Roundup Of Machine Learning Forecasts And Market Estimates For 2019
As the advancement of newer digital technologies has boomed over the last few years, they have created a name and space for themselves, benefiting each and every marketing sector possible. Read below to find out how Artificial Intelligence and Machine learning have impacted the market and what it has in store for the future. Article by Forbes.com
- AI and machine learning have the potential to create an additional $2.6T in value by 2020 in Marketing and Sales, and up to $2T in manufacturing and supply chain planning.
- Gartner predicts the business value created by AI will reach $3.9T in 2022.
- IDC predicts worldwide spending on cognitive and Artificial Intelligence systems will reach $77.6B in 2022.
Improving customer experiences by strengthening sales and marketing with greater insights is one of the primary catalysts driving AI and machine learning adoption today. EconSight, an independent Swiss economic research and consulting company, recently presented their study Artificial Intelligence As A Key Technology and Driver of Technological Progress. Using the PatentSight analytics platform, Kai Gramke, Managing Director of EconSightfound that marketing patents dominate all categories of AI patent and intellectual property development. The marketing category is easily the fastest growing of all, having already reached a Compound Annual Growth Rate (CAGR) of 29.3% between 2010 and 2018. The second- and third-fastest growing global AI patent categories between 2010 and 2018 are AI-based digital security (23.4% CAGR) and AI-based mobility (23% CAGR).
Cloud platforms are quickly becoming AI and machine learning IP and patent foundries. Cloud platform providers Amazon Web Services, Google Cloud Platform, IBM Cloud, Microsoft Azure, and others are setting a quick pace in the global race for more patents and IP. Each is focusing on developing a suite of AI-based services that can scale across as broad of a spectrum of code development, use cases, and platform extensions. Of the top four cloud platform providers, Microsoft Azure leads all others in the number and variety of AI services created.
Key takeaways from the series of machine learning market forecasts and market estimates from the last year include the following:
AI and machine learning have the potential to create an additional $2.6T in value by 2020 in Marketing and Sales, and up to $2T in manufacturing and supply chain planning.McKinsey did an analysis comparing the value created by advanced analytics versus AI and machine learning across common enterprise use cases.
IDC predicts spending on cognitive and AI systems will reach $77.6B in 2022, more than three times the $24.0B forecast for 2018. The cognitive and AI systems market will achieve an impressive 37.3% compound annual growth rate (CAGR) from 2017-2022 according to their analysis. Software will be both the largest and fastest growing technology category throughout the forecast, representing around 40% of all cognitive/AI spending with a five-year CAGR of 43.1%. The use cases that will see the fastest investment growth over the 2017-2022 forecast are pharmaceutical research and discovery (46.8% CAGR), expert shopping advisors & product recommendations (46.5% CAGR), digital assistants for enterprise knowledge workers (45.1% CAGR), and intelligent processing automation (43.6% CAGR).
47% of organizations participating in a recent survey say they have either scaled up and industrialized machine learning or are moving projects into production. 53% of survey respondents say their organizations have either scaled-up and industrialized analytics or are moving into the production phase.
Improving customer experiences and personalization is the principal reason why marketers are adopting AI and machine learning. Adobe found marketing executives are prioritizing AI and machine learning-based applications and platforms to improve customer experiences. 82% of marketing leaders are adopting AI and machine learning to improve every aspect of their personalization strategies. 64% are relying on AI and machine learning to deliver more precisely targeted content, promotions.
In 2018 McKinsey found that 82% of enterprises adopting machine learning and AI have gained a financial return from their investments. For companies across all industries, the median return on investment from cognitive technologies is 17%. Companies competing in the technology, media and entertainment, and telecommunications fields are making the most significant investments and achieving the highest ROI levels. Netflix found that if customers search for a movie for more than 90 seconds, they give up. By using AI to improve search results, Netflix prevents frustration and customer churn, saving $1B a year in potential lost revenue.
23% of North American enterprises have machine learning embedded in at least one company function as of last year. 19% of enterprises in developing markets including China and 21% in Europe also have successfully integrated machine learning into functions. The graph below shows the results of a McKinsey & Company survey of 2,135 enterprise senior executive respondents. The graph displays the percent of respondents whose organizations have embedded AI capabilities in at least one function or business unit. Respondents can select multiple AI capabilities.
Large automotive OEMs can boost their operating profits by up to 16% by deploying AI at scale across supply chain operations and production centers. Capgemini defined a conservative and optimistic scenario to predict how automotive OEMs would be able to increase their operating profit. The conservative scenario set the goal of $232M – a 5% increase from current levels. This gain would accrue from an average 0.2% reduction in operating costs, such as labor, raw materials, logistics, administration, inspection, and maintenance. In the optimistic scenario, the gain more than triples to $764M. This assumes that only 33% of financial impact materializes, delivering a 16% boost to operating profits.
China’s AI dominance reaches across multiple industries compared to the majority of nations only concentrating on a few. BCG interviewed 500 Chinese companies and found that the impact of their 2017 New Generation Artificial Intelligence Development Plan is having a successful cross-industry impact on AI piloting, adoption and success with AI initiatives. Unlike in the U.S. and other nations, China’s overall lead in the race to extract value from AI is not driven by the strong dominance of one or two particular industries; it’s succeeding as a nation and the industry-wide phenomenon that is rooted in how Chinese managers approach AI innovation.
The UK leads all European nations with $7.2B invested in AI and machine learning company acquisitions, private equity investment and mergers from 2008 to 2018. Looking at AI transaction activity across Europe, there has been a steep consistent growth trend over the past ten years, totaling 1,334 transactions involving AI by 2017 – with a six-fold increase in activity in the last five Source: Artificial Intelligence in Europe: How 277 Major Companies Benefit from AI Outlook for 2019 and Beyond by Ernst & Young (PDF, 41 pp., no opt-in).
On average, AI could boost growth in European economic activity by close to 20% by 2030 according to the McKinsey Global Institute. Europe is poised to gain the greatest market share gains from AI of any regional globally, Europe also has about 25% of AI startups, many of them focused on how to improve revenue growth across manufacturing and service industries.
Gartner predicts the business value created by AI will reach $3.9T in 2022. Innovative approaches to improving customer experiences will be the primary source of business value through the forecast period. Gartner predicts the majority of business value will be from organizations successfully increasing customer growth and retention.
IDC predicts worldwide spending on cognitive and Artificial Intelligence systems will reach $77.6B in 2022. In 2018 the market reached $24B in revenue, with the compound annual growth rate (CAGR) for 2017-2022 predicted to reach 37.3%.
Sources of Market Data on Machine Learning:
Accenture, Machine Learning In Insurance (PDF, 14 pp., no opt-in)Ark Invest Big Ideas 2019, Innovation is the Key To Growth (PDF, 94 pp., no opt-in)Artificial Intelligence: Emerging Opportunities, Challenges and Implications. U.S. Government Accountability Office, March 2018 (PDF, 100 pp., no opt-in)Artificial Intelligence in Europe: How 277 Major Companies Benefit from AI Outlook for 2019 and Beyond by Ernst & Young (PDF, 41 pp., no opt-in)Artificial Intelligence Index, 2018 Annual Report(PDF, 94 pp., no opt-in)Boston Consulting Group, AI at Scale: The Next Frontier in Digital TransformationCapgemini, Accelerating Automotive’s AI transformation: How driving AI enterprise-wide can turbo-charge organizational value, March PDF of the study is available here (PDF, 36 pp.., no opt-in)Chamakkala, Vipin, Today’s AI Software Infrastructure Landscape (And Trends Shaping The Market) Medium. May 7, 2018Deloitte, State of AI in the Enterprise, 2nd Edition, Early adopters combine bullish enthusiasm with strategic investments (PDF, 28 pp., no opt-in)Forbes, How China Is Dominating Artificial Intelligence, December 16, 2018Forbes, Microsoft Leads The AI Patent Race Going Into 2019, January 6, 2019IDC Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast to Reach $77.6 Billion in 2022, According to New IDC Spending Guide.The Economist, Risks and Rewards, Scenarios around the economic impact of machine learning (PDF, 80 pp., no opt-in)McKinsey, An Executive’s Guide to AIMcKinsey Global Institute, Tackling Europe’s gap in digital and AI, February 2019 Discussion paperMcKinsey Global Institute, Applying artificial intelligence for social good, November, 20-8 discussion paperMcKinsey Global Institute, Notes from the AI Frontier: Tackling Europe’s Gap In Digital and AI(PDF, 60 pp., no opt-in)McKinsey Global Institute, Notes from the AI frontier: Applications and value of deep learning, April 2018McKinsey Global Institute, Visualizing the uses and potential impact of AI and other analytics, April 2018MIT Sloan Management Review, Artificial Intelligence in Business Gets Real: Pioneering Companies Aim for AI at Scale, September 17, 2018, PDF available here.Statista, In-Depth: Artificial Intelligence 2019, February 2019Tractica, Artificial Intelligence: 10 Predictions for 2019(PDF, 12 pp., no opt-in)U.S. Government Accountability Office, AI technology Assessment, Emerging Opportunities, Challenges, and Implications (PDF, 100 pp., no opt-in)World Economic Forum, How to Prevent Discriminatory Outcomes in Machine Learning (PDF, 30 pp., no opt-in)
Louis Columbus is an enterprise software strategist with expertise in analytics, cloud computing, CPQ, Customer Relationship Management (CRM), e-commerce and Enterprise Resource Planning (ERP).