IDC predicts spending on AI systems will reach $97.9B in 2023, more than two and one-half times the … [+]
Machine learning’s growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Open jobs requiring TensorFlow experience is a useful way to quantify how prevalent machine learning is becoming in business today. There are 4,134 open positions in the U.S. on LinkedIn that require TensorFlow expertise and 12,172 open positions worldwide as of today. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses. There are 44,864 jobs in the U.S. today according to LinkedIn that list machine learning as a required skill, and 98,371 worldwide.
Senior management teams at enterprises who are Gartner clients initiated over 18,000 search queries last year on machine learning, with the majority from banking and financial institution clients, followed by government, services and manufacturing. One of the best reports published last year is from Stanford University’s Institute for Human-Centered Artificial Intelligence, the Artificial Intelligence Index Report 2019, (PDF, 291 PP., no opt-in)
Key takeaways from the series of machine learning market forecasts and market estimates from the last year include the following:
Source: Market Research Future, Machine Learning Market Forecast Report – Global Forecast to 2024.
- $28.5B was invested in machine learning applications in the first calendar quarter of 2019, leading all other AI investment categories. In total over $82B was invested in all AI categories shown in the chart below, with machine learning platforms and applications combining for over half of all AI investments at $42.9B. Source: Statista, Machine Learning Tops AI Dollars, May 10, 2019.
Source: Statista, Machine Learning Tops AI Dollars, May 10, 2019.
- Reducing company costs (38%), generating customer insights & intelligence (37%), and improving customer experiences are the three most popular ML use cases. Algorithma’s recent machine learning survey found the top five uses cases for ML in companies with 10,000 employees or more are reducing company costs, process automation for internal organization, improving customer experience, generating customer insights and intelligence and detecting fraud. Source: Algorithma, 2020 state of enterprise machine learning, Nov., 2019 (PDF, 29 PP., no opt-in).
Algorithma, 2020 state of enterprise machine learning, Nov., 2019 (PDF, 29 PP., no opt-in).
- Achieving price optimization by persona is now possible using machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity. Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.
Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.
- As of Q2, 2019 AI startups brought in $7.4Bn in funding, the single highest amount ever in a quarter, according to CB Insights. There were 488 artificial intelligence deals, the second-highest number in a given quarter. Despite this, for the first time, the United States made up less than half of all AI startup funding deals. Source: Statistica, AI Startup Funding Reaches Record High, July 31, 2019
Source: Statistica, AI Startup Funding Reaches Record High, July 31, 2019
- IBM was the largest owner of active machine learning and artificial intelligence (AI) patent families worldwide with 5,570 families owned as of July, 2019. In 2018, the company had claimed the leading position from Microsoft now ranked second with 5,330 active families owned. Samsung ranked third with slightly above five thousand patent families. Source: Statistica, Companies with the most machine learning & AI patents worldwide 2010-2019.
Source: Statistica, Companies with the most machine learning & AI patents worldwide 2010-2019.
Source: IDC, Worldwide Spending on Artificial Intelligence Systems Will Be Nearly $98 Billion in 2023, According to New IDC Spending Guide.
Source: IDC Worldwide Artificial Intelligence Software Platforms Market Shares, 2018: Steady Growth — Moving Toward Production, courtesy of IBM.
- PwC predicts the market for AI-related semiconductors to grow from a current $6B in revenues to more than $30B by 2022. Although AI-driven use cases are expected to find their way across every industry segment over time, their adoption will be determined by the size of investment in the technology, the pace of its development and the speed at which its benefits are realized. Source: PwC, Opportunities for the global semiconductor market; Growing market share by embracing AI (PDF, 18 pp., no opt-in).
Source: PwC, Opportunities for the global semiconductor market; Growing market share by embracing AI (PDF, 18 pp., no opt-in)
- McKinsey predicts that AI-related semiconductors will see growth of about 18% annually over the next few years—five times greater than the rate for semiconductors used in non-AI applications. By 2025, AI-related semiconductors could account for almost 20% of all demand, which would translate into approximately $67B in revenue. Opportunities will emerge at both data centers and the edge. If this growth materializes as expected, semiconductor companies will be positioned to capture more value from the AI technology stack than they have obtained with previous innovations—about 40% 50% of the total. Source: McKinsey, Artificial-intelligence hardware: New opportunities for semiconductor companies. January, 2019.
Source: McKinsey, Artificial-intelligence hardware: New opportunities for semiconductor companies. January, 2019.
- Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where machine learning can contribute to solving complex constraint, cost and delivery problems companies face today. McKinsey predicts machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus
Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus
- Machine learning algorithms are being relied on to create propensity models by persona, providing invaluable insights that predicting which customers will act on a bundling or pricing offer. By definition propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.
- 71% of today’s organizations reporting they spend more on machine learning for cybersecurity than they did two years ago. 26% and 28% of U.S. and Japanese IT professionals believe their organizations could be doing more. Additionally, 84% of respondents believe cyber-criminals are also using AI and ML to launch their attacks. When considered together, these figures indicate a strong belief that AI/ML-based cybersecurity is no longer simply nice to have; it’s crucial to stop modern cyberattacks. Source: Webroot, Knowledge Gaps: AI and Machine Learning in Cybersecurity Perspectives from the U.S. and Japanese IT Professionals (PDF, 9 pp., no opt-in)
Source: Webroot, Knowledge Gaps: AI and Machine Learning in Cybersecurity Perspectives from the U.S. and Japanese IT Professionals (PDF, 9 pp., no opt-in)
- Credit unions will adopt machine learning in 2020 to automate routine tasks and free up human underwriters to focus on providing more personalized services, including improvements in inquiry resolution & dispute and fraud management. Credit unions are built on an annuity-based business model that delivers successively higher profitability the longer a member is retained. Credit unions will capitalize on ML by driving up loan approvals with no added risk and automating more of the loan approval process. By the end of 2020, according to a Fannie Mae survey of mortgage lenders, 71% of credit unions plan to investigate, test, or fully implement AI/ML solutions – up from just 40% in 2018. AI and ML will also be adopted across credit unions to improve inquiry resolution & dispute and fraud management while improving multichannel customer experiences. Providing real-time, relevant responses to customers to expedite inquiries and dispute resolutions using AI and ML is going to become commonplace in 2020. AI and ML is predicted to make a significant contribution to automating anomaly detection and borrower default risk assessment as the graphic below from Fannie Mae’s Mortgage Lender Sentiment Survey® How Will Artificial Intelligence Shape Mortgage Lending? Q3 2018 Topic Analysis illustrates:
Fannie Mae’s Mortgage Lender Sentiment Survey® How Will Artificial Intelligence Shape Mortgage Lending? Q3 2018 Topic Analysis
- AI and machine learning will thwart compromised hardware finding its way into organizations’ supply chains. Rising demand for electronic components will expand the market for counterfeit components and cloned products, increasing the threat of compromised hardware finding its way into organizations’ supply chains.The vectors for hardware supply-chain attacks are expanding as market demand for more and cheaper chips, and components drive a booming business for hardware counterfeiters and cloners. This expansion is likely to create greater opportunities for compromise by both nation-state and cybercriminal threat actors. Source: 2020 Cybersecurity Threats Trends Outlook; Booz, Allen, Hamilton, 2019.
Machine Learning sources:
- Algorithma, 2020 state of enterprise machine learning, Nov., 2019 (PDF, 29 PP., no opt-in)
- 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 Transformation
- Capgemini, Accelerating Automotive’s AI transformation: How driving AI enterprise-wide can turbo-charge organizational value, March 2019. 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, 2018
- Deloitte, State of AI in the Enterprise, 2nd Edition, Early adopters combine bullish enthusiasm with strategic investments (PDF, 28 pp., no opt-in)
- Forbes, 10 Ways Machine Learning Is Revolutionizing Sales, December 26, 2018
- Forbes, How China Is Dominating Artificial Intelligence, December 16, 2018
- Forbes, How To Improve Supply Chains With Machine Learning: 10 Proven Ways, April 28, 2019
- Forbes, Microsoft Leads The AI Patent Race Going Into 2019, January 6, 2019
- IDC Worldwide Artificial Intelligence Market Shares, 2018: Steady Growth — POCs Poised to Enter Full-Blown Production
- IDC 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 AI
- McKinsey Global Institute, Tackling Europe’s gap in digital and AI, February 2019 Discussion paper
- McKinsey Global Institute, Applying artificial intelligence for social good, November, 20-8 discussion paper
- McKinsey 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 2018
- McKinsey Global Institute, Visualizing the uses and potential impact of AI and other analytics, April 2018
- MIT Sloan Management Review, Artificial Intelligence in Business Gets Real: Pioneering Companies Aim for AI at Scale, September 17, 2018, PDF available here.
- Stanford University, Artificial Intelligence Index Report 2019, (PDF, 291 PP., no opt-in)
- Statista, In-Depth: Artificial Intelligence 2019, February 2019
- Statista, Machine Learning Tops AI Dollars, May 10, 2019.
- Tractica, 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)