Artificial Intelligence (AI) Software Market Size: 2023 to 2030
Source Research:
Artificial Intelligence (AI) Software Market Data Overview: 3Q 2024
Highlights
- The Artificial Intelligence (AI) software market size will be valued at US$98 billion in 2024.
- Growing at a Compound Annual Growth Rate (CAGR) of 30%, the AI software market size will reach US$391.43 billion in 2030.
- Generative AI will be the fastest growing AI framework with a 49.7% CAGR over the market forecast period with foundation models, optimization software, and model deployment tools offering the largest opportunities.
- Traditional AI (AI sensing, predictive AI, Natural Language Processing (NLP)) will continue to lead total revenue in the AI software market. This significant growth will be driven by maturing enterprise AI strategies that will make these frameworks more accessible.
- Over the forecast period, the revenue gap with generative AI will continue to decrease given the wider applicability of generative AI across industries and use cases.
Over the past year, ABI Research has published numerous reports on the Artificial Intelligence (AI) market. Whether it’s the rapid growth of computer vision deployments or the latest generative Artificial Intelligence) trends, numerous developments are shaping the future of the AI industry. As potentially the most disruptive technology of our time, the AI market must be studied through both quantitative and qualitative analyses for optimal business outcomes.
Figure 1: Companies in the Generative AI Supply Chain
(Source: ABI Research)
A US$391 Billion Artificial Intelligence Software Market
The global Artificial Intelligence (AI) software market size is forecast to reach US$98 billion in 2024 and grow at a Compound Annual Growth Rate (CAGR) of 30% between 2023 and 2030. By 2030, the AI market is estimated to be valued at US$391.43 billion.
A major growth driver will be generative AI adoption across enterprises and across use cases. Moreover, maturing enterprise AI strategies lead to scaled deployments of other “traditional” AI frameworks (including computer vision, predictive AI, and Natural Language Processing (NLP)).
North America has the largest regional market size for AI powered software, driven by frontier AI innovators based in the United States. In 2024, 43% of total AI software investment will come from North American-based companies.
The Asia-Pacific region accounts for 32.7% of AI software revenue in 2024, but as China ramps up engagement in the AI race with the United States, our analysts expect the region to account for 39.9% of the market by 2030. Our forecasts indicate that China will account for two-thirds of total AI software revenue (US$156.18 billion) in Asia-Pacific by 2030. ABI Research expects this battle for AI supremacy to decrease North America’s share of artificial intelligence software revenue to 36.5% by the decade’s end.
The significant growth projection for the AI market is due to the following industry-shaping trends:
- Open Source Innovation Will Reduce Enterprise Deployment Barriers: Open source will continue to be a significant revenue generator for AI, with Machine Learning Operations (MLOps) platforms increasingly utilizing open-source models and tools for comprehensive AI solutions.
- Long-Term Graph-Based and Tabulated AI Opportunities: Revenue from graph-based and tabulated AI models will remain strong as these models become widely deployed across industries supported by maturing enterprise AI strategies.
- Generative AI Mostly Cloud-Based: Due to large model sizes and hardware limitations, Generative AI will primarily be deployed in the cloud, with limited edge deployment. However, on-device and edge deployments will grow quickly, supported by investment in AI model optimization.
- Revenue from Accuracy, Performance, and Trustworthiness: Focusing on these key areas will drive significant revenue opportunities in AI data services, observability, and model testing software tools.
- Dominance of Optimization and Training Software: Power consumption, resource limitations, memory constraints, and cost considerations mean that optimization and training/fine-tuning software will generate robust revenue over the forecast period.
- Growth of MLOps Tool Platforms: Expect significant growth, both organic and through acquisitions, in platforms that provide MLOps tools.
- Regional Revenue Dominance: North America and China will lead in AI software investment, while the Middle East & Africa, particularly Israel, will show significant growth due to AI innovation.
- No/Low-Code Platforms Essential: These platforms will become essential for enterprise AI democratization, with traditional code interfaces reserved for complex ML solutions. This will help engage the long-tail of enterprises leading to more scaled enterprise AI deployments.
- Impact of Open Source on Generative AI: Open source frontier innovation will improve accessibility, and drive substantial growth in generative AI MLOps revenue as enterprises look to harness leading models in workflows and applications.
- Importance of Enterprise Services: Talent shortages and time-to-value considerations will contribute to sustained reliance on enterprise services. Integrators, consultants, and MLOps software vendors will drive sustained revenue opportunities from enterprise generative AI deployment.
- Dominance of Cloud AI with Edge Opportunities in CV: While cloud AI will remain prevalent, Computer Vision (CV) will offer the largest edge deployment opportunities, particularly for AI inferencing.
Methodology
The AI software market is developing at a rapid pace with innovation and investment across models, frameworks, tools, applications, and services. This activity has been spurred by the recent advances in generative AI. In addition, maturing enterprise AI strategies are creating a significant amount of growth for “traditional AI” with significant growth expected in predictive AI and AI sensing (including computer vision).
Considering this development, ABI Research has developed a market data (MD) model to help our clients assess the revenue opportunity and business opportunities for AI software across different AI frameworks, MLOps software tools, model types, deployment locations, and revenue models. The forecast stretching to 2030 looks to shape the path toward AI software monetization, considering and highlighting key trends in the wider AI enterprise market.
AI powered software is not the only market dynamic that ABI Research observes. Through application analysis reports, market data forecasts, and comprehensive presentations, our analysts have identified emerging trends in generative AI, Venture Capitalist (VC) funding in AI companies, major acquisitions, on-device AI, and the vendor landscape.
Generative AI Market Outlook
ABI Research forecasts the generative AI market size to grow at a robust CAGR of 49.7%, increasing from US$10.45 billion in 2023 to more than US$176 billion by 2030. Today, North American firms invest most in generative AI software applications. However, Asia-Pacific will take the lead by 2028 as the region’s vast industrial and enterprise space adopts generative AI. Most generative AI market activity will involve software applications that run in the cloud and the edge. Enterprise services account for about 40% of the generative AI market today and 32.22% by 2030.
While applications like ChatGPT have created immense momentum for generative AI, much of the real-world use cases have been tied to the consumer markets. Though generative AI has proven useful in business applications, it has yet to fully penetrate the enterprise market. The potential for enterprise generative AI deployment is enormous, with applications spanning various verticals and business functions.
The growth trajectory for generative AI in the enterprise will be significant. ABI Research’s recent study reports that generative AI use cases will create US$434 billion in value creation for enterprises annually by 2030. Marketing, advertising & creative accounted for 46% of the enterprise value creation from generative AI in 2023, according to our forecasts. Generative AI will be pivotal in creating digital art, accelerating the development of marketing content, writing advertisement copy, and transforming Two-Dimensional (2D) content into Three-Dimensional (3D) content. As emergent as generative AI is in the marketing/advertising industry, its share of value creation is projected to dwindle to 25% by the decade’s end. This decline is due to other industries expanding their investment in generative AI applications.
Retail and e-commerce are forecast to account for 33% of the enterprise generative AI market by 2030. Generative AI growth drivers include visual search tools for customers, enhanced chatbots, summarizing product listing pages, automated website optimization, and more. The adoption of generative AI in retail & e-commerce will happen rapidly, as the industries only account for just 7% of the total value creation in 2024.
Financial services companies will be other big beneficiaries of generative AI, accounting for roughly 20% of value creation throughout the rest of the decade. For the financial services industry, generative AI can be integrated into stock market analysis and internal database management systems to improve decision-making and bolster cybersecurity. Moreover, generative AI can automate client drafting and be used for customer-facing sales interactions.
While these are the top three industries for generative AI, many other company types will benefit from its deployment. Utilities, manufacturers, law firms, pharmaceuticals, education institutions, and other sectors are expected to spend billions on generative AI applications and use cases through 2030.
Artificial Intelligence Investment Trends
As of 3Q 2023, artificial intelligence startups secured US$32.9 billion across 1,689 funding rounds, marking an 11% decrease in funding and a 33% drop in the number of rounds compared to the first three quarters of 2022. Despite the Generative AI surge, global funding for AI startups has declined in recent years, reflecting the broader funding challenges posed by high interest rates and a bear market.
Despite an overall decline in VC funding in 2022 and 2023 due to macroeconomic challenges, AI startups performed better than other tech firms, fueled by generative AI hype. In 2021-2022, VC-led funding shifted to investments and acquisitions by global AI market leaders. These larger companies aim to differentiate their AI offerings, build comprehensive ecosystems, and support overlapping business groups.
The geographical distribution of AI startup funding provides a comprehensive market view. The United States, China, and the European Union (EU) emerged as the key players, with the EU AI Act potentially impacting regional investment in 2024. Most investors targeted AI software, driven by the high demand for practical tools and innovation in the hardware market. Notably, RISC-V stands out as an open standard alternative to costly Arm/x86 architectures.
Global AI innovation is primarily fueled through numerous Mergers & Acquisitions (M&A) activities in the market, which paints a clear picture of the global landscape. Recent notable AI market developments have made headlines and underscored each decision's strategic significance. These moves include the following:
- Databricks Acquires MosaicML: MosaicML allows its customers to train Large Language Models (LLMs) on their own data and run them anywhere. The US$1.3 billion acquisition will allow Databricks to add generative AI capabilities to its Lakehouse multi-cloud service (learn more in the ABI Insight, “Databricks’ Acquisition of MosaicML Tells Us a Lot about the Direction of the Generative AI Market.”
- Cisco Acquires Splunk: AI-powered data analytics, observability, and cybersecurity firm Splunk was bought in a US$28 billion cash deal. The firm will enable Cisco to reinforce its security measures.
- AMD Acquires Nod.ai: Nod.ai builds optimized AI solutions to support hyperscalers, enterprises, and startups with reducing operational cost and improving performance for AI models. This acquisition is an important strategic move from chipmaker AMD, as it will be able to offer software services similar to its competitor, NVIDIA.
- IBM Acquires WebMethods and StreamSets: IBM has acquired Software AG's integration platforms, webMethods and StreamSets, for US$2.3 billion. The platforms, which offer application integration, and data integration services, will help IBM augment its hybrid cloud and AI offerings.
- NVIDIA acquires Deci AI: Deci AI provides pre-optimized models and Neural architectural Search (NAS) capabilities to enable deployment of computer vision and generative AI across different hardware. This acquisition will bolster NVIDIA’s newly minted AI Enterprise platform.
According to ABI Research, the United States is the leader in Large Language Model (LLM) production, closely followed by China. Both countries also top the list for artificial intelligence startup funding, supported by mature tech ecosystems, strong government backing, and a focus on Research and Development (R&D). Market leaders like Google, Microsoft, Baidu, Alibaba, and Tencent drive foundational AI investments in these regions. Total investment in AI hardware and software—not strictly startup funding—was as follows in 2023:
- United States: US$50.6 billion
- China: US$11.2 billion
- EU: US$6.1 billion
- India: US$2.84 billion
- United Kingdom: US$2.6 billion
- Israel: US$1.59 billion
- Others: US$7.5 billion
Note: Israel is grouped into the “Others” category
On-Device AI Market Heating Up
One of the biggest AI market trends ABI Research has been covering is the increased attention to on-device AI. Generative and other AI models have predominantly been cloud-based, but scaling AI across applications faces commercial and technical challenges (including reliability, availability, cost, data privacy, and networking). To address these shortcomings, on-device AI capabilities—and eventually hybrid AI—are emerging, allowing inference workloads to be performed locally. This shift will enable more “personalized AI” solutions that can securely use user data to inform model output.
For instance, Intel envisions smaller AI models running on AI PCs with Intel Core Ultra processors, using local personal data to power personal assistants and other more mission-critical use cases. In the enterprise sector, on-device AI can aid in scheduling, email drafting, note-taking, and contract creation, increasing demand for updated devices in both consumer and enterprise markets. On the consumer side, smartphones equipped with on-device AI can automate tax returns, automatically schedule get-togethers, optimize smart home appliances, and other productivity-enhancing apps. These productivity apps translate into tangible Return on Investment (ROI) through time savings and operational cost cutting.
Shipments of productivity AI chipsets for mobile and PC markets are set to begin in 2024, supported by the production of heterogeneous AI chipsets designed for on-device AI workloads. Devices equipped with these AI chipsets, such as Qualcomm’s Snapdragon 8 Gen 3 for smartphones and Intel’s Core Ultra processors for notebooks and desktops, will be capable of handling generative AI workloads on-device. Adoption of these devices is expected to grow rapidly from now until 2028, although early adoption rates may be hindered by the immaturity of the application ecosystem. As more applications begin to leverage on-device generative AI for productivity, both consumers and enterprises are anticipated to invest in new devices more frequently.
How Vendors Are Catering to the AI Market
From specialized service providers to chipset suppliers, there are a variety of companies that make up the vendor landscape in the artificial intelligence market. ABI Research’s AI/ML Market Tracker report provides us with a granular-level analysis of AI software and hardware companies, revealing the following trends:
- Software Market Fragmentation and Upcoming Consolidation: The AI software market is fragmented with dominant single service providers. Consolidation is likely in the next 2 to 3 years due to reduced VC funding and more exits to capital-heavy leaders. Major players are already acquiring startups to support market entry and solution consolidation—development of end-to-end MLOps platforms will spur M&A spending moving forward.
- Growth in the Hardware Market via RISC-V: While global AI market leaders dominate hardware, RISC-V architecture is boosting startup growth. Our research analysts note that well-established companies with deep pockets—not just capital-constrained startups—are moving from x86 or Arm to RISC-V architecture.
- Expansion of Generative AI Services: Over 70% of AI software vendors now provide some form of generative AI application or service. This proportion will grow as “traditional” AI frameworks mature, enterprise strategies are developed, and supply side innovation slows.
- AI Lifecycle Optimization Tools: 45% of software vendors offer tools for AI optimization across the lifecycle, enhancing model performance from data preparation to fine-tuning. Alibaba, Baidu, Google, H20.ai, IBM, and Mantium are just a few examples.
- Increasing Governance and Regulation Tools: Only 7% of software vendors (e.g., Holistic AI, Hugging Face, Fairly AI, Data Robot, etc.) in the AI market offer governance tools, but this is expected to grow as enterprise AI deployment scales and new internal/external regulatory constraints are imposed.
- Importance of Developer Platforms: Developer platforms are crucial in the AI value chain. Hardware vendors are increasingly recognizing their role in monetization and product differentiation. It is vital for hardware vendors to invest in hardware and software to compete with market leaders. Low/no-code platforms with visual tools are being created to facilitate deployments for enterprises with low AI skill levels.
- Hardware Vendors Expanding Software Capabilities: Hardware vendors are enhancing their software offerings, focusing on generative AI, enterprise services, developer platforms, and optimization tools. As chipset advancements become more costly, differentiation is shifting to the AI software layer. Expect most vendors to offer combined hardware and software solutions in 2 to 3 years.
- Growth in Data Services and Optimization Tools: Data services and optimization tools are currently offered by 21% and 20% of AI software vendors, respectively. Anticipate more hardware and software vendors to incorporate these tools in the coming years.
Figure 2: Percent of AI Software Vendors Offering Each Service Type
(Source: ABI Research’s AI/ML Market Tracker)
About ABI Research
ABI Research sits at the intersection of artificial intelligence technology vendors and enterprises leveraging their solutions. This unique position allows us to shape the future of the AI industry in a way that aligns software and hardware with the specific requirements of various industries and markets.
Whether your organization seeks to compare its performance with competitors or wants to identify generative AI best practices, visit our AI & Machine Learning Research Service. For a focus on generative AI, check out our Generative AI Research Spotlight for the latest research findings.
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Our AI market sizings are sourced from the following market data reports:
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Gen AI Software Market Data | 3Q 2024 | MD-AISG-101
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Artificial Intelligence (AI) Sensing Software Market Data | 3Q 2024 | MD-AISCV-101
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Artificial Intelligence (AI) Natural Language Processing (NLP) Software Market Data | 3Q 2024 | MD-AISNLP-101
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Predictive Artificial Intelligence (AI) Software Market Data | 3Q 2024 | MD-AISP-101
