AI Budget 2027: Where to Spend and Where to Cut

Budget season is here, and this year the conversation is different. For two years the answer to "should we spend on AI?" was just "yes, and more." That reflex is running out of road. Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, most of them killed by unclear value and costs nobody modelled up front. At the same time the pilots that did reach production are paying for themselves several times over. So the 2027 question is not whether to fund AI. It is which parts, at what level, and what to stop paying for.

This is a planning guide for whoever owns the number: a founder, a CFO, a head of engineering. It is not a list of tools. It is a way to sort what you are already spending, decide what deserves more, and find the line items quietly draining the budget with nothing to show. If you spent 2025 and 2026 running experiments, 2027 is the year the finance team asks what came of them. Have an answer ready.

Start from what shipped, not what was promised

Before you allocate a single euro for next year, do the unglamorous part: list every AI and automation line item you are currently paying for, and next to each one write what it actually produced in the last quarter. Not the pitch. The result. Hours saved, tickets deflected, revenue influenced, or "nothing measurable yet."

Most teams have never done this, and the exercise is uncomfortable on purpose. You will find seat licenses nobody logs into, a pilot that impressed everyone in March and has not been touched since, and one small unglamorous automation that quietly saves a person two days a month. That last one is your template. The point of the review is to enter budget planning with evidence instead of enthusiasm, because next year the people signing off have run out of patience for enthusiasm.

The four buckets your 2027 budget actually has

Every AI line item belongs in one of four buckets. Sorting them this way turns a vague "AI budget" into decisions you can defend.

• Run is what is already in production and delivering. It has users, a measurable output, and an owner. This is the safest money you will spend and usually the least discussed, which is a mistake, because it is where your credibility lives.
• Scale is a proven pilot ready to serve more people or more of the workflow. This is where the highest return sits in 2027, because the hard discovery work is done and you are buying reach, not hope.
• Experiment is deliberate, time-boxed bets with a defined question and a kill date. Fund a few of these on purpose. Just cap them and never let one drift into permanent half-funded limbo.
• Kill is everything that has been "almost there" for two quarters. Naming this bucket out loud is the single most valuable thing budget season does.

The common failure is spending 80% of attention on Experiment while Run and Scale get rubber-stamped. Flip it. The money that compounds is in the workflows already working.

What to fund in 2027

Fund the boring middle of your workflows. The wins that survive contact with a real business are rarely the flashy customer-facing demo. They are invoice processing, support triage, document handling, data entry between systems that never talked to each other. We wrote about picking that first workflow, and the logic holds for a whole portfolio: start where the work is repetitive, high-volume, and measurable.

Fund the integration work before the model work. The projects that get cancelled are usually not short of intelligence, they are short of clean data and connected systems. If your tools do not talk to each other, an agent on top of them just automates the gaps. Budget for the plumbing.

Fund total cost of ownership, not sticker price. A model API that looks cheap per call gets expensive when an agent runs it in parallel across thousands of background tasks. Model the real usage before you commit, the way we lay out in the true cost of running AI agents.

And fund ownership. A production AI system with no named owner is a cancellation waiting to happen. That is a real headcount or a real slice of someone's time, and it belongs in the budget, not in the gaps between other jobs.

What to cut

Cut the pilot that has been three months from production for nine months. It is not going to make it, and the sunk cost is not coming back.

Cut duplicate tools. Most companies accumulated three overlapping AI point solutions during the buying frenzy of the last two years. Consolidate to one and negotiate the renewal from a position of "we might leave."

Cut the seat licenses your usage data says nobody touches. Cut vanity projects with no owner and no metric. And be honest about build versus buy: some things you built in-house in 2025 are now commodity features in a tool you already pay for, and some things you bought never fit and should be built properly. We break down that decision in build versus buy.

A simple way to size the number

Skip the top-down percentage-of-revenue guess. Build the number from the buckets. Run and Scale are near-known: you have real usage and real costs, so you can forecast them within reason. Experiment is a deliberate cap you set, not a residual, maybe 15 to 25% of the total depending on how much you still need to learn. Kill is a negative line, and it should be a real one.

The output is a budget you can walk a finance team through item by item, each with a purpose and an expected return. That is a very different meeting from "we need more for AI because everyone else is spending." The teams that keep their funding through 2027 are the ones who can point at the 11% of pilots that reached production and say, plainly, "ours is in there, and here is what it returned."

Sizing a 2027 budget you can defend is a strategy problem before it is a spreadsheet problem. If you want a second read on where your spend should go, tell us what you are running today and we will help you sort it into keep, scale, and kill.