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  • Sergii Shelpuk

AI Strategies That Offer a Sustainable Competitive Advantage: Principles You Should Follow


AI Strategies That Offer a Sustainable Competitive Advantage

In my previous blog post, we discussed artificial intelligence product strategies and techniques that do not work. Although they may give you a temporary advantage, they are easy to duplicate, leaving you vulnerable to competitors who will do just that sooner or later. Today, let's take a look at artificial intelligence strategies that offer the greatest potential for the technology: creating a sustainable competitive advantage that is impossible for your competitors to replicate.

Competition is essential to consider when building a lasting business. While there is nothing wrong with capitalising on short-lived and speculative expectations, a lasting and sustainable competitive advantage can deliver exceptional results in the long run, including turning your company into a market leader and generating profits for decades to come. Companies that underestimate or misunderstand competition may succumb to it very quickly, even after years of successful performance.

Once-great companies that failed the competition

Once-great companies that failed the competition

The key to competition is the ability to copy. If a competitor can build a product or service that closely resembles yours, your market share is in danger. You may be ahead of the competition today, but ultimately, the cost of imitation determines the future of your business.

Thankfully, we know business strategies that help us make our products very difficult to copy and imitate, and one of these is artificial intelligence technology. However, not every artificial intelligence product, application, and technology builds a sustainable competitive advantage. It's easy to believe that you're safe because you're using artificial intelligence, only to discover that a swarm of competitors is silently devouring your market share. Fortunately, this blog is here to prevent you from finding yourself in that situation. Let's dive in.

Although it is referred to as "AI competitive advantage," the true source of the competitive edge is the data. AI is the tool to transform the data you have into product superiority. And if product superiority gives you more data, it establishes a positive feedback loop that ignites the network effect. After you've developed the network effect for a while, it becomes practically impenetrable: to copy your product, a competitor needs your data. But to get the data, the competitor needs your product.

AI virtuous cycle

Here are six principles you should follow to ensure that your AI product or technology contributes to the network effect and cannot be easily copied by competitors. You can also use them to audit your current AI projects to better understand whether your AI investment contributes to your business's sustainability or whether it is being consumed in the commoditising competitive arms race.

1. Your AI models should utilize scarce and limited data to make your product better

Let's talk about the first principle to ensure that your AI product or technology has a sustainable competitive advantage: use scarce and limited data to improve your product.

If you're using easily accessible data, like public datasets or scraped websites, you're doing it wrong. And if you're not even training your models, but relying on pre-trained ones or third-party AI products, that's even worse.

The data is the key to your AI competitive advantage, not the algorithms themselves. You need to train your AI algorithms on data that is hard to come by to improve your product. Consider the data your company acquires while operating and how it can enhance your product. Think about designing user journeys that encourage users to provide the right data while using your product.

2. Beware of approaching AI initiatives with a regular product development approach

The second principle to develop a sustainable AI competitive advantage is: don't treat AI initiatives like regular product development. Building an AI edge is like a tango between your AI and product teams. The AI team needs to understand how to impact the product's perceived value and build models that contribute to this core value, not just any sugary side effects. The product team, on the other hand, must know what data improves the AI models and design the product to collect this data in the most efficient way. This tango takes two, and a siloed organization or weak collaboration between the AI and product teams is a red flag.

3. Building an AI competitive advantage should be challenging, and the initial version of your AI models should not be of top quality.

If you want to stand out in the AI game, you need to be one step ahead of your competitors. Your AI models should be like a secret sauce that nobody else can replicate. That's why the initial implementation of your models should be hard, and the quality should be inferior. Replicating your AI solution should be difficult without access to your unique data. If you're able to create a widely appealing product with ease and little available data, beware! Your competitors could replicate it just as quickly, stealing a slice of your market share.

4. Your AI models should not be easily transferable across domains

The key to a successful product is versatility, but when it comes to AI models, too much versatility can be a bad thing. If your AI models can be easily applied to different markets, it's a red flag. For example, if your retail-based AI model can easily be adapted to the financial sector, you're at risk of losing your competitive edge. If your competitor can create a similar solution in another domain without spending too much time on data collection, they could quickly move into your market and take your share.

On the other hand, if your AI model performs well in one domain but not so well in another, that's a good sign. It means that expanding into a new domain will take time and resources, which is a barrier to entry for competitors.

5. The key to AI competitive advantage is data, not the algorithms

Your product team may strive to develop better algorithms to make your product faster, cheaper and more user-friendly. In addition to fixing bugs, engineers work to design, implement and test new systems and algorithms that enhance product quality. However, to gain a competitive edge with AI, the leverage should come from data rather than algorithms. You could invest in researching and developing superior machine-learning algorithms for your niche, but if you expect the future value of the product to be driven primarily by algorithmic improvement, rather than an increase in relevant data, it's a red flag. Your algorithms could be replicated by a similarly smart team of AI experts, unless they are heavily dependent on the diversity and volume of data at your disposal.

6. The fastest, not the first, mover gets the artificial intelligence competitive advantage

In some industries, the company that brings a product to market first enjoys a considerable competitive advantage. For instance, in pharmaceuticals, a patent can protect your biologics or small molecule formula for 20 years, providing a monopoly. To secure your revenue and prevent competitors from stealing your design, you must develop and patent your solution as soon as possible.

However, the AI software market works differently. To succeed, you need to accumulate the right amount of relevant data that your machine learning models can benefit from, not just any data. Being the first in the market without ensuring proper AI, product, and marketing design does not give you much of an edge. It only attracts competitors and makes your company vulnerable to latecomers who get everything right. So, to achieve a competitive advantage in AI business, speed matters more than being the first mover.

Principles for AI strategy

A shared understanding of these differences by leadership, product, and engineering teams is crucial for success. Applying regular product development patterns to AI initiatives without understanding the difference between AI and product development could lead to failure.

This blog is dedicated to AI competitive advantage, and we are doing our best to explain how it works and how you can build one for your product or company. You can check our other posts to get an extensive explanation of what the network effect is and how AI enables it, how to build an AI competitive advantage for your company, what culture helps you build the right AI products, what to avoid in your AI strategy and execution, and more.

If you need help in building an AI competitive advantage for your business, look no further. Our team of business experts and AI technology consultants has years of experience in helping technology companies like yours build sustainable competitive advantages through AI technology. From data collection to algorithm development, we can help you stay ahead of the competition and secure your market share for years to come.

Contact us today to learn more about our AI technology consulting offering.

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