2027: the year AI stops being budgeted like an experiment
Until now, many companies have treated AI as innovation spend: a flexible line, funded out of "testing" budget, that didn't face the same scrutiny as an investment in infrastructure or headcount. That era is ending. As we saw with AI governance becoming a sales requirement, buyers — and the CFOs who approve their budgets — are pulling back from funding stray pilots and starting to ask for the same thing they'd ask of any other operational investment: a business case, an estimated return, and a plan for when things don't go as expected.
If your company is closing its 2027 budget this quarter, this changes the conversation. "We want to bring AI into department X" is no longer enough. The CFO is going to ask for numbers, timelines, and a checkpoint — just as they would for any other six-figure investment.
The cost lines you'll forget if you only look at the model licence
The most common budgeting mistake isn't putting down the wrong figure — it's putting down only one figure, the subscription to the model or platform, and forgetting everything that surrounds that licence. A real AI project has, at minimum, these line items:
| Cost line | Why it gets forgotten |
|---|---|
| Integration with CRM, ERP, or data warehouses | Often exceeds the model licence cost, and nobody quotes it until the technical project starts |
| Data preparation and cleaning | If data is scattered or poorly structured, it can be the single biggest line in the whole project |
| Governance and compliance (AI Act, logs, human oversight) | No longer optional, and it costs people's time, not just tools |
| Ongoing maintenance (retraining, drift monitoring) | An AI project doesn't end at deployment — it degrades if nobody watches it |
| Exit or switching cost | What happens if the vendor raises prices or discontinues the service — few companies budget for it |
| Internal training and change management | If the team doesn't use it, the ROI on every other line is zero |
This isn't a theoretical list — it's almost literally the same reason why, as we covered in why 88% of companies use AI but almost none see ROI, most projects that fail to generate a return don't fail because of the model they chose, but because they budgeted for only part of what it actually costs to put it into production and keep it running.
Checklist: 7 things your CFO will ask for before approving 2027 budget
- A business case with an estimated ROI and a concrete timeline. Not "it will improve efficiency," but a number and a date: how much you expect to save or generate, and by which month it starts to show.
- Results from a pilot, even a small one, with validated metrics. Not a promise of what the AI will do, but real data on what it has already done at small scale.
- An estimated total cost of ownership (TCO), not just the licence. Including integration, data, governance, and maintenance, as detailed in the table above.
- A governance and compliance plan already sketched out. Who oversees it, what gets audited, and how it's documented — not "we'll figure it out later."
- An internal owner with real authority over the project. A sponsor who can make decisions, not a vague committee with nobody accountable for the results.
- A contingency plan. What happens if the vendor fails, raises prices, or the project misses its numbers at the six-month mark.
- Success and cut-off criteria defined in advance. At what point, and with what data, do you decide to keep investing, adjust scope, or stop.
The budget that doesn't survive the CFO's first question is usually the one that only includes the model licence and a promise of efficiency. The one that does survive is the one that already brings the full cost table, a pilot with real data, and a defined cut-off point — because it turns approval into a risk-management decision, not an act of faith.
Conclusion
2027 isn't the year AI gets more expensive to budget for — it's the year it gets budgeted for real, with all its line items, instead of with the smallest, most visible figure. Companies that walk into the budget meeting with a complete business case — total cost, validated pilot, governance, and an exit plan — will get a yes far more easily than those who show up with only a licence price and good intentions.
At Dataverse Solutions we help SMEs build that business case before committing budget: from the initial feasibility diagnostic through total cost estimation and the design of the pilot that produces the first real data.
Frequently asked questions
How much should an SME budget for its first AI project in 2027?
It depends heavily on the use case, but a well-scoped first project — an agent or a model for a specific process — usually falls somewhere between a few thousand and a few tens of thousands of euros, including integration and a first tuning cycle. The exact figure matters less than making sure the budget covers integration, data, and maintenance, not just the licence — that's where most projects fall short.
What's the difference between budgeting a pilot and budgeting a project at scale?
A pilot is budgeted to learn: its goal is to generate real data on whether the use case works, at the lowest possible cost. A project at scale is budgeted to produce: it needs robust integration, formal governance, a maintenance SLA, and a contingency plan. Treating a scale budget like a pilot budget — without those line items — is one of the most common causes of mid-project cost overruns.
What happens if the project doesn't deliver the promised ROI?
That's exactly why the success and cut-off criteria need to be defined before you start, not after. If you fix in advance by what deadline and with what data the project will be evaluated, missing the promised ROI isn't a surprise that paralyses the decision — it's an already-planned checkpoint, with a contingency plan (adjust scope, pause, or cancel) that was approved from day one.
Is it better to request budget for one large AI project or several small ones?
For most SMEs, several small, bounded projects are easier to get approved and to defend to the CFO than one large one: each has its own business case, its own pilot, and its own cut-off point, so a failure in one doesn't jeopardise the budget for the others. A large project only makes sense once several small use cases are already validated and you're looking for a common platform to scale them.