The State of AI 2017: What Matters Most
While hype around AI is at a peak, and some expectations may exceed results in the short term, we believe AI represents a paradigm shift in technology that warrants the attention it is receiving. In 2017 AI reached an inflection point, driven by milestones in investment, capability, entrepreneurship and adoption. The implications for consumers, companies and society will be profound.
Below, we highlight three key takeaways from our extensive report, the State of AI 2017: Inflection Point: (1) An inflection point in adoption, as AI ‘crosses the chasm’ to the early majority of buyers; (2) The profound implications of AI, including shifts in sector value chains, the creation of new business models, and benefits and risks to society; and (3) The dynamics of the UK’s 400 AI startups — including a shift to the era of AI applications.
Developed in association with Numis, our report draws on new data and over 400 discussions with ecosystem participants to go beyond the hype and explain the reality of AI today, what is to come and how to take advantage. Every chapter includes actionable recommendations for executives, entrepreneurs and investors. Download the full report here.
1. An inflection point in AI adoption
- Awareness of AI has reached an inflection point. Given media attention and vendor marketing, executives’ awareness of AI is high. In January 2016, the term ‘Artificial Intelligence’ didn’t feature in the top 100 terms searched for by executives on Gartner.com. By May 2017, the term ranked №7 (Gartner).
- However, understanding of AI among buyers is low. Technology principles, use cases and deployment methodologies are poorly understood. “Even among CIOs, understanding of AI is extremely low.” (Senior Executive, global consumer packaged goods company).
- AI adoption is crossing the chasm. 20% of AI-aware executives say they have adopted one or more AI-related technology at scale, or in a core part of their business (McKinsey Global Institute). While nascent, we believe AI adoption is ‘crossing the chasm’ from innovators and early adopters to the early majority.
- Adoption of AI will increase significantly as buyers seek to unlock value from data and avoid losing competitive advantage. 75% of executives say AI will be “actively implemented” to some degree in their organisations within three years (Economist Intelligence Unit).
- High tech, automotive and assembly, and financial service firms lead AI adoption. Spending on AI will increase most in sectors that currently lead adoption, implying that a bifurcation in adoption — by sector — may emerge.
- Poorly articulated business cases weigh on adoption. Better articulation of ROI by AI vendors can catalyse adoption. “Buyers feel there’s value, but are nervous around making bets.” (Vice President, global consumer products company)
- Three quarters of buyers are deploying AI to improve decision-making and enable process automation, while extensive media attention and numerous pilot projects relate to chatbots.
- The C-suite is key for initiating, selecting and funding AI initiatives. In two thirds of organisations, the CTO or CIO make AI technology decisions given its cross-functional implications.
- AI deployment strategies are varied, with a mix of ‘build’ and buy’ strategies, and in a state of flux. ‘Hybrid’ approaches are typical. A quarter of companies deploying AI today prefer to purchase a standalone solution.
- Lack of skills is the primary challenge for companies deploying AI. Defining an AI strategy, identifying use cases for AI, and securing funding for AI initiatives are additional difficulties.
2. AI will have profound implications
- AI’s benefits can be abstracted to four: innovation (new products and services); efficacy (the performance of tasks more effectively); velocity (the completion of tasks more quickly); and scalability (by enabling software to undertake previously human tasks).
- These benefits will have profound implications for companies, consumers and society, including the following.
1.New market participants: By automating capabilities previously delivered by human professionals, AI will reduce the cost and increase the scalability of services, significantly broadening participation in select markets.
AI will enable automated diagnosis for a growing proportion of conditions. The marginal cost of diagnosing a patient using an AI algorithm will be nil. With smartphone adoption in developing economies increasing rapidly, from 37% in 2017 to 57% by 2020E (GSMA), barriers to access will also fall. By transferring the burden of diagnosis from people to software, global access to primary care will increase. Millions of additional individuals will benefit from primary care, while the market for providers of relevant and associated technologies will expand.
2.Shifts in sector value chains: In multiple sectors AI will change where, and the extent to which, profits are made.
In the insurance sector, car insurance accounts for 42% of global insurance premiums (Autonomous Research). As AI-powered autonomous vehicles gain adoption, the frequency of accidents will reduce — and with this, insurers’ revenue. UK car insurance premiums are expected to fall by as much as 63%, causing profits for insurers to fall by 81% (Autonomous Research). Insurers must anticipate and plan for a profound shift in their sector’s value chain.
3.New commercial success factors will determine a company’s ability to be successful.
Success factors in the age of AI include: the vision to embrace AI; ownership of large, non-public data sets to train and deploy market-leading AI algorithms; a willingness to evaluate the opportunities and risks of sharing training data with partners and competitors; the ability to attract, develop, retain and integrate data scientists within an organisation; the ability to form effective partnerships with best-of-breed third-party AI software and service providers; a willingness to understand and respond to regulatory challenges posed by AI; a shift in mindset to the use of software that provides probabilistic instead of binary recommendations; the ability to manage workflow changes that result from the implementation of AI systems; and the ability to manage challenges of organisational design and culture as AI augments, and in some cases replaces, personnel.
4. Changes in companies’ competitive positioning: New leaders, followers, laggards and disruptors will emerge.
Among providers of AI:
Platforms — including Google, Amazon, IBM and Microsoft (GAIM) — provide the AI infrastructure, development environments and ‘plug and play’ AI services used by many developers and consumers of AI. With vast data sets, world-class AI teams and extensive resources, select GAIM vendors will accrue value as platforms that support the provision of AI. However, GAIM lack the strategic desire, data advantage and domain expertise to address myriad industry-specific use cases required by businesses in sectors from manufacturing and agriculture to retail and finance. This presents opportunities for Disruptors.
Disruptors are early stage, AI-led software companies tackling business problems in a novel way using AI. Disruptors will enable buyers that embrace them, while eroding the value of those that lack the foresight to do so. Select Disruptors will become tomorrow’s incumbents, or be acquired by today’s.
Among buyers of AI (today’s medium-sized and large companies):
Leaders will emerge in key industries. Leaders will extend their competitive advantage and enjoy two benefits: (1) In the ‘data economy’, economic returns will accrue disproportionally to companies that can extract value from information most effectively; (2) Data network effects create wider competitive moats. Larger volumes of training data enable better algorithms, which deliver better products and services, which attract more customers, who provide more data.
Laggards are buyers that lack the will or organisational ability to use AI effectively. While some enterprises will lack the foresight to adapt, more will falter due to limited organisational capability. In the ‘data economy’, laggards will rapidly lose competitive advantage and market share.
5. New business models: AI, growth of ‘x-as-a-service’ consumption, and subscription payment models will obviate select business models and offer new possibilities in sectors including transport, insurance and healthcare.
In the transport sector, AI will transform the economic fabric of ownership and insurance. Cars are parked for an average of 96% of their lives (UITP Millennium Cities Database). Despite the cost and inefficiency of private car ownership, the model has been necessary to enable spontaneity, point-to-point convenience, comfort, privacy and security during travel. An autonomous vehicle, summoned when required from a distributed fleet and used for the duration of a journey, will offer the same benefits while optimally utilising a fleet. With the cost of the driver removed, and the cost of the vehicle and insurance divided over a greater volume of trips in a given period, the marginal cost of a journey will be lower. [X] With growing use of transport-as-a-service subscription models, in which consumers pay a low monthly fee for on- demand access to a fleet of autonomous vehicles, private car ownership will decline.
6. Benefits and risks to society: AI will provide benefits to society including improved health, broader access to services and more personalised experiences. It will also present risks regarding job displacement, bias, conflict and privacy — which we describe in the report.
Regarding job displacement, AI will enable the automation of several occupations that involve routine and repetition — from truck driving to telemarketing. Truck driving comprises 3.6 million jobs in the US (American Trucking Association). Analysis of UK census data since 1871 shows that historically, contracting employment in agriculture and manufacturing — a result, in part, of automation — have been more than offset by rapid growth in the caring, creative, technology and business service sectors (Deloitte). Whether or not, over time, AI creates more jobs than it destroys, the short period of time in which a large number of workers could be displaced, coupled with a reduction in the availability of similar roles, could prevent those who lose their jobs from being rapidly re-absorbed into the workforce. Social dislocation, with political consequences, may result.
3. The dynamics of UK AI: the application wave
- AI entrepreneurship is thriving. The number of AI companies founded annually in the UK has doubled since 2014. A new UK AI company has been founded every five days, on average, since 2014.
- There are 400 independent, early stage software companies in the UK with AI at the heart of their value proposition:
- We’ve entered a second wave of entrepreneurship — the era of applications. While early innovation focused on AI research or core technologies applicable to multiple sectors, over 80% of today’s UK AI startups are vertically-focused business-to-business (B2B) suppliers addressing a specific business process or sector. Few companies (one in ten) sell direct-to-consumer given the difficulty of acquiring training data from a ‘cold start’ and the deployment of AI by global consumer technology companies.
- Entrepreneurial activity is unevenly spread. More UK AI companies (one in seven) address the marketing & advertising function than any other. For companies with a sector focus, finance dominates. In select sectors (manufacturing) and business functions (finance), activity appears modest relative to market opportunities.
- UK AI companies comprise nearly half the European total. AI is well represented in the UK, with a slightly higher proportion of startups focused on AI than in Europe (excluding the UK) or the US.
- UK AI companies are nascent. Two thirds of companies are in the earliest stages of their journey, with Seed or Angel funding. The sector, however, is maturing rapidly. UK companies are less embryonic than their European counterparts.
- Over 40% of companies we meet have yet to receive recurring revenue. The journey to monetisation for AI companies can be longer given technical challenges, long sales cycles in a B2B-driven market, and client integration requirements.
- Globally, investments into early stage AI firms are typically 20%-50% larger than capital infusions into general software companies of comparable stages.
- Staging of capital into UK AI companies can be atypical. One in three growth stage companies raised a significantly larger post-Angel round than is typical.
There’s more to explore in the ‘State of AI 2017’. The full report includes:
- An accessible introduction to AI for the non-specialist (Part 1).
- An analysis of AI’s proliferating applications and profound implications (Part 2).
- A market map and dynamics of the UK’s 400 AI startups – plus perspectives from the UK’s leading AI entrepreneurs (Part 3).
- Our investment framework describing the 16 keys to success for AI companies (Part 4).
Download “The State of AI 2017: Inflection Point”
