How firms plan to finance AI investment- evidence from the SAFE

2 October 2026

By Annalisa Ferrando, Sara Lamboglia, Judit Rariga, and Maurice Schmidt

What types of Artificial Intelligence (AI) investment are businesses planning for the year ahead? That was a central question posed in the latest Survey on the Access to Finance of Enterprises (SAFE). The answers paint a revealing picture of where firms see the greatest value in this rapidly evolving technology. We asked about 5,000 firms from across the euro area about their plans to invest in AI technologies and tools, data infrastructure, AI specialists and employee training over the next 12 months.

AI is increasingly part of firms’ investment plans

Nearly half of the firms surveyed (49%) expect to allocate their AI budgets to AI technologies and tools, accounting for the largest share of planned investment. Following closely behind, 46% of businesses are prioritising training for their employees, highlighting the fact that successful AI adoption depends on skills as well as technology. Data infrastructure was the focus for 40% of the firms surveyed, while just 12% are targeting the hiring of AI specialists. Meanwhile, 38% of firms did not select any of these categories.

Larger firms, perhaps unsurprisingly, are more likely than their smaller counterparts to invest across all categories, though the overall trends remain consistent regardless of size. These findings offer a glimpse into how companies are preparing to navigate the AI revolution – and where they believe the opportunities lie.

Chart 1

Types of expected AI investment

(percentage of respondents)

Internal funds are the primary source of financing, external sources play a supporting role

We also asked firms how they intend to fund their AI investments. The answer is clear: internal funds are by far the most popular source of financing. Overall, 72% of firms planning to invest in AI expect to use internal funds such as cash flow or retained earnings (Chart 2, left panel). Bank loans, grants and leasing play a secondary role, each accounting for 16% of firms. Equity and venture capital are mentioned by 6% of firms, while only 1% are looking into debt securities. Some firms have not yet chosen a particular source of financing: 18% did not select any of the financing options listed, suggesting that this segment of the sample remains undecided.

Chart 2

Type and number of financing sources for expected AI investment

Type of financing source	Number of financing sources
(percentage of respondents)	(percentage of respondents)

Most of the firms planning AI investment expect to use a single financing instrument (Chart 2, right panel). Again, internal funds dominate, while bank loans, grants and leasing are much less common as stand-alone sources (Chart 3). When firms use more than one source, they typically combine internal funds with external finance, such as leasing, grants, bank loans or equity. Combinations involving only external sources are less prevalent.

Chart 3

Combinations of financing sources for expected AI investment

(percentage of respondents)

Collateral affects how firms finance AI adoption

Whether firms use external finance depends crucially on the nature of the investment. Firms are more likely to rely on external finance when investing in tangible assets, such as hardware or data infrastructure. The advantage of these assets is that they can be pledged or used as collateral, typically lowering borrowing costs. Conversely, the investments needed to build long-term AI capabilities – such as hiring specialists or training employees – are at a comparative disadvantage. While these investments can also create valuable assets for a company, they are intangible and cannot therefore be used as collateral, making it harder to obtain bank financing.

The importance of collateral becomes clear when we compare firms in the same country, industry and size class. Firms planning to invest in AI technologies and tools, or in data and infrastructure, are substantially more likely to combine internal and external financing. Both types of investment are associated with a 16 percentage point increase in the probability of doing so (Chart 4).

The picture is different for more intangible spending. Hiring AI specialists is associated with a smaller increase in the likelihood of combining internal and external finance, at just 9 percentage points. Employee training shows no statistically significant effect. Hardware and data infrastructure often require large upfront spending and can be used as collateral, whereas training tends to involve smaller, incremental costs that are easier to fund from retained earnings. All of which points to the same conclusion: external financing for AI investment is closely tied to the availability of collateral.

Chart 4

Planned use of external finance by expected AI investment type

(percentage points)

Conclusion

AI adoption is not just a question of whether firms want to invest. It is also about how they can finance this investment. The SAFE evidence points to a clear message: euro area firms are preparing to invest in AI but expect to finance much of it themselves. While internal funds offer the advantages of accessibility, flexibility and control, heavy reliance on this type of funding could limit the scale and pace of AI adoption compared with what could be achieved if external financing were used more broadly.

The limited role of external finance raises critical questions about potential barriers in the euro area’s financial ecosystem.^[1] Bank loans, grants and leasing play a supporting role for some types of AI investment, while equity and venture capital are rarely cited. That may point to potential structural challenges that limit access to financing for intangible investment. Addressing these issues by ensuring that financing frameworks can support both tangible and intangible AI investment could help unlock greater investment potential and accelerate the diffusion of AI technologies across the euro area economy.

The views expressed in each blog entry are those of the author(s) and do not necessarily represent the views of the European Central Bank and the Eurosystem.

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