- 53% of CIOs say machine learning is one of their core priorities as their role expands from traditional IT operations management to business strategists.
- CIOs are struggling to find the skills they need to build their machine learning models today, especially in financial services.
These and many other insights are from the recently published study, Global CIO Point of View. The entire report is downloadable here (PDF, 24 pp., no opt-in). ServiceNow and Oxford Economics collaborated on this survey of 500 CIOs in 11 countries on three continents, spanning 25 industries. In addition to the CIO interviews, leading experts in machine learning and its impact on enterprise performance contributed to the study. For additional details on the methodology, please see page 4 of the study and an online description of the CIO Survey Methodology here.
Digital transformation is a cornerstone of machine learning adoption. 72% of CIOs have responsibility for digital transformation initiatives that drive machine learning adoption. The survey found that the greater the level of digital transformation success, the more likely machine learning-based programs and strategies would succeed. IDC predicts that 40% of digital transformation initiatives will be supported by machine learning and artificial intelligence by 2019.
Key takeaways from the study include the following:
- 90% of CIOs championing machine learning in their organizations today expect improved decision support that drives greater topline revenue growth. CIOs who are early adopters are most likely to pilot, evaluate and integrate machine learning into their enterprises when there is a clear connection to driving business results. Many CIO compensation plans now include business growth and revenue goals, making the revenue potential of new technologies a high priority.
- 89% of CIOs are either planning to use or using machine learning in their organizations today. The majority, 40%, are in the research and planning phases of deployment, with an additional 26% piloting machine learning. 20% are using machine learning in some areas of their business, and 3% have successfully deployed enterprise-wide. The following graphic shows the percentage of respondents by stage of their machine learning journey.
- Machine learning is a key supporting technology leading the majority Finance, Sales & Marketing, and Operations Management decisions today. Human intervention is still required across the spectrum of decision-making areas including Security Operations, Customer Management, Call Center Management, Operations Management, Finance and Sales & Marketing. The study predicts that by 2020, machine learning apps will have automated 70% of Security Operations queries and 30% of Customer Management ones.
- Automation of repetitive tasks (68%), making complex decisions (54%) and recognizing data patterns (40%) are the top three most important capabilities CIOs of machine learning CIOs are most interested in. Establishing links between events and supervised learning (both 32%), making predictions (31%) and assisting in making basic decisions (18%) are additional capabilities CIOs are looking for machine learning to accelerate. In financial services, machine learning apps are reviewing loan documents, sorting applications to broad parameters, and approving loans faster than had been possible before.
- Machine learning adoption and confidence by CIOs varies by region, with North America in the lead (72%) followed by Asia-Pacific (61%). Just over half of European CIOs (58%) expect value from machine learning and decision automation to their company’s overall strategy. North American CIOs are more likely than others to expect value from machine learning and decision automation across a range of business areas, including overall strategy (72%, vs. 61% in Asia Pacific and 58% in Europe). North American CIOs also expect greater results from sales and marketing (63%, vs. 47% Asia-Pacific and 38% in Europe); procurement (50%, vs. 34% in Asia-Pacific and 34% in Europe); and product development (48%, vs. 29% in Asia-Pacific and 29% in Europe).
- CIOs challenging the status quo of their organization’s analytics direction are more likely to rely on roadmaps for defining and selling their vision of machine learning’s revenue contributions. More than 70% of early adopter CIOs have developed a roadmap for future business process changes compared with just 33% of average CIOs. Of the CIOs and senior management teams in financial services, the majority are looking at how machine learning can increase customer satisfaction, lifetime customer value, improving revenue growth. 53% of CIOs from our survey say machine learning is one of their core priorities as their role expands from traditional IT operations to business-wide strategy.
Sources: CIOs Cutting Through the Hype and Delivering Real Value from Machine Learning, Survey Shows
- There are 29,187 Software Engineering jobs available today, making this job the most popular regarding Glassdoor postings according to the study.
These and many other fascinating insights are from Glassdoor’s 50 Best Jobs In America For 2018. The Glassdoor Report is viewable online here. Glassdoor’s annual report highlights the 50 best jobs based on each job’s overall Glassdoor Job Score.The Glassdoor Job Score is determined by weighing three key factors equally: earning potential based on median annual base salary, job satisfaction rating, and the number of job openings. Glassdoor’s 2018 report lists jobs that excel across all three dimensions of their Job Score metric. For an excellent overview of the study by Karsten Strauss of Forbes, please see his post, The Best Jobs To Apply For In 2018.
LinkedIn’s 2017 U.S. Emerging Jobs Report found that there are 9.8 times more Machine Learning Engineers working today than five years ago with 1,829 open positions listed on their site as of last month. Data science and machine learning are generating more jobs than candidates right now, making these two areas the fastest growing tech employment areas today.
Key takeaways from the study include the following:
- Six analytics and data science jobs are included in Glassdoor’s 50 best jobs In America for 2018. These include Data Scientist, Analytics Manager, Database Administrator, Data Engineer, Data Analyst and Business Intelligence Developer. The complete list of the top 50 jobs is provided below with the analytics and data science jobs highlighted along with software engineering, which has a record 29,817 open jobs today:
- Median base salary of the 50 best jobs in America is $91,000 with the average salary of the six analytics and data science jobs being $94,167.
- Tech jobs make up 20 of Glassdoor’s 50 Best Jobs in America for 2018, up from 14 jobs in 2017.
Source: Glassdoor Reveals the 50 Best Jobs in America for 2018
- While 94% of supply chain leaders say that digital transformation will fundamentally change supply chains in 2018, only 44% have a strategy ready.
- Forecast accuracy, demand patterns, product tracking traceability, transportation performance and analysis of product returns are use cases where analytics can close knowledge gaps.
These and other insights are from The Hackett Group study, Analytics: Laying the Foundation for Supply Chain Digital Transformation (10 pp., PDF, no opt-in). The study provides insightful data regarding the increasing importance of using analytics to drive improved supply chain performance. Data included in the study also illustrate how analytics is enabling business objectives across a range of industries. The study also provides the key points that need to be considered in creating a roadmap for implementing advanced supply chain analytics leading to digital transformation. It’s an interesting, insightful read on how analytics are revolutionizing supply chains in 2018 and beyond.
Key takeaways from the study include the following:
- 66% of supply chain leaders say advanced supply chain analytics are critically important to their supply chain operations in the next 2 to 3 years. The Hackett Group found the majority of supply chain leaders have a sense of urgency for getting advanced supply chain analytics implemented and contributing to current and future operations. The majority see the value of having advanced analytics that can scale across their entire supplier network.
- Improving forecast accuracy, optimizing transportation performance, improving product tracking & traceability and analyzing product returns are the use cases providing the greatest potential for analytics growth. Each of these use cases and the ones that are shown in the graphic below has information and knowledge gaps advanced supply chain analytics can fill. Of these top use cases, product tracking and traceability are one of the fastest growing due to the stringent quality standards defined by the US Food & Drug Administration in CFR 21 Sec. 820.65 for medical products manufacturers. The greater the complexity and cost of compliance with federally-mandated reporting and quality standards, the greater potential for advanced analytics to revolutionize supply chain performance.
- Optimizing production and sourcing to reduce total landed costs (56%) is the most important use case of advanced supply chain analytics in the next 2 to 3 years. The Hackett Group aggregated use cases across the four categories of reducing costs, improving quality, improving service and improving working capital (optimizing inventory). Respondents rank improving working capital (optimizing inventory) with the highest aggregated critical importance score of 39%, followed by reducing costs (29.5%), improving service (28.6%) and improving quality (25.75%).
- 44% of supply chain leaders are enhancing their Enterprise Resource Planning (ERP) systems’ functionality and integration to gain greater enterprise and supply chain-wide visibility. Respondents are relying on legacy ERP systems as their main systems of record for managing supply chain operations, and integrating advanced supply chain analytics to gain end-to-end supply network visibility. 94% of respondents consider virtual collaboration platforms for internal & external use the highest priority technology initiative they can accomplish in the next 2 to 3 years.
- The majority of companies are operating at stages 1 and 2 of the Hackett Group’s Supply chain analytics maturity model. A small percentage are at the stage 3 level of maturity according to the study’s results. Supply chain operations and performance scale up the model as processes and workflows are put in place to improve data quality, provide consistent real-time data and rely on a stable system of record that can deliver end-to-end supply chain analytics visibility. Integrating with external data becomes critically important as supply networks proliferate globally, as does the need to drive greater predictive analytics accuracy.
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.
- 50% of IT professionals believe artificial intelligence and machine learning are playing a role in cloud computing adoption today, growing to 67% 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 AWS, Google Cloud Platform, IBM Cloud, Microsoft 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 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.
- 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.
LinkedIn Blog: The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data
LinkedIn’s 2017 U.S. Emerging Jobs Report
- Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.
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.
- Aconex Limited, The Trade Desk Inc. (NASDAQ: TTD), HubSpot (NYSE: HUBS), Talentica Software, Wix.com Ltd( (NASDAQ: WIX), Coupa Software (NASDAQ: COUP), SAP AG ( NYSE: SAP), The Ultimate Software Group Inc (NASDAQ: ULTI), Extreme Networks Inc.,(NASDAQ: EXTR), Guidewire Software Inc., (NYSE: GWRE), AppFolio Inc.(NASDAQ: APPF) and Mimecast Limited are the software companies employees would most recommend to a friend today.
- CEOs who earn positive recommendations on their performance can improve the percentage of employees who recommend the company to friends by as much as 82% this year up from 70% in 2015.
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.
- 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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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)
- 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.