Generative AI in Modern Procurement Market to show Robust Growth in Upcoming Years by 2024-2032 | IBM Corp., Sap and Kaps Petroleum Services LLC, Oracle Corp.

The market for Generative AI in Modern Procurement is experiencing significant growth driven by the increasing complexity of procurement processes, the need for automation and efficiency, and advancements in AI technology. Generative AI refers to AI algorithms capable of creating new content, such as text, images, or even procurement strategies, based on input data and predefined parameters. In modern procurement, generative AI is being utilized to streamline sourcing, vendor selection, contract management, and supply chain optimization processes. These AI-driven solutions enable procurement professionals to generate insights, recommendations, and even automated actions to improve decision-making, reduce costs, mitigate risks, and enhance supplier relationships. Moreover, the COVID-19 pandemic has accelerated the adoption of generative AI in procurement as organizations seek agile and resilient procurement strategies to navigate supply chain disruptions and uncertainties.

The Global Generative AI in Modern Procurement Market is projected to grow at a CAGR of 23.40% from 2024 to 2032.

The market for generative AI in modern procurement demonstrates promising indications of continued growth and adoption. Factors such as the increasing volume and complexity of procurement data, coupled with the growing pressure to drive cost savings and operational efficiency, are driving demand for AI-driven procurement solutions. Moreover, the rising need for predictive analytics, scenario modeling, and real-time insights to anticipate market trends, mitigate risks, and optimize procurement strategies is fueling the adoption of generative AI. Additionally, the emergence of cloud-based AI platforms and software-as-a-service (SaaS) solutions is lowering barriers to entry for organizations of all sizes to leverage generative AI capabilities in their procurement operations.

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Global Key Players covered in Generative AI in Modern Procurement Market Report are:
IBM Corp., Sap and Kaps Petroleum Services LLC, Oracle Corp., GEP Worldwide, Coupa Software, Microsoft Corp., Basware, Jaggaer, Globality, and Zycus

Generative AI in Modern Procurement Market Segments:
Generative AI in Modern Procurement Market Classifies into Types:
Direct Procurement
Indirect Procurement
Goods Procurement
Services Procurement

Generative AI in Modern Procurement Market Segmented into Application:
Predictive Modeling
Risk Assessment
Supplier Identification
Product/Service Recommendation
Negotiation Support
Contract Analysis
Fraud Detection
Others

Geographically, the detailed analysis of consumption, revenue, market share, and growth rate of the following regions:
1. North America (United States, Canada, and Mexico).
2. Europe (UK, Germany, France, Russia, and Italy).
3. Asia-Pacific (China, Korea, Japan, India, and Southeast Asia).
4. South America (Brazil, Colombia, Argentina, etc.).
5. The Middle East and Africa (Saudi Arabia, UAE, Nigeria, Egypt, and South Africa).

The market for generative AI in modern procurement is characterized by dynamic movement driven by technological innovation, strategic partnerships, and market expansion initiatives. Key market players are investing in research and development to enhance the capabilities of generative AI algorithms, such as natural language processing, machine learning, and optimization techniques, to address the unique challenges and requirements of procurement processes. Strategic collaborations between AI vendors, procurement software providers, and industry stakeholders aim to integrate generative AI functionalities into existing procurement platforms, offering seamless and integrated solutions. Moreover, expansions into new geographic regions and industries, along with mergers and acquisitions to consolidate market share and expand product portfolios, are common strategies adopted by companies to capitalize on the growing demand for generative AI in procurement.

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The market research methodology employed to analyze the generative AI in modern procurement market involves a comprehensive approach integrating both primary and secondary research techniques. Primary research includes interviews and surveys conducted with key stakeholders such as procurement professionals, AI vendors, industry experts, and technology providers to gather firsthand insights into market dynamics, trends, and challenges. Secondary research involves the analysis of industry reports, academic publications, market studies, and company websites to gather relevant data on market size, growth drivers, competitive landscape, and technological developments. Furthermore, data triangulation techniques are employed to validate and corroborate primary and secondary research findings, ensuring the reliability and accuracy of the market analysis. The research methodology also incorporates qualitative and quantitative analysis to provide a comprehensive understanding of the generative AI in modern procurement market landscape, trends, and opportunities.

The years examined in this study are the following to estimate the Generative AI in Modern Procurement Market size:
History Year: 2018-2023
Base Year: 2024
Forecast Year: 2024 to 2032

Cumulative Impact of COVID-19 on Market:
Businesses all over the world are embracing a remote work culture as a result of the COVID-19 epidemic, which creates new difficulties for suppliers. The biggest challenge is keeping up with the latest trends in authoritative societies that are always changing. In any case, there has been a rise in cloud reception since the pandemic, preparing for the requirement of risky security measures.

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Frequently Asked Questions:
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Table of Contents:
1. Generative AI in Modern Procurement Market Overview
2. Market Competition by Manufacturers
3. Production by Region
4. Global Generative AI in Modern Procurement Consumption by Region
5. Segment by Type
6. Segment by Application
7. Key Companies Profiled
8. Generative AI in Modern Procurement Cost Analysis
9. Marketing Channel, Distributors and Customers
10. Market Dynamics
11. Production and Supply Forecast
12. Consumption and Demand Forecast
13. Forecast by Type and by Application (2024-2032)
14. Research Finding and Conclusion
15. Methodology and Data Source
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