The Growth Opportunity for Equipment Finance Companies in AI Infrastructure

As AI transforms business, demand for robust infrastructure is soaring. For equipment finance firms, the moment to step in and fund the future is now.

The rise of artificial intelligence (AI) and machine learning (ML) is not only transforming industries but also creating significant opportunities for equipment finance companies. As businesses seek to integrate AI-driven technologies, the demand for specialized hardware and software is surging. Companies investing in AI infrastructure require scalable solutions that balance capital expenditures and operational costs, making leasing and financing models more attractive than ever. This rapidly evolving AI-driven ecosystem requires equipment finance companies to place a heightened focus on risk mitigation and flexible financing structures to manage the risks of obsolescence proactively. AI infrastructure is poised to reshape entire industries. For equipment finance companies, this presents a significant opportunity to fund the technologies that will drive the global economy.

A NEW FRONTIER FOR EQUIPMENT FINANCING

AI infrastructure is the technology backbone that enables artificial intelligence. It represents an expanding asset class ripe for flexible financing models such as operating leases, as-a-service offerings and bundled financing. This vast category encompasses physical equipment, such as chips, servers and networking gear, as well as software systems and cloud platforms that enable companies to process large amounts of data and run intelligent applications. This market caters to a diverse range of industries, including healthcare, finance, automotive and manufacturing. Specific components of AI infrastructure include:

• High-performance computing (HPC) hardware, such as:

• Graphics processing units (GPUs): Particularly those manufactured by Nvidia, GPUs are the current gold standard for AI infrastructure.
• Tensor processing units (TPUs): Custom chips developed by Google that accelerate ML workloads.
• Field-programmable gate arrays (FPGAs): Semiconductor chips that can be programmed after manufacturing to perform specific tasks.
• Networking systems
• Storage solutions
• Software tools that optimize data processing and model training
• Orchestration platforms that enable AI to work seamlessly across on-premise, cloud and edge environments.

This infrastructure can be owned outright, leased or accessed on demand through the cloud. The projected demand growth in this category shows the enormity of the opportunity that AI infrastructure presents to the equipment finance industry.

The International Data Corporation (IDC)’s Worldwide Semiannual Artificial Intelligence Infrastructure Tracker reported in February that the AI infrastructure market is poised to surpass $200 billion by 2028.¹ Organizations increased spending on computer and storage hardware infrastructure for AI deployments by 97% year-over-year in the first half of 2024, reaching $47.4 billion, the report states.

In early 2025, Meta, Amazon, Alphabet and Microsoft indicated through CEO comments and earnings calls an intent to spend as much as $320 billion combined on AI technologies and data-center buildouts this year — up from $230 billion in 2024.²

As AI is expected to become central to productivity and competitiveness across all business sectors, firms of all sizes will face a need to make hardware and software investments to keep up. According to S&P’s Voice of the Enterprise: AI and Machine Learning, Infrastructure 2024 Survey, “Over 70% of respondents reported being inadequately prepared to handle future ML and AI workload demands in terms of the IT infrastructure that they have today.”

AI-driven businesses may not have the capital to make large upfront purchases of high-end computing hardware and software. Instead, they seek flexible financing options, such as leasing agreements and subscription-based models, to scale their AI operations efficiently. Finance companies can provide solutions that help organizations upgrade and expand AI capabilities without straining their budgets.

The list of equipment finance firms that are actively financing data centers today includes:

• DLL: Data centers and AI-related technologies, including cloud infrastructure and AI-optimized hardware
• Cisco Capital: Data centers and network infrastructure, including AI-related components
• Siemens Financial Services: AI-powered automation, IoT infrastructure and data centers
• Wells Fargo Equipment Finance: Data centers and AI infrastructure
• Key Equipment Finance: Data center infrastructure, AI hardware, networking equipment and cloud infrastructure services
• HPE Financial Services: Servers, storage devices and AI-optimized hardware

These firms offer traditional leasing and financing options. Some, like HPE and Cisco Capital, offer subscription-and consumption-based models.

FINANCING OPPORTUNITIES FOR DATA CENTERS

Data centers are the “factories” of AI production. They are the physical spaces where the vast computing power required by AI workloads lives. Growth in AI is significantly increasing demand for modern data centers with enhanced energy efficiency and high-capacity processing capabilities, creating opportunities for equipment leasing companies. While data-center financing often bundles real estate, construction and equipment costs into a single loan, there is an opportunity for equipment financing firms to provide financing for the equipment portion, which makes up a significant share of these massive projects. Real estate brokerage JLL reports that globally, data center assets valued at a combined total of $170 billion will need to secure construction lending or permanent financing in 2025. JLL projects that global data center capacity will grow at 15% per year through 2027, and this will not be sufficient to meet the growing demand.

The equipment finance industry has already seen growth from the impact of data center construction on demand for heavy construction equipment, diesel generators, HVAC systems and solar panels needed to feed their massive energy appetite. As data centers evolve to serve the needs of more power-intensive AI computing equipment, and as limited power supply and sustainability concerns weigh on the race to meet sharply rising demand, equipment finance leaders should consider additional areas of opportunity the data center boom presents. These include:

1. Power Infrastructure and Energy Efficiency Solutions

• Financing for AI-specific power solutions, including liquid cooling systems and energy-efficient data center infrastructure
• Lease-to-own agreements for backup power systems, such as uninterruptible power supplies (UPS) and AI-driven energy management software
• Green energy financing for AI data centers adopting renewable energy sources, including solar and battery storage solutions

2. Scalable Storage and Networking Equipment

• Leasing agreements for high-capacity storage systems, including all-flash arrays and distributed cloud storage
• Financing for AI-driven networking hardware, such as high-bandwidth switches, optical interconnects and AI-optimized data pipelines
• Payment structures that align with data-center expansion and technology refresh cycles

3. AI-Specific Data Center Construction and Expansion

• Equipment financing for modular and prefabricated data centers designed for rapid deployment
• Capital investment in hyperscale data centers focused on AI processing and ML model training
• Partnerships with AI-focused cloud providers and enterprises to offer custom financing solutions for dedicated AI computing environments

AI INFRASTRUCTURE FINANCING OPPORTUNITIES BEYOND DATA CENTERS

AI infrastructure financing opportunities extend well beyond the data center. Many non-technology sectors are eager to quickly scale AI applications but face capital constraints that delay their implementation. These AI-hungry sectors include:

• Healthcare: AI-driven diagnostics and medical imaging equipment often carry steep upfront costs. Equipment finance solutions can help providers adopt these technologies without straining CapEx budgets.
• Finance: Firms leveraging AI for fraud detection, credit scoring and algorithmic trading benefit from leasing high-performance compute infrastructure essential for real-time data processing.
• Manufacturing: Predictive maintenance and robotics require a combination of sensors, automation equipment and AI platforms, all of which can be financed to support smart-factory initiatives.
• Retail and Logistics: AI applications for inventory management, demand forecasting and route optimization depend on specialized hardware that can be deployed faster with leasing or consumption-based models.

By offering tailored financing solutions, equipment finance firms enable faster deployment, reduce capital burdens and support innovation at scale across industries embracing AI.

EDGE AI: NEW ASSETS REQUIRE NEW FINANCING MODELS

Edge AI refers to running artificial intelligence directly on devices that are located close to where data is generated, rather than sending that data back to a distant data center or cloud for processing. Examples of edge devices include industrial sensors, autonomous robots, AI-powered cameras, smart medical devices and embedded processors in consumer electronics. By enabling on-device AI processing, edge devices play a critical role in applications where immediate response times and reduced bandwidth usage are essential, such as healthcare, manufacturing, automotive and smart-city technologies. Stronger data security is another benefit, as edge AI eliminates the need for data to travel to remote services, introducing potential vulnerabilities.

As more businesses invest in smarter machines and devices that use edge AI, financing those assets — especially when bundled with software and services — becomes a growth area for equipment finance firms. Grand View Research estimated the global edge AI market at $20.8 billion in 2024, and projects growth at a CAGR of 21.7% from 2025 to 2030.³

The potential leasing and financing opportunities around edge AI include:

• Leasing agreements for AI-optimized edge devices, including AI-powered sensors and industrial IoT solutions.
• Financing for embedded AI hardware that enhances real-time analytics and decision-making.
• Subscription-based payment models for edge computing software and platforms that integrate with AI workloads.

RISKS AND CHALLENGES IMPACTING AI INFRASTRUCTURE FINANCING

While the growth potential in AI hardware and software leasing is significant, several risks and challenges may obstruct expansion:

1. Tariffs and Trade Restrictions: Import tariffs on AI-specific hardware, such as GPUs and networking equipment, could increase costs for leasing companies and end-users. Trade restrictions, particularly between the U.S. and China, may disrupt supply chains and hardware availability.

2. Supply Chain Disruptions: AI infrastructure relies on advanced semiconductors and specialized hardware, which are vulnerable to global chip shortages and geopolitical uncertainties. Supply chain bottlenecks can impact availability and leasing terms.

3. Regulatory and Compliance Risks: Governments worldwide are implementing AI regulations that could affect financing, leasing and deployment models. Compliance with data privacy laws, cybersecurity mandates and environmental regulations could add complexity to leasing agreements.

4. High Capital Costs and Market Volatility: AI hardware is expensive and rapidly evolving, increasing the risk of asset depreciation and obsolescence. Interest-rate fluctuations may also impact financing costs and affordability.

5. Energy and Sustainability Challenges: AI data centers require significant power, and sustainability mandates may limit expansion. Leasing firms may need to focus on green financing solutions to align with ESG initiatives.

THE FUTURE OF AI EQUIPMENT LEASING AND FINANCE

As AI adoption accelerates, businesses require flexible, cost-effective financing solutions to deploy and scale their AI infrastructure. Equipment finance companies that embrace AI-focused leasing strategies can capitalize on this trend, providing businesses with the necessary tools to drive innovation.

By offering customized payment options, financing for AI data centers and subscription-based AI hardware and software models, equipment finance firms can position themselves as key enablers of AI-driven transformation. The demand for AI infrastructure will continue to grow, and those financing companies that adapt to this rapidly evolving market will stand to benefit the most. •

¹“Artificial Intelligence Infrastructure Spending to Surpass the $200Bn USD Mark in the Next 5 years, According to IDC,” IDC, Feb. 18, 2025.

²Subin, Samantha, “Tech megacaps plan to spend more than $300 billion in 2025 as AI race intensifies,” CNBC, Feb. 8, 2025.

³“Edge AI Market Size, Share & Trends Analysis Report By Component (Hardware, Software, Services), By End-use Industry (Consumer Electronics, Smart Cities, Automotive), By Region, And Segment Forecasts, 2025 – 2030,” Grand View Research.

Valerie L. Gerard is Co-CEO of The Alta Group and leads its Strategy & Competitive Alignment practice. She helps companies design and implement value-creating solutions, and partners with leadership teams on both strategic and tactical issues ranging from growth strategies and business model optimization to multi-vendor customer financing programs and long-term capitalization. She served as a member of the Board of Trustees for the Equipment Leasing & Finance Foundation from 2018 to 2024 and chaired the Foundation’s Research Committee. She is the 2023 recipient of the Foundation’s Steven R. LeBarron Award for Principled Research. Gerard was recognized in Monitor’s inaugural class of the Top 50 Women in Equipment Finance.