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Can Europe win in vertical software?

By Cat McDonald

AI / Data Fintech


There is no hope of building European category leadership in vertical specific software, or so the argument goes. Europe is fragmented, and its ecosystem is relatively immature. The US – a single continent, with a single language and access to a larger pool of capital – has already scaled vertical software across most sectors, and everybody knows the “winner takes most” in software. What’s more, this dynamic is even more powerful in vertical software, given the tightly defined customer and focused go-to-market which enables higher market share with greater capital efficiency. Once that winning position is established in the US, the story goes, vertical winners bolt-on local solutions to expand their global footprint and European players become a footnote.

Or maybe not.

The next sectors to be served by industry specific software are precisely those which play to Europe’s industrial heritage, as the combined rising forces of AI and sustainability are the powerful catalysts these sectors need. European vertical software may soon be able to consume, rather than be consumed!

Europe’s Industrial Heritage

Europe has a historic advantage in some of the hard industrial verticals such as construction, logistics, utilities, and manufacturing. Countries like Germany and Switzerland made the “made in Europe” tag synonymous with quality, and the UK paved the way for the industrial revolution. We have a long history leading to a ton of legacy complexity in these markets. It is no coincidence therefore that these sectors underpin the industrial economy but have been relatively ignored by modern software to date. Activity in these sectors occurs in the real world, requiring many layers of interactions with real people moving physical things subject to uncontrollable external factors. These characteristics are both a blessing and a curse – whilst difficult to surmount, successful companies can become more embedded than traditionally seen even with the vertical software playbook.

The dawn of AI

Whilst the proliferation in AI adoption is set to reinforce the dominance of incumbents who have access to large, proprietary data sets and distribution, legacy industries are heavy footed and slow to react to rapidly changing environments. They are likely to suffer from innovators dilemma compared to sectors where major players were built in the last 20 years using cloud technology – sectors like retail, HR, sales and customer success are dominated by relatively tech savvy incumbents (e.g. Shopify, Workday, Hubspot, Intercom etc) who can add on AI to make existing features more powerful without having to evolve their core product or culture. In contrast, legacy markets suffer from sparse datasets, fragmented systems and a structural inability to innovate (rarely if ever does the business case stack up as innovation often requires cannibalisation!). This presents an interesting opportunity for category-defining, AI-native disruptors to emerge in legacy industries.

Take for example construction. Construction is one of the most strategically important sectors in Europe, yet the sector is extremely inefficient and dominant players have devolved into a jack of all trades, master of none. The European Union has committed to investing €350 billion in infrastructure over the next decade, and the demand for sustainable construction comes not only from the opportunity for innovation but also the requirement for existing stock to comply with net zero targets and regulation. Whilst certain categories, such as managing onsite workflows, are difficult to tackle head on (there exists a first wave of disruptors that are already entrenched), and construction software faces challenges such as inconsistent project-based decision making and a quasi-monopolistic market structure, new businesses can find white space and grow from there. Given the pure scale of the industry (in 2022, the sector contributed c.10% of European GDP) there is plenty of opportunity for initial specialisation to provide a significant wedge into the market, and a start up can beat a large incumbent if it does things the incumbent can not or will not do.

Consider planning and permitting. These processes, intrinsic to construction, rely on 40-year-old tech such as ArcGIS (1969), Autodesk (1982) and Bentley Systems (1984) supplemented with manual drawing, spreadsheets, word docs and emails to plan projects. This creates long iteration cycles and multi-year development processes. 40+ year-old companies are not well placed to benefit from AI, however new young companies are. Continuum, as one example, has built route optimisation planning for linear infrastructure (e.g. transmission lines, gas/water pipelines, renewable asset connections), using AI driven algorithms to consider thousands of potential routes whilst accounting for environmental impacts, planning considerations, built environment, geological factors etc. 12 months of pre-permit work can be undertaken in 8 weeks, reducing delays, saving cost and – for Continuum specifically, ensuring renewables can get to market more quickly.

Some examples of European AI-enabled construction tech:

We are seeing similar trends in manufacturing. The manufacturing ecosystem remains dominated by a handful of sleepy incumbents and a long tail of fragmented, technology resistant mom and pop shops. But a recent Accenture report predicts the manufacturing industry will get a huge $3.78bn boost from AI by 2035. Combine this with the scale of investment required for the Green Revolution – the entire capital stock depending on fossil fuels will have to be replaced (from vehicles to power plants) – an immense opportunity presents itself. The International Energy Agency estimates total investment required is c.$4tn by 2030 (i.e. 4% of global GDP!).

Much like construction, manufacturing is a massive market that lends itself well to pursuing a niche. Consider advanced manufacturing – the building of very specific widgets. Existing technologies face significant operational challenges including high failure rates (many have 40%+ scrap rates) and a substantial number of resources lost to unplanned downtime (amounting to ~$50B a year). Early-stage companies like Matta are tackling this, again with AI. Matta takes sensor data (usually video/camera) and AI models with an unsupervised training methodology which allows them to learn about the dynamics or physics of a manufacturing process. As the solution “watches” a component get made, it predicts with AI in real time the value of physical parameters and how to optimise those parameters for faster and more sustainable production.

Some examples of European AI-enabled manufacturing tech:

Against a backdrop of uncertainty and cost cutting, 2023 was a blockbuster year for AI and sustainability. These two megatrends have converged to create an environment for innovation in sectors that have historically been reluctant to innovate. Legacy sectors are under pressure to comply with EU regulation and deliver on sustainability goals given their significant contribution to waste (in 2020, construction contributed 37.5% to Europe’s total waste) and AI lowers the barrier to adopting new technologies, abstracting away much of the complexity that has historically prevented technology adoption in these sectors. And there is more receptiveness, budget, urgency, and pressure to experiment with AI in procurement teams right now – compounded by a new generation of users and customers that are increasingly au fait with tech. With this comes a renewed hope for Europe to build global scale solutions for critically important verticals.

I am excited to discover modern alternatives to 40-year-old incumbents with a low NPS and little ability to introduce AI in the legacy verticals that play to Europe’s industrial heritage. 

If you’re building with this vision or know someone is, please get in touch.

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