AI Business Processes: Defensibility

AI Businesses

AI Business Processes: Defensibility

Over the past 10 years, AI business technologies have grown by leaps and bounds. As the industry progresses, the advances made in AI industries continue to improve. In a recent survey, more than two-thirds of the large American auto parts companies indicated that they are using some form of AI applications in their manufacturing processes. While many people still believe that self-driving cars are a far off reality, the sheer number of car companies now using some form of AI systems in their business shows that it is a real and growing reality.

A key feature in all AI businesses is cost reduction and defensibility. This begins with the elimination of traditional purchasing methods. The adoption of more personalized purchasing methods, where sales people can actually ask customers if they need any additional services or products on offer, has made it easier for companies to save money on labor costs. These personalized selling practices also make it much easier for a company to adjust its prices as the economy changes. Even the introduction of rote inventory management processes using LMS and e-procurement has reduced the need for human support staff, which makes it more cost effective and defensible for most businesses.

Another feature of the modern era of AI business applications is defensibility. As mentioned earlier, this is primarily done through e-procurement and LMS. By eliminating the need for expensive fixed assets and labor, companies can free up cash to invest in more profitable areas. Since many as businesses require stock, the elimination of stock based costs is another way that defensibility is achieved. Eliminating stock based costs also allows companies to increase their gross margins as more units are sold. If an organization’s gross margins are up, it generally means that there are more units sold, which increases its net profit.

The use of machine learning in AI businesses can also lead to significant savings in terms of time. Since an application will typically be designed to make inferences based on past sales patterns, all the relevant data will already be available in the database. This means that the machine learning system can focus on identifying the key factors that lead to increased profitability. Instead of having to spend additional time analyzing current trends, this information can be fed directly into the program, greatly reducing the amount of time a manager must spend on such activities.

Unlike many as businesses that are based on traditional software applications, it is likely that the vast majority of AI applications will be self-funded, meaning that they will not need to pay for any employee training. This saves money for a company because traditional software companies typically charge for every hour of employee training. Many as businesses are able to eliminate the need for employee training by avoiding typical cost areas like travel expenses.

Defensive Moats As previously stated, most an AI business will rely on data to forecast patterns and make inferences. However, the ability of the software to enforce these trends without being burdened by excessive overheads is another advantage offered by the emerging network effects phenomenon. Traditional software companies have had to deal with the problem of overheads that exceed the benefits derived from the increased productivity provided by the increased efficiency. Because defensive moats exist, a company can reap the benefits of a tight labor budget while spending less money in terms of labor fees.

One important thing to consider when developing an AI network effect application is whether the business model being used can be ported to other types of artificial intelligence technologies. Many aires are being designed to work with different business models including reinforcement learning, adversarial training, decision trees, and the use of greedy and negative reinforcement. These types of machine learning techniques are already being used to train decision trees in the natural world. However, the future of artificially intelligent business applications is looking towards other business models like those found in the financial markets. In fact, there are now strong interest in the financial domain from major investment banks as well as investment companies with a wide range of investment opportunities.

In conclusion, it is clear that the use of AI technology in business has a lot to offer both the consumer and the enterprise. The biggest advantage is the defensibility of this technology. Given the right conditions, a pure software business platform can deliver accurate results even under high stress conditions. As the technology becomes more prevalent, software companies will continue to develop different ways of exploiting this powerful computing resource.