AI adoption has tripled in 12 months

Large companies are adopting AI at a rapidly accelerating rate. Just 4% of enterprises had adopted AI 12 months ago (Gartner). Today, 14% of enterprises have deployed AI. A further 23% intend to deploy AI within the next 12 months. Adoption will continue to accelerate; in two years, nearly two thirds of large companies will have live AI initiatives (Fig. 25).

AI deployment is proliferating as:

• Widespread awareness of AI drives a growing volume of enterprise test-and-learn initiatives;
• Early proof-of-concept projects mature, demonstrating value and catalysing further investment;
• Understanding of AI, although low, is improving and driving investment;
• Maturing AI technology – and a burgeoning range of inexpensive or open source AI APIs, frameworks and tooling – lower barriers to entry. Enterprises can achieve more with AI, faster, cheaper and with less expertise than 24 months ago;
• Enterprises mitigate skills shortages by recruiting chief science officers, researchers, data scientists and machine learning engineers – and up-skilling existing employees;
• Enterprises embrace a rich ecosystem of ‘best-of-breed’ third-party AI software suppliers. Europe is home to over 1,600 innovative, early stage software companies with AI at the heart of their value proposition (Chapter 7). Serving a broad range of sectors and business functions, they provide an accessible ‘on-ramp’ to AI with superior results and rapid time-to-value.

In 2019, AI ‘crosses the chasm’ to the early majority

By the end of 2019, over a third of enterprises will have deployed AI. Adoption of AI has progressed extremely rapidly from innovators and early adopters to the early majority. By the end of 2019 AI will have ‘crossed the chasm’, from visionaries to pragmatists, at exceptional pace – with profound implications for companies, consumers and society.

AI may be the fastest paradigm shift in
technology history

AI may be the fastest major paradigm shift in the history of enterprise technology. In the course of three years, the proportion of companies with AI initiatives will have grown from one in 25 to one in three (Gartner).

Companies can enjoy initial benefits from AI with relative ease. Following the cloud computing revolution, and the emergence of a rich ecosystem AI application providers (Chapter 6), enterprises can engage with ‘best of breed’ AI applications via the cloud to derive value from their data. They may also take advantage of ‘plug and play’ cloud AI services from global technology vendors including Amazon, Google, IBM and Microsoft.

While a deeper, structural embrace of AI – that may include hiring data scientists and re-mapping data pipelines – will require greater time and investment, the above factors are enabling the adoption of a new technology paradigm at unprecedented speed.

Great expectations are fuelling adoption

Adoption is being catalysed by companies’ growing conviction in AI’s potential. A greater proportion of executives believe AI will be a ‘game changer’ than any other emerging technology – including cloud, mobile, IoT, blockchain or APIs (Fig. 27).

China leads the race in AI adoption

While adoption of AI has increased in all regions, companies in Asia/Pacific have been the most proactive in embracing AI. Twice as many enterprises in Asia/Pacific – one in five – have adopted AI today, compared with one in ten companies in North America (Gartner) (Fig. 28). Within Asia/Pacific, Chinese companies lead in AI adoption. Beijing, Shanghai, Guangdong, Zhejiang and Jiangsu are primary hubs. Further, the proportion of companies in Asia/Pacific with no interest in deploying AI – one in 14 – is half that of North America (Fig. 30).

Chinese companies’ adoption of AI is being catalysed by:

1. Government policy: In 2017, China published its “Next Generation Artificial Intelligence Development Plan”. A three-step plan for leadership in AI by China and Chinese companies, the roadmap seeks to: establish Chinese competitiveness in AI by 2020; deliver breakthroughs in AI by 2025; and establish global leadership in AI by 2030.

2. A data advantage: AI systems typically improve by ingesting training data. Chinese companies have a dual advantage: more permissive policies than Europe regarding use of personal data; and less siloed data within companies. 78% of leading Chinese companies maintain their corporate data in a centralised data lake, compared with 37% of European and 43% of US pioneers (MIT Sloan Management Review).

3. Fewer legacy assets: Chinese companies typically have fewer legacy applications and processes, presenting opportunities to leapfrog European and American companies that have extensive existing systems and associated integration requirements.

Talent and personnel-related concerns are Chinese companies’ primary impediments to AI adoption. The AI talent pool in the United States is currently over 50% larger than in China (South China Morning Post). A greater proportion of pioneering Chinese companies – six in ten – highlight AI talent shortages than American or European enterprises (MIT Sloan Management Review). The impact of automation upon society is also a pressing concern for Chinese companies. Chinese companies have a greater focus on efficiency projects than revenue generating initiatives. As a result, two thirds of pioneering AI companies in China expect AI to reduce the size of their workforces, compared with a third of European peers.

Use of AI applications is advancing across a broad front

Adoption is advancing not only substantially but across a broad front. (Fig. 31). Today’s enterprises are using multiple types of experiential and analytical AI applications. One in ten enterprises now use ten or more AI applications (Gartner).

The most popular AI use cases are:

• Chatbots (26% of enterprises)
• Process automation solutions (26%)
• Fraud analysis (21%)

Prevalent applications include:

• Consumer/market segmentation (15%)
• Computer-assisted diagnostics (14%)
• Call centre virtual assistants (12%)
• Sentiment analysis/opinion mining (12%)
• Face detection/recognition (11%)
• HR applications (e.g. CV screening) (10%)

Increasingly, certain applications are becoming widespread in particular industries.

• Nearly four in ten healthcare providers use computer-assisted diagnostics;
• Three in ten utilities use process automation tools;
• Six in ten healthcare payers, nearly half of financial service firms and four in ten insurers use AI for fraud detection;
• Three in ten retailers and a quarter of wholesalers use AI for consumer segmentation;
• A third of media companies use AI for sentiment analysis.

Natural language processing and computer vision AI underpin many of the popular and prevalent AI applications, including chatbots, computer-assisted diagnostics, sentiment analysis and face detection. Companies are embracing AI’s ability to replicate traditionally human activities in software for the first
time – and the possibilities (including chatbots, computer-aided diagnostics and sentiment analysis) this enables.

Other, popular AI applications – fraud analysis, consumer segmentation and aspects of process automation – reflect AI’s ability to identify patterns in data more effectively than traditional, rules-based software. As AI has expanded the breadth and complexity of workflows that can be automated, process automation has come of age. In 2017, given its potential, 64% of enterprises highlighted process automation as a focus for future AI deployment (Gartner). As solutions have matured, companies have made good on their intentions. In 2019, process automation is the joint most popular application for AI.

Sector adoption is in flux

Adoption of AI is uneven – across and within sectors – and in a state of flux. Sectors are diverging into ‘early adopters’ of AI, ‘movers’ and ‘laggards’. Within sectors, adoption is dividing further among sub-sets of market participants.

‘Early adopters’ – sectors that proactively invested in AI – are reaping the benefits and maintaining their leadership. In 2017, financial services and high-tech & Telco companies anticipated increasing their investment in AI, in the following three years, more than companies in other sectors. Today, insurance, software & IT service and Telco companies lead in AI adoption (Fig. 32).

‘Movers’ have awoken to AI’s potential and are closing the adoption gap. In 2017, adoption of AI in retail, healthcare and media was moderate relative to other sectors. Adoption in these sectors has accelerated. More than four in ten retail, healthcare and media companies have now invested in AI or will have done so within 12 months (Fig. 32).

High rates of adoption in financial services, high-tech & Telco, retail, healthcare and media reflect the confluence of opportunity and engagement. AI offers extensive potential for value creation in these sectors. All offer: numerous prediction and optimisation challenges well suited to AI; extensive data to train AI systems; quantifiable return on investment; and, to varying extents, the resources and ability to attract high-quality talent. Participants in the above sectors are also, typically, open to engaging with AI. ‘Early adopters’ met opportunity with vision. ‘Movers’ have promptly recognised emerging opportunity – and begun to tackle impediments to adoption such as sprawling, siloed data estates.

‘Laggards’ – Government agencies, education companies and charities – are falling behind in AI adoption. While AI has potential to transform Government, in particular, given extensive data sets and numerous optimisation opportunities, AI engagement will continue to be inhibited by few AI initiatives, limited budgets for emerging technologies, siloed data and difficulty attracting AI talent. Individuals will engage with AI primarily as producers and consumers, not citizens, and in support of companies’ and consumers’ objectives. AI’s transformation of western society will be led by companies, not governments, while vulnerable members of society will be among the last to benefit from AI.

Divergence is evident within as well as across sectors. The proportion of Insurance companies that have adopted AI, or intend to within the next 12 months, is ten percentage points higher than other financial service companies. Within the healthcare sector, engagement with AI is greater among payers than providers. The value, and suitability, of particular AI use cases is driving ‘hot spots’ of activity within sectors. AI-powered fraud analysis, which can detect dishonest activity more effectively than traditional, rules-based systems, is the third most popular AI application today (Fig. 31) and is catalysing adoption among insurers and healthcare payers.

Interest in AI is diverging by department

A gulf is emerging between departments’ interest in exploiting AI’s potential. While IT departments express the greatest interest in AI, customer service teams are emergent AI champions (Fig. 33). The proportion of marketing, HR and finance departments interested in AI projects, meanwhile, is nearly double that of legal & compliance, sales and field service teams (Fig. 33).

Customer service teams’ interest in AI reflects AI’s value to both managers and workers, and low barriers to adoption. Customer service teams spend extensive time addressing repetitive, lower-value enquiries. AI, underpinned by natural language processing, enables replies to a growing proportion of enquiries to be created and sent automatically. For many other enquiries, contact centre workers’ activities can be augmented through AI. Greater efficiency, and freedom to focus on higher-value cases, suits managers and workers alike. Tailwinds to engagement – including increasing adoption of contact centre software platforms, and the availability of ‘best of breed’ AI contact centre solutions such as DigitalGenius, in which we have invested – are fuelling interest.

Extensive interest in AI from marketers, similarly, reflects the breadth of marketing activities to which AI can be usefully applied and easily adopted. AI can augment customer segmentation, channel optimisation, content personalisation, price optimisation and churn prediction. Extensive training data is available and accessible for each activity, while uplift can be readily quantified.

Modest interest in AI from Legal & Compliance teams is at odds with AI’s potential for value in these departments. While companies’ legal and compliance costs are ballooning, AI powered by natural language processing can support activities including: automated time tracking; case law review; due diligence; litigation strategy; and communication compliance. Modest adoption of technology more broadly within legal departments, and cultural resistance to change, is inhibiting interest. Our primary research, however highlights a divergence between innovative legal and compliance departments and laggards. Leaders are taking advantage of AI to gain significant competitive advantage. More broadly, we observe a tipping point in technology investment and openness to innovation among legal and compliance teams, as illustrated by the growth of ‘legal operations’ personnel whose role is to optimise efficiency through modernisation and automation. Interest in AI among legal and compliance teams is likely to increase in the medium term.

AI leaders are better informed – and learning faster

Increasing AI adoption overall masks a growing gulf between leaders and laggards in AI – in companies’ understanding, learning, strategy and investment.

Among AI laggards, fewer than two in ten believe they understand the technology–, business–, workplace– or industry implications of AI (Fig. 35, ‘passives’ and ‘experimenters’) (MIT Sloan Management Review). Among leaders (‘pioneers’ and ‘investigators’) the reverse is true; eight in ten understand its dynamics.

Laggards are set to fall further behind as their understanding of AI improves at a slower rate. In the last 12 months, between half and two thirds of AI leaders improved their understanding of AI to a great extent (Fig. 34) (MIT Sloan Management Review). During the same period, fewer than two in ten laggards did so.

Irrespective of their AI maturity, companies typically understand some considerations better than others (Fig. 35). Overall, companies are better attuned to the disruption AI will bring than the pragmatic challenges of deploying it. Companies understand best that: AI will change how companies generate value; that AI will shift industry power dynamics; and that an AI future will require different knowledge and skills to the past. Companies typically understand least: the costs of developing AI-based products and services; processes for algorithm training; and the effects AI will have on organisational behaviour.

AI leaders are extending their advantage with greater investment

Companies proactively deploying AI are compounding their competitive advantage by increasing investment in AI at a greater pace than laggards.

Nine in ten AI pioneers – companies on the leading edge of AI deployment – have increased their investment in AI in the past year. Nearly two thirds companies investigating or experimenting with the technology have also done so. Among companies with no adoption or much understanding of AI, just one in five has increased spend on AI (Fig. 36, ‘passives’) (MIT Sloan Management Review).

Laggards are falling further behind in AI strategy

Laggards’ sense of urgency regarding AI is weakening. The proportion of companies that believe developing an AI strategy is urgent for their organisation is stable overall – at six in ten. However, while the proportion of proactive adopters with this belief has increased year-on-year, the proportion of laggards who share this view has fallen during the same period (Fig. 37, ‘passives’) (MIT Sloan Management Review).

Attitudes are shaping outcomes. Overall, the proportion of companies that have implemented an AI strategy has increased – but the proportion of laggards that have done so is unchanged (Fig. 37, ‘passives’) (MIT Sloan Management Review). AI leaders are compounding their advantages in understanding and learning with strategic planning – while laggards fall further behind.

Leaders and laggards face different adoption challenges

The barriers to companies’ adoption of AI are no longer consistent. Laggards are struggling with foundational considerations. They lack general technological capabilities to embrace AI, lack leadership support for AI initiatives, and are struggling to define use cases for the technology (Fig. 38, ‘passives’ and ‘experimenters’) (MIT Sloan Management Review).

Leaders’ adoption challenges, in contrast, have shifted from ‘if’ to ‘how’. Leaders have a strong understanding of AI use cases, extensive leadership support for AI initiatives and fewer technological constraints to AI adoption. Their challenges differ. Leaders are contending with the difficulties of attracting AI talent, balancing spend on AI with competing investment priorities and addressing cultural resistance to AI-led initiatives.

AI initiation has shifted from the C–suite to the IT department

Previously, the C-suite played a vital role in initiating AI projects, making technology decisions in relation to them, and approving project funding. Two years ago, Chief Executive Officers (CEOs), Chief Information Officers (CIOs) or Chief Technology Officers (CTOs) initiated two thirds of AI projects.

Today, just one in eight respondents highlight corporate leadership as the primary driver or initiator of AI projects. Interest in AI, and its initiation, has shifted from the C-suite primarily to the IT department (Fig. 39). The  Customer Service function is also emerging as a powerful driver of AI projects.

AI engagement will continue to diffuse from the C-suite to lines of business. By providing ignition energy – identifying the disruptive potential of AI, prioritising experimentation with the technology and funding its deployment – the C-suite is necessary but insufficient to drive change. As companies’ engagement with AI evolves from ‘if’ to ‘how’ – as understanding of AI use cases improves and implementation considerations weigh more heavily – line-of-business owners will play an ever-greater role in delivering value creation through AI.

Companies prefer to buy, not build, AI

When adopting AI, more companies prefer to ‘buy’ than ‘build’. Nearly half of companies favour buying AI solutions from third parties, while a third intend to build a custom solution internally (Fig. 40). Few companies – just one in ten – are prepared to wait for AI to be embedded in their favourite software products.

For many, a ‘buy’ strategy is appropriate given limited in-house AI capability and the proliferation of verticalised, ‘best-of-breed’ software vendors with AI at the heart of their product propositions. In Europe alone 1,600 startups and scale-ups offer AI-led solutions, each focusing on a particular industry or business function (Chapter 7). Many offer best-in-class AI functionality, faster time to value and lower cost than developing in-house expertise and capability. Further, large buyers can frequently shape the product roadmaps of early stage companies to support their requirements. In sectors served by fewer early stage AI-led suppliers, such as Government and Education, propensity to ‘build’ is higher.

The low proportion of companies waiting for AI to be embedded in their favourite software products reflects buyers’ urgency for AI and desire for sustainable competitive advantage. While democratising AI, incumbents are slower to embed AI features into existing solutions and less likely to offer best-in-class capability. By providing the same tooling to large groups of market participants, the competitive advantage they provide is also limited.

Paradigm shifts in technology typically destabilise incumbents and enthrone new winners. In 2019, as buyers prioritise capability and time to value, specialist suppliers are an attractive ‘on-ramp’ to AI. In time, as AI commoditises and buyers seek to consolidate and simplify their technology stacks, buyers may favour AI-enabled incumbents once again.

Workers are concerned about job security

Workers’ views vary widely regarding the likely impact of AI on their daily activities – for example, whether AI will increase or decrease time spent with customers, or collaboration with colleagues (Fig. 41). As AI proliferates, on balance workers expect AI to increase the safety, quality and pace of their work while decreasing job security (Fig. 42).

Workers’ expectations regarding the positive impact of AI on their roles are likely to be met. By augmenting existing workflows with new tools and capabilities, and increasing automation, quality of output and pace of productivity will increase.

Regarding workers’ concerns about job security, AI is likely to enable the automation of select occupations that involve routine and repetition, such as telemarketing and truck driving. In other roles, AI will augment workers’ activities initially but displace a greater proportion of their activities over time – or obviate the need for additional hiring. In many cases, however, AI will simply augment and enrich individuals’ roles, empowering workers with greater capabilities and the opportunity to focus on higher-value tasks. We discuss AI’s potential to displace jobs, and other risks to society from AI, in Chapter 8.