Global AI In Fuel Market Size is valued at USD 2.9 Bn in 2024 and is predicted to reach USD 6.8 Bn by the year 2034 at a 9.0% CAGR during the forecast period for 2025-2034.
Artificial intelligence (AI) has substantial potential for developing and innovating all general-purpose technologies in the modern period. The gasoline supply chain is being optimized with AI, which can help create inventory control, minimize waste, and increase delivery efficiency.
The petroleum business is pressured to keep up with the rising global energy demand. Companies in the petroleum sector can benefit from AI by streamlining their operations, cutting costs, and meeting demand while remaining profitable. This is expected to fuel market expansion. Furthermore, escalating demand for cutting-edge solutions in drilling, boiler diagnostics, quality control, planning, and predictive maintenance across various operations is fueling market expansion.
The global AI in fuel market is progressing due to the quick development of new technologies, including natural language processing, machine learning, and computer vision. These technologies are helping the petroleum business by automating processes, enhancing decision-making, and lowering human error. Yet, implementing AI technology in the gasoline industry can be expensive, especially for small & medium-sized enterprises. The high implementation costs of AI technology could discourage some businesses from implementing it, which would restrict industry growth.
The AI in fuel market is segmented on the basis of type, function and application. Based on type, the market is segregated as Hardware and Software. By function, the market is segmented into Predictive Maintenance and Machinery Inspection and Material Movement. Based on application, the market is segmented as Upstream and Downstream.
The predictive maintenance and machinery inspection category is expected to hold a major share in the global AI in fuel market in 2024. Predictive maintenance makes use of Al to monitor machinery and systems, spotting possible difficulties before they develop into major concerns and enabling proactive maintenance planning. Fuel companies may cut down on expensive equipment failures and unforeseen maintenance that can disrupt operations and affect profitability by implementing Al-powered predictive maintenance. As a result, the equipment operates more effectively, has less downtime, and lasts longer.
The downstream segment is projected to grow at a rapid rate in the global AI in fuel market. Through the use of Al technology, refiners may spot chances for cost savings and improve safety protocols by spotting abnormalities and potential risks during the refining process. By monitoring emissions and implementing sustainable processes, Al can also help refiners follow environmental standards. The downstream refining segment is expected to significantly boost the adoption of Al technology in the fuel market as the demand for high-quality fuel products rises and the necessity for more environmentally friendly refining procedures grows.
The North America AI in fuel market is expected to register highest market share in terms of revenue in the near future. The region's robust economy, the high rate of adoption of AI technologies by oilfield operators and service providers, the prominence of leading AI software and system providers, and joint R&D investments by public and private organizations are all anticipated to contribute to the demand for AI in the fuel industry. In addition, Asia Pacific is projected to grow swiftly in the global AI in fuel market. The need for and usage of aluminum in the fuel industries is expanding in this region, which has a high degree of the gaseous and explosive chemical environment to monitor the tanks and gasoline business. The introduction of dependable technology in the fuel sector has led to an expansion of the market in this area.
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Report Attribute |
Specifications |
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Market size value in 2024 |
USD 2.9 Bn |
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Revenue forecast in 2034 |
USD 6.8 Bn |
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Growth rate CAGR |
CAGR of 9.0% from 2024 to 2031 |
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Quantitative units |
Representation of revenue in US$ Bn , and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report coverage |
The forecast of revenue, the position of the company, the competitive market statistics, growth prospects, and trends |
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Segments covered |
Type, Function And Application |
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Regional scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; The UK; France; Italy; Spain; China; Japan; India; South Korea; Southeast Asia; South Korea; Southeast Asia |
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Competitive Landscape |
IBM, AI, Google LLC, Microsoft Corporation, Oracle, FuGenX Technologies Pvt. Ltd, Cloudera, Cisco Systems, NVIDIA Corporation, Intel Corporation, Accenture plc, Huawei Technologies Co. Ltd, Infosys Limited, Intel Corporation, International Business Machines Corporation, Neudax and Shell plc. |
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Customization scope |
Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing and available payment methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Fuel Market Snapshot
Chapter 4. Global AI in Fuel Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Type:
5.2.1. Percutaneous Endovascular Aneurysm Repair (EVAR)
5.2.2. Fenestrated EVAR
5.2.3. Aortic Stents Biodegradable Stents
5.2.4. Self- Expanding Nitinol Stents
5.2.5. Thoracic Aortic Aneurysm Grafts
5.2.6. Other Devices
Chapter 6. Market Segmentation 2: by Function Estimates & Trend Analysis
6.1. by Function & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Function:
6.2.1. Predictive Maintenance and Machinery Inspection
6.2.2. Material Movement
Chapter 7. Market Segmentation 3: by Application Estimates & Trend Analysis
7.1. by Application & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Application:
7.2.1. Upstream
7.2.2. Downstream
Chapter 8. AI in Fuel Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.1.2. North America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Function, 2021-2034
8.1.3. North America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.1.4. North America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.2. Europe
8.2.1. Europe AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.2.2. Europe AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Function, 2021-2034
8.2.3. Europe AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.2.4. Europe AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.3. Asia Pacific
8.3.1. Asia Pacific AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.3.2. Asia Pacific AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Function, 2021-2034
8.3.3. Asia-Pacific AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.3.4. Asia Pacific AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.4. Latin America
8.4.1. Latin America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.4.2. Latin America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Function, 2021-2034
8.4.3. Latin America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.4.4. Latin America AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.5. Middle East & Africa
8.5.1. Middle East & Africa AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.5.2. Middle East & Africa AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Function, 2021-2034
8.5.3. Middle East & Africa AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.5.4. Middle East & Africa AI in Fuel Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. Accenture plc
9.2.2. C3.AI
9.2.3. Cisco Systems, Inc.
9.2.4. Cloudera, Inc.
9.2.5. FuGenX Technologies Pvt. Ltd
9.2.6. Google LLC
9.2.7. Huawei Technologies Co. Ltd
9.2.8. IBM
9.2.9. Infosys Limited,
9.2.10. Intel Corporation
9.2.11. Microsoft Corporation
9.2.12. Neudax
9.2.13. NVIDIA Corporation
9.2.14. Oracle
9.2.15. Shell plc.
9.2.16. Other Prominent Players
AI in Fuel Market By Type
AI in Fuel Market By Function
AI in Fuel Market By Application
AI in Fuel Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
