The AI budget conversation that isn’t working
Fresh insights from 2,650 finance decision-makers across Europe
Across Europe, finance teams are discussing AI investment. It’s easy to assume everyone is talking about whether the spend can be justified, whether the ROI is there, or if they should approve yet another software investment.
But the latest data suggests these conversations are focused on the wrong variable entirely. Cost isn’t the barrier; capability is.
The real bottleneck is whether anyone has been given the mandate, time, or training to deploy AI in a way that generates measurable return. That means the AI budget conversation needs to shift from software spend to building the skills needed to use these tools effectively.
The barrier to AI adoption
Eurostat found that 20% of European companies are using AI, up from 13.5% in 2024. But that growth also reveals something else: 80% of European companies still aren’t using AI.
Budgetary constraints are not what’s holding them back. The same research from Eurostat found that 71% of European enterprises say a lack of expertise is their primary barrier to AI adoption. Legal concerns and data privacy also ranked ahead of cost.
That changes the nature of the AI investment conversation. For many mid-market companies in Europe, the challenge is integrating AI tools and workflows into day-to-day processes in ways that generate value, rather than setting aside budget for them.
And regardless of whether companies are fully prepared or not, AI investment continues to accelerate. Gartner expects end-user spending on GenAI models in Europe to grow 78% in 2026.
In other words, a lot of companies already pay for AI capabilities through existing software vendors and platform upgrades. But to get a return on those investments, CFOs and finance teams need to make sure employees have the capabilities needed to use these tools successfully.
The ROI is there, but only for people who know how to use the tools
The productivity gains from AI can be impressive. Federal Reserve research found that active AI users save 5.4% of their working hours each week. In practice, that’s roughly 2-3 hours per person per week, which quickly becomes a significant saving for mid-size companies. But those gains only appear for employees who already know how to use AI effectively.
AI adoption is often treated as a technology rollout, instead of a capability challenge. Research from McKinsey shows that although 92% of companies plan to spend more on AI, only 1% of leaders believe AI is fully integrated into their company workflows. As a result, licences are purchased and emails introducing new tools are sent out, but employees are rarely given clear guidance on where AI sits within their workflows or how they should actually measure success.
As Pleo’s latest report, Europe’s AI Spending Surge, puts it: “You unlock ROI faster by funding training and a use-case framework than by squeezing licence costs.”
For finance leaders, this requires a different way of thinking about AI investment.
Shifting the conversation from cost to capability
The AI conversation can’t stop at licence costs, price negotiations, or procurement approvals. It also needs to include decisions around how to build internal capabilities through training, use-case frameworks, or sharing best practices.
Before the next AI budget request lands on your desk, start asking, “Do we have the internal expertise to get value from this, and if not, what would it take to build it?”
That shift turns AI from a procurement discussion into an operational advantage.
At Pleo, we’re not just looking at AI budget conversations. Our latest report includes a collection of key sources showing how Europe’s mid-market businesses are spending on AI, which companies are adopting, and what’s blocking the rest.
