Cognitive Computing

Cognitive Computing Market Outlook

The global cognitive computing market is anticipated to reach approximately USD 113.5 billion by 2035, growing at a compound annual growth rate (CAGR) of 31.1% from 2025 to 2035. This remarkable growth is driven by the increasing demand for advanced analytics, the proliferation of data generated across various sectors, and the rising integration of cognitive technologies into business processes to enhance decision-making and operational efficiency. Furthermore, the growing adoption of Internet of Things (IoT) devices and the necessity for organizations to leverage big data for competitive advantage are fueling the demand for cognitive computing. The convergence of artificial intelligence with cognitive capabilities is also leading to innovative applications, thus propelling the market forward. Enhanced computational power and advancements in algorithms are enabling more sophisticated cognitive solutions, drawing attention from both established firms and startups alike.

Growth Factor of the Market

Several factors are driving the growth of the cognitive computing market. Firstly, the increasing need for intelligent data analysis in business operations is compelling organizations to adopt cognitive technologies. With the explosion of big data, traditional analysis methods are becoming insufficient, thus driving the necessity for cognitive solutions that can provide meaningful insights. Secondly, advancements in artificial intelligence and machine learning are making cognitive computing technologies more accessible and effective, further encouraging their adoption across various sectors. Thirdly, the healthcare sector's growing reliance on predictive analytics and patient data management is catalyzing investment in cognitive computing solutions. Additionally, financial institutions are increasingly utilizing cognitive computing for fraud detection and risk assessment, enhancing their operational capabilities. Lastly, the rise of cloud computing is accelerating the deployment of cognitive technologies, allowing organizations of all sizes to implement these solutions without the need for substantial upfront investments in IT infrastructure.

Key Highlights of the Market

• The cognitive computing market is expected to surpass USD 113.5 billion by 2035.
• Machine learning technology accounts for the largest share of the cognitive computing market.
• Healthcare is the fastest-growing end-user segment within the cognitive computing market.
• Cloud deployment is gaining traction due to its scalability and cost-effectiveness.
• North America remains the leading region in adopting cognitive computing technologies.

By Technology

Machine Learning:

Machine learning, as the cornerstone of cognitive computing, empowers systems to learn from data, improve over time, and make predictions without being explicitly programmed. This technology has witnessed significant advancements, enabling applications across various sectors, such as finance for algorithmic trading, retail for personalized recommendations, and healthcare for predictive analytics. The proliferation of algorithms and frameworks has further democratized access to machine learning, allowing even small enterprises to implement these solutions. As organizations seek to derive actionable insights from vast datasets, the demand for machine learning solutions is on a continual rise, driving significant investments into this segment.

Natural Language Processing:

Natural Language Processing (NLP) is revolutionizing the way computers understand, interpret, and respond to human language. NLP technologies are central to chatbots, virtual assistants, and sentiment analysis tools, allowing businesses to enhance customer service and engagement. The advancement in NLP models, particularly with the rise of transformer architectures like GPT and BERT, has led to significant improvements in language understanding, making it possible for machines to comprehend context and intent more accurately. Businesses across industries are leveraging NLP to process customer feedback, automate responses, and conduct detailed market research, thus driving the growth of this segment within cognitive computing.

Image Processing:

Image processing is a critical component of cognitive computing that enables machines to interpret and analyze visual information from the world around them. This technology finds applications in various sectors, including healthcare for medical imaging diagnostics, automotive for autonomous vehicle navigation, and retail for visual search capabilities. Recent advancements in deep learning and convolutional neural networks have significantly enhanced the accuracy and efficiency of image processing applications, leading to increased adoption across industries. The ability to extract valuable insights from images not only improves operational efficiency but also enhances customer experiences, thus driving demand in this segment.

Speech Recognition:

Speech recognition technology has rapidly evolved, allowing machines to understand and process human speech. This segment of cognitive computing has gained substantial popularity due to the widespread use of voice-activated virtual assistants and automated customer service solutions. The continuous improvements in speech-to-text algorithms and the integration of contextual understanding have made voice interaction more seamless and user-friendly. Industries such as telecommunications and healthcare are leveraging speech recognition for transcription services, enhancing communication, and improving productivity. As consumers increasingly prefer voice commands for interactions, the demand for sophisticated speech recognition technologies is expected to grow significantly.

Neural Networks:

Neural networks are a pivotal technology within cognitive computing, emulating the human brain's structure and function to process information. These networks are the backbone of many machine learning models, facilitating tasks such as image and speech recognition. With advancements in deep learning techniques, neural networks have been able to tackle more complex problems, thereby expanding their applicability across various sectors. The rise of big data has further accelerated the adoption of neural networks, as they excel in extracting patterns and insights from large datasets. Their ability to improve through experience makes them invaluable for businesses aiming to enhance their analytical capabilities and drive innovation.

By User

Healthcare:

The healthcare industry is increasingly adopting cognitive computing technologies to enhance patient care, streamline operations, and improve decision-making processes. Cognitive solutions are utilized for predictive analytics, patient data management, and personalized treatment plans, significantly improving patient outcomes. Advanced algorithms can analyze vast volumes of medical data to identify trends and anomalies, aiding in early diagnosis and treatment recommendations. Additionally, healthcare providers are leveraging cognitive computing for administrative tasks, such as billing and appointment scheduling, which helps reduce operational costs and enhance service delivery. The ongoing transformation of healthcare systems to incorporate data-driven solutions positions this sector as a key user of cognitive computing technologies.

BFSI:

The Banking, Financial Services, and Insurance (BFSI) sector is at the forefront of adopting cognitive computing for various applications, including risk management, customer service, and fraud detection. Cognitive solutions enable financial institutions to analyze customer behaviors, assess risks in real-time, and enhance compliance efforts. Additionally, chatbots and virtual assistants powered by cognitive computing are transforming customer interactions by providing personalized services and support. The ability of cognitive technologies to process and analyze large datasets in real-time enhances decision-making capabilities and helps organizations stay ahead in a competitive landscape. As regulations and customer expectations evolve, the BFSI sector is increasingly relying on cognitive computing to drive innovation and efficiency.

Retail:

The retail sector is harnessing cognitive computing technologies to improve customer experiences, optimize supply chains, and enhance inventory management. Cognitive solutions facilitate personalized shopping experiences by analyzing customer preferences and behaviors, allowing retailers to offer tailored recommendations and promotions. Moreover, predictive analytics enables retailers to forecast demand and manage inventories more effectively, reducing costs and enhancing operational efficiency. The integration of cognitive technologies in retail not only streamlines operations but also fosters greater customer loyalty through improved engagement and service. As e-commerce continues to grow, the demand for cognitive computing in retail is expected to rise significantly.

IT & Telecom:

In the IT and telecommunications sector, cognitive computing is playing a vital role in network optimization, customer service automation, and cybersecurity. Cognitive solutions enable telecom companies to analyze network data in real-time, improving service quality and minimizing downtime. Additionally, chatbots and AI-driven customer service platforms are enhancing user interactions, providing faster response times, and improving overall customer satisfaction. The rising threat of cyberattacks has prompted IT firms to adopt cognitive technologies for proactive threat detection and response, safeguarding sensitive data. As the digital landscape evolves, the IT and telecom sectors are increasingly investing in cognitive computing to maintain their competitive edge and enhance service delivery.

Others:

Several other sectors, including manufacturing, education, and transportation, are leveraging cognitive computing technologies to drive innovation and efficiency. In manufacturing, cognitive solutions are being utilized for predictive maintenance, quality control, and supply chain optimization, leading to reduced operational disruptions and increased productivity. The education sector is embracing cognitive computing for personalized learning experiences, utilizing data analytics to tailor curricula to individual student needs. Meanwhile, the transportation industry is implementing cognitive technologies for route optimization and fleet management, enhancing operational efficiency. As cognitive computing continues to evolve, its applications across diverse sectors are expected to expand, unlocking new opportunities for growth and innovation.

By Deployment

On-Premises:

On-premises deployment of cognitive computing solutions allows organizations to maintain control over their data and computational resources. This deployment model is particularly attractive to industries with stringent data security and compliance requirements, such as healthcare and finance. Organizations opting for on-premises solutions benefit from enhanced security and customization capabilities, tailoring the technology to meet specific operational needs. However, the high initial setup costs and ongoing maintenance requirements can deter smaller businesses from adopting this model. Despite these challenges, the demand for on-premises cognitive computing solutions remains steady among larger enterprises that prioritize data sovereignty and operational control.

Cloud:

Cloud deployment of cognitive computing is rapidly gaining traction due to its scalability, flexibility, and cost-effectiveness. Organizations are increasingly opting for cloud-based cognitive solutions to leverage advanced analytics and computational power without the burden of extensive capital investments. This model facilitates easy access to cognitive services, allowing businesses of all sizes to benefit from sophisticated analytics and machine learning capabilities. Moreover, the cloud enables seamless collaboration across teams and locations, fostering innovation and agility in business operations. As cloud technology continues to evolve, the cognitive computing market is expected to witness substantial growth, with more organizations embracing this deployment model.

By Application

Fraud Detection:

Fraud detection is one of the most critical applications of cognitive computing, particularly in the BFSI sector. Cognitive technologies analyze vast datasets to identify patterns and anomalies indicative of fraudulent behavior. Machine learning algorithms continuously improve their accuracy by learning from historical data, enabling organizations to detect fraudulent activities in real time. This proactive approach not only minimizes financial losses for businesses but also enhances customer trust and loyalty. As fraudsters become increasingly sophisticated, the demand for advanced cognitive fraud detection solutions is expected to grow, driving investments in this application area.

Customer Service:

Cognitive computing is transforming customer service by enabling organizations to offer personalized and efficient support through advanced chatbots and virtual assistants. These technologies utilize natural language processing to understand customer inquiries and provide accurate responses, improving the overall customer experience. By automating routine inquiries, cognitive solutions free up human agents to handle more complex issues, thereby increasing operational efficiency. The ongoing advancements in AI and machine learning are further enhancing the capabilities of customer service applications, allowing businesses to anticipate customer needs and tailor their interactions accordingly. As the demand for superior customer experiences continues to rise, investment in cognitive customer service solutions is expected to expand significantly.

Risk Management:

Cognitive computing plays a vital role in enhancing risk management processes across various sectors, particularly in finance and insurance. Cognitive technologies facilitate the analysis of vast amounts of data to identify potential risks and assess their impact. By leveraging predictive analytics, organizations can develop proactive strategies to mitigate risks and protect their assets. Furthermore, cognitive solutions enable real-time monitoring of market conditions and regulatory changes, allowing businesses to adapt swiftly to evolving environments. The increasing complexity of risks in today's interconnected world is driving the demand for advanced cognitive risk management solutions, making this a crucial application within the cognitive computing market.

Predictive Maintenance:

Predictive maintenance is a critical application of cognitive computing, especially in manufacturing and industrial sectors. By analyzing data from sensors and equipment, cognitive technologies can predict when machinery is likely to fail, allowing organizations to schedule maintenance activities proactively. This approach minimizes downtime, reduces repair costs, and enhances operational efficiency. The integration of cognitive computing with IoT devices further amplifies predictive maintenance capabilities, enabling real-time monitoring and analysis of equipment performance. As industries increasingly recognize the value of predictive maintenance in optimizing operations, the demand for cognitive solutions in this area is projected to grow significantly.

Others:

Other applications of cognitive computing are emerging across various industries, ranging from healthcare diagnostics to supply chain optimization. In healthcare, cognitive solutions are being employed for personalized medicine, analyzing patient data to recommend tailored treatment plans. In logistics, cognitive technologies facilitate efficient route planning and inventory management, leading to cost savings and improved service delivery. Additionally, educational institutions are utilizing cognitive computing to create adaptive learning platforms that cater to individual student needs. As cognitive computing continues to evolve, the range of applications is expected to expand, providing new opportunities for businesses to leverage these technologies for competitive advantage.

By Region

North America is currently the leading region in the cognitive computing market, accounting for approximately 40% of the global market share. This dominance can be attributed to the region's advanced technological infrastructure, substantial investments in research and development, and a strong presence of key players in the cognitive computing space. The United States, in particular, is home to many innovative companies adopting cognitive technologies across various sectors, including healthcare, finance, and retail. The CAGR for the North American cognitive computing market is projected to be around 29.6%, driven by the increasing demand for data-driven decision-making and the integration of AI technologies.

Europe is also witnessing significant growth in the cognitive computing market, with a market share of around 27%. The region's focus on enhancing operational efficiency and fostering digital transformation across industries is driving the adoption of cognitive solutions. Countries such as Germany, the UK, and France are at the forefront of this transformation, leveraging cognitive computing in sectors like healthcare, finance, and manufacturing. The CAGR for the European market is expected to be approximately 28.4%, as organizations increasingly recognize the importance of harnessing cognitive technologies to remain competitive in a rapidly evolving landscape. Meanwhile, the Asia Pacific region is emerging as a significant player in the cognitive computing market, driven by rapid technological advancements and increasing investments in AI solutions.

Opportunities

The cognitive computing market presents numerous opportunities for businesses looking to innovate and enhance their operational capabilities. As organizations continue to embrace digital transformation, the demand for cognitive solutions is expected to surge across various sectors. One notable opportunity lies in the integration of cognitive computing with emerging technologies, such as IoT and blockchain. For instance, combining cognitive computing with IoT can lead to enhanced predictive maintenance solutions in manufacturing, enabling organizations to optimize their operations further. Additionally, the increasing focus on data privacy and security presents a unique opportunity for cognitive computing technologies, as organizations seek to leverage advanced analytics while ensuring compliance with regulations. By positioning themselves strategically, companies can capitalize on these trends to drive growth and stay ahead in the competitive landscape.

Furthermore, the growing emphasis on personalized customer experiences is creating opportunities for cognitive computing in the retail and hospitality sectors. Businesses are increasingly leveraging cognitive solutions to analyze customer data, predict preferences, and deliver tailored recommendations, enhancing customer satisfaction and loyalty. The rise of remote work and digital collaboration also presents opportunities for cognitive computing, as organizations seek solutions to streamline operations and improve productivity. As cognitive technologies continue to evolve, new avenues for innovation will emerge, allowing businesses to harness the power of data analytics and AI-driven insights to drive success in their respective industries.

Threats

Despite the promising growth of the cognitive computing market, several threats loom that could impact its trajectory. One significant threat is the increasing apprehension surrounding data privacy and security. As cognitive computing relies heavily on data analysis, organizations must navigate complex regulatory landscapes to ensure compliance with data protection laws. Breaches of sensitive information can result in severe legal ramifications and damage to a company's reputation, which may deter some businesses from adopting cognitive solutions. Moreover, the rapid pace of technological advancements means that organizations must continuously adapt to stay relevant, leading to increased competition and potential market saturation. Companies that fail to innovate or keep pace with advancements risk being left behind and losing their competitive edge.

Additionally, the reliance on algorithms and machine learning models raises concerns regarding bias and ethical implications. Cognitive solutions may inadvertently perpetuate existing biases within data, leading to unfair treatment of certain groups or individuals. Addressing these ethical concerns is essential for organizations to ensure the responsible deployment of cognitive computing technologies. Furthermore, the lack of skilled professionals trained in cognitive computing poses a significant challenge, as organizations may struggle to find talent capable of effectively implementing and managing these advanced solutions. Together, these threats highlight the need for organizations to adopt a proactive approach to navigate the evolving landscape of cognitive computing.

Competitor Outlook

• IBM
• Microsoft Corporation
• Google LLC
• Amazon Web Services (AWS)
• Salesforce
• Oracle Corporation
• SAS Institute Inc.
• Accenture
• Infosys
• Capgemini
• Deloitte
• Siemens AG
• Palantir Technologies
• Hewlett Packard Enterprise (HPE)
• Alibaba Group
• Teradata Corporation

The competitive landscape of the cognitive computing market is characterized by the presence of several key players, each striving to enhance their offerings through innovation and strategic partnerships. Companies like IBM and Microsoft are leading the charge, leveraging their extensive resources and expertise to develop advanced cognitive solutions that cater to a wide range of industries. IBM's Watson, for example, has gained prominence for its capabilities in natural language processing and machine learning, making it a go-to solution for businesses looking to harness cognitive computing. Meanwhile, Microsoft's Azure AI platform provides organizations with a robust framework for integrating cognitive capabilities into their applications, further solidifying its position in the market.

In addition to these tech giants, numerous consulting firms, such as Accenture and Deloitte, are also making significant strides in the cognitive computing market. These companies provide valuable services, helping organizations implement cognitive solutions tailored to their specific needs. By combining domain expertise with advanced technologies, consulting firms are playing a crucial role in driving the adoption of cognitive computing across various sectors. Moreover, the rise of startups specializing in niche cognitive applications is enhancing competition, leading to increased innovation and a diverse range of solutions for businesses to choose from.

The market is also witnessing a trend towards collaborative innovation, as companies partner with academic institutions and research organizations to explore new cognitive computing advancements. This collaborative approach fosters knowledge sharing and accelerates the development of next-generation cognitive solutions. Additionally, organizations are increasingly focusing on enhancing their data privacy and security measures, addressing concerns that may hinder the adoption of cognitive computing. With the competitive landscape constantly evolving, organizations must remain vigilant and adaptable to capitalize on emerging opportunities while navigating potential challenges.

• 1 Appendix

  • 1.1 List of Tables
  • 1.2 List of Figures

• 2 Introduction

  • 2.1 Market Definition
  • 2.2 Scope of the Report
  • 2.3 Study Assumptions
  • 2.4 Base Currency & Forecast Periods

• 3 Market Dynamics

  • 3.1 Market Growth Factors
  • 3.2 Economic & Global Events
  • 3.3 Innovation Trends
  • 3.4 Supply Chain Analysis

• 4 Consumer Behavior

  • 4.1 Market Trends
  • 4.2 Pricing Analysis
  • 4.3 Buyer Insights

• 5 Key Player Profiles

  • 5.1 IBM
    • 5.1.1 Business Overview
    • 5.1.2 Products & Services
    • 5.1.3 Financials
    • 5.1.4 Recent Developments
    • 5.1.5 SWOT Analysis
  • 5.2 Infosys
    • 5.2.1 Business Overview
    • 5.2.2 Products & Services
    • 5.2.3 Financials
    • 5.2.4 Recent Developments
    • 5.2.5 SWOT Analysis
  • 5.3 Deloitte
    • 5.3.1 Business Overview
    • 5.3.2 Products & Services
    • 5.3.3 Financials
    • 5.3.4 Recent Developments
    • 5.3.5 SWOT Analysis
  • 5.4 Accenture
    • 5.4.1 Business Overview
    • 5.4.2 Products & Services
    • 5.4.3 Financials
    • 5.4.4 Recent Developments
    • 5.4.5 SWOT Analysis
  • 5.5 Capgemini
    • 5.5.1 Business Overview
    • 5.5.2 Products & Services
    • 5.5.3 Financials
    • 5.5.4 Recent Developments
    • 5.5.5 SWOT Analysis
  • 5.6 Google LLC
    • 5.6.1 Business Overview
    • 5.6.2 Products & Services
    • 5.6.3 Financials
    • 5.6.4 Recent Developments
    • 5.6.5 SWOT Analysis
  • 5.7 Salesforce
    • 5.7.1 Business Overview
    • 5.7.2 Products & Services
    • 5.7.3 Financials
    • 5.7.4 Recent Developments
    • 5.7.5 SWOT Analysis
  • 5.8 Siemens AG
    • 5.8.1 Business Overview
    • 5.8.2 Products & Services
    • 5.8.3 Financials
    • 5.8.4 Recent Developments
    • 5.8.5 SWOT Analysis
  • 5.9 Alibaba Group
    • 5.9.1 Business Overview
    • 5.9.2 Products & Services
    • 5.9.3 Financials
    • 5.9.4 Recent Developments
    • 5.9.5 SWOT Analysis
  • 5.10 Oracle Corporation
    • 5.10.1 Business Overview
    • 5.10.2 Products & Services
    • 5.10.3 Financials
    • 5.10.4 Recent Developments
    • 5.10.5 SWOT Analysis
  • 5.11 SAS Institute Inc.
    • 5.11.1 Business Overview
    • 5.11.2 Products & Services
    • 5.11.3 Financials
    • 5.11.4 Recent Developments
    • 5.11.5 SWOT Analysis
  • 5.12 Teradata Corporation
    • 5.12.1 Business Overview
    • 5.12.2 Products & Services
    • 5.12.3 Financials
    • 5.12.4 Recent Developments
    • 5.12.5 SWOT Analysis
  • 5.13 Microsoft Corporation
    • 5.13.1 Business Overview
    • 5.13.2 Products & Services
    • 5.13.3 Financials
    • 5.13.4 Recent Developments
    • 5.13.5 SWOT Analysis
  • 5.14 Palantir Technologies
    • 5.14.1 Business Overview
    • 5.14.2 Products & Services
    • 5.14.3 Financials
    • 5.14.4 Recent Developments
    • 5.14.5 SWOT Analysis
  • 5.15 Amazon Web Services (AWS)
    • 5.15.1 Business Overview
    • 5.15.2 Products & Services
    • 5.15.3 Financials
    • 5.15.4 Recent Developments
    • 5.15.5 SWOT Analysis
  • 5.16 Hewlett Packard Enterprise (HPE)
    • 5.16.1 Business Overview
    • 5.16.2 Products & Services
    • 5.16.3 Financials
    • 5.16.4 Recent Developments
    • 5.16.5 SWOT Analysis

• 6 Market Segmentation

  • 6.1 Cognitive Computing Market, By User
    • 6.1.1 Healthcare
    • 6.1.2 BFSI
    • 6.1.3 Retail
    • 6.1.4 IT & Telecom
    • 6.1.5 Others
  • 6.2 Cognitive Computing Market, By Deployment
    • 6.2.1 On-Premises
    • 6.2.2 Cloud
  • 6.3 Cognitive Computing Market, By Technology
    • 6.3.1 Machine Learning
    • 6.3.2 Natural Language Processing
    • 6.3.3 Image Processing
    • 6.3.4 Speech Recognition
    • 6.3.5 Neural Networks
  • 6.4 Cognitive Computing Market, By Application
    • 6.4.1 Fraud Detection
    • 6.4.2 Customer Service
    • 6.4.3 Risk Management
    • 6.4.4 Predictive Maintenance
    • 6.4.5 Others

• 7 Competitive Analysis

  • 7.1 Key Player Comparison
  • 7.2 Market Share Analysis
  • 7.3 Investment Trends
  • 7.4 SWOT Analysis

• 8 Research Methodology

  • 8.1 Analysis Design
  • 8.2 Research Phases
  • 8.3 Study Timeline

• 9 Future Market Outlook

  • 9.1 Growth Forecast
  • 9.2 Market Evolution

• 10 Geographical Overview

  • 10.1 Europe - Market Analysis
    • 10.1.1 By Country
      • 10.1.1.1 UK
      • 10.1.1.2 France
      • 10.1.1.3 Germany
      • 10.1.1.4 Spain
      • 10.1.1.5 Italy
  • 10.2 Asia Pacific - Market Analysis
    • 10.2.1 By Country
      • 10.2.1.1 India
      • 10.2.1.2 China
      • 10.2.1.3 Japan
      • 10.2.1.4 South Korea
  • 10.3 Latin America - Market Analysis
    • 10.3.1 By Country
      • 10.3.1.1 Brazil
      • 10.3.1.2 Argentina
      • 10.3.1.3 Mexico
  • 10.4 North America - Market Analysis
    • 10.4.1 By Country
      • 10.4.1.1 USA
      • 10.4.1.2 Canada
  • 10.5 Cognitive Computing Market by Region
  • 10.6 Middle East & Africa - Market Analysis
    • 10.6.1 By Country
      • 10.6.1.1 Middle East
      • 10.6.1.2 Africa

• 11 Global Economic Factors

  • 11.1 Inflation Impact
  • 11.2 Trade Policies

• 12 Technology & Innovation

  • 12.1 Emerging Technologies
  • 12.2 AI & Digital Trends
  • 12.3 Patent Research

• 13 Investment & Market Growth

  • 13.1 Funding Trends
  • 13.2 Future Market Projections

• 14 Market Overview & Key Insights

  • 14.1 Executive Summary
  • 14.2 Key Trends
  • 14.3 Market Challenges
  • 14.4 Regulatory Landscape

Segments Analyzed in the Report

The global Cognitive Computing market is categorized based on

By Technology

• Machine Learning
• Natural Language Processing
• Image Processing
• Speech Recognition
• Neural Networks

By User

• Healthcare
• BFSI
• Retail
• IT & Telecom
• Others

By Deployment

• On-Premises
• Cloud

By Application

• Fraud Detection
• Customer Service
• Risk Management
• Predictive Maintenance
• Others

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa

Key Players

• IBM
• Microsoft Corporation
• Google LLC
• Amazon Web Services (AWS)
• Salesforce
• Oracle Corporation
• SAS Institute Inc.
• Accenture
• Infosys
• Capgemini
• Deloitte
• Siemens AG
• Palantir Technologies
• Hewlett Packard Enterprise (HPE)
• Alibaba Group
• Teradata Corporation