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.
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.
Public Cloud spending is predicted to grow at quickly, attaining 16% year-over-year growth in 2017.
Cowen’s AWS segment model is predicting Revenue and EBITDA to grow 25% and 26.8% annually from 2017 to 2022.
Microsoft Azure is viewed as the platform that customers would most likely purchase or renew going forward (28% of total vs. AWS at 22%, GCP at 15%, and IBM at 10%).
These and many other fascinating insights are from Cowen’s study published this week, Public Cloud V: AWS And Azure Still Leading The Pack (58 pp., PDF, client access reqd.). Cowen partnered with Altman Vilandrie & Company to complete the study. The study relies on a survey sample of 551 respondents distributed across small, medium and enterprises who are using Public Cloud platforms and services today. For purposes of the survey, small businesses have less than 500 employees, medium-sized businesses as 500 to 4,999 employees, and enterprises as more than 5,000 employees. The study provides insight on a range of topics including cloud spending trends, workload migration dynamics, and vendor positioning. Please see pages 5,6 & 7 for additional details regarding the methodology.
The more AWS and Azure compete to win customers, the greater the innovation and growth in public cloud adoption as the following key takeaways illustrate:
Existing Public Cloud customers predict spending will grow 16% year-over-year in 2017. Existing mid-market Public Cloud customers predict spending will increase 18% this year. SMBs who have already adopted Public Cloud predict a 17% increase in spending in 2017, and enterprises, 13%. Public Cloud providers are the most successful upselling and cross-selling mid-market companies this year as many are relying on the cloud to scale their global operations to support growth.
AWS dominates awareness levels with SMBs who have existing Public Cloud deployments, with Microsoft Azure the most known and considered in enterprises. Consistent with many other surveys of Public Cloud adoption, IBM SoftLayer scored better in enterprises than any other segment including SMBs (71% vs. 58%). Google Cloud Platform has its strongest awareness levels in SMBs, attributable to the adoption of their many cloud-based applications in this market segment. They trail AWS, Azure, and SoftLayer in the enterprise, however. Across all existing companies who have adopted Public Cloud, the majority are most aware of AWS and Microsoft Azure. The second graphic provides an overview of awareness across the entire respondent base.
Microsoft is the most-used Public Cloud and the most likely to be purchased or renewed by 28% of all respondents. While AWS is the most reviewed Public Cloud across all respondents, Microsoft Azure is the most used. When asked which Public Cloud provider they are likely to purchase or renew, the majority of respondents said Microsoft Azure (28%), followed by AWS (22%), Google Cloud Platform (15%) and IBM SoftLayer (10%). The following graphic compares awareness, reviewed and use levels by Public Cloud platform.
Only 37% of current Azure users expect to add or replace their Public Cloud provider, compared to 53% of current AWS users and 50% of GCP users. The study found that approximately 40% of respondents expect to add or replace their cloud provider in the next two years, compared to 43% who predicted that last year. Companies who have adopted Microsoft Azure are least likely to replace/add other vendors, as only 37% of current Azure users expect to add or replace, compared to 53% of current AWS users and 50% of GCP users.
AWS and Azure dominate all seven facets of user experience included in the survey. AWS has the best User Interface, API Complexity, and Reporting & Billing. Microsoft Azure leads all Public Cloud providers globally in the areas of Management & Monitoring, Software & Data Integration, Technical Support and Training & Google Cloud Platform is 3rd on all seven facts of user experience.
18% of workloads are supported by Public Cloud today with SMBs and mid-market companies slightly leading enterprises (16%). Overall, 38% of all workloads are supported with on-premise infrastructure and platforms, increasing to 43% for enterprises. The following graphic illustrates the percentage of workloads supported by each infrastructure type.
77% of existing Public Cloud adopters are either likely or very likely to add a SaaS workload in the next two years, led by mid-market companies (81%). SMBs (76%) and enterprises (73%) are also likely/very likely to add SaaS workloads in the next two years. The majority of these new SaaS workloads will be in the areas of Testing & Development, Web Hosting, and e-mail and communications.
Cowen’s AWS segment model is predicting Revenue and EBITDA to have a five-year Compound Annual Growth Rate (CAGR) of 25% and 26.8% from 2017 to 2022. AWS Net Income is predicted to increase from $2.7B in 2017 to $8.2B in 2022, attaining a projected 24.5% CAGR from 2017 to 2022. Revenue is predicted to soar from an estimated $16.8B in 2017 to $51.5B in 2022, driving a 25% CAGR in the forecast period.