Europe is home to 1,600 early stage AI software companies

With every paradigm shift in technology, innovative early stage companies emerge to improve and then reimagine business processes and consumer applications.

Over time, the distinction between ‘AI companies’ and other software providers will blur and then disappear, as AI becomes pervasive. Today, however, it is possible to highlight a sub-set of early stage software companies that have AI at the heart of their value proposition.

We individually reviewed the activities, focus and funding of 2,830 purported AI startups in the 13 EU countries most active in AI – Austria, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Portugal, Spain, Sweden and the United Kingdom. Together, these countries also comprise nearly 90% of EU GDP. In approximately 60% of the cases – 1,580 companies – there was evidence of AI material to a company’s value proposition.

AI entrepreneurship is becoming mainstream

In 2013, just one in 50 new startups embraced AI. Today, one in twelve put AI at the heart of their value proposition (Fig. 72). In 2019, entrepreneurs are disrupting incumbents by leading the paradigm shift to AI.

AI-led startups have proliferated since 2016, as technological enablers for AI meet triggers for entrepreneurship. Maturing AI enablers included: enhanced algorithms offering improved results; specialised hardware that accelerated AI system training; and greater availability of training data.

Against this background, more entrepreneurs are taking advantage of AI as: cloud-based AI infrastructure and open source AI frameworks reduce initiation and scaling costs; startups successfully access pools of AI talent at leading universities; venture capital funding for European AI startups has increased as providers of capital recognise opportunity for returns; and successful AI exits (Blue Vision Labs, Deep Mind, MagicPony, SwiftKey) and scale-ups (including Ada Health, Babylon Health, Benevolent AI, Darktrace, Graphcore, Kreditech and Meero) highlight demand and recycle capital and leadership experience within the European ecosystem.

Within ten years, most companies will use AI in select business processes, either directly or via their suppliers. Widespread adoption of AI among today’s entrepreneurs is a leading indicator of a near-term future in which AI is pervasive.

For incumbents, the growth of AI entrepreneurship is a double-edged sword. AI startups are valuable suppliers – an ‘on-ramp’ to AI – for companies that embrace them, while disrupting those that do not. Select early stage companies will be acquired by today’s incumbents or become the incumbents of tomorrow.

The European AI ecosystem is maturing

While AI entrepreneurship is nascent (six in ten AI startups in Europe are at the earliest stages of their journey, with Angel or Seed-stage funding), it is maturing. One in six European AI companies has passed through Angel, Seed and Early Stage phases to a ‘Growth’ phase catalysed with over $8m in venture funding (Fig. 73).

Countries with a large number of AI companies (the UK, France and Germany) typically have more mature ecosystems (Fig. 74). In the UK, France and Germany, one in five AI startups are later, ‘Growth’-stage companies; in Sweden, just one in ten. Spain is an exception. While there are almost as many AI companies in Spain as in Germany, just one in ten is mature.

As the ecosystem matures, we expect:

• an increasing number of exits, as incumbents acquire disruptive, early stage companies gaining critical mass;
• a positive, ‘flywheel’ effect as lucrative exits recycle capital and talent within the ecosystem;
• selective, high-profile failures among companies that have raised significant sums of capital;
• startups competing with ‘scale-ups’ as well as incumbents;
• increasing competition for technical talent and executive leadership, as ‘scale-ups’ offer attractive salaries and impact as well as innovation;
• the European AI sector to better compete with larger US vendors as, to an increasing extent, multinational companies procure vendors internationally.

The UK is the powerhouse of European AI

With nearly 500 AI startups – a third of the European total and twice as many as the next most active country – the UK is the heartland of European AI (Fig. 75). With the largest internet economy in the G20 (as a percentage of GDP), extensive academic talent including a quarter of the world’s top 25 universities, a growing number of AI exits (DeepMind, SwiftKey, MagicPony) recycling capital and talent, supportive Government policy in relation to AI, and a global financial services hub, the UK has significant assets.

The market map, overleaf, places the UK’s 500 startups according to:

• Purpose: Does the company focus on a business function (for example, marketing or human resources), a sector (healthcare, education) or core AI technology with crossdomain application?
• Customer: Does the business predominantly sell to other businesses (B2B) or to consumers (B2C)?
• Funding: How much funding has the company disclosed to date? We categorise companies as: Angel or Seed stage (under $500,000 to $2m); or Early or Growth stage (over $2m to c. $200m).

With approximately 200 AI startups each, Germany and France are thriving AI hubs in Europe. High quality talent, increasing volumes of capital and an expanding roster of successful AI companies are creating feedback loops of growth and investment.

Spain is an outlier whose contribution to European AI exceeds its size. Despite a population half the size of Germany, Spain houses almost as many AI startups. Extensive immigration may have deepened the Country’s already broad pool of talent. Spain has the second highest rate of immigration in the EU, and entrepreneurial activity is higher among immigrants than native citizens (Global Entrepreneurship Monitor).

Germany and France are extending their influence

The dynamics of AI entrepreneurship in Europe are in flux. While the UK remains the powerhouse of European AI, and will house more AI startups than other European countries for years to come, its share of European AI startups, by volume, has slightly reduced (Fig. 76). Brexit could accelerate this dynamic. AI developers are skilled, few in number and may select opportunities from the many offers they receive. More broadly, one in five London technology workers is an EU national from overseas (London Tech Advocates). If free movement of workers between the EU and UK ends, visas are unforthcoming, or rhetoric is unwelcoming, the UK’s access to talent could reduce. France, Germany and other countries may extend their influence in the decade ahead, spreading the benefits of entrepreneurship more evenly across Europe.

Italy, Sweden and Germany ‘punch above their weight’ in core technology

While two thirds of Europe’s core technology AI startups are located in the UK, Germany, Spain and France, adjusting for countries’ ‘size’ – their number of AI startups – reveals a different dynamic.

Relative to their size, Italy, Sweden and Germany are core technology hubs; in each, approximately one in five AI startups is a core technology provider compared with the European average of one in eight (Fig. 77). There is also support for Nordic countries’ reputation for deep tech expertise; in Finland, Denmark and Norway one in seven AI startups is a vendor of ‘core’ AI technology.

While countries with large AI ecosystems, such as the UK, benefit from a large number of leading universities, broad pools of talent and extensive investment, smaller hubs ‘punch above their weight’ for varying reasons. In addition to exceptional talent, their ecosystems benefit from: leading research and engineering centres (Germany); effective core technology incubators (Finland); the AI laboratories of internet giants (Paris); and the ‘halo’ effect of multiple successful scale-ups in other fields (Sweden). Flows of venture capital into smaller core technology hubs are also increasing, creating a virtuous circle of investment and success.

Nine in ten AI startups are B2B

Nine in ten of Europe’s 1,600 AI startups are business-tobusiness (B2B) vendors, developing and selling solutions to other companies (Fig. 78). Just one in ten sells directly to consumers (B2C).

Historically, B2C AI has been inhibited by the ‘cold start’ data challenge. Training AI algorithms typically requires large volumes of data. While B2B companies can analyse the extensive data sets of the businesses they serve, customer-facing companies usually begin without large volumes of consumer data to analyse (in the absence of public or permissioned data, such as Facebook profile information). B2C companies typically deploy AI later, as their user bases and data sets grow.

B2C AI is on the rise as the ‘cold start’ thaws

While most existing AI companies are B2B, a growing proportion of new AI startups – in 2018, a quarter – are B2C (Fig. 79). B2C AI startups are mitigating or circumventing the cold start challenge.

From their inception, a greater proportion of new B2C companies are planning effective data acquisition strategies for AI. By integrating with existing customer data (such as financial transaction information), capturing data earlier in their customers’ journeys, or developing partnerships with data providers and other companies, companies are mitigating the cold start challenge to gain value from AI earlier in their lives. While incumbent consumer companies struggle with sprawling, siloed data estates, AI startups are turning a limitation to an advantage by creating a data collection and processing pipeline optimised for AI.

Entrepreneurs are also circumventing the challenge by imaginatively applying AI techniques to a wider range of consumer processes. Without extensive third-party data sets, early stage consumer companies can present new forms of engagement (such as human-computer interaction via chatbots) and offer new services and experiences (by using AI to optimise their supply chains).

The rise of B2C AI also reflects a shift in entrepreneurship to B2C-leaning sectors. There is a higher proportion of B2C AI companies in which data is more readily available: media & entertainment (47% B2C); finance (26%); and health & wellbeing (27%) (Fig. 80). In the last 24 months, the sectors attracting the highest proportion of new AI startups have been: finance (23% of new startups); health & wellbeing (17%); and media & entertainment (10%) (Fig. 81). As entrepreneurs tackle B2C-leaning sectors, B2C AI is on the rise.

AI entrepreneurship remains vertically-focused

Nine in ten AI startups address a need in a specific ‘vertical’ (business function or sector) (Fig. 82). Just one in ten is developing a core, ‘horizontal’, AI technology (a sectoragnostic capability or platform). This mix has remained consistent over time (Fig. 83).

The proportion of core technology providers will remain modest. Google, Amazon, IBM and Microsoft (GAIM) offer an extensive, and expanding, suite of core AI technologies, primarily in the fields of computer vision and language. Their solutions – ranging from audio transcription, language translation and sentiment analysis to object recognition and facial analysis – are capable and leave limited room for any but the most specialised direct competitors. Further, developing core technology requires world-class technical expertise (frequently stemming from academic research) which is limited in supply.

GAIM solutions, however, are generic and sector-agnostic. AI startups are addressing the myriad sector- and function-specific opportunities which GAIM vendors lack the strategic desire, domain expertise and data advantage to address.

The healthcare and financial services sectors are well served by AI startups

The health & wellbeing, finance, retail and media & entertainment sectors are well served by AI startups (Fig. 84). Activity in these sectors is high, in part, because they are well positioned to benefit from AI technology while offering attractive commercial characteristics for entrepreneurs. Active sectors offer:

• Large market opportunities with domain-specific challenges unaddressed by the generic AI offerings of platform vendors Google, Amazon, IBM and Microsoft (GAIM).
• numerous prediction and optimisation challenges well suited to the application of AI;
• large data sets for training and deployment, although access to data in healthcare can be challenging;
• a path to better-than-human performance, through AI, that is technically achievable;
• opportunity for significant, demonstrable value creation, such as improved trading performance (financial services) or improved purchase conversion (retail);
• alternatives to automation that are impractical (healthcare) or expensive (finance).

In select areas, activity is modest relative to market opportunities. In manufacturing, few startups address a substantial need. Manufacturers could reduce material costs with improved analysis of product quality. Buffering (the storage of raw materials to compensate for unforeseen production inefficiencies) could be reduced by up to 30% with more predictable production. The requirement for significant domain expertise serves as an inhibitor to younger entrepreneurs in this area.

In other sectors, such as education, activity is inhibited by technology fit (stakeholders spend a lower proportion of time collating and processing data – 23% in education versus 50% in finance) and commercial considerations (challenging buyer dynamics).

Healthcare is a focal point for AI entrepreneurship

More AI startups – one in five – serve the health & wellbeing sector than any other (Fig. 84). In the coming decade, developers will have a greater impact on the future of healthcare than doctors. Healthcare is a focal point for AI entrepreneurship as:

• AI offers profound new opportunities for process automation and cost reduction in healthcare, as AI technologies (computer vision, natural language processing and improved pattern matching) enable formerly human processes to be undertaken in software at scale and low cost. AI can improve most stages of an individual’s healthcare journey (including diagnosis, treatment and monitoring) and associated workflows (triage, drug discovery and fulfilment);
• challenges to healthcare systems reach a ‘tipping point’. Ageing populations and new medical treatments are increasing costs. In many European countries, since 1970 healthcare costs as a percentage of GDP have doubled to approximately ten per cent (OECD). Further, as austerity pressures governments’ spending, consumer expectations continue to rise;
• increasingly, healthcare system stakeholders are willing to embrace innovation and early stage companies (in October 2018, the UK Health Minister published a vision for the future of UK healthcare with modern technologies at its core);
• the already vast market opportunity in healthcare expands with the rise of wellbeing-related applications (fitness, meditation, talking therapies and preventative testing); and
• a cohort of bold entrepreneurs, many who combine medical expertise with commercial acumen, seek to effect structural change at scale.

The UK is the heartland of European healthcare AI

With one in three of the Continent’s startups, the UK is the heartland of European healthcare AI. In addition to having more AI startups, overall, than any other European country, and larger quantities of venture capital investment, UK healthcare entrepreneurs benefit from:

• Many of the world’s top-rated universities for medicine, and teaching hospitals, that create a large pool of expert practitioners and opportunities for collaboration between researchers, startups and care providers;
• the ‘flywheel’ effect of a critical mass of healthcare scaleups. Companies including Babylon Health, Benevolent AI, DeepMind Technologies and Sophia Genetics are stimulating, attracting and recycling talent, capital and commercial engagement in the UK ecosystem;
• increasing openness to innovation in the NHS. ‘The tech revolution is coming to the NHS’ (UK Health and Social Care Secretary). While engaging with the NHS remains challenging given its scale, fragmentation and procurement procedures, early stage companies are benefiting from more accessible deployment opportunities as the Government seeks to ‘transform the NHS into an ecosystem of enterprise and innovation that allows technology to flourish and evolve’ and to establish ‘open standards’ (Department for Health and Social Care);
• a Government commitment to increase the budget of NHS England above inflation by an average of 3.4% each year until 2023/24, and policies to catalyse healthcare AI including a £50m investment in five new AI medical technology centres in 2019.

There remain inhibitors and sources of uncertainty for healthcare innovation in the UK – including disparate data standards and conflicting IT systems within the NHS, unclear data permissioning protocols, budget pressures in areas including social care, and Brexit.

Marketing and customer service teams enjoy a rich ecosystem of suppliers

Marketers are well served by Europe’s AI entrepreneurs. Among AI companies serving a business function, more – a quarter – focus on marketing departments than any other. Customer service and IT departments also receive significan attention (one in six startups, respectively) (Fig. 85).

While the UK contributes half of Europe’s AI marketing startups (Fig. 86), France is Europe’s hub for AI customer service with a fifth of the Continent’s startups (Fig. 87).

Modern marketing represents a sweet-spot for AI. Consumers have billions of touch points with websites and apps, providing a rich stream of complex data that is difficult to analyse using traditional, rules-based software but well suited to AI-powered analytics. In addition, natural language AI enables supplementary data, such as social media, to be analysed at scale for the first time. Most stages of the marketing and advertising value chain are ripe for optimisation and automation, including: consumer segmentation; consumer targeting; programmatic advertising; consumer purchase discovery; and consumer sentiment analysis. Competition and commoditisation are primary challenges for early stage AI marketing and advertising companies.

Customer Service departments are well served following a recent wave of new, AI-powered vendors. Among those addressing a business function, one in five AI startups founded since 2017 sell customer service solutions (Fig. 88). Entrepreneurs are taking advantage of advances in natural language processing AI to offer new augmented or automated customer service capabilities including: social listening (identifying and responding to customers automatically); intelligent classification and routing of contact centre enquiries; drafting or full automation of contact centre responses; chatbots (for customer engagement); and automated customer care analytics.

An influx of AI startups is driving process automation

While currently underserved, the operations function is benefitting from an influx of new, AI-led startups in the last 24 months. Among those addressing a business function, one in seven AI startups founded since 2017 serve operations teams (Fig. 88).

Traditional data mining techniques are less effective for process control given systems’ varying media and data formats, concurrency, loops and decision-making (Chabanoles). Advances in AI computer vision, natural language processing, understanding and reasoning are expanding the breadth of materials accessible to digital automation, offering greater understanding of their content, and enabling more intelligent responses.

AI is profoundly expanding the ‘envelope’ of automation – the breadth and value of processes susceptible to digital mechanisation. Improved capabilities include: recommending the ‘next, best action’ in a workflow; better automation of document processing; and more expansive robotic process automation (RPA). In the short term we expect the number of vendors serving the Operations function to increase further. In the medium term, commoditisation and competition will become challenges. Vendors focusing on a particular industry may develop the domain expertise, deep workflow integrations, data network effects and referenceability to develop lasting competitive advantage.

AI companies raise larger investment rounds

Since 2015, when securing investment AI companies have raised larger volumes of capital than traditional software companies (Fig. 89). A difference exists across all stages of maturity, from Seed stage through Series A, B and C funding (Fig. 90).

Early stage AI companies are attracting larger funding rounds due to sector fundamentals and dynamics in the supply and demand of capital.

AI companies’ capital requirements can justify greater investment, given the longer cycles required to achieve develop a minimum viable product, the high cost of AI talent, and the larger teams required for complex deployments.

Beyond fundamentals, capital infusions are being inflated by extensive supply of capital and limited demand. Many venture capitalists wish to invest in AI but there are relatively few AI companies in which to invest. Globally, venture capital investment in early stage AI companies has increased 15-fold in five years, while the number of investable prospects remains limited. As the number of AI-led startups has increased (today, one in 12 new startups in Europe is an AI-led startup) differences in round sizes are reducing.

AI companies attract premium valuations

AI companies are securing higher valuations, as well as securing larger capital infusions. Distributing companies founded since 2016 along a valuation curve reveals that a smaller proportion of AI companies are valued at lower amounts, and a greater proportion are valued at higher amounts, than equivalent non-AI startups. This is the case across most stages of maturity (Fig. 91) and within the early phases a company’s life (Fig. 92).

Pragmatically, entrepreneurs raising large volumes of capital seek higher valuations to avoid unpalatable ownership dilution. Investors may also be willing to value highly AI companies that have attracted scarce AI talent, developed advanced and defensible technology, or have a data advantage delivering data network effects. Beyond industry fundamentals, an imbalance in demand for capital and its supply is inflating valuations. AI companies’ valuations benefit from investors competing to deploy capital into a limited number of AI prospects. With AI entrepreneurship becoming mainstream (page 99), this tailwind will reduce.

Core technology providers attract a disproportionate share of funding

Core technology providers – ‘deep tech’ companies developing ‘horizontal’, sector-agnostic capabilities instead of ‘vertical’ solutions focused on individual sectors or business functions – attract a disproportionate share of venture capital (Fig. 93). While core technology companies comprise a tenth of AI startups, they attract a fifth of venture capital investment.

Core technology companies, from developers of autonomous systems to computer vision and language companies, exhibit more fully the capital dynamics latent in AI:

• developing core technology demands expensive, worldclass talent;
• the time required to develop minimum viable products can be longer in the technically demanding field of core AI, increasing cash burn; and
• a greater proportion of core technology companies pursue atypical revenue models, such as licensing agreements, in place of traditional licenses or software-as-a-service subscription agreements, elongating time to revenue.

Core technology entrepreneurs should adequately capitalise their businesses for longer, deeper periods of expenditure, while their investors develop syndicates with deep pockets. Doing so can enable core technology companies to realise their potential: capturing vast market opportunities with differentiated, defensible technology.

Talent, data and productisation are AI startups’ key challenges

Competition for AI talent, the limited availability of training data, and the difficulty of creating production-ready technology are consistently entrepreneurs’ key challenges when developing AI.

1. Recruiting AI talent is challenging
Startups compete with multiple categories of competitors – including large technology companies (Google, Amazon, IBM, Microsoft, Facebook), banks, professional service firms, and other early stage companies – for data scientists, AI experts and AI engineers. Recruiting staff that have a balance between theoretical expertise and commercial experience, and experience running an AI team, are additional difficulties.

“Access to talent, and its competitiveness, is the biggest challenge.”
David Benigson, Signal

“London is a good place to be, when looking for AI talent.”
Dmitry Aksenov, DigitalGenius

“London has one of the best pools of AI talent in the world – which is the main reason why we are here.”
Fabio Kuhn, Vortexa

To identify and attract talent, AI-led companies are building deep relationships with academic institutions, being active member of research communities, publishing papers, and collaborating with universities.

“We try to engage with developers well before they’re looking for a job, and let them do what they love.”
David Benigson, Signal

2. Access to training data is critical
Access to initial data sets for training poses a challenge.

“It’s a classic chicken and egg problem. Early customers, and thus data, are hard to get when you don’t have any existing reference clients.”
Tim Sadler, Tessian

Companies are mitigating the difficulty by developing powerful use cases for access to client data and by implementing a data acquisition strategy from early in their lives.

“We started collecting data very early in our journey.”
Timo Boldt, Gousto

For many early stage AI companies, compromising on early pricing to secure access to valuable customer data is proving effective.

3. Developing production-ready AI is difficult
Entrepreneurs recommend moving from ‘lab to live’ as soon as possible, testing development systems on low-risk real world data. Cross-functional collaboration is also key.

“Taking what works well in a lab and getting it to work in a diverse and sick population is a big challenge.”
Chris McCann, Current Health

“The real world is full of black swans and exceptions. We’ve learned to overcome them by getting great at cross functional collaboration, building integration with the tech team, and constant monitoring of risk.”
Timo Boldt, Gousto

The UK is home to a third of Europe’s AI start-ups. Below, we feature 16 leading start-ups, spanning a range of sectors and functions, that are using AI to create new possibilities.