The pace of AI development has shifted from steady progress to an absolute sprint. Capabilities we expected to see years from now are already here, changing how we run businesses.
The real breakthrough isn't a specific tool; rather, it's the speed of the innovation. The gap between imagining a complex business asset and creating it is gone.
Instead of spending hours or days building financial models from scratch, you can now describe what you need in plain English. Within minutes, an LLMs transforms your prompt into a functional spreadsheet with multi-year revenue models, monthly cohorts, and detailed unit economics.
Formulas. Formatting. Tabs. Charts. It looks like a financial model. It feels like a financial model. The numbers compute. The structure is logical.
It’s a vibe-coded financial model. And just like vibe-coded software, it’s a prototype, not a production artefact.
I want to be clear about something upfront: I’m not here to criticise these tools. We use AI extensively at Radley. The question isn’t whether AI belongs in financial modelling — it does. The question is where in the process it belongs, and what happens when you put it in the wrong place.
The Logic Test Founders Need to Apply
Here’s an observation that should give every founder pause.
Nobody opens Claude Code and prompts: “Build me a complete project management SaaS application with user authentication, role-based permissions, a billing system, and integrations with Slack and Jira.” And if they did, nobody would push the output straight to production. They’d review it. Refactor it. Test it. Harden it. Run it through CI/CD. Have other engineers review the architecture. The vibe-coded version would be the starting point for weeks of proper engineering work.
A fundraising model requires that exact level of engineering discipline. It is a production asset. Professional investors will download it, audit the underlying logic, and stress-test every variable. The spreadsheet will populate internal investment memos and dictate valuation discussions. Millions of dollars hang on the integrity of this single file. When that file is generated in under two minutes by an LLM, the foundation of your fundraise becomes inherently unstable.
If you wouldn’t bet your product on vibe-coded software, why would you bet your fundraise on a vibe-coded financial model?
The Illusion of Structural Expertise
Generative tools amplify existing capability rather than creating it from scratch. In the hands of an experienced corporate finance professional, an AI tool accelerates production. The expert already understands market benchmarks, complex accounting relationships, and regulatory nuances. They can review a generated output and instantly spot an unrealistic customer acquisition cost or an omitted operational liability.
When a non-expert relies on the same tool, the dynamic changes. Generative systems produce highly polished outputs regardless of whether the underlying data makes contextual sense. A model featuring clean charts and flawless formatting looks identical whether it was built by a seasoned CFO or generated by a basic prompt.
This creates a dangerous visual symmetry. The quality gap between a prompt-engineered model and an expert-vetted model is massive, but the visual differences are virtually non-existent. This creates false confidence for the founder, while remaining immediately obvious to an experienced investor during a live review.
The Context Problem
When you prompt an LLM to build a financial model, the AI knows exactly what you told it. Nothing more.
It doesn’t know what industry you’re in beyond whatever you mentioned in the prompt. It doesn’t know what stage you’re at, what your competitive landscape looks like, or what investors in your segment expect to see. It doesn’t know whether a 2% monthly churn rate is exceptional for your market or slightly above average. It doesn’t know whether your CAC assumptions are reasonable for your acquisition channels. It doesn’t know whether your gross margins are consistent with how your business actually operates at your stage.
It builds exactly what you ask for, using exactly the numbers you provide, with no ability to assess whether those numbers make sense.
A financial model for fundraising isn’t just a calculation engine. It’s a credibility document. Every assumption will be challenged by someone who knows your market intimately. If your churn assumption is half the industry average and you can’t explain why, the model works against you. If your CAC payback period is 20 months and nobody flagged it during the build process, you’ll discover the problem in the investor meeting — which is the worst possible time to discover it.
The Benchmarking Blind Spot
When an experienced accountant or CFO builds a financial model, they’re constantly cross-referencing assumptions against what they know about the industry. They’ll challenge a growth rate that’s too aggressive. They’ll flag a gross margin that doesn’t match the cost structure. They’ll ask why the hiring plan doesn’t include customer success at 200 customers.
This benchmarking layer — the constant comparison of your specific assumptions against what’s normal, what’s achievable, and what’s credible — is entirely absent from prompt-based model generation.
Ask Claude or Copilot to build a SaaS model with 5% monthly growth and 1% monthly churn, and it will. It won’t tell you that 5% monthly growth is ambitious for your stage, or that 1% monthly churn is top-decile performance that investors will demand evidence for. It won’t benchmark your LTV:CAC ratio against the 3:1 to 5:1 range that’s considered healthy. It won’t flag that your revenue-per-employee metric implies efficiency most companies don’t achieve until well past Series B.
The model will be internally consistent. The formulas will compute. But it won’t have been tested against anything outside itself. And that’s exactly what investors do in the first 90 seconds.
The Unit Economics Trap
Unit economics is where more fundraising processes fail than any other section of the model. Not because founders don’t know they matter, but because getting them right requires deep understanding of both your specific business and the benchmarks investors use to evaluate them.
A vibe-coded model will calculate LTV if you give it an ARPU and a churn rate. But it won’t ask whether ARPU should be blended or segmented by customer size. It won’t distinguish between logo churn and revenue churn. It won’t flag that your LTV calculation assumes steady-state churn when you only have 12 months of data and the retention curve hasn’t stabilised yet.
It won’t tell you that CAC should include founder time. It won’t differentiate between scalable acquisition channels and personal network connections that won’t repeat at scale. It won’t model the transition from founder-led sales to a repeatable sales motion and how that changes your unit economics trajectory.
An investor who opens the unit economics tab and finds a simple ARPU/Churn LTV calculation with no cohort analysis, no segmentation, and no acknowledgment of data limitations will conclude that the founder doesn’t deeply understand the economics of their own business. The model becomes evidence of shallow thinking, even if the product is exceptional.
The Hallucination and Consistency Problem
Every developer who’s used vibe coding knows the consistency issue. The same prompt on Monday and Tuesday produces different code. Different structure, different approaches, different edge case handling. For a prototype, this is fine. For production code, it’s unacceptable.
The same problem exists with AI-generated financial models. Ask for the same model twice and you may get different formula structures, different tab organisations, different calculation approaches. The numbers might even differ because the LLM chose a slightly different methodology for computing growth or allocating costs.
For the financial model that forms the basis of your fundraise — the one investors will download, audit, and reference in their IC memo — inconsistency is a serious problem. You need the model to be the same every time. Update one assumption and only that assumption should change. Everything else stays stable, traceable, and auditable.
There’s also the hallucination risk. AI tools can generate formulas that look correct but contain subtle errors: a SUM range that doesn’t extend to all cells, a growth rate applied to the wrong base, a circular reference that produces a plausible but incorrect output. These are dangerous precisely because the spreadsheet looks professional and the numbers look reasonable. Nobody catches the problem until an investor’s associate spends their day auditing your formulas.
The irony: one of the arguments against founders building their own models manually in Excel is that human formula errors cause serious problems. The same argument applies to AI-generated formulas. The error source is different — probabilistic generation versus human typos — but the consequence is identical.
The Central Tendency Problem
Here’s something more subtle that nobody is talking about yet.
When every founder uses the same AI tools with similar prompts, the outputs converge. The structure looks the same. The assumptions framework is the same. The presentation is the same. Investors start seeing dozens of models that feel identical because they were all generated by the same underlying system.
Financial models are supposed to demonstrate that you understand the unique economics of your specific business. If your model looks like every other AI-generated model the investor has seen this month, it doesn’t demonstrate understanding. It demonstrates that you used an AI tool. And while there’s nothing wrong with using tools, the model has failed its primary purpose.
A marketplace model should look fundamentally different from a SaaS model. A hardware business with a software recurring revenue component has completely different cash flow dynamics than a pure-play subscription business. A services company with project-based revenue has different metrics than one with retainer-based revenue. When every model comes from the same prompt template, these structural differences disappear. Everything converges to a generic mean.
We don’t want a central tendency where every financial model has exactly the same structure and similar numbers. We want models that are tailored to how each specific business actually works. That’s what investors are evaluating: do you understand your business?
The Use Cases for AI in Financial Modelling
None of this means AI has no role in building financial models. It means the role needs to be carefully and specifically defined.
At Radley Finance, our model engine is AI-free. This means that AI is not used to generate any formulas, any calculations, or any financial logic in the outputs. The entire model — every cell in the spreadsheets, every unit economics calculation, every scenario cascade, every cash flow projection — is driven by deterministic algorithms, variables, and tested formulas. It is impossible for the financial logic to hallucinate, because there is no probabilistic generation involved.
Instead, we've used AI to handle the tasks it’s genuinely excellent at:
Data extraction: AI reads your pitch deck, your historical financials, and your existing forecasts to pre-populate assumptions. It pulls structured data out of unstructured documents so you don’t re-enter numbers manually.
Narrative generation: AI writes the walkthrough script that helps you present the model, the assumptions document that explains the logic, and the Q&A prep that anticipates investor questions. Words and context around numbers, not the numbers themselves.
CFO-grade analysis: AI powers the review layer that analyses the completed model against 22 rules covering unit economics, runway, growth credibility, revenue concentration, and market benchmarks. It tells you where the model is strong and where it’s vulnerable.
Conversational intelligence: AI powers the virtual CFO chat, where you can ask questions about your model, run scenarios, and explore the implications of changes. The chat references your actual cells and metrics — it’s grounded in your data.
And the financial model itself? That’s built from frameworks developed by chartered accountants and finance professionals with decades of experience. The structure is determined by your business model type — SaaS, marketplace, e-commerce, services, or hardware+software — each with fundamentally different architectures, different question flows, and different metrics. The assumptions are captured through a structured discovery process that asks questions tailored for your specific business, compares your answers against industry benchmarks, and flags where your assumptions fall outside credible ranges.
Every formula is programmatic and validated. 100% formula-driven, zero hardcoded values. Run it again with the same inputs and you get exactly the same model. There are no hallucinations, no structural drift, and no formula errors introduced by probabilistic generation.
This is the architectural decision that makes the difference: AI where AI excels (extraction, language, analysis), deterministic logic where certainty matters (every financial calculation in the model).
What Fundraising Actually Requires
A financial model that raises capital needs to do five things that a vibe-coded model cannot:
Capture your unique environment: Not a generic template populated with your numbers, but a model that reflects the actual mechanics of how your specific business acquires customers, generates revenue, and manages costs. This requires asking the right questions, not accepting generic prompts.
Benchmark against reality: Every assumption tested against what’s known about your segment, stage, and market. LTV:CAC ratios, churn rates, gross margins, growth rates — all compared against what investors consider credible for a company like yours.
Prepare you for scrutiny: The model isn’t just a document you hand over. It’s the basis for a conversation. You need an assumptions deck, a walkthrough script, and Q&A prep that anticipates the hard questions. You need to be able to explain every number.
Integrate your actuals: Your historical financials merge with your projections. The model calibrates against what actually happened, tracks variance, and builds forward from reality, not from a blank prompt.
Produce consistent, auditable outputs: The same every time. Not probabilistically similar — exactly the same. Fully auditable by an investor’s associate who’s never seen it before.
The Speed Question
The strongest argument for using AI to generate a financial model is speed. And it’s a fair argument. Building manually traditionally takes weeks.
But the comparison isn’t between AI-generation and manual building. It’s between AI-generation and purpose-built systems designed for this specific task.
The entire Radley Finance process — from answering discovery questions through to receiving a complete financial model, along with all supporting assets, takes under an hour. Five integrated outputs, all consistent, all benchmarked, all investor-ready. That’s comparable speed to prompting an LLM, except the output has been stress-tested against industry benchmarks, structured for your specific business model type, built on deterministic formulas that cannot hallucinate, and prepared for due diligence from the start.
The model is also repeatable. Update an assumption and only that assumption changes. No structural drift. No formula variation. No praying that today’s generation matches last week’s.
The Team Behind the Tech
The logic behind Radley Finance was built by people who’ve spent decades in exactly this discipline. Cumulatively, we have over 30 years of financial modelling experience across M&A transactions, IPOs, and trade sales in more than 20 jurisdictions, and 20 years as an operator in growth-stage companies. Our team includes finance professionals with multiple international qualifications who lecture in finance at university level, as well as having board-level oversight from a Big Four-adjacent firm partner and the former chair of an accounting software company.
This matters because the quality of a financial model depends entirely on the quality of the thinking behind it. When the framework encodes decades of experience regarding what investors look for, which benchmarks matter at each stage, and which structural decisions separate a credible model from a superficial one, the output reflects that depth. A model generated using an LLM has access to everything on the internet but direct experience with none of it.
The Bottom Line
AI tools like Claude for Excel are genuinely excellent. In the hands of an experienced accountant or financial analyst, they’ll be transformative — just as Claude Code is transformative in the hands of an experienced developer. Use them for analysis, manipulation, exploration, and extending existing models. They’re already better and faster than manual approaches for these tasks.
But for the financial model that underpins your fundraise — the one investors will download, audit, and use to decide whether to write a cheque — a vibe-coded prototype isn’t enough. You need a model that captures your unique environment, benchmarks your assumptions against reality, prepares you for scrutiny, integrates your actual performance, and produces consistent, deterministic outputs.
You need production-grade, not prototype.
Develop a financial model with Radley Finance
This post is the latest installment in a series on fundraising and investment for founders. Previously, we've covered topics like:
The key assumptions that VCs will scrutinise
Developing reliable revenue predictions
The due diligence questions that you need to prepare for before your pitch
The key differences between your accountant and your CFO
Next, we will be looking at how to model SaaS unit economics when you only have twelve months (or less) of data to draw from.
