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Artificial intelligence is widely spoken, and AI applications are seen around us. Yet, the concept is not fully understood, and that leads to different interpretations and thinking that artificial intelligence is something altogether different, which it is not. Also, there is a bit of skepticism when one encounters it, for how should we be able to imitate human intelligence and get a computer to make decisions as humans make?

It is all coming from the hype, that artificial intelligence has created, and it needs to be simplified to truly democratize it. The fact is that AI is just the next step in the development of computer software, driven by the interest in exploiting the enormous amounts of data we collect.

Artificial intelligence automates tasks that previously required a human brain to solve

Artificial intelligence automates tasks that previously required a human brain to solve, and it is already prevalent in many industries, including online retail, where you may have noticed that the system presents you with products that it predicts you will like. You also know it from your email system, which today almost perfectly sorts spam emails from legitimate ones.

This is possible because artificial intelligence oversees and analyzes huge amounts of data and makes decisions that are often based on both probability and facts.

What is machine learning and deep learning?

AI is an "umbrella" for a broader concept of what could be called mechanical intelligence. Below we find machine learning, which are systems or algorithms designed to learn structures and predict future results.
As well as deep learning, which is a category under machine learning, where algorithms try to model high-level abstractions from the data by applying many processing layers in a complex structure.
In recent years deep learning systems have been able to achieve human or even super-human performance on high-level tasks like computer vision, text analysis and strategic planning.

The "machine" wins over the rules

AI is growing in many industries, and this is basically due to the fact that technology is overtaking the method we have used so far to be able to make something happen automatically or predict something.

For many years we have used code writing so that our systems could do something specific in one situation or another.

But machine learning trumps that method. It is not based on a written logic but on a trained computer model, which is constantly learning new things from the changing world around it.

The differences between rule-based and machine learning are noticeable:

Rule-based systems are static and implemented independently of data. The rules are formulated by experts. Historical data is not used. And the systems are best suited for behaviors and scenarios that do not change.

For many years we have used code writing so that our systems could do something specific in one situation or another.

Machine learning systems are dynamic and deliver output based on data. They require far fewer experts in the form of system architects and programmers, bring large amounts of historical data into play, and can adapt to change.

AI can help you build smarter processes

Most companies have some Achilles heels that can pose a greater or lesser threat to reliability, customer satisfaction and ultimately the bottom line.

One of them may be poor quality or even failure in delivery from subcontractors.

84 percent of all companies have experienced errors in their deliveries from subcontractors. Therefore one obvious strategy is to let AI and machine learning help eliminate the problems.

If you are in the Manufacturing Industry and are interested in Microsoft's ERP-, Power Platform news, I recommend you to watch our webinar: Microsoft Ignite 2021 - Takeaways from a Data & Analytics perspective in the Manufacturing industry. 

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Especially in the case of a large number of suppliers, it is almost impossible to make a qualified selection of the suppliers who live up to expectations on a number of different parameters. But with the help of machine learning, we can continuously process all data on prices, quality, compliance, delivery time and risks. The result is a supplier and risk ranking, which enables us to select the best suppliers.

Another example might be waste.

Annually, $ 750 billion worth of food is wasted due to poor logistics planning. It is a very complex area where a large number of variables have to be worked on, including start time and destinations, distances and weather conditions. This is exactly where machine learning has its great strength and can help with a route planning that can reduce waste drastically by taking all conditions into account.

Machine learning can also help reduce unnecessary capital tied up in the warehouse and prevent technical breakdowns in production.

Getting started with AI is easy

Few people today doubt that AI is a crucial differentiator. But many ask themselves whether AI is right for them, whether the timing is ideal, how to get started, and whether a sensible ROI can be achieved.

In fact, it is relatively easy to get started, and the individual company does not have to invent it all from scratch. AI is probably already a part of some of the systems you work with, so you only need a little help from consultants and some technical assistance to take the first step in a journey towards working with artificial intelligence where it makes sense and can help you be more competitive. You only have to add data.

Concerning the ROI, many companies have experienced, that getting the full benefit of the large amounts of data can lead to a surprisingly impressive ROI.

 

I hope that this blog found you well and feel free to get in touch if you have any questions. If you are looking for a way to get started, I recommend our e-book: ‘6 Steps to Be Successful With Advanced Analytics’.

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