The State of AI 2019: Divergence

Chapter 7: Europe’s AI startups

The landscape for entrepreneurs is changing. Europe’s 1,600 AI startups are maturing, bringing creative destruction to new industries, and navigating new opportunities and challenges. While the UK is the powerhouse of European AI, Germany and France may extend their influence.


  • Europe is home to 1,600 early stage AI software companies. AI entrepreneurship is becoming mainstream. In 2013, one in 50 new startups embraced AI. Today, one in 12 put it at the heart of their value proposition.
  • The European start-up ecosystem is maturing. One in six European AI companies is a ‘growth’-stage company with over $8m of funding. Expect: acquisitions to recycle capital and talent; startups competing with ‘scale-ups’ as well as incumbents; and increasing competition for talent.
  • The UK is the powerhouse of European AI with nearly 500 AI startups – a third of Europe’s total and twice as many as any other country. We provide a map of the UK’s AI startups and feature 14 leading companies.
  • Germany and France are thriving European AI hubs. High-quality talent, increasing investment and a growing roster of breakout AI companies are creating feedback loops of growth and investment.
  • Spain’s contribution to European AI exceeds its size. Immigration, which correlates with entrepreneurship, has deepened the Country’s talent pool.
  • The European AI landscape is in flux. While the UK remains the powerhouse of European AI, its share of European AI startups, by volume, has slightly reduced. Brexit could accelerate this. 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 AI technology, while there is support for Nordic countries’ reputation for deep tech expertise.
  • Nine in ten AI startups address a business function or sector (‘vertical’). Just one in ten provides a ‘horizontal’ AI technology.
  • A quarter of new AI startups are consumer companies, as entrepreneurs address or circumvent the ‘cold start’ data challenge. Many focus on finance or health & wellbeing.
  • Healthcare, financial services, retail and media & entertainment are well served by AI startups. In sectors including manufacturing and agriculture, entrepreneurial activity is modest relative to market opportunities.
  • Health & wellbeing is a focal point for AI entrepreneurship; more startups focus on the sector than any other. In the coming decade, developers will have a greater impact on the future of healthcare than doctors. Activity is thriving given profound new opportunities for process automation and a tipping point in stakeholders’ openness to innovation.
  • The UK is the heartland of European healthcare AI, with a third of the Continent’s startups. UK entrepreneurs benefit from healthcare scale-ups stimulating talent and increasing openness to innovation within the NHS.
  • Marketing and customer service departments enjoy a rich ecosystem of suppliers. A quarter of AI startups serving a business function focus on marketing teams.
  • An influx of AI startups supporting operations teams is driving increasing process automation.
  • AI companies raise larger amounts of capital, due to technology fundamentals and extensive capital supply.
  • Core technology providers attract a disproportionate share of funding. While comprising a tenth of AI startups, they attract a fifth of venture capital.
  • AI entrepreneurs’ key challenges are the availability of talent, access to training data and the difficulty of creating production-ready technology.




  • A growing proportion of startups and scale-ups are using AI to deliver new capabilities. Assess the extent to which your suppliers are taking advantage of AI.
  • Use our market map to explore the rich ecosystem of early stage UK companies putting AI at the heart of their value proposition. Most are B2B vendors and some will offer market-leading solutions to challenges in your organisation.
  • Take advantage of the influx of new suppliers serving operations teams to reassess the potential for process automation in your organisation.
  • Early stage AI companies value the training data, and testimonials, your organisation can provide. Suppliers may be willing to adapt their pricing, or solution, to your requirements in return for your data and public endorsement.


  • In a crowded market, prioritise customer acquisition over short-term revenue to take advantage of data network effects that enable long-term differentiation.
  • Identify potential competitors and partners in the UK using our market map.
  • If beginning a venture, explore functions and sectors where activity is limited relative to market opportunity, including agriculture and manufacturing.
  • Europe’s AI ecosystem is maturing. If yours is a later stage company, leverage product maturity, customer references and capital to secure competitive advantage. If yours is an early stage company, prioritise adaptability and speed of execution.
  • To overcome challenges regarding talent, data and productising AI, read our ‘AI Playbook’ ( that offers best practices.
  • AI companies are raising larger volumes of capital than others. Capitalise your business adequately to create and maintain competitive advantage.


  • With select sectors and functions over-supplied by startups, others under-served, and some witnessing an influx of new participants, identify areas of opportunity aligned with emerging dynamics and the themes on which you focus.
  • As AI startups mature, evaluate opportunities to support portfolio companies with emerging challenges including international expansion and acquisitions.
  • With investments into AI companies larger than average, valuations can be elevated. Consider whether or not you are willing to ‘overpay’ to access opportunities.


  • Competition for talent and capital is increasingly pan-European. Support your country’s early stage companies by removing impediments to the flow of skilled talent and international capital.
  • Expand public sector organisations’ openness to innovation, and simplify procurement processes, to catalyse opportunities for early stage companies and deliver improved public services.

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.

Fig 72. One in twelve new European startups is an AI company

Source: MMC Ventures (2018 data to October)

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.
Fig 73. Six in ten startups are at the Angel or Seed stages

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

Fig 74. Larger ecosystems are typically more mature; Spain is an exception

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

Fig 75. With twice as many AI startups as any other country, the UK is the powerhouse of European AI entrepreneurship

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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).

Download UK market map

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.

“The dynamics of AI entrepreneurship are in flux. While the UK remains the powerhouse of European AI, other countries may extend their influence in the decade ahead.”

Fig 76. France and Germany are increasing their share of European AI entrepreneurship

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

Fig 77. While many core technology startups are in the UK or Germany, relative to their size, Italy, Sweden and Germany are core technology hubs

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

Fig 78. Nine in ten AI startups are B2B

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

Fig 79. B2C AI is on the rise – a quarter of new AI companies are B2C

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

Fig 80. There is a higher proportion of B2C AI companies in sectors where data is readily available

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

Fig 81. Half of new AI startups target the finance, health or media sectors

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

Fig 82. One in ten AI startups focuses on core tech

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

Fig 83. The proportion of ‘horizontal’ core technology providers has remained consistent over time

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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).
Fig 84. More AI startups – one in five – serve the health & wellbeing sector than any other

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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).

Fig 85. More startups – one in four – serve the marketing function than any other

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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).

Fig 86. The UK contributes four in ten of Europe’s AI marketing startups (% of European AI marketing startups)

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

Fig 87. France is Europe’s hub for AI Customer Service (% of European AI Customer Service startups)

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

“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 well suited to AI-powered analytics.”

Fig 88. Among new startups addressing a business function, one in seven serve operations teams

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

Fig 89. Since 2015, AI companies have raised larger investment rounds

Source: Crunchbase, MMC Ventures

Fig 90. AI companies are raising larger rounds at all stages of maturity

Source: Crunchbase, MMC Ventures

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.

Fig 91. A greater proportion of AI startups are highly valued

Source:, MMC Ventures

Fig 92. A greater proportion of AI startups are highly valued (detail – lower values)

Source:, MMC Ventures

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.

“While core technology companies comprise a tenth of AI start-ups, they attract a fifth of venture capital investment.”

Fig 93. Core technology companies attract a disproportionate share of funding

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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.

Featured company - Audio Analytic.

Above: The Audio Analytic team in Cambridge. and the Free Devialet Player from pan-European telecoms operator Iliad.

Audio Analytic

Customers Businesses
Core technology Sound

We are teaching machines to “hear.” We are mapping the world of sounds – beyond speech and music – to give a wide range of devices, across a number of market sectors, the ability to understand local context.

  • We give devices the ability to understand local context, making them more intelligent and helpful to users.
  • Our licensees, including leading consumer technology brands, can offer unique, intelligent features in highly competitive markets.
  • Our licensees can offer valuable, subscription-based services to users – for example, alerts and recordings of events at home while they are away.

We believe sound recognition is a fundamental AI technology. By 2023, sound recognition will be a ‘must-have’ component in a wide variety of intelligent and connected devices, from smart speakers and devices to cars, mobiles and hearables. We also expect sound recognition technology to proliferate into areas beyond consumer technology.

Featured company images_Current Health

Current Health

Customers Healthcare facilities
Sector Healthcare

We help healthcare teams stay close to the patients who need them most, at home and in hospital. Our solution is centred around an all-in-one, wireless wearable we offer, which enables remote monitoring of the human body to detect warning signs of illness and deterioration. By alerting the physician, nurse, home healthcare team or hospital to warning signs, we enable earlier, proactive intervention.

We work with hospitals, home healthcare teams and nursing facilities to:

  • automate vital sign capture and data entry (in a hospital with 750 beds, five years of nursing time is spent every twelve months collecting vital sign data and inputting this into patients’ medical records).
  • detect deterioration earlier, to intervene sooner and save lives.
  • proactively treat deterioration, to reduce (re-)hospitalisations, length of stay and the cost of healthcare.

In 1950, average life expectancy globally was 48. Today it’s 70. As we age, we experience more chronic diseases, including heart failure, cancer and diabetes, which strain healthcare services. An ageing population will require a radical shift in the delivery of healthcare. We are also generating scientific and medical knowledge at a greater rate than ever before – and at a rate greater than the human mind can retain and apply. AI can scale and multiply the efforts of our doctors and nurses. With AI, our solution enables a healthcare professional to monitor hundreds of patients at once and identify the few who require help.

Featured company images - Digital Genius

Digital Genius

Customers Businesses
Function Customer Service

Our AI platform puts customer support on autopilot – by understanding conversations, automating repetitive processes and delighting customers. Our customer service automation platform is powered by deep learning, which understands customers’ objectives and drives automated resolutions through APIs that connect seamlessly to a company’s back-end systems. Our platform is used by KLM Royal Dutch Airlines, The Perfume Shop, Air France and other forward-looking businesses to drive conversational process automation through the use of deep learning.

Our platform can:

  • automate repetitive, expensive tickets. A third of all Course Hero’s customer service tickets are handled by our platform; half of these are resolved without involving an agent.
  • provide shorter customer response times. With our solution, Imagine Learning’s cases are answered 70% faster.
  • deliver higher customer satisfaction. With our solution servicing 40% of tickets, The Perfume Shop reported a boost in customer satisfaction to 88%.

AI is transforming customer service. First, increasing automation – of conversations and processes – is inevitable given the volume of repetitive conversations in contact centres. While there will always be unique customer enquiries, increasingly sophisticated AI will increase the complexity of addressable questions. Second, customer service teams will evolve into customer happiness teams. Instead of fire-fighting they will focus on exception-handling and proactive outreach.

Teams will be measured on customer loyalty, retention, and repeat purchasing – not response time or average call duration. Finally, customer expectations regarding the quality and ease of conversations with machines will shift. Any matter that cannot be resolved in a matter of seconds will be deemed a failure in customer experience.


Growth Intelligence

Customers Businesses
Function Sales

We use machine learning to help B2B businesses discover their future customers.

Our technology mines subtle and nuanced information – ‘signals’ – from all over the public web, on every UK business. These signals are mapped against clients’ customers to create a unique AI prediction model. The AI then offers recommended, future clients and advice on how to approach them: who will buy; who will be most valuable; when should they attempt to contact them; and what to say when they do.

Our AI models transform the economics of customer acquisition by using Machine Learning to:

  • reveal customers’ true addressable market;
  • prioritise it by probability to buy;
  • predicting their dollar value and the best time to contact.

Our customers experience, on average: a 25% reduction in acquisition costs; a 2x boost in lead conversion rates; and up to 2x increase in revenue generated per new customer acquired.

We are likely to see more ‘consumerisation’ in the B2B world as it catches up with B2C dynamics. For example, AI is already widely  used in consumer digital marketing. However, it hasn’t been widely utilised in the B2B ecosystem because of a lack of publicly available information. As a technology company, we are solving this challenge by gathering our own data from publicly available data sources, to help businesses make better marketing decisions.

We predict that, eventually, traditional segmentation models will be replaced by AI-driven modelling in search of better marketing return on investment. As the industry matures more AI use cases will be adopted, including decisions that would have been regarded as strategic or creative in the past – for example, modelling buyer personas using unstructured information about a business as a whole.

Featured company images - Gousto


Customers Consumers
Sector Retail

A recipe kit company, we offer consumers precise ingredients,
delicious recipes and a dollop of adventure. We supply
subscribers with recipe kit boxes that include ready-measured,
fresh ingredients and easily followed recipes.

We offer customers:
• variety – customers can choose from 30 weekly recipes, compared with the average six to seven cooked by UK families.
• convenience – customers can order recipes in minutes, saving hours in weekly supermarket shopping.
• sustainability – we deliver ingredients in pre-portioned measures, eliminating household food waste.

Increasingly, the food industry is characterised by consumers’ demands for choice and convenience. Given the complexity of meeting these expectations, AI is a vital enabler. AI powers our business, from what a customer sees on our website to how boxes are routed through our facilities. Through ever advancing AI we will continue to differentiate our customer proposition and offer greater choice, convenience and value.

Featured company images - Kheiron Medical

Kheiron Medical

Customers Healthcare facilities
Function Healthcare

We are a London-based AI cancer diagnostics company, with an initial focus on breast cancer screening. We believe in deep clinical rigour and robust validation – and are the first UK company to receive regulatory approval for a deep learning application in radiology.

Our first solution, Mia, provides intelligent assessment of breast cancer screening mammograms as an independent reader. Mia is the first and only software suited to making ‘call-back’ decisions as a doctor would.

The product has the potential to increase the diagnostic accuracy of population screening initiatives, reduce false positives, reduce scan-to-report times, and slash the workload of an over-stretched workforce. There is also exciting potential to bring breast cancer screening to countries where no such service currently exists.

AI in medical imaging will be a $2bn global industry by 2023, with algorithms being integrated and deployed at an accelerating rate. Patients, radiologists and healthcare systems will benefit immensely from a plethora of diagnostic decision support tools, automation of repetitive tasks, and standardised disease screening. Kheiron is leading the way in this important field.

Featured company images - Omnius


Customers Businesses
Sector Insurance

Our solution enables the automation of insurance claims handling, by using AI to extract structured data from varying documents and support decision-making. Our state-of-the-art AI technology allows insurers to unlock efficiencies and focus their resources on customer experience.

We offer:

  • up to 90% automation of insurance claim handling, via cutting-edge AI which automates data extraction from varying document streams.
  • deep understanding and insight into insurance. We work with eight of the 10 top global insurers.
  • gains in efficiency and efficacy, by automating classification and semantic extraction of data from documents. Insurance carriers are undergoing a once-in-a-generation technology shift, embracing new digital platforms and adopting best-in-class digital tools and analytics. The industry is moving from being process-driven to data-driven, to meet increasing customer expectations and deliver a better customer experience. AI is the game-changer that enables insurers to better understand their customers, and launch new products and services bespoke to the customer.

The Peak team


Customers Businesses
Sector Marketing & Supply Chain

We enable companies to do great things with data. Our AI system, “the brain”, sits at the centre of the enterprise, aligning with a company’s strategic priorities to solve challenges of revenue growth, profitability and cost reduction – particularly important in today’s uncertain macroeconomic and political environment. It’s difficult to know where to begin with AI – let alone make it successful for your business. Our unique proposition, comprising software and data science services, enables customers to solve critical business challenges – with a focus on more personalised customer experiences, supply chain optimisation and more efficient fulfilment. Our AI works across the entire value chain to drive competitive advantage, and has delivered consistently great results across a broad range of sectors.

Our solution has delivered:

  • +28% digital campaign revenues for Footasylum, by enabling a hyper-personalised, AI-driven experience for Footasylum customers. Our AI enables Footasylum to personalise every marketing touch-point and engage with customers at the ideal moment.
  • Significantly reduced inventory and increased return on capital for Speedy Hire PLC, through improved demand forecasting. Our forecast AI tool enables Speedy to hold less stock, while maintaining service levels across regions.
  • +27% sales in 30 days for Regit, an online platform for motorists that provides leads for automotive industry customers. Using AI, Peak enables Regit to predict when a customer is in the market for a new car – which Regit was unable to do previously.

As AI becomes more prevalent in the enterprise, it will have a greater impact on how businesses use their data to support improvements to key performance indicators. The market has already made the shift from backwards-facing business intelligence tools to predictive analytics. At the bleeding edge is companies using data from across the enterprise to drive AI, which is more powerful than employing ‘black box’ AI tools for discrete business functions.

Featured company images -

Customers Businesses
Core Technology Autonomous Systems

Our mission is to enable leaders and organisations to make better business decisions by optimising resources. Our decision engine, VUKU™, can process data in real time, adapt to uncertainty, act on sparse information and learn from experience. Our goal is to ensure that business is powered by people and empowered by AI.

VUKU unlocks benefits for businesses through better decisions – for example:

  • Lower operational costs: our platform enables 15% improvement in last mile delivery fleet utilisation.
  • Greater financial returns: finance industry customers are improving the performance of their investment portfolios and delivering better returns than hedgefunds at a lower cost.
  • Improved education: education industry customers are helping students to learn independently, faster. VUKU develops an automatically self-organising curriculum for students by rapidly discovering their current skill-sets and precisely tracking changes.

AI-enabled decisions will drive the world economy by 2025. New AI technologies, such as our VUKU engine, help industries optimise their business processes in ways that current technologies cannot. The expansion of AI, and the efficiencies it brings, will be as transformational for the revenues and margins of companies as computers were in the 1980s and 1990s.

Featured company images - Seldon


Customers Businesses
Core Technology Tooling

Our technology accelerates the adoption of large-scale machine learning for some of the world’s leading businesses. In 2017 we released Seldon Core, which has grown into one of the most popular open-source platforms for managing and deploying machine learning models. It’s built on cloud-native technologies that allow models, built in any toolkit, to be run on any cloud and on-premise. Our solution is integrated into the Google Kubeflow and IBM FfDL (Fabric for Deep Learning) open-source machine learning platforms.

Our solution:

  • takes machine learning from research into production, providing continuous integration and delivery (CI/CD), monitoring, optimisation and compliance to deliver increased model performance and reduced risk.
  • provides a uniform machine learning deployment platform across an entire organisation, to enable companies to build and deploy multi-model inference graphs across clouds and on-premise.
  • streamlines model testing, deployment and optimisation workflows. Our technology enables 6x efficiency gains for faster R&D iteration and a better feedback loop between data science and DevOps teams.

In 2011, Marc Andreessen said ‘software is eating the world’. Now, AI is eating software. Over the next five years, AI will catalyse organisations’ switch from monolithic infrastructures to cloud-native, open-source stacks, which leverage containers and microservices for hybrid cloud and edge deployments. New, open standards and governance frameworks will boost consumers’ and regulators’ confidence that model-driven decisions are accurate, explainable, and free from bias – accelerating the rate of AI adoption on an industrial scale.

Featured company images - Sensat


Customers Businesses
Sector Construction

We use AI to solve problems that cause delays and costs on construction sites. Our goal is to teach computers to understand the physical world in which we live. To do this, we’re creating an intelligent ecosystem that simulates reality in real time, by combining real-time information and temporal data with 2D and 3D information. By better understanding our physical world, we enable computers to make intelligent decisions on our behalf – impacting the way we live and work. We were ranked by Crunchbase as Europe’s top AI company in 2018.

Our visualisation platform, Mapp, allows users to measure, analyse and collaborate on their construction projects in real time.

• offers intelligence that automates measurement, reporting and communication to make construction sites safer and more profitable.
• improves capability, reducing the need for surveyor ‘boots on the ground’ by up to 80%.
• increases safety, by offering asset monitoring and project management through real-time visualisation.

AI will have a profound impact on construction. Narrow intelligence, which solves individual problems well, will evolve to more general intelligence that can manage projects holistically. AI will transform safety, by removing humans from dangerous environments, and productivity, by combining on-site and off-site data to create predictive ecosystems.

Featured company images - Senseon


Customers Businesses
Function Cybersecurity

We use AI to protect organisations from emerging cyberattacks. We move beyond traditional rule-based applications, which are too rigid to keep pace with evolving threats, and ineffective systems that cannot differentiate between unusual behaviour and malicious activity. Our unique approach, ‘AI Triangulation’, understands and blends information from multiple perspectives across an organisation’s entire digital estate, allowing organisations to accurately detect even the most complex, subtle cyber threats and reduce false positive alerts.

Our solution:

  • reduces risk – our AI provides accurate and actionable detection that identifies cyber threats at a speed and scale that humans and legacy systems cannot.
  • saves time – automated investigation frees up teams to focus on what matters: investigating genuine threats.
  • saves money – our platform replaces the need for multiple single-point security solutions.

As AI capabilities have helped to improve network defence, attackers have begun to use AI for malicious purposes. The ability to evade detection or adapt attack techniques has already allowed primitive forms of machine learning-fronted attacks to breach systems and existing security tools. This is just the beginning of AI attacks – a cat and mouse game between attackers and defenders that cannot be won; instead, it is constantly evolving. Because our technology learns and adapts to change, we can keep pace with the evolution of cyber threats.

Featured company images -Senseye


Customers Businesses
Sector Manufacturing

We offer the leading cloud-based software for industrial predictive maintenance. Our solution helps manufacturers avoid downtime and save money, by automatically forecasting machine failure without the need for expert manual analysis. Its intelligent machine learning algorithms enable it to be used with any machine, from any manufacturer, and to absorb information from existing industrial IoT sensors and platforms to automatically diagnose failures. Uniquely, it also forecasts the remaining useful life of machinery.

Our solution can:

  • monitor tens of thousands of assets, in real-time, without increasing effort for the end-user.
  • maintain, as well as build, models of normal and abnormal machine behaviour, saving extraordinary effort and cost.
  • reduce the cost of continuous condition monitoring by orders of magnitude
  • reduce unplanned downtime by 50%, reduce the cost of continuous condition monitoring by orders of magnitude, and deliver a return on investment in under three months.

AI is frequently perceived as a threat; scaremongering in the press has caused unnecessary apprehension. These fears are misplaced. The AI we use helps talented engineers and maintenance professionals achieve more, and with greater focus. AI will not replace human experts – it will augment their abilities and allow them greater visibility of matters that require their attention and expertise. With AI taking care of the mundane, a symbiotic relationship paves the way for a new era of productivity.

Featured company images - Signal


Customers Businesses
Function Information & Regulation

Our platform uses AI to aggregate and analyse information in real time. Our technology translates and categorises content from 2.8 million digital, print and broadcast sources to deliver quick and easy access to relevant global data. We provide clients with the capability to monitor whatever subject matter they choose – organisations, people, events or topics – for myriad use cases including reputation management, regulatory compliance, business development and account management.

Our clients receive:

  • real-time access to premium and exclusive content sources and datasets, in a single platform that saves time and money compared with subscribing to individual systems from multiple providers.
  • our insights dashboard, which enables clients to discover emerging trends and track established topics that would be difficult, or impossible, to otherwise follow.
  • a report builder, exportable graphs and email alerts, which simplify the process of sharing relevant news and insights clients’ wider audiences, including their C-suites and external stakeholders.

Data is everywhere. The digital revolution has resulted in the ‘datasphere’ growing at an unprecedented rate. IDC predict there will be more than 163 zettabytes (163 trillion gigabytes) of data by 2025 – a tenfold increase from 2016. It’s no longer humanly possible to process and extract salient information from this volume of data, and the challenge will continue to increase. Alongside this data deluge there is greater risk, competition, regulation and opportunity. As a result, AI will have a profound role to play in the corporate world.

Featured company images - StoryStream


Customers Businesses
Function Marketing

We offer a smart content platform for automotive brands. Using patent-pending AI, our solution transforms the carbuying experience by automatically delivering more real, relevant content, at scale, to every customer touchpoint. Our platform enables better lead quality and increased conversion, and improved ROI by driving efficiency into content management across global teams.

Using our solution, brands experience dramatic increases in marketing efficiency across their organisations and up to: a 22% increase in website conversions; a 4.5x increase in customer engagement through targeted content; and a 5x ROI on their platform investment. Our proprietary AI, Aura, solves problems for automotive brands by:

  • enriching their content at scale by surfacing assets’ hidden, previously unavailable context.
  • using image tagging and sorting to automatically create an organised content ecosystem, which underpins high quality marketing campaigns
  • automatically preparing and distributing content to brand stakeholders and customers, including intelligent content segmentation for personalised website experiences.

AI enables intelligent context searching, which will enable marketing to become hyper-personalised. Marketers will readily be able to match individuals with the most appropriate content based on their buying behaviours. With the increasing power and performance of generative AI networks, we will begin to see artificial images used pervasively, compressing the time from concept to campaign and eliminating the need for expensive photo shoots or post-processing.

Featured company images - Synthesia


Customers Businesses
Core Technology Computer Vision

We offer responsible video synthesis technology to empower visual content creation. We remove the language barrier from video, by enabling ‘native’ translation that re-animates an actor’s face to make it appear they speak a foreign language. In addition to synthesis technology, we are also developing tools to prevent malicious use of generative AI.

Our solution:

  • delivers native translation of video content, which enables advertisers, celebrities and film & television producers to reach new, global audiences and establish new markets.
  • enables content creators to develop different permutations of a single video, to create personalised adverts.
  • allows celebrities to create new video content without having to be present on set.

AI will impact the media landscape in three ways:

  • ‘Native’ translation of video will foster cultural exchange and deeper understanding – just as written translations have done for centuries.
  • Hollywood will face increasing global competition, as high-end visual effects are democratised and the original language of video content becomes unimportant.
  • Verification of digital identity and media will become as ubiquitous as secure socket layer (SSL) technology.