“One in 12 startups is now an ‘AI startup’. Europe’s 1,600 AI startups — ‘The Disruptors’ — are maturing, bringing creative destruction to new industries, and raising larger amounts of capital at higher valuations. The UK is the powerhouse of European AI but Germany and France may extend their influence. Securing talent, accessing training data and moving from ‘lab to live’ are entrepreneurs’ greatest challenges.”

Explore our State of AI 2019 Report; highlights deck; and keynote video.

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For our groundbreaking State of AI 2019 Report, we undertook a unique, company-by-company analysis of AI startups across Europe, and spoke with hundreds of AI entrepreneurs, to deliver unprecedented insight into the European AI ecosystem.

Previously (‘Part 1') we revealed that one in 12 European start-ups have embraced AI; the European ecosystem is maturing (one in six AI companies is a ‘growth’-stage company); and the UK is the powerhouse of European AI with a third of the Continent’s AI start-ups.

Today (‘Part 2’) we reveal 12 further findings. We explore how:

• Nine in ten AI startups address a specific 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.
• Healthcare, financial services and retail are well served by AI startups. In manufacturing and agriculture, activity is modest relative to market opportunities.
• Health & wellbeing is a focal point for AI entrepreneurship. In the next decade, developers will have a greater impact on the future of healthcare than doctors.
• The UK is the heartland of European healthcare AI, with a third of the Continent’s startups.
• Marketing and customer service departments enjoy a rich ecosystem of suppliers.
• An influx of AI startups supporting operations teams is driving increasing process automation.
• AI companies raise larger amounts of capital at higher valuations, due to technology fundamentals and extensive capital supply.
• Core technology providers attract a disproportionate share of funding.
• AI entrepreneurs’ key challenges are the availability of talent, access to training data and the difficulty of creating production-ready technology.

1. Nine in ten AI startups are B2B...

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

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

Historically, B2C AI has been inhibited by the ‘cold start’ data challenge. Training AI algorithms typically requires extensive data. While B2B companies can analyse the large 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). B2C companies typically deploy AI later, as their user bases and data sets grow.

2. …But B2C AI is on the rise as the ‘cold start’ thaws

A growing proportion of new AI startups — in 2018, a quarter — are B2C. 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 customer data (such as financial transaction information), capturing data earlier in their customers’ journeys, or developing partnerships with data providers and other companies, startups are mitigating the cold start challenge to gain value from AI earlier. While incumbent consumer companies struggle with sprawling, siloed data estates, AI startups are turning a limitation into an advantage by creating data collection and processing pipelines 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%). 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%). As entrepreneurs tackle B2C-leaning sectors, B2C AI is on the rise.

3. AI entrepreneurship remains vertically-focused

Nine in ten AI startups address a need in a specific ‘vertical’ (business function or sector).

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

Just one in ten is developing a core, ‘horizontal’, AI technology (a sector-agnostic capability or platform). This mix has remained consistent over time.

The proportion of core technology providers will remain modest. Google, Amazon, IBM and Microsoft (GAIM) offer an extensive, 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.

4. Healthcare and Financial Services are well served by AI startups

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

The health & wellbeing, finance, retail and media & entertainment sectors are well served by AI startups. 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 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).

5. Healthcare is a focal point for AI entrepreneurs

More AI startups — one in five — serve the health & wellbeing sector than any other. 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.

6. The UK is a 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 scale-ups. 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 health care 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.

7. 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 significant attention (one in six startups, respectively).

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

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. 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.

8. 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.

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.

9. AI companies raise larger investment rounds

Source: MMC Ventures, Crunchbase

Since 2015, AI companies have raised larger volumes of capital than traditional software companies. A difference exists across all stages of maturity, from Seed stage through Series A, B and C funding.

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.

10. 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 and within the early phases a company’s life.

Source: Dealroom.co, MMC Ventures

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, this tailwind will reduce.

11. Core technology providers attract a disproportionate share of funding

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

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. While core technology companies comprise a tenth of AI startups, they attract a fifth of venture capital investment.

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

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, world-class talent;
• the time required to develop minimum viable products can be longer in technically demanding core AI, increasing cash burn;
• 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.

12. Talent, data and productisation are entrepreneurs’ greatest 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 Media

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

Explore our State of AI 2019 Report; highlights deck; keynote video.

Interested in AI? Sign up for our blog posts.

This post originally appeared on MMC Writes.