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Posts from the ‘Machine learning’ Category

Machine Learning’s Greatest Potential Is Driving Revenue In The Enterprise

  • Enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020.
  • International Data Corporation (IDC) is forecasting spending on artificial intelligence (AI) and machine learning will grow from $8B in 2016 to $47B by 2020.
  • 89% of CIOs are either planning to use or are using machine learning in their organizations today.
  • 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

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Roundup Of Internet Of Things Forecasts And Market Estimates, 2018

 

  • According to IDC, worldwide spending on the IoT is forecast to reach $772.5B in 2018. That represents an increase of 15% over the $674B that was spent on IoT in 2017.
  • The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%.
  • Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each.
  • Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector.
  • Internet Of Things Market To Reach $267B By 2020 according to Boston Consulting Group.
  • According to IDC FutureScape: Worldwide IoT 2018 Predictions, By the end of 2020, close to 50% of new IoT applications built by enterprises will leverage an IoT platform that offers outcome-focused functionality based on comprehensive analytics capabilities.

The last twelve months of Internet of Things (IoT) forecasts and market estimates reflect enterprises’ higher expectations for scale, scope and Return on Investment (ROI) from their IoT initiatives. Business benefits and outcomes are what drives the majority of organizations to experiment with IoT and invest in large-scale initiatives. That expectation is driving a new research agenda across the many research firms mentioned in this roundup. The majority of enterprises adopting IoT today are using metrics and key performance indicators (KPIs) that reflect operational improvements, customer experience, logistics, and supply chain gains. Key takeaways from the collection of IoT forecasts and market estimates include the following:

  • The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%. According to GrowthEnabler & MarketsandMarkets analysis, the global IoT market share will be dominated by three sub-sectors; Smart Cities (26%), Industrial IoT (24%) and Connected Health (20%). Followed by Smart Homes (14%), Connected Cars (7%), Smart Utilities (4%) and Wearables (3%). Source: GrowthEnabler, Market Pulse Report, Internet of Things (IoT), 19 pp., PDF, free, no opt-in.

  • Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector. Advisory firm Bain predicts the most competitive areas of IoT will be in the enterprise and industrial segments. Bain predicts consumer applications will generate $150B by 2020, with B2B applications being worth more than $300B. Globally, enthusiasm for the Internet of Things has fueled more than $80B in merger and acquisition (M&A) investments by major vendors and more than $30B in venture capital, according to Bain’s estimates. Source: Bain Insights: Choosing The Right Platform For The Internet Of Things

  • The global IoT market is growing at a 23% CAGR of 23% between 2014-2019, enabling smart solutions in major industries including agriculture, automotive and infrastructure. ― Key challenges to growth are the security and scalability of all-new connected devices and the adherence to open standards to facilitate large-scale monitoring of different systems. Source: Export opportunities of the Dutch ICT sector to Germany (25-04-17), PDF, 95 pp., no opt-in

  • According to  Variant Market Research, the Global Internet of Things (IoT) market is estimated to reach $1,599T by 2024, from $346.1B in 2016, attaining a CAGR of 21.1% from 2016 to 2024. Asia-Pacific is predicted to grow at the fastest CAGR over the forecast period 2016 to 2024. The growth is attributed to increasing adoption of IoT in emerging countries such as India and China, high rate of mobile and internet usage, and development of next-generation technologies. Source: Global Internet of Things (IoT) Market: Rising Adoption of Cloud Platform Noticed by Variant Market Research. 

  • Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each. Improving the accuracy, speed, and scale of supply chains is an area many organizations are concentrating on with IoT. IoT has the potential to redefine quality management, compliance, traceability and Manufacturing Intelligence. Business-to-Consumer (B2C) companies are projected to spend $25B on IoT in 2020, up from $5B in 2015. The following graphic compares global spending by vertical between 2015 and 2020. Source: Statista, Spending on the Internet of Things worldwide by vertical in 2015 and 2020 (in billion U.S. dollars).

 

  • By 2020, 50% of IoT spending will be driven by discrete manufacturing, transportation, and logistics, and utilities BCG predicts that IoT will have the most transformative effect on industries that aren’t technology-based today. The most critical success factor all these use cases depend on secure, scalable and reliable end-to-end integration solutions that encompass on-premise, legacy and cloud systems, and platforms.Source: Internet Of Things Market To Reach $267B By 2020.

  • The hottest application areas for IoT in manufacturing include Industrial Asset Management, Inventory and Warehouse Management and Supply Chain Management. In high tech manufacturing, Smart Products, and Industrial Asset Management are the hottest application areas. The following Forrester heat Map for 2017 shows the fastest growing areas of IoT adoption by industry. Source: IoT Opportunities, Trends, and Momentum Robert E Stroud CGEIT CRISC.

  • B2B spending on IoT technologies, apps and solutions will reach €250B ($296.8B) by 2020 according to a recent study by Boston Consulting Group (BCG). IoT Analytics spending is predicted to generate €20B ($23.7B) by 2020. Between 2015 to 2020, BCG predicts revenue from all layers of the IoT technology stack will have attained at least a 20% Compound Annual Growth Rate (CAGR). B2B customers are the most focused on services, IoT analytics, and applications, making these two areas of the technology stack the fastest growing. By 2020, these two layers will have captured 60% of the growth from IoT. Source: Internet Of Things Market To Reach $267B By 2020.

  • Manufacturers most relied on the Industrial Internet of Things (IIoT) in 2017 to help better understand machine health (32%) on the shop floor, leading to more accurate Overall Equipment Effectiveness (OEE) measurements. Changing how plant maintenance personnel will work and interact with all levels of operation (29.5%) and helping to better prevent and predict shutdowns (27.1%) are the top three use cases of IIoT according to Plant Engineering and Statista. 

  • Improving customer experiences (70%) and safety (56%) are the two areas enterprises are using data generated from IoT solutions most often today. Gaining cost efficiencies, improving organizational capabilities, and gaining supply chain visibility (all 53%) is the third most popular uses of data generated from IoT solutions today. 53% of enterprises expect data from IoT solutions to increase revenues in the next year. 53% expect data generated from their IoT solutions will assist in increasing revenues in the next year. 51% expect data from IoT solutions will open up new markets in the next year. 42% of enterprises are spending an average of $3.1M annually on IoT. Source: 70% Of Enterprises Invest In IoT To Improve Customer Experiences.

  • McKinsey Global Institute estimates IoT could have an annual economic impact of $3.9T to $11.1T by 2025. Their forecast scenario includes diverse settings and use cases including factories, cities, retail environments, and the human body. Factories alone could contribute between $1.2T to $3.7T in IoT-driven value. Source: McKinsey & Company, What’s New With The Internet of Things?

  • Business Intelligence Competency Centers (BICC), R&D, Marketing & Sales and Strategic Planning are most likely to see the importance of IoT. Finance is considered among the least likely departments to see the importance of IoT. The study also found that sales analytics apps are increasingly relying on IoT technologies as foundational components of their core application platforms.These and many other insights are from Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study. The study defines IoT as the network of physical objects, or “things,” embedded with electronics, software, sensors, and connectivity to enable objects to collect and exchange data. The study examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. Please see page 11 of the study for details regarding the methodology.

  • Manufacturing, Consulting, Business Services and Distribution/Logistics are IoT industry adoption leaders. Conversely, Federal Government, State & Local Government are least likely to prioritize IoT initiatives as very important or critical. IoT early adopters are most often defining goals with clear revenue and competitive advantages to drive initiatives. Manufacturing, Consulting, Business Services and Distribution/Logistics are challenging, competitive industries where revenue growth is often tough to achieve. IoT initiatives that deliver revenue and competitive strength quickly are the most likely to get funding and support. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • IoT advocates or early adopters say location intelligence, streaming data analysis, and cognitive BI to deliver the greatest business benefit. Conversely, IoT early adopters aren’t expecting to see as significant of benefits from data warehousing as they are from other technologies. Consistent with previous studies, both the broader respondent base and IoT early adopters place a high priority on reporting and dashboards. IoT early adopters also see the greater importance of visualization and end-user self-service. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • Business Intelligence Competency Centers (BICC), Manufacturing and Supply Chain are among the most powerful catalysts of BI and IoT adoption in the enterprise. The greater the level of BI adoption across the 12 functional drivers of BI adoption defined in the graphic below, the greater the potential for IoT to deliver differentiated value based on unique needs by area. Marketing, Sales and Strategic Planning are also strong driver areas among IoT advocates or early adopters. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • IoT early adopters are relying on growing revenue and increasing competitive advantage as the two main goals to drive IoT initiatives’ success. The most successful IoT advocates or early adopters evangelize the many benefits of IoT initiatives from a revenue growth position first. IoT early adopters are more likely to see and promote the value of better decision-making, improved operational efficiencies, increased competitive advantage, growth in revenues, and enhanced customer service when BI adoption excels, setting the foundation for IoT initiatives to succeed. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • The most popular feature requirements for advanced and predictive analytics applications include regression models, textbook statistical functions, and hierarchical clustering. More than 90% of respondents replied that these three leading features are “somewhat important” to their daily use of analytics. Geospatial analysis (highly associated with mapping, populations, demographics, and other Web-generated data), recommendation engines, Bayesian methods, and automatic feature selection is the next most required series of features. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

  • 74% of IoT advocates or early adopters say location intelligence is critical or very important. Conversely, only 26% of the overall sample ranks location intelligence at the same level of importance. One of the most promising use cases for IoT-based location intelligence is its potential to streamline traceability and supply chain compliance workflows in highly regulated manufacturing industries. In 2018, expect to see ERP and Supply Chain Management (SCM) software vendors launch new applications that capitalize on IoT location intelligence to streamline traceability and supply chain compliance on a global scale. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.

Sources:

10 Predictions For The Internet Of Things (IoT) In 2018

2017 Internet Of Things (IoT) Intelligence Update

Bain Insights, Three Ways Telcos Can Win On The Internet Of Things [Infographic]

Bain Insights: Choosing The Right Platform For The Internet Of Things

Big Data & Analytics Is The Most Wanted Expertise By 75% Of IoT Providers

Cambridge Consultants, Review of latest developments in the Internet of Things, 7 March 2017, 143 pp., free, no opt-in.

Cognizant Trend Study: Digital Industrial Transformation with the Internet of Things: How can European companies benefit from IoT?

Ernst & Young,  Internet of Things Human-machine interactions that unlock possibilities –  Media & Entertainment. 24 pp., PDF, no opt-in.

GrowthEnabler, Market Pulse Report, Internet of Things (IoT), 19 pp., PDF, free, no opt-in

IDC, Worldwide Spending on the Internet of Things Forecast to Reach Nearly $1.4 Trillion in 2021, According to New IDC Spending Guide

IHS Markit IoT Trend Watch 2017, pdf, 26 pp., free, no opt-in

Internet Of Things Market To Reach $267B By 2020

Internet Of Things Will Revolutionize Retail

PwC, Leveraging the Upcoming Disruptions from AI and IoT, 20 pp., PDF, free, no opt-in

McKinsey & Company, Beyond The Supercycle: How Technology Is Reshaping Resources

McKinsey & Company,  Digital machinery: How companies can win the changing manufacturing game

McKinsey & Company, Taking the pulse of enterprise IoT

McKinsey & Company, What’s New With The Internet of Things?

IoT: Landscape and Nasscom Initiatives, May 2017. 36 pp., PDF, free, no opt-in

Stanford University Course EE392B, Industrial IoT: Applications Overview April 4, 2017, Dimitry Gorinevsky

Verizon, State of the Market: Internet of Things 2017 Making way for the enterprise

What Makes An Internet Of Things (IoT) Platform Enterprise-Ready?

Woodside Capital Partners, The Industrial Internet of Things: Making Factories “Smart” For The Next Industrial Revolution, PDF, 126 pp., free, no opt-in

THE INTERNET OF THINGS 2017 REPORT: How the IoT is improving lives to transform the world

The IoT Platforms Report: How software is helping the Internet of Things evolve

 

 

 

 

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.

Artificial Intelligence Will Enable 38% Profit Gains By 2035

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  • By 2035 AI technologies have the potential to increase productivity 40% or more.
  • AI will increase economic growth an average of 1.7% across 16 industries by 2035.
  • Information and Communication, Manufacturing and Financial Services will be the top three industries that gain economic growth in 2035 from AI’s benefits.
  • AI will have the most positive effect on Education, Accommodation and Food Services and Construction industry profitability in 2035.

Today Accenture Research and Frontier Economics published How AI Boosts Industry Profits and Innovation. The report is downloadable here (28 pp., PDF, no opt-in).The research compares the economic growth rates of 16 industries, projecting the impact of Artifical Intelligence (AI) on global economic growth through 2035. Using Gross Value Added (GVA) as a close approximation of Gross Domestic Product (GDP), the study found that the more integrated AI is into economic processes, the greater potential for economic growth.  One of the reports’ noteworthy findings is that AI has the potential to increase economic growth rates by a weighted average of 1.7% across all industries through 2035. Information and Communication (4.8%), Manufacturing (4.4%) and Financial Services (4.3%) are the three sectors that will see the highest annual GVA growth rates driven by AI in 2035. The bottom line is that AI has the potential to boost profitability an average of 38% by 2035 and lead to an economic boost of $14T across 16 industries in 12 economies by 2035.

Key takeaways from the study include the following:

  • AI will increase economic growth by an average of 1.7% across 16 industries by 2035 with Information and Communication, manufacturing and financial services leading all industries. Accenture Research found that the Information and Communication industry has the greatest potential for economic growth from AI. Integrating AI into legacy information and communications systems will deliver significant cost, time and process-related savings quickly. Accenture predicts the time, cost and labor savings will generate up to $4.7T in GVA value in 2035. High growth areas within this industry are cloud, network, and systems security including defining enterprise-wide cloud security strategies.

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  • AI will most increase profitability in Education, Accommodation and Food Services and Construction industries in 2035. Personalized learning programs and automating mundane, routine tasks to free up colleges, universities, and trade school instructors to teach new learning frameworks will accelerate profitability in the education through 2035.  Accommodation & Food Services and Construction are industries with manually-intensive, often isolated processes that will benefit from the increased insights and contextual intelligence from AI throughout the forecast period.

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  • Manufacturing’s adoption of Industrial Internet of Things (IIoT), smart factories and comparable initiatives are powerful catalysts driving AI adoption. Based on the proliferation of Industrial Internet of Things (IIoT) devices and the networks and terabytes of data they generate, Accenture predicts AI will contribute an additional $3.76T GVA to manufacturing by 2035. Supply chain management, forecasting, inventory optimization and production scheduling are all areas AI can make immediate contributions to this industry’s profits and long-term economic

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  • Financial Services’ greatest gains from AI will come automating and reducing the errors in mundane, manually-intensive tasks including credit scoring and first-level customer inquiries. Accenture forecasts financial services will benefit $1.2T in additional GVA in 2035 from AI. Follow-on areas of automation in Financial Services include automating market research queries through intelligent bots, and scoring and reviewing mortgages.

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  • By 2035 AI technologies could increase labor productivity 40% or more, doubling economic growth in 12 developed nations. Accenture finds that AI’s immediate impact on profitability is improving individual efficiency and productivity. The economies of the U.S. and Finland are projected to see the greatest economic gains from AI through 2035, with each attaining 2% higher GVA growth.The following graphic compares the 12 nations included in the first phase of the research.

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Sources:

Machine Learning Is The New Proving Ground For Competitive Advantage

  • 50% of organizations are planning to use machine learning to better understand customers in 2017.
  • 48% are planning to use machine learning to gain greater competitive advantage.
  • Top future applications of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).

These and many other insights are from a recent survey completed by MIT Technology Review Custom and Google Cloud, Machine Learning: The New Proving Ground for Competitive Advantage (PDF, no opt-in, 10 pp.). Three hundred and seventy-five qualified respondents participated in the study, representing a variety of industries, with the majority being from technology-related organizations (43%). Business services (13%) and financial services (10%) respondents are also included in the study.  Please see page 2 of the study for additional details on the methodology.

Key insights include the following:

  • 50% of those adopting machine learning are seeking more extensive data analysis and insights into how they can improve their core businesses. 46% are seeking greater competitive advantage, and 45% are looking for faster data analysis and speed of insight. 44% are looking at how they can use machine learning to gain enhanced R&D capabilities leading to next-generation products.
If your organization is currently using ML, what are you seeking to gain?*

If your organization is currently using ML, what are you seeking to gain?

  • In organizations now using machine learning, 45% have gained more extensive data analysis and insights. Just over a third (35%) have attained faster data analysis and increased the speed of insight, in addition to enhancing R&D capabilities for next-generation products. The following graphic compares the benefits organizations who have adopted machine learning have gained. One of the primary factors enabling machine learning’s full potential is service oriented frameworks that are synchronous by design, consuming data in real-time without having to move data. enosiX is quickly emerging as a leader in this area, specializing in synchronous real-time Salesforce and SAP integration that enables companies to gain greater insights, intelligence, and deliver measurable results.
your organization is currently using machine learning, what have you actually gained?

If your organization is currently using machine learning, what have you actually gained?

  • 26% of organizations adopting machine learning are committing more than 15% of their budgets to initiatives in this area. 79% of all organizations interviewed are investing in machine learning initiatives today. The following graphic shows the distribution of IT budgets allocated to machine learning during the study’s timeframe of late 2016 and 2017 planning.
What part of your IT budget for 2017 is earmarked for machine learning?

What part of your IT budget for 2017 is earmarked for machine learning? 

  • Half of the organizations (50%) planning to use machine learning to better understand customers in 2017. 48% are adopting machine learning to gain a greater competitive advantage, and 45% are looking to gain more extensive data analysis and data insights. The following graphic compares the benefits organizations adopting machine learning are seeking now.
If your organization is planning to use machine learning, what benefits are you seeking?

If your organization is planning to use machine learning, what benefits are you seeking?

  • Natural language processing (NLP) (49%), text classification and mining(47%), emotion/behavior analysis (47%) and image recognition, classification, and tagging (43%) are the top four projects where machine learning is in use today.  Additional projects now underway include recommendations (42%), personalization (41%), data security (40%), risk analysis (41%), online search (41%) and localization and mapping (39%). Top future uses of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).
  • 60% of respondents have already implemented a machine learning strategy and committed to ongoing investment in initiatives. 18% have planned to implement a machine learning strategy in the next 12 to 24 months. Of the 60% of respondent companies who have implemented machine learning initiatives, 33% are in the early stages of their strategies, testing use cases. 28% consider their machine learning strategies as mature with between one and five use cases or initiatives ongoing today.

5 Ways Integration Is Enabling The Factory Of The Future

  • factory-of-the-future-report93% of global product leaders say that predictive maintenance combined with real-time equipment monitoring enabled by integration is a must-have for factory planning today.
  • 75% of global product leaders plan to implement factory of the future initiatives and programs in the next five years or less, starting with Industry 4.0
  • 67% of automotive executives expect that new technologies enabled by real-time integration will enable their teams to reach and exceed lean management and continuous improvement goals starting this year and accelerating through 2030.

Boston Consulting Group’s recent article, The Factory of the Future provides insights into a recent global survey the consulting firm conducted of more than 750 manufacturing product leaders from leading companies in three industrial sectors: automotive (which includes suppliers and original equipment manufacturers, or OEMs), engineered products, and process industries. The survey’s objective is to define the vision for the factory of the future in 2030.  Determining long-term benefits and the roadmap to implementation are also goals of the study Boston Consulting Group (BCG) and its research partner, the Laboratory for Machine Tools and Production Engineering at RWTH Aachen University, achieved. The Factory of the Future is a vision for how manufacturers should enhance production by making improvements in three dimensions: plant structure, plant digitization, and plant processes.

5 Ways Integration Fuels The Factory Of The Future’s Growth

Real-time integration based on intelligent objects that connect diverse enterprise systems including SAP, Salesforce and others is the foundation that manufacturing companies must adopt to excel in their Factory of the Future efforts. These real-time objects illustrate the future of Application Programmer Interfaces (API).  APIs that will fuel and drive the Factory of the Future will enrich each real-time integration points across manufacturing networks. Intelligent Objects pervasively used today are the precursors to the most valuable APIs that will enable Factories of the Future tomorrow. With APIs continually improving and gaining the capability to provide insight and intelligence, the essential role of real-time integration in all factories of the future becomes clear.

The following are the five ways integration is enabling the Factory of the Future today:

  1. Real-time integration enables the value chains supporting the Factories of the Future to continually accelerate, excel and improve with additional insight that drives future growth strategies. Bringing greater intelligence into each integration point across the value chains supporting the Factories of the Future leads to new technologies delivering greater lean management benefits. Real-time integration will deliver strong benefits in the areas of lean management, predictive maintenance, modular line setups, and the orchestration and collaboration of smart robots.

factory-of-the-future-1

  1. The Implementation Roadmap for the Factory of the Future shows how critical real-time integration is to the Factory of the Future’s vision being attained. Multidirectional layouts, modular line setups, sustainable production, the orchestration of smart and collaborative robotics and attainment of big data and analytics plans all are dependent on real-time integration. The following graphic from the study illustrates just how central integration is to the optimizing of plant structure and plant digitization.

factory-of-the-future-2

  1. By integrating large-scale enterprise systems including those from SAP, Salesforce and others with legacy, 3rd party and homegrown systems, every area of production quality will improve. The most urgent need global manufacturers have is finding new ways to improve product, process and service quality without raising costs. Improving the quality of these three dimensions makes any manufacturer more trusted and successful in selling next-generation products.  By aggregating data using real-time integration so that Big Data and advanced analytics can be used to find new patterns, some of the world’s most well-known manufacturers are excelling on product quality. To produce cylinder heads at its plant in Untertürkheim, Germany, Mercedes-Benz uses predictive analytics to examine more than 600 parameters that influence quality. Mercedes-Benz is an early adopter of using Big Data and advanced analytics to improve quality management and bring high precision to engineering. Bosch has implemented software that analyzes data about its production of fuel injectors in real time. The software monitors process adherence and recognizes trends. It automatically transmits information about deviations to operators, allowing them to improve the process accordingly.
  1. Real-time integration across and within manufacturing systems enables multi-directional layouts of production workflows. The Audi R8 manufacturing facility in Heilbronn, Germany, does not have a fixed conveyor so the teams there has greater multidirectional flexibility in building customized vehicles.  Real-time integration across the Audi factory floor is essential to provide R8 production teams with the specifics of how they can best collaborate and deliver the highest quality vehicles in the shortest amount of time. Real-time integration is enabling driverless transport systems, guided by a laser scanner and radio frequency identification technology in the floor, which moves the car bodies through the assembly process. These systems enable assembly layout changes quickly with no impact on existing production. Enabling real-time integration often involves extensive field mapping between different systems, which is a lengthy and error-prone process. Integration technology provider enosiX has developed a unique, real-time integration technology that obsoletes the need for field mapping and supports bi-directional data updates.
  1. Enabling the Factory of the Future’s production operations to flex in response to rapidly changing customer requirements is entirely dependent on real-time, reliable integration of production and customer-facing systems. The implications of the study on the future of manufacturing underscore just how critical it is for manufacturers to be agile enough to create entirely new business models while gaining insight and intelligence into how they can continually improve lean manufacturing. When real-time integration unifies a value chain for any manufacturer, their speed, scale and ability to simplify the complex processes required to serve customers turns into a formidable competitive advantage.

 

McKinsey’s 2016 Analytics Study Defines The Future Of Machine Learning

  • U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years.
  • Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.
  • 40% of all the potential value associated with the Internet of Things requires interoperability between IoT systems.

These and many other insights are from the McKinsey Global Institute’s study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month. You can get a copy of the Executive Summary here (28 pp., free, no opt-in, PDF) and the full report (136 pp., free, no opt-in, PDF) here. Five years ago the McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity (156 pp., free no opt-in, PDF), and in the years since McKinsey sees data science adoption and value accelerate, specifically in the areas of machine learning and deep learning. The study underscores how critical integration is for gaining greater value from data and analytics.

Key takeaways from the study include the following:McKinsey Analytics

  • Location-based services and U.S. retail are showing the greatest progress capturing value from data and analytics. Location-based services are capturing up to 60% of data and analytics value today predicted by McKinsey in their 2011 report. McKinsey predicts there are growing opportunities for businesses to use geospatial data to track assets, teams, and customers across dispersed locations to generate new insights and improve efficiency. U.S. Retail is capturing up to 40%, and Manufacturing, 30%.  The following graphic compares the potential impact as predicted in McKinsey’s 2011 study with the value captured by segment today, including a definition of major barriers to adoption.

uneven-progress

  • Machine learning’s greatest potential across industries includes improving forecasting and predictive analytics. McKinsey analyzed the 120 use cases their research found as most significant in machine learning and then weighted them based on respondents’ mention of each. The result is a heat map of machine learning’s greatest potential impact across industries and use case types.  Please see the report for detailed scorecards of each industry’s use case ranked by impact and data richness.

machine-learning-impact

  • Machine learning’s potential to deliver real-time optimization across industries is just starting to evolve and will quickly accelerate in the next three years. McKinsey analyzed the data richness associated with each of the 300 machine learning use cases, defining this attribute as a combination of data volume and variety. Please see page 105 of the study for a thorough explanation of McKinsey’s definition of data volume and variety used in the context of this study The result of evaluating machine learning’s data richness by industry is shown in the following heat map:

rich-data-is-an-enabler

  • Enabling autonomous vehicles and personalizing advertising are two of the highest opportunity use cases for machine learning today. Additional use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics; predicting personalized health outcomes, and optimizing merchandising strategy in retail. McKinsey identified 120 potential use cases of machine learning in 12 industries and surveyed more than 600 industry experts on their potential impact. They found an extraordinary breadth of potential applications for machine learning.  Each of the use cases was identified as being one of the top three in an industry by at least one expert in that industry. McKinsey plotted the top 120 use cases below, with the y-axis shows the volume of available data (encompassing its breadth and frequency), while the x-axis shows the potential impact, based on surveys of more than 600 industry experts. The size of the bubble reflects the diversity of the available data sources.

machine-learning

  • Designing an appropriate organizational structure to support data and analytics activities (45%), Ensuring senior management involvement (42%), and designing effective data architecture and technology infrastructure (36%) are the three most significant challenges to attaining data and analytics objectives. McKinsey found that the barriers break into the three categories: strategy, leadership, and talent; organizational structure and processes; and technology infrastructure. Approximately half of executives across geographies and industries reported greater difficulty recruiting analytical talent than any other kind of talent. 40% say retention is also an issue.

barriers-to-analytics-and-machine-learning-adoption

  • U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising including pricing, assortment, and placement optimization is leading to an additional 16% in operating margin improvement. The following table illustrates data and analytics’ contribution to U.S. retail operations by area.

us-retail-data-sheet

  • Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing. McKinsey estimates that analytics have increased manufacturer’s gross margins by as much as 40% when used in design-to-value workflows and projects. Up to 15% of after-sales costs have been reduced through the use of analytics that includes product sensor data analysis for after-sales service. There are several interesting companies to watch in this area, with two of the most innovative being Sight Machine and enosiX, with the latter enabling real-time integration between SAP and Salesforce systems. The following graphic illustrates the estimated impact of analytics on manufacturing financial performance by area.

manufacturing

10 Ways Machine Learning Is Revolutionizing Manufacturing

machine learningBottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.

Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

The ten ways machine learning is revolutionizing manufacturing include the following:

  • Increasing production capacity up to 20% while lowering material consumption rates by 4%. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric and cited in a National Institute of Standards (NIST) provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.

typical production improvemensSource: Focus Group: Big Data Analytics for Smart Manufacturing Systems

  • Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints. In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible.

factory and demand analytics

Source:  GE Global Research Stifel 2015 Industrials Conference

  • Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google, and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.

Azure IOT Services

Source: Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft

  • Enabling condition monitoring processes that provide manufacturers with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%. An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent.  By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown

OEE Graphic

Source: Using Big Data for Machine Learning Analytics in Manufacturing

  • Machine learning is revolutionizing relationship intelligence and Salesforce is quickly emerging as the leader. The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post,     Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.

Salesforce AI Acquisitions

Salesforce revenue sources

  • Revolutionizing product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide. Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
  • Increasing production yields by the optimizing of team, machine, supplier and customer requirements are already happening with machine learning. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customized products to use more of their production capacity, and machine learning help to optimize the best possible selection of machines, trained staffs, and suppliers.
  • The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
  • Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale.  For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
  • Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning.   Machine learning is extending what enterprise-level price optimization apps provide today.  One of the most significant differences is going to be just how optimizing pricing along with suggested strategies to close deals accelerate sales cycles.

Additional reading:

Cisco Blog: Deus Ex Machina: Machine Learning Acts to Create New Business Outcomes

Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft 

Focus Group: Big Data Analytics for Smart Manufacturing Systems

GE Predix: The Industrial Internet Platform

IDC Manufacturing Insights reprint courtesy of Cisco: Designing and Implementing the Factory of the Future at Mahindra Vehicle Manufacturers

Machine Learning: What It Is And Why It Matters

McKinsey & Company, An Executive’s Guide to Machine Learning

MIT Sloan Management Review, Sales Gets a Machine-Learning Makeover

Stanford University CS 229 Machine Learning Course Materials
The Economist Feature On Machine Learning

UC Berkeley CS 194-10, Fall 2011: Introduction to Machine Learning
Lecture slides, notes

University of Washington CSE 446 – Machine Learning – Winter 2014

Sources:

Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.

Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.

Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).

Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.

Internet of Things, Machine Learning & Robotics Are High Priorities For Developers In 2016

  • 200213603-00156.4% of developers are building robotics apps today.
  • 45% of developers say that Internet of Things (IoT) development is critical to their overall digital strategy.
  • 27.4% of all developers are building apps in the cloud today.
  • 24.7% are using machine learning for development projects.

These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. 1,441 developers completed the survey globally. Please see page 17 of the study for additional details on the methodology.

Key takeaways from the study include the following:

  • Big Data analytics developers are spending the majority of their time creating Internet of Things (IoT).  The second-most popular Big Data analytics applications are in professional, scientific and technical services (10%), telecommunications (10%), and manufacturing (non-computer related) (9.6%). The following graphic provides an overview of where Big Data analytics developers are investing their time building new applications.

Best Describes App

  • Robotics (56.4%), Arts, Entertainment and Recreation (56.3%), and Automotive (52.9%) are the three most popular industries data mining app developers are focusing on today. Additional high priority industries include telecommunications (48.3%), Internet of Things (47.1%) and manufacturing (46.7%). A graphic from the study is shown below for reference.

Data Mining adoption

  • Nearly one-third (27.4%) of all app developers globally are planning to build new apps on the cloud. 66.9% expect to have a new cloud app within 12 months. Overall, 81.3% of all developers surveyed are building cloud apps today. The following graphic compares developers’ predicted timeframes for cloud app development over the next two years.

Plans for Apps In the Clouds

  • Better security (51.9%), more reliability (42%) and better user experience (41%) are the top three areas that motivate developers to move to new cloud platforms. Additional considerations include a better breadth of services (39.4%), networking and data center speed (37.8%), better pricing options (37.5%), better licensing structures (34.6%) and completeness of vision (30.9%). The following graphic compares the key factors that most motivate developers to switch cloud platforms.

key factors

  • 45% of developers say that Internet of Things (IoT) development is very important to their overall digital strategy. 7% say that IoT is somewhat important to their digital strategy. The study also found that 29.5% of all developers are creating Internet of Things (IoT) apps today. The following graphic illustrates the relative level of importance of IoT to developers’ digital strategies.

importance of IoT strategy

  • 41% say that cognitive computing and artificial intelligence (AI) are very important to their digital strategies. In speaking with senior executives at services firms, the opportunity to provide artificial intelligence-based services using a subscription model is gaining momentum, with many beginning to fund development projects to accomplish this on a global scale.

AI Importance

  • Most frequently created machine learning apps include those for the Internet of Things (11.4%), Professional, Scientific and Technical Services (10%), and Manufacturing (9.4%) industries.  Additional industries include telecommunications (8.3%), utilities/energy (8.1%), robotics (7.2%) and finance or insurance (6.8%). The following graphic breaks out the industries where machine learning app development is happening today.

Machine learning industries final

  • The majority of developers (84.2%) say that analytics is important for enabling their organizations to operate today. Of that group, 45.7% say that analytics are very important for their organizations to attain their goals.
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