How Much Does AI Cost: A Financial Analysis for C-Level Executives

AI is becoming more accessible every day, and more businesses are exploring how to integrate it into their operations. But let’s face it: one of the biggest questions people have about AI is, “How much does AI cost?” Spoiler alert: it can cost anywhere from $5,000 to over $1,000,000, depending on the scope of your project—whether it’s a customer support chatbot, a RAG system, or a full-blown prescriptive analytics engine.

Here’s the thing, though: AI pricing isn’t set in stone. There’s so much more involved, like preparing your data, training large language models, maintaining your system, and scaling it as your needs grow. This guide is here to help you break it all down.

Curious what an AI project might cost you? Keep reading to learn all the details you need to make informed, budget-friendly decisions.

Factors Affecting the Cost of AI Implementation

There are only two ways to start using AI today - you either build your custom AI system or use off-the-shelf software; both options come with a heavy price tag, but the exact amount ranges. A lot also depends on what you want your AI solution to do. Are you scaling a nascent project? Are you looking to optimize your hiring process by purchasing talent acquisition software? These categories (and more) are unique to each business and industry, but they must be considered if you want your AI project to be a high-reward venture.

AI Cost Breakdown: What Should C-Level Executives Expect?

Fortunately, analysts from Epoch AI broke down the three main categories that executives need to consider when evaluating the cost of implementing artificial intelligence and how much it costs to build an AI system from the ground up rather than purchasing an already-built product. The three categories are hardware, R&D (personnel), and energy consumption. They also determined the average range of what an AI budget would need to spend:

Taking these percentage ranges as their baseline, executives can now better anticipate the financial requirements of AI projects. However, these percentages only apply to what it would cost to build an AI system. As we discussed, companies also have the option to purchase already-existing software, which has the advantage of being tried and tested. Buying an off-the-shelf product has other benefits for those companies and their projects, which we’ll discuss in the next section.

Scope and Scale of AI Projects

Scaling any project is a significant investment, and scaling an AI project is no different. Researchers at Stanford found that training an advanced model such as GPT-3 costs over $12 million, a figure that has ballooned since then. The researchers also discovered that the costs of teaching an AI language model increased 2.4 times yearly. The researchers then surmised that training an AI model could run into $1 billion over time, putting AI innovations out of reach for most small and medium-sized businesses.

Of course, large enterprises with the money to scale massive AI projects also want their AI to carry a heavy load, meaning they want it to do a lot, such as inventory management, accounting, and marketing research. Along with the astronomical training costs, these enterprises must also budget for expanding their infrastructure, renting cloud space, and investing enough R&D talent to match their plan’s ambition.

Custom vs. Off-the-Shelf AI Solutions

We’ve already distinguished between spending the capital to build your AI and buying an off-the-shelf product. We’ve also emphasized the capital outlay for an in-house AI solution runs into the billions. Now, what about an off-the-shelf solution? Unsurprisingly, Gartner research indicates that off-the-shelf AI software costs are often much lower. Licensing fees average between $30,000 to $50,000 annually per user. Compare those figures to the range of $500,000 to several million dollars, and you can see how using an already-made product holds tremendous appeal for budget-conscious executives. Of course, “How much does AI cost?” should not be the only question.

Sure, custom-built solutions demand greater investments in R&D and elsewhere, but you have to weigh that against the tailored capabilities that a custom solution can have that a generic, off-the-shelf product does not. The one thing to take from this discussion is that there is an obvious tradeoff you must consider when thinking about how much it costs to build an AI system versus buying one.

Infrastructure and Hardware Costs

We’ve seen how when figuring out how much AI costs, infrastructure and hardware can take between 47-67% of your budget, so this category alone is among the most substantial costs in implementing AI.

For example, a single NVIDIA A100 GPU—commonly used in training models—can exceed $10,000. For one GPU.

Now, imagine you have a large-scale AI project that requires hundreds to thousands of these units. And that’s only for hardware.

Now factor in other infrastructure costs, such as renting cloud-based infrastructure. BCG reports that cloud rental fees for training an advanced model can range from $5,000 to $20,000 per hour, depending on the model’s complexity, and building your own AI solution seems less plausible than you might have thought it would be.

This expense may be a drop in the ocean for giant, multinational firms, but smaller companies may balk at this expenditure. However, having a well-funded infrastructure budget is an inescapable reality for any company thinking about harnessing the power of AI.

Data Requirements and Preparation

Data is the lifeblood of AI, but collecting, collating, and preparing all that data can be costly. One cost we didn’t mention above but can perhaps be categorized as a personnel and hardware expenditure is the cost associated with preparing data, which, according to a recent O’Reilly report, estimates can consume 60% to 80% of an AI project’s time and resources.

This figure also considers data engineering salaries, averaging about $120,000 annually. Even outsourcing data gathering can add up. Companies like Thomson Reuters and Bloomberg charge up to $2 million annually to access large, proprietary datasets.

These costs can become a headache since AI needs data to improve. As AI models become more data-hungry, the never-ending costs associated with feeding an AI model data, processing it, and refining it will climb exponentially, ultimately impacting the overall cost of implementing artificial intelligence. This sobering fact has led many organizations to prioritize data strategy to optimize resources and manage the significant expenses of high-quality data requirements.

Talent Acquisition and Development Costs

The demand for AI talent has surged. Salaries for machine learning engineers and data scientists have reached new heights.

A report by Deloitte estimates that the average annual wage for experienced machine learning engineers is around $150,000, while specialized AI research scientists often earn between $200,000 and $300,000. In some cases, high-level AI researchers at leading companies may earn over $1 million annually.

But then there is also the matter of finding this talent. According to the same report, talent acquisition and development costs can account for 30–40% of the cost to build an AI solution. This figure is only one more line of their balance sheet that executives must consider, especially given how essential skilled professionals are to AI projects. Many organizations will have to reckon with the high cost of talent, as it is a necessary consideration when assessing whether to pursue in-house development or collaborate with established AI service providers.

ROI of AI Projects

Now that we’ve answered questions of cost, we can take a closer look at the reason why everyone is interested in AI, which is the return on investment (ROI). The ROI of AI can be substantial. Some studies have estimated that in one industry alone (banking), AI could add over $1 trillion in value over the next ten years. In cases such as those, when AI is aligned with strategic goals like enhancing products and reducing inefficiencies, the ROI is obvious.

However, many companies are measuring ROI in different ways. Increased profits and revealing unrealized investment opportunities are one thing, but not every organization is centered around profits. A recent survey by the Harvard Business Review of 250 executives showed that over half prioritized AI initiatives to improve existing products.

In contrast, only 22% cited workforce reduction as a primary goal, indicating that cost reduction is often secondary to enhancing business value.

AI’s ROI also varies based on the type of application:

1. Process Automation: For robotic process automation (RPA) applications, ROI can be seen through immediate time and labor cost reductions. NASA, for instance, achieved considerable savings through RPA, automating 86% of HR transactions and planning to introduce more intelligent RPA bots to manage various departments.
2. Data Analysis and Insights: Cognitive insights are another ROI-driving area. By integrating data across units, GE realized $80 million in savings in its first year alone by eliminating redundant contracts and achieving better-negotiated terms. This data-driven insight offers substantial ROI, particularly in large, decentralized organizations with extensive procurement needs.
3. Cognitive Engagement: Given their current limitations, the ROI of customer-facing AI engagement tools, such as chatbots, is mixed. Companies like Facebook found that while chatbots offer some value, they are not yet mature enough for complex customer inquiries, with a 70% human intervention rate needed for Facebook Messenger bots. As a result, businesses often restrict these bots to specific conversation types, meaning the ROI is limited primarily to straightforward, predictable customer interactions.

To maximize AI’s ROI, executives should focus on deploying AI solutions that support process automation, improve data-driven decisions, and engage customers with a clear benefit. While the initial investment in AI is substantial, the long-term value—cost savings, productivity gains, and enhanced product quality—can justify the expense, making AI an investment with high potential returns when strategically implemented.

AI in Different Sectors: Cost Analysis by Industry

There’s no sugarcoating that AI is a significant investment, regardless of whether you opt for a bespoke solution or an off-the-shelf one. But what’s curious is how different industries can reconcile the advantages of implementing AI and its enormous expenses. Can some industries offset the costs of implementing artificial intelligence by reducing costs elsewhere? Will some industries have more pointed reasons for implementing AI, or are they looking for a centralized, catch-all solution to optimize their businesses? Let’s find out.

AI in Manufacturing

AI’s role in manufacturing will be potentially transformative, especially in predictive maintenance and quality control; transformative but costly. Companies like Siemens and Bosch have already implemented AI-driven quality assurance systems, saving millions by reducing downtime and preventing equipment failures. The cost of implementing AI for these purposes can range significantly, with Siemens spending close to $500 million annually on R&D for AI and digital factory technology.

One concern specific to manufacturers is infrastructure. They will likely have to invest in the physical infrastructure that powers their industry, such as building large-scale factories or even something smaller but more precise, like advanced sensors. Siemens’ AI models, for example, analyze real-time data from thousands of ultra-sensitive sensors, but maintaining this level of precision requires substantial ongoing investment. Even the most conservative estimates put the costs of building an AI system in manufacturing into the millions or more.

AI in Healthcare and Pharma

AI in healthcare is an enormous advancement with unlimited applications. Pharmaceutical companies, for instance, rely heavily on AI to analyze massive datasets and speed up research. Insilico Medicine, a company using AI for drug discovery, developed an AI-designed drug that entered clinical trials in less than 18 months—a process that traditionally takes years. While highly promising, these AI systems are expensive, with drug discovery models often requiring $10 million to $20 million to develop and test.

The costs of artificial intelligence in healthcare also include specialized datasets, secure cloud storage, and data privacy compliance, adding to the overall expense. As used by PathAI for pathology slide analysis, AI diagnostics demonstrate the efficiency AI can bring to patient care but also underscore the high cost of implementation. Medical-grade AI platforms can cost $500,000 to several million dollars, depending on the level of customization, software sophistication, and regulatory compliance.

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AI in Retail and E-commerce

In retail and e-commerce, AI is all about the customer, but not only. AI in retail is frequently used to personalize customer experiences. However, giant retailers like Walmart and IKEA have applied AI solutions to everything from inventory management to supply chain efficiency. Another example is Amazon, which spent over $700 million in 2022 on warehouse robotics and AI-driven logistics systems to predict demand and manage inventory precisely. But Amazon has Amazon budgets. Their AI solutions can perform tasks such as data processing and run a team of robots. These are some of the more flagrant examples of the high costs of implementing AI.

However, all is not lost for smaller retailers. Thanks to online e-commerce platforms embracing AI, smaller businesses can use these platforms to introduce low-scale AI solutions, such as personalized recommendation engines or chatbots. These off-the-shelf solutions have lower upfront costs, starting usually at $20,000. However, custom solutions, like Amazon’s proprietary AI recommendation algorithms, involve more high-end services, such as extensive data integration, which can lead to benefits such as increased customer retention rates and higher purchase prices, thereby helping costly AI solutions pay for themselves.

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AI in Financial Services

AI in financial services is not having the impact people might think. Rather than increasing profits and finding new investment opportunities, AI in financial services is more of a crime fighter than a cash cow. JPMorgan Chase, to use one example, uses AI to analyze vast amounts of economic data and identify fraudulent transactions, spending nearly $10 billion annually on AI and digital initiatives. AI systems in finance are complex and require both robust data infrastructure and high regulatory compliance, making the cost of implementing AI significant.

Depending on the scale and specific security requirements, an AI system might cost a financial institution between $500,000 and $2 million for fraud detection alone. Of course, there is also a lot of money to be made with AI in banking and financial services, which is why algorithms have been introduced into trading platforms or as robo-advisors.

However, algorithmic trading platforms and robo-advisors are costly to develop. They are not static platforms. They need sophisticated algorithms, live data feeds, and constant updates to adapt to market conditions, which costs a lot of money. Anyway, financial firms need to be aware that the high cost of building AI systems sometimes justifies the outcomes, which are streamlined operations and reduced risks in investing.

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AI in Aerospace and Defense

The U.S. Department of Defense, which has the largest military budget in the world, has committed $2 billion to fund AI projects over the next few years. This sum covers mostly autonomous drones, logistics, and data analysis. The potential of AI in defense, though, has far-reaching implications. Any innovations discovered by introducing AI into defense could blend into civilian life, such as GPS or drone technology.

Because of enhanced security, the cost of implementing artificial intelligence in the military is enormous. Protecting such highly sensitive data requires advanced technology, such as AI-predictive maintenance. Similar to GPUs, only one of these systems has a $1 to $5 million price tag. However, if you must protect and maintain a fleet of aircraft, ships, and vehicles, the high cost of implementing AI becomes evident.

A well-funded organization such as the Defense Department can afford the custom AI software, high-powered sensors, and continuous data analysis it requires to maintain and protect its hardware. The Department of Defense can also involve any of its private sector partners, such as Lockheed Martin, which created an AI-enhanced system for mission planning, specifically for the armed forces, to fill the gap between its development capabilities and its AI necessities.

Indeed, the US military is an ideal patron for dealing with how much AI costs. It has the resources, access to highly skilled professionals, and the energy capacity to bring many projects to fruition. The US military is an outlier, but it is still a persuasive example of how AI's long-term savings and operational efficiency can justify these high upfront costs for government and defense contractors despite the high initial costs.

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Vodworks – a Trusted Software Partner for AI Implementation

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With expertise across multiple AI applications—from process automation and predictive analytics to cognitive engagement—Vodworks leverages advanced technology and data-driven insights to optimize costs and maximize ROI.

Our team ensures seamless integration of AI into your operations, taking care of every aspect from infrastructure setup to ongoing support so you can focus on achieving meaningful, measurable results without the stress of high implementation barriers.

Ready to unleash the power of AI? Contact Vodworks today and start your AI journey.