The Problem With Generic AI Suitability Reports

Generic AI can make suitability report writing look easy. Paste in some meeting notes, add a few client details, ask for a report, and within seconds you have something that sounds polished, structured, and convincing.

But for UK advice firms, a suitability report is not just a writing exercise. It is a regulated advice document that needs to reflect the client’s circumstances, objectives, risk profile, product information, recommendation rationale, firm-standard wording, and approval process.

The real question is not whether AI can produce text that looks like a suitability report. It is whether your team can review it, trace it, evidence it, and approve it without rebuilding the work from scratch.

Generic AI is good at text. Advice work needs more than text.

• Accurate client context

• Clear recommendation reasoning

• Firm-standard wording

• Controlled disclosures

• Correct template structure

• Visible sources

• Version history

• A review step

• Human approval before anything leaves the firm

A generic AI draft might read well, but still leave the paraplanner asking where a statement came from, whether anything has been invented, whether wording has drifted, and who approved the final version.

Problem 1: Hidden sources

A suitability report needs to be defensible. If the report says the client wants to retire at 60, the reviewer should be able to see where that came from. Generic AI tools often produce confident wording without showing the evidence behind each statement. That turns review into reconstruction.

Problem 2: Unsupported claims

Generic AI can smooth over gaps, make assumptions, or turn incomplete information into confident prose. A safe AI-assisted workflow should be comfortable saying when information is missing, sources conflict, a section needs review, or there is not enough evidence to draft confidently.

Problem 3: Template drift

Most firms have approved Word templates, standard sections, disclosures, required wording, house style, and established review processes. Generic AI tools usually do not understand that structure by default. The first draft arrives quickly, then the paraplanner spends time copying, pasting, reordering, rewriting, and checking controlled wording.

Problem 4: No clear version history

Suitability reports often move through several stages. If AI is involved, firms need to understand what the AI generated, what a human changed, which source was used, who reviewed the section, when approval happened, and which version became final.

Problem 5: The output feels final too early

Generic AI tools are designed to answer. In advice documentation, the output should not feel final by default. A suitability report draft should be treated as a draft: something to inspect, edit, challenge, and approve.

Problem 6: Generic tools do not fit the advice workflow

The work between a client meeting and a suitability report lives across meeting notes, transcripts, fact-finds, policy documents, CRM records, research files, email threads, templates, and previous reports. Generic AI tools usually sit outside that workflow, so the user still has to move everything manually.

What good should look like instead

• Start from the real case materials

• Structure meeting and fact-find information

• Draft inside the firm’s existing template

• Preserve required wording and disclosures

• Keep sources visible

• Flag gaps and conflicts

• Record changes between versions

• Make human review explicit

• Require approval before anything leaves the firm

How Templi approaches this

Templi is built for UK financial advice workflows, not generic document generation. It helps advice teams move from meeting to report by capturing meeting context, structuring the case, reconciling fact-finds, and drafting firm-standard documents inside the templates and systems the firm already uses.

Templi does not make advice decisions. It does not replace advisers, paraplanners, or compliance responsibility. It handles the structured work around human advice, so the team can spend less time rebuilding the case and more time reviewing the work that matters.

A simple test for any AI suitability report draft

• Can we see where each key statement came from?

• Does the report match our existing template?

• Has any controlled wording changed?

• Are missing facts flagged clearly?

• Can a paraplanner review this without rebuilding it?

• Can we see what changed between versions?

• Is human approval required before use?

• Does the workflow fit our systems?

The safer alternative to generic AI reports

Generic AI can be useful for exploring wording. But for suitability reports, firms need more than fast text. They need a workflow that is reviewable, traceable, template-native, source-backed, approval-led, and built around human responsibility.