How to Value an AI Business – Key Metrics and Factors

AI: The Transformative Market of Automated Efficiency

The artificial intelligence (AI) market is driven by rapid technological advancements across hardware development, software engineering, and AI application – daily innovations in machine learning, automation, and the creative potential of AI drive consistent growth across these segments.

As AI continues to revolutionize industries, understanding how to value an AI business is essential for investors, entrepreneurs, and stakeholders.

This essay explores what makes an AI business valuable, starting with AI industry segments and continuing through categories of AI solutions and AI funding history. Most critically, it ends with the key value indicators of AI businesses. Finally, we will examine the types of solutions AI companies offer and the critical ROI metrics that determine their valuation. spiders

Market Value

The global AI market is experiencing exponential growth. It was valued at $197 billion in 2023 and is projected to grow at a CAGR of 36.6% to reach $1,745 billion in 2030.

This growth is fueled by advancements in machine learning, natural language processing, and the integration of AI into business operations across various sectors.

The generative AI market, which is especially popular in consumer-facing and enterprise application markets, could contribute up to $15.7 trillion to the global economy by 2030.

AI Funding History

AI companies have attracted significant investment from venture capital (VC) and private equity (PE) firms, reflecting the industry’s growth potential. Between 2020 and 2023, AI startups secured over $100 billion in funding.

Notable examples include OpenAI, which raised $1 billion in a Series A funding round in 2019, and Anthropic, which secured $580 million in early-stage funding in 2022.

2023 saw a record number of AI startup investments, with seed and early-stage rounds increasingly focusing on AI-driven automation and analytics platforms.

Notable AI Startup Funding

In addition to investments in foundational AI companies like OpenAI, significant funding rounds for AI startups outside of big tech partnerships have also occurred. For instance, Inflection AI raised $225 million in its seed round, and Cohere AI secured $125 million in Series A funding. Other notable examples include:

• DataRobot, an AI-driven enterprise automation platform, raised $300 million in Series G funding in 2021, bringing its valuation to $6.3 billion.

• Hugging Face, known for its open-source AI tools, received $100 million in Series C funding in 2022, reflecting growing interest in democratizing AI technology.

• UiPath, a company specializing in AI-driven automation, garnered $750 million in a Series F funding round in 2021, achieving a valuation of $35 billion.

AI Industry Segments

Foundational Models

Foundational models, such as GPT-4 and DALL-E, serve as the backbone of modern AI applications. These models are pre-trained on massive datasets and can be fine-tuned for specific tasks, making them highly versatile. The value of foundational models lies in their finely-honed data processing power as well as their wide-ranging applicability across industries, from healthcare to finance.

As AI adoption grows, companies that develop and enhance these foundational models are well-positioned to secure a substantial portion of the market. However, the valuation of AI firms with proprietary foundational models can differ significantly, influenced by factors such as scalability and the ability to integrate these models across a range of applications.

AI Applications

The application of AI technologies spans every industry. Three prominent examples: search algorithms, chatbot services, and business operation management.

Foundational AI models enhance search engines’ ability to comprehend complex queries and improve content ranking. Beyond delivering precise answers to complex questions, these tools help to personalize in-house search algorithms for increased customer satisfaction and to rapidly process business data for dynamic transactions.

AI-driven analytics tools can predict market trends, optimize inventory levels, and automate financial reporting, allowing businesses to make data-driven decisions more effectively.

Generative AI (GenAI) furthers the potential of technology to inspire new users and reduce operational costs for businesses. For example, GenAI-powered chatbots have become essential in customer service, offering 24/7 support, automating routine inquiries.

Cloud and Data Processing

Data processing is the foundation of AI business operations, with cloud computing enabling the scalable processing of large datasets essential for training and deploying AI models.

Companies like Google Cloud, Amazon Web Services (AWS), and Microsoft Azure have built robust infrastructures that support AI applications by providing the necessary computational power and storage for data-intensive tasks.

The physical construction of data centers, coupled with the availability of clean and well-labeled data, directly impacts the performance and value of AI models. Additionally, choosing the right infrastructure for AI development can significantly affect both initial and ongoing costs, influencing the overall ROI of AI projects.

Database and AI Operational Integrity

The integrity of an AI company’s operational infrastructure—ranging from its development team to its hardware and software architecture—plays a significant role in its valuation.

The hardware that underpins AI operations, including GPUs and TPUs, is critical for model training. This is what is underpinning recent market demand for computer chips with more sophisticated processing capabilities.

Clean data is another cornerstone of effective AI; without it, even the most sophisticated algorithms can produce flawed outputs. Companies that can demonstrate robust, secure, and scalable operations are more likely to attract higher valuations.

Categories of AI Solutions

Product Innovation

AI businesses are at the forefront of product innovation, developing faster chips, enhancing cloud computing capabilities for AI, and continuously updating foundational models.

Innovations in prompt engineering and new training methods are pushing the boundaries of what AI can achieve. For example, advancements in natural language processing have enabled more sophisticated chatbots and virtual assistants, increasing both consumer-use and the adoption of in-house enterprise.

Process Innovation

Equally, the application of AI across various domains will transform the cost of both business operations and the management of personal affairs.

AI-powered chatbots managing customer experience, real-time payment processing systems, and generative AI for image and text creation are just a few examples of daily AI use-cases.

These applications open new revenue streams for businesses, making them attractive to strategic and financial investors alike. The path to realizing value from generative AI involves not only the technology itself but also the integration of AI into business processes in a way that drives measurable outcomes.

How to Value an AI Business

To learn the value of an AI business or investment, understanding the overall AI market is the first step. The valuation of AI businesses hinges on different metrics depending on the type of solution as well as its target segment and industry.

That said, there is a clear trend in maximizing the value of an AI business across categories. SaaS and application AI products especially will find more value if leaders can confidently share the following metrics:

5 key metrics to value an AI business in 2024:

1. Proprietary Technology: The uniqueness of a company’s technology – with certified patents – can significantly influence its valuation. Companies with proprietary AI models or processes that provide a competitive edge are more likely to command higher valuations.

2. Revenue Growth: Consistent and strong revenue growth is a clear indicator of a business’s market penetration potential. AI companies that demonstrate year-over-year revenue increases, particularly in rapidly growing markets, are more attractive to investors.

3. Path to Profitability: While many AI startups operate at a loss during their early years, a clear and achievable path to profitability is essential for long-term viability. Investors look for companies with a realistic plan to achieve profitability, as increased cash flow can be reinvested into the business to drive further growth. Understanding and measuring projected ROI in AI-driven solutions is crucial for companies looking to attract investment and demonstrate long-term value.

4. Sustainable Use-Case: AI businesses that solve real-world problems with sustainable solutions are more likely to succeed. Whether improving healthcare outcomes or optimizing supply chains, solving specific problems provides a long-term value add.

5. Regulatory Risk: AI companies operate within a complex regulatory landscape. Key risks include: data privacy, ethical considerations, and compliance with international standards. Companies that proactively manage these risks are better positioned to sustain long-term value creation. Managing regulatory and ethical risks is a critical aspect of AI governance that can significantly impact a company’s reputation and long-term success.

The firm recently published a full AI Market Report for 2024, including recent AI financing and M&A activity. Read the complete analysis here.

To learn more about how to value a SaaS business in 2024, check out the firm’s recent guide here.