Artificial Intelligence (AI) is the talk of the world and it features prominently in predictions for 2019 (see here and here) and recent surveys by consulting firms and other observers of the tech scene. Here are the key findings:
Consumer adoption: “Smart speakers” lead the way to the AI-infused home of the future
Smart speakers (e.g., Amazon Echo and Google Home) will become the fastest-growing connected device category in history, with an installed base projected to surpass 250 million units by the end of 2019. With sales of 164 million units at an average selling price of $43 per unit, total smart speakers’ revenues will reach $7 billion, up 63% from 2018. (Deloitte)
Enterprise adoption: Timid first steps
47% of business executives say their companies have embedded at least one AI capability in their business processes and just 21% say their organizations have embedded AI in several parts of the business. 30% say they are piloting AI. (McKinsey)
20% of business executives say their companies will deploy AI across the business in 2019. (PwC)
In 2019, among companies using AI, 70% will obtain AI capabilities through the cloud. Cloud-based AI software and services will make it easier for companies to benefit from AI, accelerating their adoption and spreading their benefits. 65% will create AI applications using cloud-based development services. By 2020, the penetration rate of enterprise software with AI built in, and cloud-based AI development services, will reach an estimated 87 and 83 percent respectively. (Deloitte)
55% of government officials says they are aware of AI but it is not being utilized; 37% are utilizing AI. (RELX)
Only 15% of enterprises have appointed a single enterprise-wide AI leader. 3% said they were not sure who was in charge of AI, and 0% said there was a single executive who was in charge of AI at their firm. 24% said their enterprise-wide AI efforts were being led by an AI “center of excellence.” (PwC)
58% of business say less than one-tenth of their companies’ digital budgets goes toward AI and 71% expect AI investments will increase in the coming years. (McKinsey)
Only 18% of senior leaders plan to increase investment in AI and machine learning. (RELX)
46% of business executives say they use AI to manage risk, fraud, and cybersecurity threats. (PwC)
The three most popular uses for AI and machine learning are to increase efficiencies or worker productivity (51%), to inform future business decisions (41%) and to streamline processes (39%). (RELX)
Robotic process automation (RPA), computer vision, and machine learning are the most commonly deployed AI applications. (McKinsey)
While 88% of senior leaders polled agree machine learning or AI help their businesses to be more competitive, only 56% of organizations are currently utilizing these emerging technologies. 69% of respondents believe that machine learning/AI technologies are having a positive impact within their industry and 39% believe their organizations are getting the most value out of AI and machine learning. (RELX)
Among top challenges to AI adoption, 43% cite lack of clear AI strategy and 42% point to lack of AI talent. (McKinsey)
AI and jobs: More, not less
In 2018, six out of the 15 emerging jobs are related in some way to AI. (LinkedIn)
38% of business executives say that AI will lead to an increased headcount while only 19% say AI will lead to job cuts in their organization. Right now, the challenge is to fill jobs, and 31% of executives are worried about the inability to meet the demand for AI skills over the next five years. (PwC)
On average, more than one in four (26%) of all skills reported in 2017 by LinkedIn members based in the US can be classified as interpersonal or soft skills. Soft skills like project management and leadership are also among the fastest-growing “unique” skills. There is no doubt that AI skills are on the rise, but some typically human skills that today cannot be replicated by machines have been growing almost as fast and are here to stay. (LinkedIn)
AI talent and skills are in great demand
AI skills are among the fastest-growing skills on LinkedIn, and globally saw a 190% increase from 2015 to 2017. (LinkedIn)
While ML is the largest skill cited as a requirement, deep learning (DL) is growing at the fastest rate from 2015 to 2017 the number of job openings requiring DL increased 35x. (AI Index 2018)
Analytics and AI teams will merge into on organization. Organizations can accelerate AI adoption if they take advantage of what IIA has called Analytic Athleticism. Analytic Athleticism describes the fact that the same underlying skills that drive success for one type of analytics also drive it for others. In other words, the underlying mindset and skillset required for success is very similar across different types of analytics. This also holds true for AI—particularly the machine learning components of it. In organizations where AI wasn’t placed with analytics in the first place, the integration of AI into the analytics organization is starting to happen already and will accelerate through 2019. (IIA)
There are 720,000+ people worldwide skilled in machine learning. (Diffbot)
AI Startups
There are 6,305 AI startups in the Crunchbase database which reports 230 acquisitions by Google, 214 by Microsoft and 91 by Intel. (Crunchbase)
From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x. For the most part, growth in all active startups has remained relatively steady, while the number of AI startups has seen exponential growth. From 2013 to 2017, AI VC funding increased 4.5x while all VC funding increased 2.08x. (AI Index 2018)
More than 1,200 AI companies have been established in Israel since 2010, of which 79% are still active, 6% have been acquired, and 15% have ceased to operate. AI capital raising has grown significantly since 2013, both in the number of deals and amounts. AI exits in the first half of 2018 were higher than for all of 2017. ( IVC Research)
Trust in AI
37% of executives said ensuring AI systems were trustworthy was their top priority, 61% would like to create transparent, explainable, and provable AI models, and 55% would like to create ethical, legal, and understandable AI systems. (PwC)
Only 16% of employees trust AI-generated results. (McKinsey)
AI’s impact on society
63% of AI experts say they are hopeful that most individuals will be mostly better off because of AI in 2030, but 37% said people will not be better off. (Pew)
Articles on AI became 2.5x more positive from 2016 to 2018. (AI Index 2018)
Mentions of the terms Artificial intelligence and Machine learning in transcripts of the U.S. Congressional Record and the records of proceedings of the Parliaments of Canada and the United Kingdom have spiked since 2016. Additionally, in the three countries, Machine learning is rarely mentioned before 2016, and has remained a small fraction of total mentions, relative to Artificial intelligence. (AI Index 2018)
Source: Forbes
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