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10 Charts That Will Change Your Perspective On Artificial Intelligence’s Growth

  • There has been a 14X increase in the number of active AI startups since 2000.
  • Investment into AI start-ups by venture capitalists has increased 6X since 2000.
  • The share of jobs requiring AI skills has grown 4.5X since 2013.

These and many other fascinating insights are from Stanford University’s inaugural AI Index (PDF, no opt-in, 101 pp.). Stanford has undertaken a One Hundred Year Study on Artificial Intelligence (AI100) looking at the effects of AI on people’s lives, basing the inaugural report and index on the initial findings. The study finds “that we’re essentially “flying blind” in our conversations and decision-making related to Artificial Intelligence.” The AI Index is focused on tracking activity and progress on AI initiatives, and to facilitate informed conversations grounded with reliable, verifiable data. All data used to produce the AI Index and report is available at aiindex.org. Please see the AI Index for additional details regarding the methodology used to create each of the following graphs.

The following ten charts from the AI Index report provides insights into AI’s rapid growth:

  • The number of Computer Science academic papers and studies has soared by more than 9X since 1996. Academic studies and research are often the precursors to new intellectual property and patents. The entire Scopus database contains over 200,000 (200,237) papers in the field of Computer Science that have been indexed with the key term “Artificial Intelligence.” The Scopus database contains almost 5 million (4,868,421) papers in the subject area “Computer Science.”

  • There have been a 6X increase in the annual investment levels by venture capital (VC) investors into U.S.-based Ai startups since 2000. Crunchbase, VentureSource, and Sand Hill Econometrics were used to determine the amount of funding invested each year by venture capitalists into startups where AI plays an important role in some key function of the business. The following graphic illustrates the amount of annual funding by VC’s into US AI startups across all funding stages.

  • There has been a 14X increase in the number of active AI startups since 2000. Crunchbase, VentureSource, and Sand Hill Econometrics were also used for completing this analysis with AI startups in Crunchbase cross-referenced to venture-backed companies in the VentureSource database. Any venture-backed companies from the Crunchbase list that were identified in the VentureSource database were included.

  • The share of jobs requiring AI skills has grown 4.5X since 2013., The growth of the share of US jobs requiring AI skills on the Indeed.com platform was calculated by first identifying AI-related jobs using titles and keywords in descriptions. Job growth is a calculated as a multiple of the share of jobs on the Indeed platform that required AI skills in the U.S. starting in January 2013. The study also calculated the growth of the share of jobs requiring AI skills on the Indeed.com platform, by country. Despite the rapid growth of the Canada and UK. AI job markets, Indeed.com reports they are respectively still 5% and 27% of the absolute size of the US AI job market.

  • Machine Learning, Deep Learning and Natural Language Processing (NLP) are the three most in-demand skills on Monster.com. Just two years ago NLP had been predicted to be the most in-demand skill for application developers creating new AI apps. In addition to skills creating AI apps, machine learning techniques, Python, Java, C++, experience with open source development environments, Spark, MATLAB, and Hadoop are the most in-demand skills. Based on an analysis of Monster.com entries as of today, the median salary is $127,000 in the U.S. for Data Scientists, Senior Data Scientists, Artificial Intelligence Consultants and Machine Learning Managers.

  • Error rates for image labeling have fallen from 28.5% to below 2.5% since 2010. AI’s inflection point for Object Detection task of the Large Scale Visual Recognition Challenge (LSVRC) Competition occurred in 2014. On this specific test, AI is now more accurate than human These findings are from the competition data from the leaderboards for each LSVRC competition hosted on the ImageNet website.

  • Global revenues from AI for enterprise applications is projected to grow from $1.62B in 2018 to $31.2B in 2025 attaining a 52.59% CAGR in the forecast period. Image recognition and tagging, patient data processing, localization and mapping, predictive maintenance, use of algorithms and machine learning to predict and thwart security threats, intelligent recruitment, and HR systems are a few of the many enterprise application use cases predicted to fuel the projected rapid growth of AI in the enterprise. Source: Statista.

  • 84% of enterprises believe investing in AI will lead to greater competitive advantages. 75% believe that AI will open up new businesses while also providing competitors new ways to gain access to their markets. 63% believe the pressure to reduce costs will require the use of AI. Source: Statista.

  • 87% of current AI adopters said they were using or considering using AI for sales forecasting and for improving e-mail marketing. 61% of all respondents said that they currently used or were planning to use AI for sales forecasting. The following graphic compares adoption rates of current AI adopters versus all respondents. Source: Statista.  

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By 2020 83% Of Enterprise Workloads Will Be In The Cloud

  • Digitally transforming enterprises (63%) is the leading factor driving greater public cloud engagement or adoption today.
  • 66% of IT professionals say security is their most significant concern in adopting an enterprise cloud computing strategy.
  • 50% of IT professionals believe artificial intelligence and machine learning are playing a role in cloud computing adoption today, growing to 67% by 2020.
  • Artificial Intelligence (AI) and Machine Learning will be the leading catalyst driving greater cloud computing adoption by 2020.

These insights and findings are from LogicMonitor’s Cloud Vision 2020: The Future of the Cloud Study (PDF, free, opt-in, 9 pp.). The survey is based on interviews with approximately 300 influencers LogicMonitor interviewed in November 2017. Respondents include Amazon Web Services AWS re:Invent 2017 attendees, industry analysts, media, consultants and vendor strategists. The study’s primary goal is to explore the landscape for cloud services in 2020. While the study’s findings are not statistically significant, they do provide a fascinating glimpse into current and future enterprise cloud computing strategies.

Key takeaways include the following:

  • 83% Of Enterprise Workloads Will Be In The Cloud By 2020. LogicMonitor’s survey is predicting that 41% of enterprise workloads will be run on public cloud platforms (Amazon AWSGoogle Cloud PlatformIBM CloudMicrosoft Azure and others) by 2020. An additional 20% are predicted to be private-cloud-based followed by another 22% running on hybrid cloud platforms by 2020. On-premise workloads are predicted to shrink from 37% today to 27% of all workloads by 2020.

  • Digitally transforming enterprises (63%) is the leading factor driving greater public cloud engagement or adoption followed by the pursuit of IT agility (62%). LogicMonitor’s survey found that the many challenges enterprises face in digitally transforming their business models are the leading contributing factor to cloud computing adoption. Attaining IT agility (62%), excelling at DevOps (58%), mobility (55%), Artificial Intelligence (AI) and Machine Learning (50%) and the Internet of Things (IoT) adoption (45%) are the top six factors driving cloud adoption today. Artifical Intelligence (AI) and Machine Learning are predicted to be the leading factors driving greater cloud computing adoption by 2020.

  • 66% of IT professionals say security is their greatest concern in adopting an enterprise cloud computing strategy. Cloud platform and service providers will go on a buying spree in 2018 to strengthen and harden their platforms in this area. Verizon (NYSE:VZ) acquiring Niddel this week is just the beginning. Niddel’s Magnet software is a machine learning-based threat-hunting system that will be integrated into Verizon’s enterprise-class cloud services and systems. Additional concerns include attaining governance and compliance goals on cloud-based platforms (60%), overcoming the challenges of having staff that lacks cloud experience (58%), Privacy (57%) and vendor lock-in (47%).

  • Just 27% of respondents predict that by 2022, 95% of all workloads will run in the cloud. One in five respondents believes it will take ten years to reach that level of workload migration. 13% of respondents don’t see this level of workload shift ever occurring. Based on conversations with CIOs and CEOs in manufacturing and financial services industries there will be a mix of workloads between on-premise and cloud for the foreseeable future. C-level executives evaluate shifting workloads based on each systems’ contribution to new business models, cost, and revenue goals in addition to accelerating time-to-market.

  • Microsoft Azure and Google Cloud Platform are predicted to gain market share versus Amazon AWS in the next three years, with AWS staying the clear market leader. The study found 42% of respondents are predicting Microsoft Azure will gain more market share by 2020. Google Cloud Platform is predicted to also gain ground according to 35% of the respondent base. AWS is predicted to extend its market dominance with 52% market share by 2020.

Data Science And Machine Learning Jobs Most In-Demand on LinkedIn

  • Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
  • Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles.
  • There are currently 1,829 open Machine Learning Engineering positions on LinkedIn.
  • Job growth in the next decade is expected to outstrip growth during the previous decade, creating 11.5M jobs by 2026, according to the U.S. Bureau of Labor Statistics.

These and many other insights are from the recently released LinkedIn 2017 U.S. Emerging Jobs Report. LinkedIn has provided an overview of the methodology in their post, The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data. “Emerging jobs” refers to the job titles that saw the largest growth in frequency over that five year period. LinkedIn reports that based on their analysis, the job market in the U.S. is brimming right now with fresh and exciting opportunities for professionals in a range of emerging roles.

Key takeaways from the study include the following:

  • There are 9.8 times more Machine Learning Engineers working today than five years ago based on LinkedIn’s research, with 1,829 open positions listed on the site today. There are 6.5 times more Data Scientists than five years ago, and 5.5 times more Big Data Developers. The following graphic illustrates the rapid growth of key data scient, machine leanring, big data and full stack developers in addition to sales development and customer success managers.

  • Software engineering is a common starting point for professionals who are in the top five fasting growing jobs today. The career path to Machine Learning Engineer and Big Data Developer begins with a solid software engineering background. The top five highest growth job typical career paths are shown below:

  • The skills most strongly represented across the 20 fastest growing jobs include management, sales, communication, and marketing. Additional skills represented across the highest growing jobs include marketing expertise (analytics and marketing automation), start-ups, Python, software development, analytics, cloud computing and knowledge of retail systems.
  • LinkedIn interviewed 1,200 hiring managers to determine which soft skills are most in-demand and adaptability came out on top. Additional soft skills include culture fit, collaboration, leadership, growth potential, and prioritization.

Sources:

LinkedIn Blog: The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data

LinkedIn’s 2017 U.S. Emerging Jobs Report

53% Of Companies Are Adopting Big Data Analytics

  • Big data adoption reached 53% in 2017 for all companies interviewed, up from 17% in 2015, with telecom and financial services leading early adopters.
  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.
  • Data warehouse optimization remains the top use case for big data, followed by customer/social analysis and predictive maintenance.
  • Among big data distributions, Cloudera is the most popular, followed by Hortonworks, MAP/R, and Amazon EMR.

These and many other insights are from Dresner Advisory Services’ insightful 2017 Big Data Analytics Market Study (94 pp., PDF, client accessed reqd), which is part of their Wisdom of Crowds® series of research. This 3rd annual report examines end-user trends and intentions surrounding big data analytics, defined as systems that enable end-user access to and analysis of data contained and managed within the Hadoop ecosystem. The 2017 Big Data Analytics Market Study represents a cross-section of data that spans geographies, functions, organization size, and vertical industries. Please see page 10 of the study for additional details regarding the methodology.

“Across the three years of our comprehensive study of big data analytics, we see a significant increase in uptake in usage and a large drop of those with no plans to adopt,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “In 2017, IT has emerged as the most typical adopter of big data, although all departments – including finance – are considering future use. This is an indication that big data is becoming less an experimental endeavor and more of a practical pursuit within organizations.”

Key takeaways include the following:

  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.  Big Data ranks 20th across 33 key technologies Dresner Advisory Services currently tracks.  Big Data Analytics is of greater strategic importance than the Internet of Things (IoT), natural language analytics, cognitive Business Intelligence (BI) and Location intelligence.

  • 53% of companies are using big data analytics today, up from 17% in 2015 with Telecom and Financial Services industries fueling the fastest adoption. Telecom and financial services are the most active early adopters, with Technology and Healthcare being the third and fourth industries seeing big data analytics Education has the lowest adoption as 2017 comes to a close, with the majority of institutions in that vertical saying they are evaluating big data analytics for the future. North America (55%) narrowly leads EMEA (53%) in their current levels of big data analytics adoption. Asia-Pacific respondents report 44% current adoption and are most likely to say they “may use big data in the future.”

  • Data warehouse optimization is considered the most important big data analytics use case in 2017, followed by customer/social analysis and predictive maintenance. Data warehouse optimization is considered critical or very important by 70% of all respondents. It’s interesting to note and ironic that the Internet of Things (IoT) is among the lowest priority use cases for big data analytics today.

  • Big data analytics use cases vary significantly by industry with data warehouse optimization dominating Financial Services, Healthcare, and Customer/social analysis is the leading use case in Technology-based companies. Fraud detection use cases also dominate Financial Services and Telecommunications. Using big data for clickstream analytics is most popular in Financial Services.

  • Spark, MapReduce, and Yarn are the three most popular software frameworks today. Over 30% of respondents consider Spark critical to their big data analytics strategies. MapReduce and Yarn are “critical” to more than 20 percent of respondents.

  • The big data access methods most preferred by respondents include Spark SQL, Hive, HDFS and Amazon S3. 73% of the respondents consider Spark SQL critical to their analytics strategies. Over 30% of respondents consider Hive and HDFS critical as well. Amazon S3 is critical to one of five respondents for managing big data access. The following graphic shows the distribution of big data access methods.

  • Machine learning continues to gain more industry support and investment plans with Spark Machine Learning Library (MLib) adoption projected to grow by 60% in the next 12 months. In the next 24 months, MLib will dominate machine learning according to the survey results. MLib is accessible from the Sparklyr R Package and many others, which continues to fuel its growth. The following graphic compares projected two-year adoption rates by machine learning libraries and frameworks.

The Best Software Companies To Work For In 2018, According To Glassdoor

These and other findings are based on an analysis of Glassdoor rankings of Software Magazine’s 2017 Software 500 list of the leading software companies globally. An Excel spreadsheet was first created using the 2017 Software 500 list as the basis of the Glassdoor company comparisons. Rankings from Glassdoor were added today for the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO.The Software 500 list was used to preserve impartiality in the rankings.  The original data set the analysis is based on is available for download here in Microsoft Excel format.

To gain greater insights into the data sets a series of cross-tabulations and correlation analyses were done using IBM SPSS Statistics Version 25. The analysis shows CEOs have an even greater impact on improving their company’s recommendation scores, rising to 82% this year from 70% in 2015. The analysis also showed that companies who flood Glassdoor with fake reviews hit a wall around 10 posts, down from 15 in 2015. This doesn’t stop some companies from offering cash, prizes, and merchandise to their employees in exchange for positive reviews. Relying on Glassdoor and ideally in-office visits to see how a company culture is and how your potential boss treats others is ideal.

The following are the highest rated software companies to work for in 2018, based the (%) of employees who would recommend the company to a friend:

The following companies scored between 80% and 89% on the rating % of employees who would recommend this company to a friend:

Please see the entire data set for the rankings of all companies included in the Software Magazine 500 here in Microsoft Excel format.

Gartner’s Top 10 Predictions For IT In 2018 And Beyond

  • In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs.
  • By 2020, IoT technology will be in 95% of electronics for new product designs.
  • By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related.

These and many other insights are being presented earlier this month at the Gartner Symposium/ITxpo 2017 being held in Orlando, Florida. Gartner’s predictions and the series of assumptions supporting them illustrate how CIOs must seek out and excel in the role of business strategist first, technologist second. In 2018 and beyond CIOs will be more accountable than ever for revenue generation, value creation, and the development and launch of new business models using proven and emerging technologies. Gartner’s ten predictions point to the future of CIOs as collaborators in new business creation, selectively using technologies to accomplish that goal.

The following are Gartner’s ten predictions for IT organizations for 2018 and beyond:

  1. By 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase digital commerce revenue by 30%. Gartner has found that voice-based search queries are the fastest growing mobile search type. Voice and visual search are accelerating mobile browser- and mobile app-based transactions and will continue to in 2018 and beyond. Mobile browser and app-based transactions are as much as 50% of all transactions on many e-commerce sites today. Apple, Facebook, Google and Microsoft’s investments in AI and machine learning will be evident in how quickly their visual- and voice-search technologies accelerate in the next two years.
  2. By 2020, five of the top seven digital giants will willfully “self-disrupt” to create their next leadership opportunity. The top digital giants include Alibaba, Amazon, Apple, Baidu, Facebook, Google, Microsoft, and Tencent. Examples of self-disruption include AWS Lambda versus traditional cloud virtual machines, Alexa versus screen-based e-commerce, and Apple Face ID versus Touch ID.
  3. By the end of 2020, the banking industry will derive $1B in business value from the use of blockchain-based cryptocurrencies. Gartner estimates that the current combined value of cryptocurrencies in circulation worldwide is $155B (as of October 2017), and this value has been increasing as tokens continue to proliferate and market interest grows. Cryptocurrencies will represent more than half of worldwide blockchain global business value-add through year-end 2023 according to the Gartner predictions study.
  4. By 2022, most people in mature economies will consume more false information than true information. Gartner warns that while AI is proving to be very effective in creating new information, it is just as effective at distorting data to create false information as well. Gartner predicts that before 2020, untrue information will fuel a major financial fraud made possible through high-quality falsehoods moving the financial markets worldwide. By the same year, no significant internet company will fully succeed in its attempts to mitigate this problem. Within three years a significant country will pass regulations or laws seeking to curb the spread of AI-generated false information.
  5. By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust. AI and machine learning systems today can categorize the content of images faster and more consistently accurate than humans. Gartner cautions that by 2018, a counterfeit video used in a satirical context will begin a public debate once accepted as real by one or both sides of the political spectrum. In the next year, there will be a 10-fold increase in commercial projects to detect fake news according to the predictions study.
  6. By 2021, more than 50% of enterprises will be spending more per annum on bots and chatbot creations than traditional mobile app developments. Gartner is predicting that by 2020, 55% of all large enterprises will have deployed (used in production) at least one bot or chatbot. Rapid advances in natural-language processing (NLP) make today’s chatbots much better at recognizing the user intent than previous generations. According to Gartner’s predictions study, NLP is used to determine the entry point for the decision tree in a chatbot, but a majority of chatbots still use scripted responses in a decision tree.
  7. By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related. By 2019, IT technical specialist hires will fall by more than 5%. Gartner predicts that 50% of enterprises will formalize IT versatilist profiles and job descriptions. 20% of IT organizations will hire versatilists to scale digital business. IT technical specialist employees will fall to 75% of 2017 levels.
  8. In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs. By 2020, AI-related job creation will cross into positive territory, reaching 2 million net-new jobs in 2025. Global IT services firms will have massive job churn in 2018, adding 100,000 jobs and dropping 80,000. By 2021 Gartner predicts, AI augmentation will generate $2.9T in business value and recover 6.2B hours of worker productivity.
  9. By 2020, IoT technology will be in 95% of electronics for new product designs. Gartner predicts IoT-enabled products with smartphone activation emerging at the beginning of 2019.
  10. Through 2022, half of all security budgets for IoT will go to fault remediation, recalls and safety failures rather than protection. Gartner predicts IoT spending will increase sharply after 2020 following better methods of applying security patterns cross-industry in IoT security architectures, growing at more than 50% compound annual growth rate (CAGR) over current rates.The total IoT security market for products will reach $840.5M by 2020, and a 24% CAGR for IoT security from 2013 through 2020. Combining IoT security services, safety systems, and physical security will lead to a fast-growing global market. Gartner predicts exponential growth in this area, exceeding more than $5B in global spending by year-end 2020.

Gartner has also made an infographic available of the top 10 Strategic Technology Trends for 2018, in addition to an insightful article on Smarter with Gartner.  You can find the article here, at Gartner Top 10 Strategic Technology Trends for 2018.

Sources:

Gartner Reveals Top Predictions for IT Organizations and Users in 2018 and Beyond

Smarter With Gartner, Gartner Top 10 Strategic Technology Trends for 2018

Top Strategic Predictions for 2018 and Beyond: Pace Yourself, for Sanity’s Sake (client access reqd)

73% of Executives Are Researching & Launching IoT Projects In 2017

  • Manufacturing-based IoT connections grew 84% between 2016 and 2017, followed by energy & utilities (41%).
  • 73% of executives are either researching or currently launching IoT projects.
  • The IoT platform market is expected to grow 35% per year to $1.16B by 2020.
  • B2B uses can generate nearly 70% of the potential value enabled by IoT.

These and many other fascinating findings are from Verizon’s State of the Market: Internet of Things 2017, Making way for the enterprise (16 pp., PDF, free, opt-in). The Verizon study found that the Internet of Things (IoT) gained significant momentum in 2016, with 2017 IoT investments accelerating. The majority of investments today are in IoT projects that are still in the concept or pilot phase, concentrating on tracking data and sending alerts. While easier to initiate and manage, the majority of pilots aren’t providing the depth of analytics data and insights IoT has the potential to deliver.

Key takeaways from the study include the following:

  • Manufacturing-based IoT connections grew 84% between 2016 and 2017, followed by energy & utilities (41%). Transportation and distribution (40%), smart cities and communities (19%) and healthcare and pharma (11%) are the remaining three industries tracked in the study who had positive growth in the number of IoT connections. The following graphic compares year-over-year growth by industry for the 2016 to 2017 timeframe.

  • Manufacturing is predicted to lead IoT spending in 2017 with $183B invested this year. Verizon’s study predicts that transportation and utilities will have the second and third-largest capital expenses in IoT this year. Insurance, consumer and cross-industry IoT investments including connected vehicles and smart buildings will see the fastest overall growth in 2017.

  • The IoT platform market is expected to grow 35% per year to $1.16B by 2020. From well-established enterprise service providers to startups, the platform market is becoming one of the most competitive within the global IoT ecosystem. The design objective of all IoT platforms is to provide a single environment for enabling API, Web Services and custom integrations that securely support enterprise-wide applications. Please see the post What Makes An Internet Of Things (IoT) Platform Enterprise-Ready? for an overview of the Boston Consulting Group’s recent IoT study, Who Will Win The IoT Platform Wars?
  • Improving the customer experience and excel at customer service by gaining greater insights using IoT leaders enterprises’ investment priorities. 33% of enterprises interviewed prioritize using IoT technologies and the insights it’s capable of providing to excel at customer service. 26% intend to use IoT technologies to improve asset management and increase Return on Assets (ROA) and Return on Invested Capital (ROIC). Consistent with how dominant manufacturing’s investment plans are for IoT this year, production and delivery capabilities are the top deployment priority for 25% of all businesses interviewed.
  • IoT has the potential to revolutionize pharmaceutical supply chains by drastically reducing drug counterfeiting globally. It’s estimated that counterfeit drugs cost the industry between $75B to $200B annually. The human costs of treating those who have been sold counterfeit drugs back to health are incalculable. IoT platforms and systems have the potential to drastically reduce the costs of counterfeiting, both on a personal impact and market standpoint. Drug manufacturers operating in the United States have until November 2017 to mark packages with a product identifier, serial number, lot number and expiration date, plus electronically store and transfer all transaction histories, including shipment information, across their distribution supply chains. Pharmaceutical manufacturers have a high level of urgency to make this happen and stay in compliance with the US Drug Supply Chain Security Act. IoT solutions are flourishing in this industry as a result.

How Artificial Intelligence Is Revolutionizing Business In 2017

  • 84% of respondents say AI will enable them to obtain or sustain a competitive advantage.
  • 83% believe AI is a strategic priority for their businesses today.
  • 75% state that AI will allow them to move into new businesses and ventures.

These and many other fascinating insights are from the Boston Consulting Group and MIT Sloan Management Review study published this week, Reshaping Business With Artificial Intelligence. An online summary of the report is available here. The survey is based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. For additional details regarding the methodology, please see page 4.

The research found significant gaps between companies who have already adopted and understand Artificial Intelligence (AI) and those lagging. AI early adopters invest heavily in analytics expertise and ensuring the quality of algorithms and data can scale across their enterprise-wide information and knowledge needs. The leading companies who excel at using AI to plan new businesses and streamline existing processes all have solid senior management support for each AI initiative.

Key takeaways include the following:

  • 72% of respondents in the technology, media, and telecommunications industry expect AI to have a significant impact on product offerings in the next five years. The technology, media and telecommunications industry has the highest expectations for AI to accelerate new product and service offerings of all industries tracked in the study, projecting a 52% point increase in the next five years. AI-based improvements are expected to deliver Business Process Outsourcing (BPO) gains in the Financial Services and Professional Services industries as well. The following graphic compares expectations for AI’s expected contributions to business offerings and process improvements over the next five years by industry.

  • Customer-facing activities including marketing automation, support, and service in addition to IT and supply chain management are predicted to be the most affected areas by AI in the next five years. Demand management, supply chain optimization, more efficient distributed order management systems, and Enterprise Resource Planning (ERP) systems that can scale to support new business models are a few of the many areas AI will make contributions to the in the next five years. The following graphic provides an overview of operations, IT, customer-facing, and corporate center functions where AI is predicted to contribute.

  • 84% of respondents say AI will enable them to obtain or sustain a competitive advantage. 75% state that AI will allow them to move into new businesses and ventures. The research shows that AI will be the catalyst of entirely new business models and change the competitive landscape of entire industries in the next five years. 69% of respondents expect incumbent competitors in their industry to use AI to gain an advantage. 63% believe the pressure to reduce costs will require their organizations to use AI in the next five years.

  • Despite high expectations for AI, only 23% of respondents have incorporated it into processes and product and service offerings today. An additional 23% have one or more pilots in progress, and 54% have no adoption plans in progress, 22% of which have no current plans. The following graphic provides insights into the current adoption of AI with survey respondents.

  • By completing a cluster analysis of survey respondents based on AI understanding and adoption questions, four distinct maturity groups emerged including Pioneers, Investigators, Experimenters, and Passives. 19% of the respondent base is Pioneers or those organizations who understand and are adopting AI. The study says that “these organizations are on the leading edge of incorporating AI into both their organization’s offerings and internal processes.” Investigators (32%) are organizations that understand AI but are not deploying it beyond the pilot stage. Experimenters (13%) are organizations that are piloting or adopting AI without deep understanding. Passives (36%) are organizations with no adoption or much knowledge of AI.

  • Pioneers and Investigators are finding new ways to use AI to create entirely new sources of business value. Pioneers (91%) and Investigators (90%) are much more likely to report that their organization recognizes how AI affects business value than Experimenters (32%) and Passives (23%). One of the most differentiating aspects of the four maturity clusters is understanding the differences and value of investing in high-quality data and advanced AI algorithms. Compared to Passives, Pioneers are 12 times more likely to understand the process for training algorithms and ten times more likely to comprehend the development costs of AI-based products and services.

  • Organizations in the Pioneer cluster excel at analytics expertise versus competitors and have exceptional data governance processes in place, further accelerating their AI-driven growth. Pioneers are excellent at change management, citing their senior management’s vision and leadership as a foundational strength in accomplishing their AI-based initiative Early adopter Pioneers are also adept at product development, capable of changing existing products and services to take advantage of new technologies.

  • 61% of all organizations interviewed see developing an AI strategy as urgent, yet only 50% have one done today. The research found that regarding company size, the largest companies (those with more than 100K employees) are the most likely to have an AI strategy, but only half (56%) have one. The following graphic compares the percentage of respondents by maturity cluster who say developing a plan for Al is urgent for their organization relative to those that have a strategy in place today.

  • 70% of respondents are personally looking forward to delegating the more mundane, repetitive aspects of their jobs to AI. 84% believe employees will need to change their skill sets to excel at delivering AI-based initiatives and strategies. Taking this approach provides career growth and a chance to become more marketable for many whose jobs that are being increasingly automated. Cautious optimism regarding AI’s effects on employment dominates early adopter organizations, not dire fatalism. The bottom line is that AI is providing opportunities for career growth that will only accelerate in the future. Those that seize the chance to learn and earn more will end up having AI removing the mundane tasks from their jobs, leaving more time for the most challenging and rewarding work.

McKinsey’s State Of Machine Learning And AI, 2017

  • Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions.
  • Artificial Intelligence (AI) investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.
  • U.S.-based companies absorbed 66% of all AI investments in 2016. China was second with 17% and growing fast.
  • By providing better search results, Netflix estimates that it is avoiding canceled subscriptions that would reduce its revenue by $1B annually.

These and other findings are from the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in) published last month. McKinsey Global Institute published an article summarizing the findings titled   How Artificial Intelligence Can Deliver Real Value To Companies. McKinsey interviewed more than 3,000 senior executives on the use of AI technologies, their companies’ prospects for further deployment, and AI’s impact on markets, governments, and individuals.  McKinsey Analytics was also utilized in the development of this study and discussion paper.

Key takeaways from the study include the following:

  • Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. The current rate of AI investment is 3X the external investment growth since 2013. McKinsey found that 20% of AI-aware firms are early adopters, concentrated in the high-tech/telecom, automotive/assembly and financial services industries. The graphic below illustrates the trends the study team found during their analysis.

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  • AI is turning into a race for patents and intellectual property (IP) among the world’s leading tech companies. McKinsey found that only a small percentage (up to 9%) of Venture Capital (VC), Private Equity (PE), and other external funding. Of all categories that have publically available data, M&A grew the fastest between 2013 And 2016 (85%).The report cites many examples of internal development including Amazon’s investments in robotics and speech recognition, and Salesforce on virtual agents and machine learning. BMW, Tesla, and Toyota lead auto manufacturers in their investments in robotics and machine learning for use in driverless cars. Toyota is planning to invest $1B in establishing a new research institute devoted to AI for robotics and driverless vehicles.

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  • McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area with the Compound Annual Growth Rate (CAGR) reaching approximately 80% from 20-13 to 2016. The following graphic illustrates the distribution of external investments by category from the study.

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  • High tech, telecom, and financial services are the leading early adopters of machine learning and AI. These industries are known for their willingness to invest in new technologies to gain competitive and internal process efficiencies. Many start-ups have also had their start by concentrating on the digital challenges of this industries as well. The\ MGI Digitization Index is a GDP-weighted average of Europe and the United States. See Appendix B of the study for a full list of metrics and explanation of methodology. McKinsey also created an overall AI index shown in the first column below that compares key performance indicators (KPIs) across assets, usage, and labor where AI could contribute. The following is a heat map showing the relative level of AI adoption by industry and key area of asset, usage, and labor category.

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  • McKinsey predicts High Tech, Communications, and Financial Services will be the leading industries to adopt AI in the next three years. The competition for patents and intellectual property (IP) in these three industries is accelerating. Devices, products and services available now and on the roadmaps of leading tech companies will over time reveal the level of innovative activity going on in their R&D labs today. In financial services, for example, there are clear benefits from improved accuracy and speed in AI-optimized fraud-detection systems, forecast to be a $3B market in 2020. The following graphic provides an overview of sectors or industries leading in AI addition today and who intend to grow their investments the most in the next three years.

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  • Healthcare, financial services, and professional services are seeing the greatest increase in their profit margins as a result of AI adoption. McKinsey found that companies who benefit from senior management support for AI initiatives have invested in infrastructure to support its scale and have clear business goals achieve 3 to 15% percentage point higher profit margin. Of the over 3,000 business leaders who were interviewed as part of the survey, the majority expect margins to increase by up to 5% points in the next year.

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  • Amazon has achieved impressive results from its $775 million acquisition of Kiva, a robotics company that automates picking and packing according to the McKinsey study. “Click to ship” cycle time, which ranged from 60 to 75 minutes with humans, fell to 15 minutes with Kiva, while inventory capacity increased by 50%. Operating costs fell an estimated 20%, giving a return of close to 40% on the original investment
  • Netflix has also achieved impressive results from the algorithm it uses to personalize recommendations to its 100 million subscribers worldwide. Netflix found that customers, on average, give up 90 seconds after searching for a movie. By improving search results, Netflix projects that they have avoided canceled subscriptions that would reduce its revenue by $1B annually.

73% Are Using Internet Of Things Data To Improve Their Business

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  • According to the Cisco Visual Networking Index, M2M connections will represent 46% of connected devices by 2020.
  • 95% of execs surveyed plan to launch an IoT business within three years.

These and many other insights are from the recently published Cisco Internet of Things (IoT) study, The Journey to IoT Value: Challenges, Breakthroughs, and Best Practices published on SlideShare last month. The study is based on a survey of 1,845 IT and business decision-makers in the United States, UK, and India. Industries included in the analysis include manufacturing, local government, retail/hospitality/sports, energy (utilities/oil & gas/mining), transportation, and health care. All respondents worked for organizations that are implementing or have completed IoT initiatives. 56% of all respondents are from enterprises.

Key takeaways from the study include the following:

  • 73% Are Using Internet Of Things Data To Improve Their Business. The data and insights gained from IoT are most often used for improving product quality or performance (47%), improving decision-making (46%) and lowering operational costs (45%). Improving or creating new customer relationships (44%) and reducing maintenance or downtime (42%) are also strategic areas where IoT is making a contribution today according to the Cisco study.

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  • IT executives often see IoT initiatives as more successful (35%) than their line-of-business counterparts (15%). With IT concentrating on technologies and line-of-business users focused on strategy and business cases, the potential exists for differences of opinion regarding IoT initiatives’ value. The following graphic provides an overview of how stark these differences are.

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  • Engaging with the IoT partner ecosystem in every phase of a project or initiative improves the probability of success. The most valuable phases to engage with ecosystem partners include strategic planning (60%), implementation and deployment (58%) and technical consulting or support (58%). The following graphic provides an overview of most and less successful organizations by their level of involvement in the IoT partner ecosystem.

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  • Only 26% of all companies are successful with their IoT initiatives. The three best practices that lead to a successful IoT implementations include collaboration between IT and business, the availability of internal and external partnerships to gain IoT expertise; and a strong technology-focused culture.
  • 60% of companies believe IoT projects look good on paper but prove more complex that expected. This finding underscores how critical it is for IT and line-of-business executives to have the same goals and objectives going into an IoT project. Being selective about which integration, technology, and professional services partners are chosen needs to be a shared priority between both IT and line-of-business executives.
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