The Path to Autonomous AI: Drivers, Regulations & the Rise of Europe's AI with Hanah-Marie Darley

AI expert Hanah-Marie Darley analyzes Europe's surging AI market, the EU AI Act's impact, startup challenges, and the talent gap driving the shift to agentic AI

The European AI market's explosive growth, poised to expand tenfold by 2031, is not merely a response to technological advancement but a strategic alignment of regulatory foresight, ethical imperatives, and targeted investment. This positions Europe to lead the global shift toward agentic AI (autonomous systems that execute tasks), transforming industries while navigating complex governance challenges.

To explore this evolution, we spoke with Hanah-Marie Darley, a leader at the intersection of AI strategy and policy. With deep expertise in European tech ecosystems, Darley unpacks the drivers behind the continent’s AI surge, the impact of landmark regulations like the EU AI Act, and the critical hurdles facing startups and talent pipelines. Her insights reveal how Europe is forging a unique path; one where competitiveness hinges on balancing innovation with accountability.

What are the key drivers behind the recent growth of the AI market in Europe, and which sectors are seeing the most rapid adoption?

AI is the engine behind tomorrow’s economy, reshaping every industry and powering the technologies of the future. The significant growth in Europe’s AI market signals that this transformation is well underway. The market, valued at approximately $58 billion - up nearly 30 percent from last year - is on a trajectory for exponential growth, with forecasts suggesting it could expand nearly tenfold by 2031.

This momentum follows a surge in European AI investment and a broader boom in the tech ecosystem, catalysed by global gatherings like the international AI Safety Summits in Bletchley, Seoul, and Paris. These high-level gatherings bring together government, academic, and technology leaders and have helped drive both legislative shifts and infrastructure development to support AI adoption. This is particularly visible in regions like the US, Europe, and Singapore, where regulatory maturity and deep talent pools have become accelerators.

While AI is not a new technology, its applications are evolving. The focus is shifting from statistical and predictive models to more experiential and autonomous use cases. Although recent attention has centred around large language models and generative AI, the most transformational shift may lie in the rise of AI agents. These systems move beyond content creation and begin to deliver real operational leverage.

This pivot toward agentic AI has further fuelled investment in Europe, especially in the technology sector, where businesses are already building, acquiring, and deploying autonomous agents into their workflows. As these agents scale, the conversation is shifting from experimentation to enterprise-grade deployment, prompting a fundamental rethink around oversight, accountability, and the frameworks needed to safely govern autonomous decision-makers.

How is the European Union’s regulatory approach, such as the AI Act, impacting innovation and competitiveness in the AI sector?

Regulation is essential to both innovation and sustainable growth in AI. Most experts advocate for a balanced approach that combines oversight with the freedom to experiment. The EU’s AI Act is one of the first and most comprehensive attempts to define global standards for AI governance. The challenge is whether legislation can keep pace with the speed of innovation, particularly as foundation models and deployment use cases multiply.

Since the AI Act’s initial draft, ongoing revisions have raised questions about whether such robust regulation might hinder innovation. The EU maintains that the Act is not meant to block progress, but rather to anchor it in transparency and accountability, especially critical as more businesses move from passive AI tools to autonomous AI agents capable of acting independently within systems.

Compared to the more open-ended approach taken by other countries, the EU aims to provide a scalable framework for safety. This helps reduce uncertainty for businesses and enables faster, more responsible deployment.

What are the biggest challenges European AI startups face compared to their counterparts in the U.S. and Asia?

European startups face the universal challenges of finding product-market fit and focusing on real customer problems. However, they also deal with specific structural constraints. Regulatory compliance in Europe demands greater up-front accountability, often requiring startups to demonstrate safety, transparency, and ethical data practices before they’ve reached scale.

While this may seem like a disadvantage compared to less regulated markets with deeper funding pools, the long-term payoff is a more resilient ecosystem. European AI companies, shaped by regulatory pressure, are building defensible technologies that can better withstand security risks, ensure data integrity, and adapt to evolving legal frameworks.

These higher entry thresholds, however, can slow time to market. That is a critical disadvantage in a global AI race where talent and funding are limited.

How is the talent gap in AI and machine learning affecting the scalability of AI solutions across Europe?

Europe faces a well-known challenge: a persistent shortage of AI and machine learning expertise. This echoes long-standing talent gaps in the cybersecurity field. While there is strong momentum around academic AI programs, the university pipeline alone cannot meet current demand, let alone the needs of future growth.

Many EU member states are investing in up-skilling and re-skilling, particularly by moving professionals from data-centric roles such as data science into AI-focused positions. Even with these efforts, the scale of retraining required is immense and the window to close the gap is shrinking.

As a partial solution, some businesses are turning to AI itself. Specifically, AI agents are being developed to act as functional experts in targeted domains, often deployed in roles previously thought to require deep human oversight.

As these agents become more embedded in operations, there is a rising need for frameworks that ensure they align with business goals, regulatory standards, and ethical constraints. Human oversight will remain essential, particularly for safety and governance, but core machine learning skills will continue to be both highly valuable and scarce, much like in cybersecurity.

Another issue is compensation. Europe's ability to retain AI talent is challenged by global competition, where salaries can be dramatically higher. To stay competitive, organisations must create environments where employees can continuously grow alongside the technology.

To what extent is access to high-quality data and computing infrastructure a barrier to AI development in Europe?

Access to high-quality data and compute infrastructure is a global challenge, not one unique to Europe. Around the world, countries are grappling with the power, sustainability, and supply chain demands associated with scaling AI workloads.

The EU has launched several initiatives to address this, including investments in hyper-scale data centres and semiconductor manufacturing. These efforts aim to establish sovereign compute capacity while meeting environmental commitments such as achieving net-zero emissions by 2050.

Europe is home to global AI leaders like the UK, France, and Germany, with other member states also making bold commitments. Long-term success will depend on access to raw materials, stable supply chains, and sustainable infrastructure for supercomputing and cloud-based AI services.

This will be essential as AI agents become more sophisticated and mission-critical, requiring infrastructure that can support secure, scalable agentic operations - a defining factor in enterprise resilience and national digital competitiveness.

We’re just beginning to see the real impact of AI on business and to understand the implications of its trajectory with autonomous AI agents. The real breakthroughs will come not just from investment in AI and what it can do, but in how we govern and scale that autonomy.