AI Stats News: 34% Of Employees Expect Their Jobs To Be Automated In 3 Years

Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the precarious nature of the future of work (long after the coronavirus pandemic ends), the continuing mixed attitudes of consumers about data privacy, and the possible resilience of this year’s investments in AI.

78% of employees say they are ready to learn new skills GETTY

AI and the future of work

34% of employees expect their jobs to be replaced in three years; 61% of employees believe their employers are preparing them for the future of work and 55% trust their organization to reskill them if their job changes as a result of automation; 78% of employees say they are ready to learn new skills, but 38% claim they do not have enough time for training; business executives think that only 45% of the workforce is able to adapt to the new world of work; 34% of HR leaders are investing in workforce learning and reskilling as part of their strategy to prepare for the future of work and 40% do not know what skills their workforce has today; 67% of HR leaders are confident they can ensure AI is not institutionalizing bias; the use of predictive analytics has nearly quadrupled in five years (from 10% in 2016 to 39% today), but only 43% of organizations use metrics to identify employees likely to leave, 41% know when critical talent is likely to retire, 18% know the impact of pay strategies on performance, 15% can determine if it is better to buy/build/borrow employees and 12% are using analytics to correct inequities and prevent them recurring [Mercer’s 2020 Global Talent Trends survey of 7,300 senior business executives, HR leaders and employees in 16 geographies around the world]

The IT department’s need for AI talent has tripled between 2015 and 2019, but the number of AI jobs posted by IT is still less than half of that stemming from other business units; departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development. These business units are using AI talent for customer churn modeling, customer profitability analysis, customer segmentation, cross-sell and upsell recommendations, demand planning, and risk management

By 2025, at least two of the top 10 global retailers will establish robot resource organizations to manage nonhuman workers; 77% of retailers plan to deploy AI by 2021, with the deployment of robotics for warehouse picking as the No. 1 use case。

By 2024, AI, virtual personal assistants, and chatbots will replace almost 69% of the manager’s workload。

AI in business

85% of organizations are using AI and (of these) most are using it in production; top three areas for AI use are research and development, IT, and customer service; the biggest bottleneck to AI adoption was reported to be a lack of institutional support (22%), followed by "Difficulties in identifying appropriate business use cases" at 20%; top skills shortages are of ML modelers and data scientists (58%) and data engineering (40%); more than 26% of respondents say their organizations plan to institute formal data governance processes and/or tools by 2021 and nearly 35% expect this to happen in the next three years [O’Reilly worldwide survey of 1,388 respondents in tch-related jobs]

96% of IT decision makers say they currently use cybersecurity products with AI and machine learning; 74% don’t care if their cybersecurity uses AI/ML, as long as it’s effective; 84% think their company has everything it needs to stop cyberattacks; 70% have experienced a damaging attack in the last 12 months [Webroot survey of 800 IT decision makers in the US, UK, Japan, and Australia/New Zealand]

70% of law firms report the use of Legal Analytics, and 98% of those using Legal Analytics say it has made them a better lawyer; the biggest drivers of legal analytics adoption are competitive pressures (58%), meeting client expectations (56%), and a favorable viewing by clients (81%) [Lex Machina]

The Life of Data, the fuel for AI

96% of Americans say that online data privacy is important to them, and many are willing to take actions to proactively protect themselves; 92% would delete a regularly used app if they found out their personal information had been sold to a third party; 67% would be willing to pay to completely delete all their past personal data and their online footprint off the internet forever, including 4% who said they would pay more than $1,000 to do so; 42% said they use Facebook less or deleted their account entirely as a result of Facebook’s privacy scandals; 37% regularly read through the terms and conditions of all the apps on their phones, up from 19% last year [ExpressVPN survey of 1,200 American adults]

AI market forecasts and predictions

IDC forecasts 2020 AI spending ranging from $48 billion, an increase of 25% from 2019, to $50.7 billion, a 32% increase [Wall Street Journal]

45% of the surveyed industrial users in China said that their spending would be delayed in the first quarter of 2020 and their annual spending is also expected to be reduced; among the positively impacted ICT segments, enterprise collaboration platforms will benefit most from the epidemic, with 76% of the surveyed industrial users choosing to adopt such platforms, followed by cloud computing, robotics, AI, big data and 5G [IDC survey of CXOs in China]

Over 40% of privacy compliance technology will rely on AI by 2023, up from 5% today [Gartner]

33% of corporate legal departments will have a dedicated legal technology expert to support the increasing automation of core in-house workflows [Gartner]

AI quotes

"Supervised machine learning doesn’t live up to the hype. It isn’t actual artificial intelligence akin to C-3PO, it’s a sophisticated pattern-matching tool… Rather than seeing exponential improvements in the quality of AI performance (a la Moore’s Law), we’re instead seeing exponential increases in the cost to improve AI systems"—Stefan Seltz-Axmacher, founder, Starsky Robotics

“…why are we holding our hands behind our back trying to build AI without mechanisms that infants have?”—Gary Marcus

“We haven’t really gone to great depth with deep learning yet. We’ve had a limited amount of training data so far. We’ve had limited structures with limited compute power. But the key point is that deep learning learns the concept, it learns the features. It’s not a human-engineered thing”—Danny Lange

“…such capabilities [as “deepfake” transformation of the human face] were called image processing 15 years ago, but are routinely termed AI today. The reason is, in part, marketing. Software benefits from an air of magic, lately, when it is called AI. If “AI” is more than marketing, then it might be best understood as one of a number of competing philosophies that can direct our thinking about the nature and use of computation’—Glen Weyl and Jaron Lanier

“It might be well at this point to dispel some of the fuzzy sensationalism of the popular press regarding the ability of existing digital computers to think… The digital computer can and does relieve man of much of the burdensome detail of numerical calculations and of related logical operations, but perhaps it is more a matter of definition than fact as to whether this constitutes thinking”—Arthur L. Samuel , 1953 [developed the first checkers-playing computer program in 1952 and coined the term “machine learning” in 1959]

“Recently there have been a good deal of news about strange giant machines that can handle information with vast speed and skill….These machines are similar to what a brain would be if it were made of hardware and wire instead of flesh and nerves… A machine can handle information; it can calculate, conclude, and choose; it can perform reasonable operations with information. A machine, therefore, can think”—Edmund Berkeley, Giant Brains or Machines that Think, 1949