How to plan your 2027 AI budget

What does Gartner say about 2027?

The figures most quoted this budget season come from Gartner. Its 2027 CIO and Technology Executive Agenda survey, run from May to July 2026 with 2,002 CIOs and technology executives, found:

Indicator	Figure published by Gartner
Average IT budget growth in 2027	3.7% (marginal for many organisations once inflation is accounted for, Gartner notes)
Average increase in agentic AI funding	31.8%, the fastest-growing investment area
Already deployed AI agents	37%
Plan to deploy within 12 months	34%
Report significant or transformational value from AI so far	13%
Report increased pressure to deliver cost improvement from AI	58%
Expect AI to increase the total cost of ownership of technology solutions	51%

The table shows a gap: spending and ambition are climbing fast, while few organisations see the results. The wider market looks similar. Gartner’s forecast of 16 September 2026 puts worldwide AI spending at about $2.7 trillion ($2.67 trillion) in 2026, up 49.5% year on year, with infrastructure as the largest category.

Gartner’s advice for 2027 planning is cost discipline: set up curated AI marketplaces, ruthlessly eliminate “zombie AI” projects, and require AI investments to show business value within 90 days. The same survey found that organisations with guidelines for keeping unsuccessful pilots out of production are 2.3 times more likely to be what Gartner calls “vanguards”, CIOs delivering better performance on business outcomes, and those with criteria for abandoning low-value AI already in production are 2.6 times more likely. Only 31% and 17% of CIOs, respectively, said they had such practices in place.

The respondents are CIOs and technology executives. The lesson for a mid-sized company is the same: the problem is less the size of the budget than what gets funded and when it gets stopped.

Why should the budget start from processes, not tools?

When a budget opens with a “licences” line, spending without a measurable outcome is legitimised from the start. A sounder order is: process first, then expected outcome, then tool.

For each candidate process, answer four questions in writing:

1. Process and owner. Which work, in which department, owned by whom? For example, entering supplier invoices into the ERP, owned by the finance manager.
2. Today’s baseline. How many transactions a month, how many minutes each, what error rate? If you cannot measure today, you cannot measure improvement.
3. Target outcome. What change do you expect in time, cost, errors or revenue? Pick one primary metric.
4. Data readiness. Where is the data, in what format, and who can access it?

To score the shortlist, use the criteria in our guide to choosing your first AI use case. A digital maturity assessment is also a useful start: for a company with scattered data and undocumented processes, the first budget line is often data and process order, not AI.

How do you write a 90-day pilot with a kill criterion?

Gartner’s 90-day recommendation works for mid-sized firms too. What matters is that the criterion is written before the pilot starts.

Prepare a one-page decision note for each pilot:

• Scope: which process, which team, which data. The narrower the scope, the clearer the result.
• Primary metric and target: for example “handling time per invoice from 6 minutes to 2” or “first-contact resolution up 20%”.
• Kill criterion: “If handling time has not fallen by at least 30% by day 90, the project is closed.” It must be numeric and agreed in advance.
• Checkpoints: short reviews on day 30 and day 60, so data problems surface early.
• Decision owner: who decides to continue, narrow or close.

A kill criterion is there to protect the budget, not to punish the pilot team. A closed pilot costs less than a project that never ends. In a 2025 prediction, Gartner said more than 40% of agentic AI projects would be cancelled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls. Better that such a cancellation happens in your budget on day 90, as planned, than two years later.

Which costs get left out of AI budgets?

The most common mistake in AI budgets is treating the build cost as the total cost. Once live, an AI application keeps costing money every month.

Line	Build (one-off)	Run (monthly)
Software and integration	Development, connection to ERP, CRM or document systems	Keeping up with system updates, bug fixes
Model usage	Test usage during the pilot	Token or call fees that scale with volume
Licences	Initial setup	Per-user or usage-based subscriptions
Data	Cleaning, labelling, preparing the knowledge source	Keeping the knowledge source current
Monitoring and quality	Test scenarios, evaluation set	Regular output reviews, cost tracking
Training	User and admin training	New joiners, new features
Governance	Policy, risk assessment, data protection review	Inventory, periodic audit

Model usage in particular grows with volume. A cost that looks small at a hundred transactions a day in the pilot can multiply when the tool is opened to the whole company. Measure the real cost per transaction during the pilot and set a monthly spending cap in the provider’s console.

Give maintenance its own line as well. When the ERP you connect to is updated, when a provider retires a model or when business rules change, someone has to do the work. If that owner is not in the budget, the application slowly turns into a zombie project.

Where do training and governance fit?

Whether an AI application creates value is usually decided by the team using it, not by the technology. If people do not know why they should use a tool, when not to trust its output and whom to tell about errors, even a good build goes unused. Make training part of the pilot, not something added later.

Governance means knowing which AI tools are in use, which data goes where and who approved it. Gartner’s curated marketplace is the large-company version; a mid-sized firm can start with a catalogue of approved tools, the data rules for each and a way to request a new one. Any application that processes personal data also needs a privacy notice and an international transfer assessment under GDPR or, for data about people in Türkiye, KVKK. Do that with your legal counsel.

Most of the five mistakes in our post on why digital transformation fails show up in AI budgets as well, starting with buying tools without a business goal and measuring activity instead of results.

Where to start

1. Put every AI idea under discussion on a single list, and for each write the process, the owner and today’s baseline.
2. Pick no more than three processes; for each, agree the primary metric, the 90-day target and the kill criterion up front.
3. Cost build and run separately for each process, without skipping model usage, maintenance, training or governance.
4. Review AI experiments already under way against the same criteria; close or redefine those without an owner or a metric.
5. Release the budget by quarter, so second-quarter money depends on the results of first-quarter pilots.

This matters most for small and mid-sized businesses that have to put a limited budget in the right place. If you would rather argue the budget with evidence than with assumptions, our AI discovery sprint reviews your processes and produces a prioritised list of use cases, each with a success criterion and its cost lines.

At Corrplus, our AI consulting work scores candidate processes, designs pilots around 90-day decision points and accounts for run costs from the start. For companies treating AI as part of a broader roadmap, we work through digital transformation strategy. You can see how we engage on our pricing page, or get in touch for a free first call.