Artificial Intelligence in the UK: Landscape and learnings from 226 startups

David Kelnar
Dec 21, 2016 · 12 min read
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Figure 1 (above): The landscape of early stage UK AI companies.

The UK AI landscape: 226 companies and counting

Over time, we expect the distinction between ‘AI’ companies and other software providers to blur and then disappear, as machine learning is employed to tackle a wide variety of business processes and sectors. Today, however, it is possible to point to a sub-set of early stage software companies defined by their focus on AI.

  • Customer Type: Does the company predominantly sell to other businesses (‘B2B’) or to consumers (‘B2C’)?
  • Funding: How much funding has the company received to date? We bracket this from ‘angel’ investment (under $500,000) through to ‘growth’ capital ($8m to ~$100m).

1. A focus on AI for business functions

Most early stage UK AI companies — five in every six — are applying machine learning to challenges in specific business functions or sectors (Figure 2, below). Reflecting the nascent stage of the field, however, one in six is developing an AI technology — a capability, platform or set of algorithms — applicable across multiple domains. These companies’ activities range from the development of computer vision solutions to the creation of algorithms for autonomous decision-making.

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2. AI entrepreneurship is unevenly spread

A heat map highlights areas of early stage activity, as measured by the number of companies in each segment (Figure 4, below).

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Figure 4: A heat map of early stage AI companies in the UK

Activity is greatest within:

  • The Marketing & Advertising, Information Technology, and Business Intelligence & Analytics functions; and
  • The Finance sector.

Activity is extensive within:

  • The Human Resources function; and
  • The Infrastructure, Healthcare and Retail sectors.

Areas of lower activity

In a number of areas, activity appears modest relative to market opportunities. In the Manufacturing sector, for example, there are few startups to address a substantial need. Machine learning has the potential to unlock 20% more production capacity through predictive, optimised maintenance of machines. Raw material costs and re-working can be reduced through improved analysis of product quality data. Further, ‘buffering’ — storing raw materials and part-developed products to compensate for unforeseen inefficiencies during production — can be reduced by up to 30% given more predictable production capacity. The proliferation of sensors in the manufacturing industry, including sensor data from the production line, machine tool parameters and environmental data, has also increased significantly the data available for machine learning.

3. AI entrepreneurship has doubled

The number of AI companies founded annually in the UK (Figure 5, below) has doubled in recent years (2014–2016) compared with the prior period (2011–2013). Over 60% of all UK AI companies were founded in the last 36 months. During this period, a new AI company has been founded in the UK on almost a weekly basis.

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4. A nascent sector relative to global peers

The UK AI sector is at a nascent stage in its development relative to global peers, presenting both opportunities and challenges.

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5. The journey to monetisation can be longer

Over 40% of the AI companies we meet are yet to generate revenue (Figure 9, below). This is not an artefact of us meeting ‘early stage’ companies; the median profile of a company we meet is one founded 2–3 years ago that has raised £1.3m, has a team of nine and is spending £76,000 per month.

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  1. 90% of AI companies are B2B. The long sales cycles typical in B2B sales are exacerbated by many AI companies’ focus on sectors, such as finance, with sprawling and sensitive data sets.
  2. Deployment periods can be lengthy given extensive per-client data integration, data cleansing and customisation requirements. Half the AI companies we meet have a pure software-as-a-service model; as many monetise significant client integration and customisation work in the form of project revenue (Figure 10, above).
  3. The limited number of personnel available for implementation in early stage companies is inhibiting many AI companies’ growth. In a sentiment echoed by several companies, one told us “we couldn’t implement more sales even if we had them.” One third of many teams are engaged in deployment support.

6. Investments are larger and staging is atypical

Globally at least, investments into AI firms are typically 20% to 60% larger than average capital infusions (Figure 11, below, shows 2015 data). This reflects company fundamentals and dynamics in the supply and demand of capital. AI companies’ capital requirements can be higher given longer development periods prior to product viability, the high cost of machine learning talent and the larger teams required for complex deployments. Beyond these fundamentals, however, capital infusions are being inflated by extensive supply (many venture capitalists seek opportunities to invest in artificial intelligence companies) and limited demand (there are relatively few AI companies in which to invest). Venture capital investment in early stage AI companies has increased seven-fold in five years, while the number of investable prospects remains limited.

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Conclusion: An inflection point in UK AI

The last 36 months have marked an inflection point in early stage UK AI. Entrepreneurship has doubled, as AI technology comes of age and investment has increased. Yet, companies are early in their development relative to global peers, offering entrepreneurs and employees unprecedented opportunity and challenge. Three quarters of UK AI companies are at the earliest stages of their journey and activity remains uneven. Startups have concentrated on readily addressable business functions, where data sets are plentiful and optimisation challenges are pronounced. Today, business processes are being optimised. In the future, they will be re-imagined. Within the last 24 months, additional functions and sectors are starting to be tackled by AI entrepreneurs. The path to monetisation for today’s AI companies can be longer, but effective entrepreneurs are taking advantage of attractive capital dynamics to raise sufficient sums of money earlier in their journey.


MMC writes

A collection of stories and experiences from the…

David Kelnar

Written by

Head of Numis Growth Capital Solutions. 2x start-up/scale-up CEO/CFO. Love tech, scale-ups, trends and triathlon. http://www.linkedin.com/in/kelnar

MMC writes

A collection of stories and experiences from the early-stage technology and venture capital communities. Curated by MMC Ventures.

David Kelnar

Written by

Head of Numis Growth Capital Solutions. 2x start-up/scale-up CEO/CFO. Love tech, scale-ups, trends and triathlon. http://www.linkedin.com/in/kelnar

MMC writes

A collection of stories and experiences from the early-stage technology and venture capital communities. Curated by MMC Ventures.

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