- 66% of organizations that consider themselves completely successful with Business Intelligence (BI) initiatives currently use the cloud.
- 86% of Cloud BI adopters name Amazon AWS as their first choice, 82% name Microsoft Azure, 66% name Google Cloud, and 36% identify IBM Bluemix as their preferred provider of cloud BI services.
These and other many other fascinating insights are from Dresner Advisory Services 2018 Cloud Computing and Business Intelligence Market Study (client access reqd.) of the Wisdom of Crowds® series of research. The goal of the 7th annual edition of the study seeks to quantify end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that employ one or more cloud deployment models. Dresner Advisory Services defines the scope of Business Intelligence (BI) tools and technologies to include query and reporting, OLAP (online analytical processing), data mining and advanced analytics, end-user tools for ad hoc query and analysis, and dashboards for performance monitoring. Please see page 10 of the study for the methodology. The study found the primary barriers to greater cloud BI adoption are enterprises’ concerns regarding data privacy and security.
Key takeaways from the study include the following:
- Cloud BI’s importance continues to accelerate in 2018, with the majority of respondents considering it an important element of their broader analytics strategies. The study found that mean level of sentiment rose from 2.68 to 3.22 (above the level of “important”) between 2017 and 2018, indicating the increased importance of Cloud BI over the last year. By region, Asia-Pacific respondents continue to be the strongest proponents of cloud computing regarding both adjusted mean (4.2 or “very important”) and levels of criticality. The following graphic illustrates Cloud BI’s growing importance between 2012 and 2018.
- Over 90% of Sales & Marketing teams say Cloud BI apps are important to getting their work done in 2018, leading all respondent categories in the survey. The study found that Cloud BI importance in 2018 is highest among Sales/Marketing and Executive Management respondents. One of the key factors driving this is the fact that both Sales & Marketing and Executive Management are increasingly relying on cloud-based front office applications and services that are integrated with and generate cloud-based data to track progress towards goals.
- Cloud BI is most critical to Financial Services & Insurance, Technology, and Retail & Wholesale Trade industries. The study recorded its highest-ever levels of Cloud Bi importance in 2018. Financial Services has the highest weighted mean interest in cloud BI (3.8, which approaches “very important” status shown in the figure below). Technology organizations, where half of the respondents say cloud BI is “critical” or “very important,” are the next most interested. Close to 90% of Retail/Wholesale respondents say SaaS/cloud BI is at least “important” to them. As it has been over time, Healthcare remains the industry least open to managed services for data and business intelligence.
- Cloud BI adoption is soaring in 2018, nearly doubling 2016 adoption levels. The study finds that the percentage of respondents using Cloud BI in 2018 nearly doubled from 25% of enterprise users in 2016. Year over year, current use rose from 31% to 49%. In the same time frame, the percentage of respondents with no plans to use cloud BI dropped by half, from 38% to 19%. This study has been completed for the last seven years, showing a steady progression of Cloud BI awareness and adoption, with 2018 being the first one showing the most significant rise in adoption levels ever.
- Sales & Marketing leads all departments in current use and planning for Cloud BI applications. Business Intelligence Competency Centers (BICC) are a close second, each with over 60% adoption rates for Cloud BI today. Operations including manufacturing and supply chains and services are the next most likely to use Cloud BI currently. Marketing and BICC lead current adoption and are contributing catalysts of Cloud BI’s soaring growth between 2016 and 2018. Both of these departments often have time-constrained and revenue-driven goals where quantifying contributions to company growth and achievement ad critical.
- Financial Services (62%), Technology (54%), and Education (54%) industries have the highest Cloud BI adoption rates in 2018. The retail/wholesale industry has the fourth-highest level of Cloud BI adoption and the greatest number of companies who are currently evaluating Cloud BI today. The least likely current or future users are found in manufacturing and security-sensitive healthcare organizations, where 45% respondents report no plans for cloud-based BI/analytics.
- Dashboards, advanced visualization, ad-hoc query, data integration, and self-service are the most-required Cloud BI features in 2018. Sales & Marketing need real-time feedback on key initiatives, programs, strategies, and progress towards goals. Dashboards and advanced visualization features’ dominance of feature requirements reflect this department’s ongoing need for real-time feedback on the progress of their teams towards goals. Reporting, data discovery, and end-user data blending (data preparation) make up the next tier of importance.
- Manufacturers have the greatest interest in dashboards, ad-hoc query, production reporting, search interface, location intelligence, and ability to write to transactional applications. Education respondents report the greatest interest in advanced visualization along with data integration, data mining, end-user data blending, data catalog, and collaborative support for group-based analysis. Financial Services respondents are highly interested in advanced visualization and lead all industries in self-service. Healthcare industry respondents lead interest only in in-memory support. Retail/Wholesale and Healthcare industry respondents are the least feature interested overall.
- Interest in cloud application connections to Salesforce, NetSuite, and other cloud-based platforms has increased 12% this year. Getting end-to-end visibility across supply chains, manufacturing centers, and distribution channels requires Cloud BI apps be integrated with cloud-based platforms and on-premises applications and data. Expect to see this accelerate in 2019 as Cloud BI apps become more pervasive across Marketing & Sales and Executive Management, in addition to Operations including supply chain management and manufacturing where real-time shop floor monitoring is growing rapidly.
- Retail/Wholesale, Business Services, Education and Financial Services & Insurance industries are most interested in Google Analytics connectors to obtain data for their Cloud BI apps. Respondents from Technology industries prioritize Salesforce integration and connectors above all others. Education respondents are most interested in MySQL and Google Drive integration and connectors. Manufacturers are most interested in connectors to Google AdWords, SurveyMonkey, and The Healthcare industry respondents prioritize SAP Cloud BI services and also interested in ServiceNow connectors.
- 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
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.
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.
- Artificial Intelligence (AI) investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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 $1.2T in additional GVA in 2035 from AI. Follow-on areas of automation in Financial Services include automating market research queries through intelligent bots, and scoring and reviewing mortgages.
- By 2035 AI technologies could increase labor productivity 40% or more, doubling economic growth in 12 developed nations. Accenture finds that AI’s immediate impact on profitability is improving individual efficiency and productivity. The economies of the U.S. and Finland are projected to see the greatest economic gains from AI through 2035, with each attaining 2% higher GVA growth.The following graphic compares the 12 nations included in the first phase of the research.
- 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI).
- Cowen predicts AI will drive user productivity to materially higher levels, with Microsoft at the forefront.
- Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption.
- According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.
- Cowen sees Salesforce ($CRM), Adobe ($ADBE) and ServiceNow ($NOW) as well-positioned to deliver and monetize new AI-based application services.
These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd). The study is based on interviews with 146 leading AI researchers, entrepreneurs and VC executives globally who are involved in the field of artificial intelligence and related technologies. Please see the Appendix of the study for a thorough overview of the methodology. This study isn’t representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning. Cowen finds the market is still nascent, with CIOs eager to invest in new AI-related initiatives. Time-to-market, customer messaging, product positioning and the value proposition of AI solutions will be critical factors for winning over new project investments.
Key takeaways from the study include the following:
- Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption. Customer self-service, Enterprise Resource Planning (ERP), Human Resource Management (HRM) and E-Commerce are additional areas that have upside potential for AI/ML adoption. The following graphic provides an overview of the areas in software that Cowen found the greater potential for AI/ML investment.
- 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more. The following graphic provides an overview of company readiness for machine learning and AI projects.
- Market forecasts vary, but all consistently predict explosive growth. IDC predicts that the Cognitive Systems and AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 55%. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. IBM claims that Cognitive Computing is a $2T market, including $200B in healthcare/life sciences alone. Tractica forecasts direct and indirect applications of AI software to grow from $1.4B in 2016 to $59.8B by 2025, a 52% CAGR.
- According to CBInsights, the number of financing transactions to AI start-ups increased 10x over the last six years, from 67 in 2011 to 698 in 2016. Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include FinTech, Healthcare, Transportation and Retail/e-Commerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.
- Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the b top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years. The following graphic compares the top 10 uses cases by projected global revenue.
- Machine Learning is predicted to generate the most revenue and is attracting the most venture capital investment in all areas of AI. Venture Scanner found that ML raised $3.5B to date (from 400+ companies), far ahead of the next category, Natural Language Processing, which has seen just over $1Bn raised to date (from 200+ companies). Venture Scanner believes that Machine Learning Applications and Machine Learning Platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.
- Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future. Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behavior, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.
- According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years. Machine Learning-based Applications and Deep Learning Neural Networks are experiencing the largest and widest amount of investment attention in the enterprise.
- Accenture leverages machine learning in 40% of active Analytics engagements, and nearly 80% of proposed Analytics opportunities today. Cowen found that Accenture’s view is that they are in the early stages of AI technology adoption with their enterprise clients. Accenture sees the AI market growing exponentially, reaching $400B in spending by 2020. Their customers have moved on from piloting and testing AI to reinventing their business strategies and models.