Artificial intelligence continues to expand and evolve because of numerous technological advancements, such as smart software and machine learning algorithms. Many small businesses are using AI technology to improve their place in the global market by offering innovative products and services. Even though their main focus is artificial intelligence, there are many questions about their business model, market opportunity, and scope for innovation.
As investors and stakeholders try to find sustainable and innovative businesses to invest in, hence it is important to get the exact value of early-stage AI companies. In this blog, we will talk about AI valuation, how to value early-stage AI companies, different valuation methods, and challenges in valuing such start-ups.
What are early-stage AI companies?
An early-stage AI company is a small business or start-up that uses AI technology to create new tools or software. Since they are in their early stages, hence they do not have a fully mature product but an early version of the product that has been developed and tested multiple times. Additionally, these companies are just a few years old and are still building their presence in the market.
Hence, they have limited revenue but good growth potential as their main focus is on innovative and cutting-edge AI technology.
What is an AI startup’s valuation?
AI startup valuation is the process of calculating the worth of an AI company. In simple words, it is like putting a price tag on a company so that investors and funders can understand its future and current worth. For founders, it decides how much part of the company they must give to investors in exchange for funding, whereas the investors can decide what they are getting in return for their money.
This process is very important as a strong valuation can attract beneficial partners and collaborators for the future. Moreover, by valuation, a company can examine its financial health and ensure that its strategies meet the industry and market standards for both growth and sustainability.
How is early-stage AI valuation different?
Before we understand how to value early-stage AI companies, it is necessary to know how this process is different from the traditional valuation of companies. Here are a few points to understand:
| Technology & Data | A traditional valuation looks at physical assets and income, such as machines and hardware. However, an AI valuation looks for intellectual property, such as patents, innovative technology, computer algorithms, and the quality of data used by the company. |
| Demand In Market | For traditional business valuation, it is easy to understand what a customer’s product demand is. However, an AI valuation looks for a company’s products and services that can meet future customer needs and also solve important problems. |
| Growth Potential | A traditional business valuation focuses on the present earnings and assets of a company. However, an early-stage AI valuation examines the future scope and growth potential of the company. Additionally, the company’s new technology and ideas are also analysed to see how much it can grow in the future. |
9 Metrics for assessing AI company value
There are specific metrics that you should keep in mind when assessing AI company value. Here are the following factors:
Market potential
Market potential means the size of the market where the company is looking to target customers. This also includes room for growth, competitors, and the challenges that come when you try to enter a new market. For example, if a company is trying to make an AI healthcare product, then you must see how relevant the product will be to the healthcare industry and if people will benefit from it in the future. If the market is large, then the chance of growth and revenue increases, making the company more valuable.
Technology Assessment
One of the most important factors of an AI valuation is checking the technology and innovative products the company has. The same can be done for examining the product that the company has introduced. For example, if a company has developed cutting-edge AI technology that solves healthcare problems, it can stand out in the market and attract investors.
Moreover, if a patent or intellectual property is added to the technology, then it makes the company more important and increases its AI valuation.
Business Model
A business model tells investors and competitors how the company will make money. AI startups can use many business models that target customers directly, sell their business to other companies, or make partnerships with similar organisations. Hence, a good business model shows investors that the company has the ability to make money and generate revenue and profit.
Scope of innovation
The uniqueness of a product with AI technology can lead to high AI valuation as companies with new innovations can quickly become profitable and change the game in the market. Additionally, if the technology is supported by patents, then the company has a good chance of becoming more valuable for investors and founders.
Team & leadership
The people and leadership behind an early-stage AI company are very important. They are the individuals who determine how far the company will grow in the future. Hence, you must always consider the experiences and success stories of the team that works on an innovative product or service. A skilled and talented team increases the company’s chances for high valuation and attracting investors.
Development phase
Another important factor that determines the valuation of a company is the developmental phase. Startups and AI companies with fully developed products or services have more chances of high AI valuation as it shows the market and investors that they can deliver more than just ideas. However, the progress of every AI product must be studied as those early-stage companies that are further in development are seen as more reliable and profitable than others.
Financial health & revenue
An AI valuation is different from traditional valuation as it does not focus on the present earnings or revenue. However, it does not mean that investors will provide external funding for an AI startup without considering the company’s profit plan. Hence, an early-stage AI company has to show a solid income plan or revenue generation method so that its future potential can be understood along with its value. In addition, this shows that the company is heading in the right direction and can be trusted for future investment.
Sustainability
If an AI company solves problems and also follows its ethical responsibility towards the climate and people, then it is considered of high value. It is not just about profit but also adjusting to market trends, following ethical practices, and minimising the environmental impact. All these things lead to a socially responsible AI company, which makes it less risky and more valuable in the market.
Rules & regulations
Every industry, government and businesses have ethical practices and regulatory rules like data privacy, good governance, sustainability, intellectual property rights, and international standards. An AI company that follows these has an increased chance of being more valuable in the market. This increases its value and is a necessary metric that must be kept in mind when assessing the AI valuation of a company.
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Book a ConsultationDifferent valuation methods for AI startups
Here are different valuation methods that are used to assess the valuation of an AI startup:
Market valuation
This method is used to determine the AI valuation of a company by comparing it with other AI technology companies that have recently been bought or raised revenue in the market. The following are the factors that are used in the market valuation method:
- Revenue or Profit
- Number of users
- Growth potential
- Scalability
- Technology & innovation
- Partnership or Collaboration
Discounted cash flow valuation method
The discounted cash flow method is a way to determine a business worth by predicting how much money it may make in the future. In simple words, it examines how profitable the early-stage AI company will be considering the risks and future market trends.
This method estimates the company’s future earnings and applies a discounted rate on it. This discount rate considers risks and challenges like inflation and the time value of money. Hence, the DCF method calculates the value of the business for the present day. It is an accurate tool that is widely used by investors and founders who value an early-stage AI company.
Asset-Based valuation method
You can easily understand by the name of the method that it assesses the AI valuation of a startup by examining the tangible and intangible assets. It also determines how much cost it would take to build the company from scratch. Following are the tangible and intangible assets it checks for AI valuation:
| Tangible | Office equipment, machines and hardware |
| Intangible | Intellectual property, patents, advanced algorithms, special AI tools and technology |
Berkus valuation method
The Berkus valuation method also assess a few tangible and intangible factors to determine the value of an AI early startup. First, it considers the idea and innovation behind the business and how impactful the technology might be in the future. Second, it checks the development stage of the AI technology. Has the company have a fully developed and test model or early version of the product?
Then comes the most imporatnt factor, and that is the skills and team leadership. What are the sucess stories behind the people working on the AI startup and what experience do they have with other AI companies. Moreover, it also checks partnerships and collaborations with similar businessess. In the last, it determines the profit and revenue generation of the company with the product or service.
Each of these factors are given a monetary value, and the total gives the company’s AI valuation. As this method combines both qualitative and quantiative methods, it is very useful for investors and stakeholders.
Venture Capital Method
This method assess the AI valuation by predicting the future worth of the company and working backward to determine what value it holds today. It answers the question “ What would the company be in 5-10 years? Or how much money it can make for investors?”
Scorecard Method
This method is similar to Berkus’s valuation method and is based on grading a startup on different tangible and intangible factors. Here is how it works:
| Team (40%) | How capable and experienced are the people that are running the company? Can they be trusted with the success of the company? A startup with a strong team is given a higher score. |
| Market (30%) | How big is the market, and how well does the AI technology fit into it? What problems is the company solving? Bigger markets mean a high score. |
| Product (20%) | Does the product stand out from its competitors? Is it useful in the AI industry, and is it in demand among the customers? How innovative is the product for future growth? |
| Financials (10%) | Even though the startups do not have any revenue or profit, their financial heath still matters. This includes analysing the cash flow and if the company has enough funds for operational costs, machines and other resources. Moreover, the history is also covered to see how much capital they have raised from previous external refunds from investors. |
Challenges in valuating early-stage AI companies
Here are the challenges in valuating early-stage AI companies:
- Most of the AI startups and early-stage companies are in their initial phases of development. They have no consistent revenue or income which makes it hard for investors to understand the value of the company. Hence, they have to rely on the potential of the company as the performance of the AI technology or product is uncertain in different market conditions as it is still developing.
- Another major challenge in valuing early-stage companies is scalability. A new AI product or technology may work well with small amounts of data but it can become difficult to determine whether it will work on large data or not. Hence, scaling an AI system can be very difficult.
- As AI valuation depends on scope of innovation and business models, it can be hard to determine the worth of the company as most of the products and services are still in their early stages of development, and have not yet been tested in the real-world. Moreover, assessing the value of intellectual property or quality of data is equally challenging.
- AI companies often face industry and government regulations. Such laws can affect the company’s value and can also make it hard to predict how the company will perform against its competitors.
Future of AI valuation for early-stage companies
The future of AI valuation for early-stage companies will keep changing as long as technology evolves. As AI applications and technology continue to advance, more standards and methods will be developed for assessing the AI valuation. Investors will create more benchmarks so that an accurate value can be set for early-stage companies and business startups.
Apart from this, AI depends heavily on data quality, processing and availability. Companies that can handle large amounts of data in the future and have innovative solutions for data processing will be valued higher among others. Moreover, companies that follow ethical regulations, regulatory standards and can solve specific problems will be assessed as more valuable. Hence, by keeping an eye on proper valuation methods and metrics, stakeholders and investors can make better decisions about early startups.
Read More: How does artificial intelligence work?
