Let’s Lean into Europe’s Complexity
Europe isn’t simple to operate in. It’s a patchwork of markets, regulations, currencies, payment methods, and cultures. This complexity creates friction that many view as challenging. But it’s precisely this complexity that birthed companies like Revolut, Adyen, and Wise*.
Our economies also largely consist of traditional industries — manufacturing, aerospace, retail, pharma, financial services. These sectors are relatively low growth, and have been somewhat untouched by software.
But it’s precisely these conditions that will support the creation of some of the biggest and most enduring AI companies globally over the coming years.
Traditional Industries Will Leapfrog SaaS
Traditional European industries largely missed the SaaS revolution — not because there wasn’t value to be gained, but because it was too hard to realise it. Integrations into legacy systems was a nightmare, employee upskilling was too hard, and critical input data was impossible to capture.
New AI models, especially LLMs, change this equation entirely. Data can be extracted from legacy systems, spreadsheets and emails without deep integrations. Inputs can be easily synthesized from voice or images, not just structured text and numbers. And large parts of repetitive language-based tasks can now be automated.
This is giving rise to a wave of new vertical AI companies. The first players focused on highly paid service industries — lawyers, doctors, accountants — automating portions of their work with clear ROI benefits, but without complex integrations. Now we’re seeing companies move into more traditional spaces: wealth management, insurance broking, procurement, structural engineering, construction.
None of these industries has a core SaaS operating system to displace, so by automating parts of employees’ work and owning the engagement layer between workers and their existing systems, startups will be able to capture significant value over the long-term. And since human labour costs typically dwarf software budgets, companies that dominate these industries could be much larger than the vertical software winners of the last decade.
Complexity Creates Moats
The key question many have when looking at AI applications is: where are the moats? Traditional software companies mostly built defensibility through switching costs — coming from integrations, data lock-in, or general user familiarity — moats that LLMs can largely erode.
This is where Europe’s complexity becomes an advantage. The nuanced needs of traditional industries, operating without modern software, will make it hard to switch providers easily. And as we’ve seen in vertical SaaS will also likely trend to winner-takes-most dynamics in each market.
Let’s consider two examples to highlight where complexity creates opportunities:
Wealth Management: The FCA introduced regulations in 2023 requiring financial advisors to be able to justify the advice they give clients — creating an enormous documentation burden. Startups like Saturn developed specialized transcription and note-taking products that automatically structure notes from client calls. This high-value wedge product captures a rich dataset that will act as a bridge to automating more of the financial advisors’ workflow and replacing parts of their legacy systems. Similar examples are emerging in healthcare (eg Heidi*) and education, where comprehensive documentation is mandatory in Europe.
Accounting: This $100B+ services market has significant complexity. Each country has its own dominant accounting software, standards, and practices. Xero dominates the UK with modern APIs and an ecosystem of supporting tooling and software, while Germany’s DATEV or France’s Cegid present different opportunities. Startups like Tabular* are using LLMs to automate part of accountants’ work, but the best approach and long-term opportunity vary significantly by country due to these structural differences.
Different Approaches Will Win
Building products for complex, often regulated markets demands precision. An accountant needs absolute confidence in automated work — quite different from generating marketing copy. Selling into these industries can also be challenging too — we’re unlikely to see Cursor or Lovable type ARR charts, but these users should be stickier, with fewer competitors gaining traction.
Different approaches are emerging:
- Human-in-the-loop co-pilots for high-trust industries like law, accounting, and medicine.
- Agents automating entire job functions, like we’ve seen horizontally with 11x in sales.
- AI-enabled service businesses that aim to outcompete incumbents with faster, cheaper, and better offerings to capture market share organically or through roll-up strategies.
The optimal approach will depend on the nature of the buyer, the stickiness of their existing customer relationships, the strength of the value proposition and whether the economic value is realised through headcount reduction or revenue expansion.
So, let’s lean into Europe’s complexity — it’ll be the foundation for the next generation of enduring tech giants.
*: LocalGlobe portfolio company