November 11, 2019
Artificial Intelligence (AI) is a strong trend today in terms of technology innovation, start-up company focus and investment firm interest. Much hype can be found in the typical investment pitch-deck being circulated. But the term “AI” has become so common, even overused, it can often be misleading and unnecessary as a value-add to these materials. It does not always help in assessing the value of an early stage company.
The commercial market for AI is still in its early days, but it will no doubt continue to develop across multiple business sectors, and in some cases do so rapidly. However many companies exaggerate their AI proficiency and product capabilities. A clear understanding of the innovation and disruption potential of the technology is a core element driving investor interest, particularly in an emerging field such as AI. The complexity of the AI landscape continues to grow and the field is sub-dividing into different areas (Advanced AI and more advanced Analytics, for example). Therefore, different approaches are used for determining AI technology and start-up value, and these are blended with traditional valuation criteria of company performance to date, customer adoption, churn rates, leadership and team attributes, etc. The following are some examples.
AI Class is a means of categorizing the focus of business activity for the start-up. It helps simplify the AI landscape somewhat, suggesting the way in which the technology will used by customers. It also points to the way that technology will be acquired by customers, and therefore the way in which it will need to be brought to market. Here are some general classes:
AI used to enhance business operations — by automating manufacturing processes, for example. This form of AI is used primarily in an organizational support capacity, to lower costs or increase efficiencies.
AI used to increase business user or team effectiveness — by analyzing customer data in real time to improve an offer to a retail customer, for example. This type of AI is integrated into existing applications or offered incrementally, extending user capabilities, speeding processes or improving results generated.
AI offered as tools to build custom applications for or by a customer — to create a reseller portal, for example. This class of AI is marketed as a tool set, including architecture frameworks, best-practices, etc. and may or may not be supported by the vendor with professional services.
Classes of AI are not mutually exclusive. A company may offer a hybrid product or service but each class helps frame the nature of the target customer, the market opportunity, the competition to be expected, the cost structures to be planned for and other factors that will influence the value and viability of a new AI company or product. One AI class should not be rated higher or lower than another in isolation, nor should it suggest a strong or a poor investment (i.e., there are market opportunities across all these classes). The final preference, if there is one, will be more a reflection of the investment group objectives.
The next step in assessing the value of an AI start-up is a deeper dive into the dynamics of the target market, and that all-important competitive landscape. For example, it is important to get a clear understanding of:
This list could go on easily. And while these questions apply to any technology assessment, they are particularly relevant when talking about an emerging innovation such as AI, to avoid getting caught in the early-market hype and assuming undue risk in the investment.
Depending on the degree of technology development, the start-up will have a level of AI ‘assets’ that can be factors in a final valuation process. The deeper the company is embracing AI, such as those in the AI Platform class, the more value these assets may hold. For example:
Based on the preceding, determining an AI investment valuation can be more subjective than objective. The goals, timelines and priorities of the investor can carry more significance than in other technology due-diligence exercises. But in an effort to take more of the guess work out of the process, the following score card is an example of a tool that can help capture and rank the resulting data during an assessment.
|Company, AI Class
Subjective Ratings: 1 = minimal value; 2-3 = marginal/average value; 4 = clear added value; 5 = best in class
Artificial Intelligence is in the early stages of commercial application, and given the technology and human resources needed to become a fully established AI company, it is likely a start-up has several more steps to take on that journey. It is also likely they are using AI technology either sourced openly or developed elsewhere to augment a software product rather than craft something completely innovative, and there is nothing wrong with that. The assessment process outlined in this article is merely one starting point for investment discussions. And all of these factors will continue to evolve and mature as the technologies and markets inevitably do as well. alacrityglobal.com