Simple contained artificial intelligence (AI) based systems are increasingly becoming entrenched in our day to day lives. We adopt them and they disappear into the fabric of our daily lives, going unnoticed. AI in some form is encountered by most of us from the time we wake up in the morning until we go to bed at night. Just think of your conversations with Alexa, opening your phone with facial recognition, or the recommendations you receive when shopping online. When you use Amazon for example, AI algorithms go to work to understand your historical transactions, your preferences, and purchases made by people like you, and then it makes recommendations for items you will likely buy, all with a tremendous deal of accuracy. In fact, Amazon is so confident in its algorithms that it ships products towards your location in anticipation, even before you click buy.

 

How AI is Driving Autonomous Technology

 

In other more complex areas, AI and its connected technology is quickly evolving and working its way to the point of near autonomy. The top-of-mind example is of course the self-driving car. Today they are not fully autonomous but still semi-autonomous, requiring the person behind the wheel to oversee the system and remain responsible. The fundamental challenge with the development of autonomous cars is the issue of edge cases, which are the situations not typical of normal everyday driving. They are the circumstances which are new and consequently where the AI and technology has not been tested and therefore has not learned. The outcome in these cases is that the AI periodically fails, resulting in serious consequences and an appearance in the news cycle. However, this is changing and eventually the edge cases will be infrequent enough that trust can be placed in self-driving cars.

 

How is AI Used in Today’s Business Models?

 

In business it is a different situation. Most of the edge cases are worked out in simulators beforehand. The ones that are encountered in the real world are not a matter of life and limb, but rather learning experiences that ultimately help the AI learn and adapt over the long-term. Today’s best-in-class AI technology in this area typically makes successful choices at an extremely high rate and makes them thousands of times faster than even the most advanced traditional combination of people and analytics.

A great illustration of autonomous AI technology working in business today is software that provides recommendations and makes decisions for merchandise planning – decisions such as what items to promote, at what prices, and how much inventory to carry for each item in order to meet sales and financial targets. Here the best-in-class technology is making successful choices more than 98% of the time at lightning speed, with the Merchants only jumping into situations (edge cases) that are new to the technology or to shift the strategic direction. This is analogous to what drivers of semi-autonomous cars are required to do today, jumping in in new situations and setting the destination of the journey.

Autonomous self-driving cars are probably not right around the corner. However, in the business world, AI systems are improving on what people can do, saving labour, moving people away from mundane tasks, and allowing them to focus on the more strategic, high return activities.

Daisy is a leading vendor in this space. We run AI software on behalf of our retail clients and output recommended decisions for merchandise planning. Our unique autonomous system has no code, no infrastructure, and no data scientists, only unbiased decisions that are getting better every day.

 

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