European Commission President Ursula von der Leyden described artificial intelligence (AI) alongside climate change as the “tipping point of our time” during her State of the Union address on September 16, 2026.
Evidence shows that AI is transforming world economies: for example, the Development Bank of Singapore (DBS) recently declared more than S$1 billion in measurable value from AI initiatives this year, which is roughly €680million, an increase from the 2025 figure of S$750 million. This value was generated by the deployment of AI at scale: 370 use cases and 1,500 models.[1]
Examples like these illustrate how much value can be delivered from implementing AI at scale.
In the financial services sector, human-led financial services are currently being scaled to AI-enabled (and increasingly AI-delegated) financial services, requiring certain operational foundations that are proving challenging.
The debate is not about whether AI can create value in Europe; the evidence increasingly suggests that it can and does. Now, the debate is about how Europe can use it and deploy at scale.
"Europe's ability to scale and deliver value from AI is no longer primarily in the hands of regulators or financial institutions. It is a question of appetite, access to risk capital and computing infrastructure."
Dera McLoughlin Partner and Head of Consulting, Forvis Mazars Ireland
The discussions at EUROFI suggested that institutions successfully scaling AI are separated by five key factors from those that remain trapped in experimentation:
1. Data quality and accessibility remain the single most important prerequisite.
The EUROFI conference was the occasion to remind that the issue is rarely the volume of data available, but rather it’s the quality of and access to data that are key
Financial services organisations have a lot of data, but it remains fragmented across product lines, countries, legal entities and legacy systems.
However, the effectiveness of AI depends less on the volume of data and more on the ability of AI to access high-quality, governed, interoperable data at scale.
It also requires context; what does "exposure", "claim" or "counterparty" mean across systems built over decades for humans and applications, not for AI and agents? Most firms don’t have an “agent ready” layer in their data infrastructure, and that is increasingly exposing those who can move beyond simple efficiency and labour substitution applications of AI to more fundamental use cases.
The Financial Stability Board (FSB) has identified data quality and governance as one of the key vulnerabilities associated with wider AI adoption in finance, and that third-party concentration, model risk, cyber risks and data governance remain among the most significant constraints on wider adoption. [fsb.org]
2. Cloud and digital infrastructure are becoming strategic enablers
Generative AI and advanced machine learning require scalable computing, storage, model development environments and access to specialised hardware. The challenge for Europe and the UK is that much of the AI value chain – including cloud infrastructure, semiconductor manufacturing and frontier models – remains concentrated elsewhere.
The European Central Bank (ECB) has noted that widespread use of AI could increase operational concentration risk and dependency on a limited number of AI and technology providers. The same concern appears prominently in the FSB's assessment of systemic AI risks.
3. Governance and control frameworks must mature at the same pace as innovation
Scaling AI requires model governance, validation, explainability, monitoring, human oversight, data lineage and accountability structures that are already familiar to financial institutions through prudential regulation and model risk management. In fact, governance was not seen as a threat to innovation. Many simply see it as a precondition for scaling safely.
Fortunately, the industry's comparative advantage is that FS is already one of the most heavily governed sectors in the economy. The challenge is extending existing governance models to incorporate large language models and more autonomous AI systems.
We can see that AI deployment in European FS is becoming widespread across customer support, fraud detection, AML risk assessment and internal processes, reinforcing the need for robust governance frameworks.
4. Skills are rapidly becoming a binding constraint.
It is becoming increasingly clear that financial institutions need AI-literate risk managers, compliance officers, auditors, lawyers and business leaders to address the many challenges AI introduces and ensure it brings value to the organisation.
Those firms that have been successful in scaling increasingly recognise that AI transformation is as much an organisational change programme as a technology programme, and they acquire and retain skilled talent accordingly.
5. Executive sponsorship and enterprise operating models
Examples from large banks in Europe and North America suggest that decentralised experimentation creates numerous pilots but little scale. Conversely, organisations that treat AI as a strategic transformation programme with central oversight tend to achieve greater value creation and faster deployment.
Importantly, most successful deployments today remain focused on augmentation rather than autonomy. AI is proving highly effective as a co-pilot for employees, analysts, customer service teams, software developers and compliance functions.
Fully autonomous decision-making, on the other hand, remains comparatively limited, and that’s where the challenge of scaling becomes more complex. The Bank of England and FCA report that only a small proportion of AI use cases involve fully autonomous decision-making.
Financial institutions can largely face these five challenges. However, successful AI adoption also depends on other factors, like access to capital, computing capacity, cloud infrastructure and technology ecosystem, that are beyond these institutions. They depend on Europe itself.
The UK and European challenge at the moment is not a shortage of AI use cases, but more a shortage of risk capital and infrastructure.
Take, for example, these three key regional models, each of which is very different:
| United States | Europe | Asia |
|---|---|---|
| Deeper capital markets, greater availability of AI investment and large technology ecosystems. | Strong regulatory frameworks and trust, but more fragmented capital markets, data environments and technology ecosystems. | Particularly in Singapore and parts of East Asia, rapid digital adoption and modern infrastructure, often supporting faster deployment with significant state-backed investment. |
One of the central questions over the next few years is whether Europe can create the conditions for AI scaling at the same pace as the US and parts of Asia. Notably, Europe must address two main challenges to unlock further scaling potential:
1. Capital availability and investment capacity
History tells us that transformative technologies have been scaled in environments with deep pools of risk capital and strong innovation ecosystems.
The United States continues to have significant advantages through its venture capital markets, private equity ecosystem, technology sector and concentration of global AI providers. The largest AI foundation model developers, hyperscale cloud providers and semiconductor innovators are predominantly US-based. This creates a cycle in which capital, talent and technological capabilities reinforce each other.
The US benefits from an integrated AI ecosystem. Europe and the UK, however, remain highly dependent on importing critical elements of the AI value chain
European financial institutions are generally well capitalised, but Europe has traditionally been less effective at mobilising large-scale private investment into frontier technologies and scaling technology companies. As a result, many European firms remain consumers of AI technologies developed elsewhere rather than producers of them.
This creates economic dependence on non-European foundation models, cloud providers and computing and semiconductor capacity.
The ECB has highlighted the growing concentration risks associated with a small number of AI and technology providers, while the FSB has similarly identified third-party dependencies and concentration risk as potential systemic concerns.
2. Computing capacity and infrastructure sovereignty
The other key scaling issue in Europe is the access to computing power.
Former President of the European Central Bank, Mario Draghi, highlighted in his recent Financial Times article that Europe hosts only three times as much AI computing capacity as a single data centre site being completed in Malaysia this year[2] .
European model developers collectively earn less than 2% of the revenue of just two US counterparts. Additionally, European companies and institutions were recently cut off from access to frontier AI capabilities.
The next phase of AI deployment, particularly generative AI and agentic systems, will require greater computational capacity. This raises questions a global European plan to have our own:
● Data centre availability
● Energy infrastructure
● GPU and semiconductor access
● High-performance computing capacity
Notably, AI successes in the United States and China are also the product of significant investment by these countries in AI infrastructure. Singapore, South Korea and some Gulf jurisdictions are also making substantial strategic investments. The US in particular leads the AI market thanks to a combination of innovation, funding and scale.
As such, Europe faces a risk that financial institutions may want to scale AI faster than the underlying regional infrastructure permits. This is not simply an operational issue but increasingly a question of industrial policy and strategic autonomy.
The next phase of AI scaling will likely be determined not only by what individual institutions can do but by the capacity of Europe to create the conditions necessary for scaling. The question is whether we can move quickly enough to ensure that European financial institutions remain producers of value from AI rather than merely consumers of technologies developed elsewhere.
The real test for financial services is no longer whether AI models can be built or deliver value. They can, and they do. It is whether institutions can combine trusted data, resilient infrastructure, strong governance and skilled people. In the end, successful AI adoption at scale will depend less on the intelligence of the technology and more on the readiness of the organisation, the availability of risk capital and the computing infrastructure to support AI for European financial services organisations at scale.
[1] DBS declared a revenue uplift of over S$1 billion this year from AI
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