AI has moved quickly from novelty to practical tool in financial advice, but not every use is equal. Some applications deliver real, low-risk value today; others are either overhyped or sit too close to the regulated advice decision to be sensible. For firm owners, advisers and paraplanners deciding where to start, the useful question is not “should we use AI?” but “where does AI actually earn its place?”
This article sets out the best AI use cases for financial advice firms — the ones that genuinely save time and support good outcomes — and the clear line that separates them from the uses to avoid.
The guiding principle: assist, don’t advise
Before the list, one principle frames all of it. The strongest AI use cases are assistive and back-office — taking on the repetitive, mechanical work that surrounds advice — while the advice itself, and the judgement behind it, stays firmly human. This is not just good practice; it is what the regulatory framework expects. The FCA’s approach to AI in financial services relies on existing rules, and under the Senior Managers and Certification Regime a named person remains accountable whatever tool was involved. The best use cases all keep a human reviewing and editing the output.
With that in mind, here are the applications worth prioritising.
1. Suitability report writing
This is the standout use case, because it combines a huge time cost with highly structured, rules-bound work. A complex suitability report can take the best part of a day to write by hand, most of it spent on mechanical tasks: structuring source material, drafting, formatting and rekeying figures. Purpose-built suitability report software takes a firm’s own template and source documents and produces a first draft in minutes, which the paraplanner then reviews and edits. Ammonite states that its Planbot platform at least halves the time to produce suitability reports and annual reviews, with a complex report able to fall from around six hours to under one.
2. Annual review and periodic report production
Annual reviews are high-volume, repetitive and predictable — exactly what AI handles well. Producing review letters from a consistent template clears the backlogs that leave clients waiting and applies a uniform standard every time, which matters under the Consumer Duty‘s focus on clear communication and good outcomes. For many firms, this is where the biggest cumulative time saving sits.
3. Meeting notes and file notes from transcriptions
Writing up client meetings is a reliable drain on time. AI can turn a meeting transcription into a structured file note or an updated fact find, filtering the relevant information from the conversation. The adviser moves on to the next client instead of the next write-up — while still checking the note for accuracy before it goes on file.
4. Fact finds and data capture
Populating fact finds and data-capture forms from information a firm already holds — transcripts, documents, spreadsheets — removes another layer of manual entry. Structured correctly, it also improves the hand-off between adviser and paraplanner, a long-standing friction point in many firms.
5. Client onboarding documents
New-client onboarding generates welcome packs, brochures and engagement documents, all of which can be produced from the client’s details in the firm’s house style. It is low-risk, repetitive and a good early win that gives every new client a polished, consistent first impression.
6. Broader paraplanning support
Beyond individual documents, AI increasingly supports the paraplanning function as a whole — structuring information, drafting across document types and holding the firm’s house style steady. This is the territory of dedicated paraplanning software and AI for paraplanners: tools built around the role rather than generic assistants, designed to free skilled paraplanners for analysis and judgement rather than replace them.
Where AI does not belong (yet)
The line is as important as the list. AI should not make the suitability decision or the personal recommendation — that is regulated advice, and it belongs with a qualified human. It should not be handed sensitive client data through free, general-purpose consumer tools. And it should not be used as a “generate and send” shortcut that skips the human check, because AI can produce confident, plausible-looking errors. For complex client needs in particular, the recommendation must come from a person, not a model.
Keeping AI on the assistive side of that line is what makes these use cases both valuable and safe.
Choosing where to start
For most firms, the sensible sequence is to begin where the time cost and the structure are both highest — suitability reports and annual reviews — then extend into file notes, fact finds and onboarding once the approach is proven. Choose purpose-built tools designed for UK financial services over general-purpose AI, keep a human reviewing every output, and scrutinise any tool on how it handles client data: where it is processed, whether it is stored, and whether it is used to train external models.
Where Ammonite fits
Ammonite built Planbot around exactly these use cases — the assistive, document-heavy work that surrounds advice. It turns a firm’s own templates into AI-powered documents a team can populate in minutes and then reviews and edits, keeping the firm in control of wording, structure, tone and calculations. Its suitability report software targets the single biggest time cost; its paraplanning software extends the same approach across review letters, file notes, fact finds and welcome documents; and its AI for paraplanners is built to support the role, not replace it. On data, Planbot stores no client information and never uses it to train AI models, and runs on UK-based Google Cloud.
The perspective behind it is first-hand: Ammonite was founded by Caroline Duff, a Chartered Financial Planner, and Rob Harradine, a Chartered Member of the CISI and former adviser, who between them have written thousands of suitability reports and built Planbot to automate the parts of the work that never needed a human in the first place.
You can see how Planbot works, read about the team behind it, or explore more in Ammonite’s Insights. For wider standards, PIMFA is a useful industry reference as firms set their own approach.
Frequently asked questions
What is the best use of AI in a financial advice firm?
Suitability report writing is usually the highest-value starting point: it carries a large time cost and is highly structured, so AI can draft from the firm’s template in minutes while the paraplanner reviews and edits. Annual reviews, file notes, fact finds and onboarding documents are close behind.
What should AI not be used for in advice firms?
AI should not make the suitability decision or personal recommendation — that is regulated advice and stays with a qualified human. It should not receive client data through free consumer tools, and it should never be used as a “generate and send” shortcut that skips the human review.
Will AI replace advisers or paraplanners?
No. The best use cases take on repetitive drafting and admin so professionals can focus on analysis, judgement and client relationships. Ammonite’s founders frame the shift as moving “from report writer to advice architect.”
How should a firm choose where to start with AI?
Begin where the time cost and structure are highest — suitability reports and annual reviews — then extend to file notes, fact finds and onboarding. Use purpose-built tools, keep a human reviewing every output, and check carefully how each tool handles client data.
