Global market for AI in Manufacturing to grow to USD 40.01 billion by 2025

The Global Artificial Intelligence in Manufacturing Market is expected to grow from USD 5.9 billion in 2019 to USD 40.01 billion by the end of 2025, at a CAGR of 46.56%.

Automotive accounts the largest share in the industry segment with around 26.15% market share in global AI in the manufacturing market. ML segment commands largest technology adoption share of AI

Definition / Scope
Market Overview
Key Metrics
Market Risks
Market Drivers
Market Restraints
Industry Challenges
Technology Trends
Other Key Market Trends
Market Size and Forecast
Market Outlook
Technology Roadmap
Competitive Landscape
Competitive Factors
Key Market Players
Strategic Conclusion
References

Definition / Scope

AI is a broad area of computer science that makes machines replicate human intelligence with the system broadly called Machine Learning and Deep Learning.

Other inter-related technologies include Supervised Learning, Unsupervised Learning, Natural Language Processing (NLP), Computer Vision, Speech and Robotics. Machine Learning (ML) is an application of AI that provides systems with the ability to learn and improve from experiences and training, focusing on programs that can access data and use it to learn.

Deep learning, a sub-set of ML, is a form of AI that mimics the neural networks of human body. Machine is fed with terabytes of information then it studies them, learns from them, reiterates the process, and pivots its own moves.

Supervised Learning uses labelled data to train a ML model while Unsupervised Learning is algorithm that take a set of data that contain only inputs and finds structure such as clustering. AI is first coined in 1956 by Dartmouth professor named John McCarthy.

There are many use cases of AI in solving pain points in manufacturing and can be divided into smart production, business operation and management, supply chain, and business model decision making.

Moreover, manufacturing sector is heavily undergoing transformation due to AI implementation. Within manufacturing, AI has evolved in various aspects of business and found numerous applications.

In fact, by industry, the market for AI is largest within manufacturing globally and will remain at top comparing other sectors like communication & media, retail, transportation, insurance, trade, etc.

Market Overview

AI in manufacturing sector has a significant demand to optimize the manufacturing cycle, efficiency, improve product quality, and reduce labor cost. At manufacturing facilities, AI empowers robots, enabling them to have human like perception, coordination, and decision making. Currently, such robots have general functions as packaging, positioning, sorting, assembly, and detection.

By comparing the number of AI financing projects in different fields, China has the nearest normal distributed financing in different fields of AI, while other countries have more emphasis on vertical industry applications (AI+).

The total scale of global AI investment surpassed USD 50 billion in 2019. China has the highest investment reaching USD 30 billion, accounting for 60, making it the world’s most capital absorbing country in the world.

The USA has around 40% funding share worldwide. In the USA, Canada, and UK, the financing is more skewed towards AI applications, more than 60%. Big data is the second most attractive field of AI for number of financing projects.

Big data segment constitute about 10% of total number of financing projects in these countries. Majority of the countries shows least number of financed projects in UAV and AR/VR fields.

Key Metrics

Key Indicators	Base Year 2019
Market Size	USD 5.9 Billion
Growth Rate	CAGR 46.56%
Market Outlook	USD 40.01 Billion

Market Risks

Risk of cyber-attack

Manufacturing is the third-most targeted sector in terms of cyber attacks, and around half of manufacturers report suffering financial or operational losses due to cyber-attacks.

Cyber-attack risks are widely expected to increase due to digital transformation from AI. Manufacturing now ranks as the biggest investor in AI compared to any other industry. As manufacturers integrate more digital sensors and other connected devices into their daily operations, their attack surfaces will widen.

Risks introduced by third-parties

Manufacturers have always had to contend with having a large supplier ecosystem, but as more of their production facilities and products involve digital elements, they now need to re-assess the digital risks that their partners introduce. In fact, there’s evidence that many data breaches are linked to direct or indirect third-party access.

One interesting example is product tampering where malicious technology can be introduced into a product as it moves through the supply chain. This has led to the rise of e-pedigree, which is essentially electronic documents that show exactly who has handled the product. Lack of block chain technology integration has posed risk to the manufacturers.

Risks around collecting and storing data

AI runs on data and learns from the data. Alongside the growing use of new technologies, collection and sharing of data has become a prominent part of the industry.

At the same time, the mistreatment of data that has been collected for commercial reasons can lead to infringement of privacy regulations.

Risk of disruption to operations

As business-critical infrastructure becomes more complex, connected and data-driven, this can have a significant impact on how businesses should deal with disruption to their operations. Manufacturers going transition from legacy system to AI enabled digital system will impose many losses.

Market Drivers

Digital Move

With manufacturing connected digitally, it is challenging to rely on legacy-based processes and operate in silos; there is no longer a place for manual, time-consuming processes.

To be efficient and provide employees a flexible approach to work seamlessly, manufacturers need a modern and agile digital solution that replaces outdated and error-prone paper-based processes and converts them to digital.

Digital Data

Capturing, processing and analyzing digital data allow predictive analysis. Engineering Analytics solution enables manufacturer to leverage data from all aspects of the manufacturing value chain to derive the insights required for process reengineering, value creation and value delivery.

For AI, the utmost prey is data for it to make correct decisions. Industrial manufacturers also have the opportunity to use big data resources to control costs, optimize consumption of resources and manage sustainability efforts amid changing regulations. In the asset-intensive manufacturing industry, equipment breakdown and scheduled maintenance is a regular feature.

Big data analytics can reduce breakdowns by as much as 26 percent and unscheduled downtime by as much as 23 percent. In automotive manufacturing, robotic arms in assembly lines are a regular feature. These robots perform various tasks like welding of parts in an automobile, gluing, cabling etc. As per study, downtime costs auto industry approximately USD 22000/min.

Automation

Combining traditional technologies with artificial intelligence is increasingly giving rise to systems that work autonomously and organize themselves. This reduces error rates, adds speed and cuts operating costs.

A study conducted in 2015 found that 478 billion of the 749 billion working hours (64 percent) spent on manufacturing-related activities globally were automatable with currently demonstrated technology.

These 478 billion working hours represent the labor equivalent of 236 million out of 372 million full-time employees (USD 2.7 trillion out of USD 5.1 trillion of labor) that could be eliminated or repurposed, assuming that demonstrated technologies are adapted for use in individual cases and then adopted.

These figures suggest that, even though manufacturing is one of the most highly automated industries globally, there is still significant automation potential within the manufacturing sites.

Connectivity

Interconnecting the entire value chain via mobile or fixed-line high-bandwidth telecom networks synchronizes supply chains and shortens both production lead times and innovation cycles. The network capability enables mobility for connected devices, agility in operations and an increasing level of device density.

Wireless allows manufacturers to connect widespread assets and processes in real time, allowing integration with contributing workflows. Compared to a fixed network, the scope and ease of wireless contributes to new connections and services that can increase value limit waste and address more pain points.

The wireless connectivity, moreover the upcoming 5G technology means there will be more number of IoT devices surrounded around the manufacturing facilities.

Digital Customer Access

The Internet gives new intermediaries direct access to customers to whom they can offer full transparency and new kinds of services. This capability can lead to increased customer loyalty, as customers are more loyal to brands that create differentiated and personalized experiences.

Consumer brands have led the way in developing compelling digital customer experiences at this stage; many digital commerce platforms have shaped customer expectations around the ability to easily research, evaluate, buy, and service purchases online across devices. More granular customer information captured through digital commerce and service platforms can help manufacturers better understand the risk of attrition or service defection within their existing customer base and potentially address it preemptively.

Legacy manufacturers lack visibility into customers’ usage of the manufacturer’s products, leading to an inability to predict maintenance requirements and the potential to affect uptime and performance.

Market Restraints

Lack of Available stored data

AI heavily depends on the data that it requires to feed for training the cognitive approach. To learn for AI to solve data problem, it first needs to look and analyze the data. AI makes decisions based on the available data. Therefore, the shortage of available day may choke its performance.

High Budget

Implementing AI is a capital-intensive investment. The project implementation cost is much higher when adopted in full-fledged mode. However, the custom solutions from third party may be somewhat less but without having the company’s goal and strategy the cost saving from implementing AI could be non-reportable and eventually, they stop using other AI services in fear of its return on investment.

Industry Challenges

Shift from legacy manufacturing to digital manufacturing

Any digital transformation initiative can place demands on the IT department’s technology stack and development structure. This may require the use of new release cycles, processes, APIs, or innovating in other areas of digital performance. There’s a significant skill gap when it comes to completing the type of change that most businesses need. Digital transformation can be most difficult in traditional organizations with a long running history of success and low employee turnover. Employees’ resistant to change can become a challenge when people start to confuse their daily. When a digital transformation is taking place, it forces people to change their daily working norms and culture.

Cost and resource factor

Digitalization in the manufacturing industry incurs costs on human resources. The workforce can feel disillusioned in the face of changing workplace realities. Employee reluctance and communication issues also pose a challenge to manufacturers.

Being in a dynamic and cash-sensitive industry, manufacturers need to carefully address any budget and resource limitations. This can lead to reservations about sticking to their digital transformation strategy.

Lack of talent

It is a real problem for most manufacturers wanting to adopt AI as well as move to other data-driven models of digital transformation. Global AI in manufacturing market growth is restricted by the limited presence of skilled workforce. A bottleneck will exist due to the shortage of data and technology professionals with the experience and training needed to implement the required infrastructure and organizational change.

Although AI research has been ongoing for decades, it’s only relatively recently that these skills have been in demand by industry.

Technology Trends

Intelligent Hardware

The intelligent hardware, unlike traditional hardware, has superior independent performance with parallel computing capability and supports artificial neural network algorithm. The intelligent hardware includes sensors such as touch, visual, ultrasonic, temperature, proximity, etc. The global market for intelligent sensor will reach USD 70.62 billion by 2023, a CAGR of 17.45% during the forecasted period. Likewise, the market for AI chip is forecasted to grow at a CAGR of 53.6% to reach USD 25.48 billion by 2025.

Machine Vision Technology

Vision technology enables machines to have a visual perception and cognitive mechanism similar to the vision that humans have to classify image features. It identifies, classifies, positions, and detects object within the picture or video widely useful in surveillance, autonomous driving, facial recognition, and autonomous robot navigation. The global market size for AI-based computer vision is estimated to reach USD 55.12 billion by 2025 with CAGR of 47.54%.

NLP

Natural Language Processing (NLP) entails a wide variety of computer directions to make computer understand natural language. NLP includes machine translation, automatic summary, etc. The global NLP market is expected to grow at a CAGR of 16.1% to reach USD 29.19 billion by 2025.

Regulatory Trends

Regulatory principles, guidance, plans, and acts specifically related to AI in some major AI markets are discussed below:

Regulation in the USA

Self Drive Act

The aim of Self Drive Act is to establish a federal framework for the regulation of self-driving cars, dramatically increasing the possible number of autonomous vehicles on the road.

Before the promulgation of this act, automakers and companies interested in testing self-driving technology had to apply for exemptions to the National Highway and Traffic Safety Administration’s (NHTSA) federal motor vehicle safety standards, and the agency only grants 2,500 per year.

The Self Drive Act would increase that cap to 25,000 per year initially, and expand it up to 100,000 annually in three years’ time. The act is approved in 2017.

AI Policy Principles

10 AI principles are released in US that will provide official guidance and reduce uncertainty for innovators about how their own government is approaching the regulation of artificial intelligence technologies. These principles include:

Public trust in AI: The government must promote reliable, robust, and trustworthy AI applications.
Public participation: The public should have a chance to provide feedback in all stages of the rule-making process.
Scientific integrity and information quality: Policy decisions should be based on science.
Risk assessment and management: Agencies should decide which risks are and aren’t acceptable.
Benefits and costs: Agencies should weigh the societal impacts of all proposed regulations.
Flexibility: Any approach should be able to adapt to rapid changes and updates to AI applications.
Fairness and nondiscrimination: Agencies should make sure AI systems don’t discriminate illegally.
Disclosure and transparency: The public will trust AI only if it knows when and how it is being used.
Safety and security: Agencies should keep all data used by AI systems safe and secure.
Interagency coordination: Agencies should talk to one another to be consistent and predictable in AI-related policies.

Regulation in China

Three Year Action Plan for Promoting the Development of a New Generation of Artificial Intelligence Industry

It focuses on the in-depth integration of information technology and manufacturing technology, with the industrialization and integration of the new generation of AI technology application as the focal point, to promote the in-depth integration of AI and the manufacturing industry and speed up the building of China into a manufacturing superpower and a cyber superpower.

Released in 2017, this action plan aims to foster certain guiding ideology, basic principles, action goals, and foster the development of smart products such as intelligent networked vehicles, intelligent service robots, intelligent UAV, intelligent image identification system, medical diagnostic system, voice interactive system, transportation system, and smart home products.

Regulation in Japan

Strategy for Comprehensive Innovation

It was proposed in 2018 that highlights reforms including university reform, strengthening government support for innovation, artificial intelligence, agricultural development, and environment/energy.

The university reform will include introduction of the Japanese version of the Fraunhofer model. On agricultural development, a smart food supply chain system will be built, including automatic sensing, agricultural machinery automation, AI agricultural product supply etc, by 2025.

In the manufacturing sector, the priority is towards realization of Connected Industries and bases for data utilization to be newly built for exchange data for manufacture to distribute and utilize data among different companies.

Other Key Market Trends

Covid-19 Impact:

Scenario A: A Slow Positive Recovery

This scenario considers the slow positive impact of the COVID-19 pandemic on the AI in manufacturing market. In this scenario, the market is estimated to experience a moderate decline of around average (2.5%) in 2020 and comparatively fast recovery from 2021 onwards.

Considering the lockdowns and restriction, most of the operations in the manufacturing industry across the globe remain closed, a moderate decline of (8-10%) is considered in the 1st and 2nd quarter of 2020.

Post second quarter, the manufacturing sector is expected to recover much faster compared to the first two quarters; however, the AI in manufacturing market will still experience a moderate decline throughout 2020.

Post 2020, as soon as operations in the manufacturing industry would back on the track, it is expected to recover at much faster rate and the investments in this market segment are expected to recover at a rapid pace resulting in market recovery for AI in manufacturing solutions from 2021 onwards.

Scenario B: A Fast Positive Recovery

This scenario considers the fast positive impact of the COVID-19 pandemic on the AI in manufacturing market. This scenario considers a moderate market decline in the Q1 & Q2 of 2020. However, the markets are expected to recover strongly in Q3 & Q4 2020, resulting in mild decline of around average (1.0%) for 2020 and fast recovery from 2021 onwards.

The scenario considers that although there were stringent lockdowns across the globe, most of the component designing and R&D department for decision making and product enhancements were functions.

The leading players are focusing on moderating their manufacturing strategies with the help of advanced technologies are expected to help business to back on track more rapidly than expected.

Moreover, positive government initiatives to uplift different sectors affected from COVID- 19 are also expected to help businesses to recover at much faster rate as compared to previous scenario, and support the strong recovery of AI in manufacturing market.

Market Size and Forecast

Overall Market Size:

The Global Artificial Intelligence in Manufacturing Market is expected to grow from USD 5.9 billion in 2019 to USD 40.01 billion by the end of 2025, at a CAGR of 46.56%.

Market Size by Technology:

Based on technology, the machine learning technology segment commanded the largest share of the overall AI in manufacturing market in 2019.

This is primarily attributed to the factors such as increasing demand for predictive maintenance & machinery inspection, material handling, production planning; and an increase in the unstructured data generated by the manufacturing industry.

The following segmented market size is based on the overall application segments not limited to manufacturing industry.

Machine Vision Technology

Vision technology enables machines to have a visual perception and cognitive mechanism similar to the vision that humans have to classify image features.

It identifies, classifies, positions, and detects object within the picture or video widely useful in surveillance, autonomous driving, facial recognition, and autonomous robot navigation.

The global market size for AI-based computer vision is estimated to reach USD 55.12 billion by 2025 with CAGR of 47.54%.

NLP

Natural Language Processing (NLP) entails a wide variety of computer directions to make computer understand natural language.

NLP includes machine translation, automatic summary, etc. The global NLP market is expected to grow at a CAGR of 16.1% to reach USD 29.19 billion by 2025.

Market Size by End-Use:

Automotive:

Automotive accounts the largest share in the industry segment with around 26.15% market share in global AI in the manufacturing market. The market for AI in automotive segment was recorded USD 1 billion in 2019 and is estimated a CAGR of 35% to reach USD 6.05 billion by 2025.

Energy:

The artificial intelligence in energy market is expected to reach USD 7.78 billion by 2024. The market is projected to witness a CAGR of 22.49% from 2019 to 2024. In 2019, the market was worth USD 2.17 billion.

Food and Beverage:

The global market of AI in Food & Beverage projected a CAGR rise of 42.5% to reach USD 12.58 billion by 2026. The market for AI in food and beverage sector is growing in North America, with the United States leading the way.

North America held a market share of 29.1% in 2017, which is second-largest region for AI in the food and beverage market. The market for AI in F & B industry was worth USD 261.41 million in 2019.

Fashion:

IN fashion industry, the application of AI market will reach USD 1.26 billion by 2024, at a CAGR of 40.8% from USD 91.61 million in 2019.

North America is expected to account for the largest market size in the AI in the fashion market by region during the forecast period.

Agriculture:

The global artificial intelligence in agriculture market size was valued at USD 773.56.9 million in 2019 and is anticipated to register a CAGR of 25.4% from 2019 to 2025.

Artificial intelligence techniques for farming help increase productivity and yield.

AI-enabled applications cater to several areas in the agriculture industry, such as predictive and recommendation analytics, identifying plant diseases, detecting pest infestations, and soil monitoring.

Pharmaceutical:

The market for AI in the pharmaceutical industry is expected to increase from USD 303.50 million in 2019 to USD 3.88 billion in 2025, with a CAGR of 52.9%.

Drug discovery represents the largest market size during the forecast period, increasing from USD 242.91 million in 2019 to USD 2,999.7 million in 2025, at a CAGR of 52.0%.

Market Outlook

Asia-Pacific market is expected to hold the highest global share AI in manufacturing market revenue during the projected horizon.

This is ascribable to factors such as increasing investment for AI-based platforms in countries such as India, China, and Japan in the region. Currently, the APAC region accounts for 39% global share in AI while North America, the largest market, holds about half of the AI market.

Regional Outlook:

North America accounts for majority the market for AI application in manufacturing. In this region, the market size will increase from USD 4 billion in 2019 to USD 28.7 billion by 2025.

Similarly, APAC market will experience an increase of about USD 19 billion from 2019 to 2025. European manufacturing sector will face AI’s entry making the market worth USD 7 billion by 2025.

Competitive Landscape

The market for AI technology vendors in manufacturing is highly competitive, characterized by a large number of participants and subject to rapid change.

Competitors may include systems integration firms, contract programming companies, application software companies, cloud computing service providers, traditional consulting firms, professional services groups of computer equipment companies, infrastructure management companies, outsourcing companies and digital companies.

The competitors may also provide custom solutions to verticals of manufacturing such as pharmaceutical, energy, food, fashin, etc. Some of the major competitors include, among others, Amazon Web Services (AWS), Oracle, SAP, NVIDIA Corporation, IBM Corporation, Alphabet Inc. (Google), Microsoft Corporation, Intel Corporation, and Siemens AG.

The market for AI technology vendors in manufacturing is highly competitive, characterized by a large number of participants and subject to rapid change.

In global intelligent hardware technology field, international giants such as Honeywell, BOSCH, and ABB have dominated the market. In terms of intelligent chips, NVIDIA, Intel, IBM, ARM, Qualcomm, and Google have captured the market.

Amazon, Google, Microsoft, and Facebook present the competitive market for AI-based computer vision technology.

International giants in the AI-based robots are ABB, Fanuc, Yaskawa, and Kuka and all are based in China.

North America holds the majority of the market share.

Key Market Players

Amazon Web Services (AWS)

Amazon Web Services, Inc. provides information technology services. The company offers website hosting, backup, digital marketing, analytics, application integration, blockchain, networking, and other related services. AWS also offers set of AI, ML, and deep learning services for thousands of customers globally.

AWS contributes over 11% (USD 25 billion) in revenue for its parent company Amazon Inc.

Intel Corporation

Intel Corporation provides computing, networking, data storage, and communication solutions worldwide. It operates through Data Center Group, Internet of Things Group, Non-Volatile Memory Solutions Group, Programmable Solutions Group, Client Computing Group, and All Other segments.

The company offers platform products, such as CPU and chipset, system-on-chip, and multichip package products for cloud, enterprise, and communication infrastructure markets. Further, the company develops computer vision and machine learning- based sensing, data analysis, localization, mapping, and driving policy technologies for advanced driver assistance systems and autonomous driving.

It serves original equipment manufacturers, original design manufacturers, industrial and communication equipment manufacturers, and cloud service providers.

The revenue reported in 2019 was USD 72 billion, half of which came from data centric business. MOBILEYE segment Includes development of computer vision and machine learning-based sensing, data analysis, localization, mapping, and driving policy technology for ADAS and autonomous driving.

Mobileye is the global leader in driving assistance and automation solutions fuelled by AI. This segment accounts for % of its data centric revenue.

Siemens AG

Siemens Limited is engaged in manufacturing of electric motors, generators, transformers and electricity distribution, and control apparatus; general purpose machinery, and electrical signalling, safety or traffic-control equipment.

Its segments include Power and Gas, Energy Management, Building Technologies, Mobility, Digital Factory, Process Industries and Drives, Healthcare, Metals Technologies, and design and engineering, and others.

It generated USD 10.6 billion revenue in 2019. Its digital industries segment holds 32% revenue share.

Salesforce

Salesforce Inc. is a provider of enterprise software, delivered through the cloud, with a focus on customer relationship management (CRM). The company focuses on cloud, mobile, social, Internet of Things (IoT) and artificial intelligence technologies.

The company made total revenue of USD 16.04 billion in 2019. Its Salesforce Platform segment contributed USD 4.47 billion (27.87% contribution in total revenue) in 2019. Nearly 71% of its market is captured in the Americas.

Qualcomm Technologies

Qualcomm Technologies was incorporated in 1991 as a wireless technology company. The company is engaged in the development, launch and expansion of technologies like fifth-generation (5G).

The company operates through three segments: QCT (Qualcomm CDMA Technologies) segment, QTL (Qualcomm Technology Licensing) segment and QSI (Qualcomm Strategic Initiatives) segment. It is specialized in offering third-generation (3G), fourth-generation (4G) wireless technologies and 5G wireless technologies.

Its offered technologies and products are used in mobile devices and other wireless products, including network equipment, broadband gateway equipment, consumer electronic devices and other connected devices.

Its technologies and products are also used in industry segments and applications beyond mobile, including AI, Internet of Things (IoT) and networking.

The company generated USD 24.27 billion in revenue in 2019. Revenue from QSI segment amounted USD 152 million (less than 1% of total revenue). Qualcomm Strategic Initiative (QSI) segment covers early stage investments in variety of industries and applications including artificial intelligence among others.

IBM

International Business Machines Corporation (IBM) is a technology company offering products and services through five segments: Cognitive Solutions, Global Business Services (GBS), Technology Services & Cloud Platforms, Systems and Global Financing.

The GBS segment provides clients with consulting, application management services and global process services. The Technology Services & Cloud Platforms segment provides information technology infrastructure services. The Systems segment provides clients with infrastructure technologies.

IT earned USD 77.15 billion in revenue in 2019. GBS segment accounted for 21.57% of revenue. GBS segment is further divided into Consulting (USD 7.99 billion in revenue), Application Management (USD 7.65 billion in revenue), and Global Process Services (USD 995 million in revenue). Global Technology Service segment held 35.46% share of revenue.

Cisco

Cisco was incorporated in 1984, engaging in designing and selling a range of technologies across networking, security, collaboration, applications and the cloud. The company’s product and technologies includes infrastructure platforms; applications; security and other products. It also offers technical support services and advanced services.

Infrastructure Platforms consists of company’s core networking technologies of switching, routing, data center products and wireless that are designed to work together to deliver networking capabilities and transport and store data.

These technologies consist of both hardware and software offerings that help to build networks, automate, orchestrate, integrate and digitize data.

Application product category consists primarily of software-related offerings that utilize the core networking and data center platforms to provide their functions. It consists of both hardware and software-based solutions, including both software licenses and software-as-a-service.

Security product category primarily includes company’s unified threat management products, advanced threat security products, and web security products.

Security offerings cover the following network-related areas: network and data center security, advanced threat protection, web and email security, access and policy, unified threat management, and advisory, integration, and managed services.

It reported USD 51.9 billion as revenue for 2019. The infrastructure platform segment constituted for 58% of the company’s revenue while American market contributed 60% towards total revenue.

Oracle

Oracle Corporation supplies software for enterprise information management. The company offers databases and relational servers, application development and decision support tools, and enterprise business applications.

Oracle’s software runs on network computers, personal digital assistants, set-top devices, PCs, workstations, minicomputers, mainframes, and massively parallel computers.

Oracle Corporation supplies software for enterprise information management. The Company offers databases and relational servers, application development and decision support tools, and enterprise business applications.

Oracle’s software runs on network computers, personal digital assistants, set-top devices, PCs, workstations, minicomputers, mainframes, and massively parallel computers.

In 2019, it made total revenue of USD 39.51 billion. 55.33% of revenue came from American market, EMEA region held for 28.52% share of revenue, and Asia-Pacific market represented the remaining revenue share.

Cloud and license business accounted for 83% of its revenue and the remaining 17% revenue came from hardware and service offering.

SAP

SAP offers enterprise application software. The company operates through three segments:

Applications, Technology & Services segment, which is engaged in the sale of software licenses, subscriptions to its cloud applications, and related services, primarily support services and various professional services, and support services, as well as implementation services of its software products and education services on the use of its products;
The SAP Business Network segment, which includes its cloud-based collaborative business networks and services relating to the SAP Business Network, including cloud applications, professional services and education services, as well as the Company markets and sells the cloud offerings developed by SAP Ariba, SAP Fieldglass and Concur,
The Customer Experience segment, which comprises on-premise and cloud-based products that run front office functions across the customer experience.

Total revenue earned was USD 30.25 billion in 2019. Cloud and software revenue held for 85% revenue share.

Strategic Conclusion

Automotive manufacturing is the largest segment for AI deployment globally.

Full-stack self-driving system that is geared with camera-centric backbone, robust GPS, intelligent sensoring, and AR/VR experience in productizing cutting-edge technology in the automotive industry will drive economically competitive market for AI adoption.

Thus, AI will enter the mass market segment as an end-to-end service provider at scale in the road ahead. Other segments of manufacturing will also see gradual increase in the use of AI.

References

Appendix

AI: Artificial Intelligence
ML: Machine Learning
NLP: Natural Language Programming
AR: Augmented Reality
VR: Virtual Reality