In the last article, we examined three of the challenges facing AI startups: commoditization, demonstrating ROI, and managing risk. In this article, we’ll examine a fourth challenge: building a defensible “moat” to protect market position.
Warren Buffett has said, “A truly great business must have an enduring ‘moat’ that protects excellent returns on invested capital.” Buffett warns that “competitors will repeatedly assault any business ‘castle’ that is earning high returns.” He concludes that a “formidable barrier” is essential for sustained success.
The barrier – or moat – is a competitive advantage that makes a company unique and desirable. This article will examine three moats geared for today’s challenges. Each forms one layer that reinforces the others to improve defensibility.
The first challenge for any AI startup is to be noticed. With market incumbents adding new products and features rapidly – and with new challengers coming to market just as rapidly – making a strong first impression is critical. That makes distribution the first moat needed to build defensibility.
Bundling and embedding. Two conventional distribution strategies are bundling and embedding.
The benefit of these approaches is the ability to tap into an existing user base, but there are barriers to entry and the risk of becoming just a replaceable “widget.” In addition, bundling is often limited to well-known incumbents, with little room for startups.
Embedding offers broader access, provided the developer abides by the terms and conditions of the existing SaaS platform. However, deeper integrations may need approvals from enterprise system administrators or SaaS developers. That can bring embedding closer to the bundling model and may limit its utility for startups.
Trailblazing distribution. Instead of traditional distribution routes, some AI startups blaze their own trails by creating new categories, new cultural signifiers, or by courting controversy. Below are just a few examples:
Getting the right mix of users. AI is unlike SaaS and other tech business models because the cost of adding new users can be substantial. An article in The VC Corner notes, “In AI, every click burns the compute.” Without a disciplined distribution plan, costs can explode without corresponding revenue. Getting attention is necessary but AI startups need the right mix of users, particularly those willing to pay for the features they use.
Once an AI startup has gained traction, it needs to protect its user base. The next two “moats” are designed to keep users coming back – providing layers of defensibility to shield developing businesses.
Network effects describe a system in which each new use – and each new user – makes the product or system better. The key to creating network effects is to leverage data.
User data as a resource. To build network effects, AI startups can begin with user data. “Every click, search, and transaction holds value,” according to Pitchdrive.
In the last article, we discussed proprietary data as a hedge against commodification. For most startups, the biggest source of that data will be user interactions.
User data has the benefit of being unique and exclusive. AI startups need to protect it like the valuable asset it is. To reap its full benefits, though, they need to “invest in making the data clean, structured, and useful” both for model training and personalization, writes Latitude Media.
The data flywheel. Another way to build network effects is the “data flywheel,” a self-reinforcing loop that learns by interacting with users. Streaming services and Amazon product recommendations (such as “You may also like…”) are just two examples.
Designing a system that learns from each user paves the way for continuous improvement. Incorporating feedback loops into product design is an excellent way to improve the system: “When users correct errors, rate experiences, or share opinions, they are teaching your system,” writes Pitchdrive.
A third moat is brand trust. “Once considered a weaker defensibility, brand has become paramount,” according to NFX. That’s because many products have “similar functionality” while “concerns around hallucinations and data privacy” are growing, according to the authors. Becoming a trusted brand creates a moat that competitors will find very difficult to overcome.
Data strategy. In our previous article, we saw that “responsible AI” metrics for factuality, safety, and security are widely ignored by the AI industry.
But users, and especially enterprise users, want reassurance that their data is safe and that AI products are reliable. That creates opportunities for AI startups to build their brands by building trust.
Implementing a strong data strategy is key to developing a trusted brand. Protecting customer and partner data from data breaches and cyber-attacks is the top priority. The strategy must also ensure that only high-quality data from trusted sources is used for model training, personalization, and customer interactions.
Data governance. The organization should also develop data governance standards that keep data “fit, permitted, traceable, and secure.” Data governance standards must guide what data is allowed to enter the AI model, how the model operates, and how the model’s outputs are used.
Weak data governance can lead to “disinformation, bias, regulatory exposure, and security gaps,” according to TechRadar. Surprisingly, more than half of organizations fail to track even “basic data quality metrics,” the authors write. This weakness provides more opportunities for AI startups to build their brands through trust.
IP Ownership and Defense. From inception and throughout growth, a successful AI startup’s intellectual property strategies, ownership, and defense must be woven into both long term mission and short term planning, as well as at every stage of execution.
The three moats described in this article reinforce each other to enhance the defensibility of AI startups as they develop and grow. If you would like to learn more about helping your business reach the next level of development, please contact us.
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