Use-of-Funds Analysis for an AI Company

A use-of-funds analysis answers a specific question: if an investor provides this capital, what will the company do with it, and what will management be able to demonstrate as a result?

It is not a budget summary or a list of hiring plans. It is a structured argument about how capital converts into operating capability and, eventually, into evidence that the business works. Investors use it to evaluate whether management has a coherent plan, whether the funding amount is sized appropriately for the plan, and whether the stated milestones are achievable within the stated timeline and budget.

What investors expect

The minimum standard for a use-of-funds analysis is category-level specificity with milestone ties. “Hire engineers” does not meet that standard. “Expand the engineering team by eight people in the first four quarters to build and launch two infrastructure improvements that reduce per-token inference cost by a target range” does.

Investors are looking for three things:

Traceability. Each major use should connect to the financial model. If the use-of-funds plan shows $4 million in headcount, the headcount plan in the model should show the same roles, start dates, and compensation. Inconsistencies between the slide and the model generate immediate follow-on questions.

Milestones, not activities. An activity is something the company does. A milestone is a state management expects to reach. Hiring a head of sales is an activity. Having a qualified pipeline of eight enterprise opportunities worth a combined $2 million in ACV by the end of the second quarter is a milestone. The milestone is testable; the activity is not.

A credible downside. Management should be able to explain what happens if revenue arrives later than planned. Which spending categories are fixed, which are discretionary, and at what revenue shortfall does management act? Investors who back early-stage companies expect variability. They are evaluating whether management has thought through the scenarios, not whether the base case will land precisely.

Standard categories for an AI company

The following structure covers the core allocation areas for most AI companies at the pre-Series B stage:

Category	What it covers
Product and engineering	Engineering headcount and salaries; product roadmap work; technical infrastructure for product delivery
AI model and infrastructure	Development-stage compute (training, evaluation, fine-tuning); serving infrastructure; data licensing or acquisition
Go-to-market	Sales headcount; marketing programs; customer success capacity; partnership development
Finance and operations	Finance, legal, and compliance roles; finance systems and reporting infrastructure; office or remote work infrastructure
Working capital and contingency	Short-cycle timing differences between revenue collection and operating costs; contingency for cost surprises

Not every category needs equal weight. A company that has already built its core model and infrastructure but needs to grow its go-to-market function will allocate differently from one using the round to fund a significant product development cycle. The allocation should reflect the actual plan, not a generic template.

AI-specific cost considerations

AI companies face two cost categories that most software companies do not: development compute and operational compute.

Development compute covers the work required to train, fine-tune, evaluate, and update models. It is tied to the product roadmap. If the product plan includes a major model upgrade or a new domain-specific capability, the compute cost for that development work belongs in the use-of-funds analysis, not hidden in an overhead category.

Operational compute covers the cost of serving customer workloads through inference. At early scale, this cost is often small relative to headcount. At growth scale, it can become the largest single line item. Investors who have reviewed AI company financials will ask about the compute cost trajectory: how does it grow with revenue, and what product or infrastructure investments can improve it?

A common mistake is presenting use-of-funds with a single compute line that blends development and operational costs. Separating them is more transparent and more useful to an investor building their own model.

A second AI-specific consideration is data. If the growth plan depends on acquiring or licensing training data, expanding datasets, or building proprietary data assets, that investment belongs in the use-of-funds analysis with its own rationale. Data investment that is material to the product roadmap should not appear as a miscellaneous operational cost.

Common mistakes

Categories without milestones. A use-of-funds plan that allocates by spending category without connecting each category to a milestone is not yet a plan. It is a budget. The milestone structure is what allows investors to evaluate the plan against their own judgment about timing and execution risk.

Allocation that does not reconcile to the model. If the use-of-funds slide and the financial model show different headcount, different spending levels, or different timing, the inconsistency undermines both. Investors and their advisors will notice. The use-of-funds analysis should be derived from the model, not assembled separately.

Single scenario only. A use-of-funds plan built entirely around the base case does not answer the question investors are actually asking: if the plan does not work as expected, what happens? Including the key assumption that drives the biggest variance — customer acquisition pace, model upgrade timing, sales cycle length — and showing what management would do if that assumption is wrong strengthens the analysis considerably.

Ignoring model cost evolution. AI companies that plan to launch a significantly more capable model during the funding period, or whose current model is an older foundation model that will need replacement, face compute cost trajectories that are non-linear. A use-of-funds plan that presents flat or smoothly declining compute costs when the product plan requires a major model transition will generate questions the team may not be ready to answer.

No connection to the next decision point. The use-of-funds analysis should show what state the company expects to be in when the round is spent. That state should be specific enough to describe what the next financing or operational decision looks like. If the goal is to reach the metrics that support a Series B, name those metrics and show how the plan gets the company there.

Tying use of funds to milestones

The most useful use-of-funds format organizes allocation by time period, shows the milestone or measurable state each period is intended to achieve, and shows the cumulative cash position under the base case.

A simplified structure for a company raising an 18-month operating round:

Period	Major allocations	Expected milestone
Months 1–6	Engineering team expansion; go-to-market initial hires; model infrastructure upgrade	Product capability improvement deployed; qualified pipeline established
Months 7–12	Full go-to-market team in place; data investment completed; finance systems installed	First enterprise contracts signed; gross margin trajectory improving; reporting in place
Months 13–18	Customer success capacity; second product cycle; finance for next-round readiness	Defined ARR target reached; renewal class showing retention; company ready to present metrics to Series B investors

The exact periods and milestones depend on the company, the product, and the funding amount. What matters is that the structure is specific enough for an investor to evaluate it, and consistent enough with the underlying model that the numbers trace.

The CFO’s role

Building the use-of-funds analysis is a finance function responsibility. The CFO or fractional CFO owns the model from which the analysis is derived, knows the headcount plan, and is responsible for ensuring the allocation is internally consistent and defensible.

In practice, the use-of-funds analysis is often the last deliverable to come together in the fundraising preparation process, because it requires the operating plan, the headcount schedule, the product roadmap budget, and the cash model to all be settled. That is why it is worth building the model first and deriving the use-of-funds from it, rather than drafting the slide first and reverse-engineering consistency into the model afterward.

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For the broader fundraising readiness process this analysis supports, see How to Prepare an AI Company for Fundraising. For how the use-of-funds slide fits into the investor deck, see AI Startup Investor Deck. For the financial model the use-of-funds should reconcile to, see How to Build a Financial Model for an AI Company. For cash position and runway analysis, see Cash and Runway Planning for an AI Company. If your team is preparing for a funding round and needs to build the financial model and use-of-funds analysis, contact us or learn about the fractional CFO retainer.

This article is general business information, not accounting, tax, legal, investment, securities, or brokerage advice.