How Much Should a Fund Spend on AI?

1. The Wrong Question and the Right One

The question lands in almost every first conversation. What should we spend on AI? People want one number, a line in next year's budget, a figure they can defend to the partners.

There is no one number, and the firms that insist on one are asking the wrong question. "What should we spend on AI" is like asking what a firm should spend on people. It depends entirely on what you are trying to get done. A single license and a system you build over a year are both AI, and they are three orders of magnitude apart.

The right question is smaller and sharper. For this specific thing we want to do, what does it cost, and what does it return. Asked that way, the budget stops being a guess and becomes arithmetic. You are no longer pricing "AI." You are pricing a workflow against the hours it removes from your most expensive people.

So this guide does not give you a number. It gives you four levels of spend, what each one buys, and the only yardstick that makes the cost legible: the loaded cost of the hours you are trying to win back.

2. The Unit of Measure: A Loaded Hour

Every AI spend should be measured against one thing: the fully loaded cost of a deal professional's hour. That is the yardstick that turns a price into a decision.

Take an associate or a vice president. Salary, bonus, benefits, and the overhead that sits behind every seat add up to a loaded cost that runs well into the hundreds of dollars an hour. You do not need a precise figure to use it. You need to internalize that an investment professional is an expensive way to do assembly work, and assembly work is most of what AI removes.

Now the spend becomes readable. A 30-dollar-a-month license that gives an analyst back two hours a week has paid for itself many times over by Wednesday. A training that costs a few thousand dollars and changes how a deal team works for a year is rounding error against the hours it returns. The numbers only look large next to zero. They look small next to the cost of the people whose time they free.

This is the move that makes the whole budget honest. Stop comparing the price of AI to nothing. Compare it to the loaded cost of the hours it removes, and most of the decisions answer themselves. The same logic runs through every ROI calculation for AI at a PE firm.

3. The Four Levels of Spend

AI spend at a firm sorts into four levels. They are not a ladder you must climb in full. They are a menu, and most firms live across two or three of them at once, depending on the workflow.

Read the levels as increasing commitment, not increasing virtue. Level 1 is right for a firm testing the water. Level 4 is right for a firm that has already proven the value and wants it everywhere. Spending at level 4 before you have done level 2 is how firms buy a system nobody adopts.

4. Level 1: Per-Seat Licenses

The first level is the cheapest and the most underrated. A capable model on a business or enterprise tier costs roughly 20 to 30 dollars per user a month. For a ten-person firm that is a few thousand dollars a year, less than what most firms spend on coffee and data subscriptions.

What it buys is access, and only access. Every person can now do their work the new way if they choose to. That qualifier is the whole catch. A license is permission, not a result. It is the floor of the spend, and on its own it is also the floor of the value, because a seat nobody opens returns nothing.

Buy the right tier, not the cheapest. The business and enterprise tiers carry the data terms that let you put real deal material through the tool: content is not used to train public models, and administration sits with the firm. The consumer tier is fine for learning and wrong for live work, and that distinction matters more than the few dollars between them.

Level 1 is necessary and never sufficient. It is the cost of admission, and almost every firm should pay it. The mistake is believing the work ends there, because the gap between a license bought and a workflow changed is the entire rest of this guide.

5. Level 2: Training and Adoption

The second level is where licenses become use. This is the spend most firms skip, and skipping it is the single most common way the rest of the budget gets wasted.

The shape of the spend runs from a single briefing to a guided launch across the firm. A 90-minute Executive Briefing or a Partner and IC Review runs 7,500 dollars. A hands-on Deal Team Intensive is 12,500 dollars. Associate Onboarding, so new hires inherit the firm's way of working instead of inventing their own, is 7,500 dollars. A Guided Launch that takes the whole firm live one function at a time is 35,000 dollars. The full menu and what each format is for sits in the cost of AI training for an investment firm.

Measured against the loaded hour, this level is almost always the highest-return money a firm spends on AI, because the return is multiplied. A few thousand dollars of training does not save a few thousand dollars of time. It changes how a team of expensive people works for a year, and that compounds across every deal they touch. Licenses without training is the most expensive way to do AI, because you pay for the tool and get none of the result.

If a firm could only spend at one level, this is the one. A trained team with good licenses beats an untrained team with a custom build, every time, because adoption is the thing that was scarce, not capability.

6. Level 3: A Built Workflow

The third level is where you stop helping a person do a task and start having the task run itself. A Custom Build, from 75,000 dollars, is a fixed-scope engagement that automates one recurring workflow end to end.

The economics are specific, and the word that decides them is recurring. A build pays off when the same job runs on a schedule and the format does not change: a board pack rebuilt every quarter for twenty portfolio companies, a screening memo produced for every inbound deal, an LP report assembled the same way each period. The build cost is fixed and the saving repeats, so the math improves every time the workflow runs.

It is the wrong spend for a one-off. If a job happens twice a year, a person with a good license and a good setup will do it more cheaply than any build. The trap at this level is building something bespoke for a workflow that a well-configured off-the-shelf tool already handles. That trade, when to configure and when to build, is the whole of AI build, buy, or partner.

Reach for a build when a recurring, high-volume workflow is costing real hours every cycle and the inputs come from a system you control. That is the point where a fixed price buys a saving that repeats forever, and the loaded-hour math turns decisively in its favor.

7. Level 4: A System and a Partner

The fourth level is the whole firm running on AI rather than a few people using it. It has two parts, and they are usually bought together.

The system. An AI Operating System, from 150,000 dollars, connects the winning workflows into one place: shared firm knowledge, standing agents that run on their own, and real data behind them. It is what a pile of separate tools becomes once they are wired together and built around how the firm actually works.

The partner. A system needs an owner who keeps it working as the firm and the models change. An AI Operating Partner, from 10,000 dollars a month, is fractional AI leadership: the person who builds the setups, fixes what breaks, trains new people, and shows the partners the number. It is the answer for a firm that has the budget for AI but not the in-house owner to run it.

This level is large, and it should be earned, not jumped to. A firm that reaches level 4 after proving the value at the levels below it is buying scale on top of evidence. A firm that starts here is buying a system on belief, which is the most expensive kind of pile.

The honest framing of level 4 is that it is a commitment to AI as infrastructure, not a tool you try. Make it when the workflows below have already paid off and the constraint is no longer whether AI works but how to run it everywhere at once.

8. The Cost of Doing Nothing

Every budget conversation prices the spend. Almost none price the alternative. The cost of doing nothing is real, it is just invisible, because it does not show up as a line item.

It shows up as hours. Your analysts still read every CIM in full. Your associates still rebuild the board pack by hand. Your team still answers the same LP question for the ninth time. None of that is on an invoice, so it feels free, but it is the most expensive thing in the building: the loaded hours of your best people spent on assembly instead of judgment.

It also shows up as a widening gap. The firms that started a year ago are not a year ahead in software. They are a year ahead in adoption, in the habits and setups that only accumulate by doing. That gap is hard to see and harder to close, because it is built from practice, not purchase.

So the real comparison is never spend versus zero. It is the cost of a level of AI against the cost of the hours and the ground you lose by skipping it. Framed that way, doing nothing is rarely the cheap option it pretends to be.

9. Phasing the Spend

The budget should follow the roadmap, not lead it. You do not write a check for a firm-wide system before a single workflow has paid off. You fund a stage, see the number, and let the result earn the next stage.

In practice that means starting small and letting evidence open the wallet. Buy the licenses and the training first, because they are cheap and they prove whether the firm will actually adopt. Win one workflow completely. Then, once a real number exists, fund the build for the recurring job that is clearly costing hours, and only after several of those have paid off does a system and a partner make sense.

Phasing this way does two things. It keeps the spend honest, because each stage stands on the proof of the one before it. And it keeps the spend safe, because the largest commitments come last, when the question is no longer whether AI works at your firm but how far to take it. The sequence the budget follows is laid out in the AI strategy and roadmap for investment firms.

The firms that overspend almost always did it by buying a level before they had earned it. The firms that get the most from a modest budget almost always did it by phasing: small spend, real proof, larger spend, repeat.

10. Where to Start

Stop trying to budget for "AI." Pick one workflow that is costing your best people real hours, and price that. The number you get is defensible, because it sits against the loaded cost of the hours it removes, not against zero.

Then phase it. Licenses and training first, because they are cheap and they tell you whether the firm will adopt. A build later, once a recurring job has obviously earned it. A system and a partner last, when the value is proven and the only question left is scale.

If you want the spend mapped to your firm rather than a generic number, that is what an AI Readiness Sprint produces: the first workflow to win, the hours it returns, and a phased budget across the four levels. When the work points to a built workflow, a Custom Build prices the recurring job; when it points to running the whole thing for you, an AI Operating Partner is the owner who does it.