soon, one company is going to build a single platform for this, and tech stacks will be confined to history. Saturn, Marloo, Ningi? Who’s going to be first?
The next three–to-five years are going to bring exponential growth in AI’s capabilities. Will we see Artificial General Intelligence (AGI) in that time? Maybe. But what I do know is this:
If you don’t start using AI now to streamline your business, you may not have a business in two generations.You don’t need to be a tech wizard to get started. But you do need to start.
Ian Else is a financial planner at 4 Financial Planning, based in Bristol.
