Over the course of the next several months, we will be doing a blog series covering artificial intelligence (AI) and machine learning (ML). The series will focus on the use of AI and ML in Dynamics 365, the Power Platform, Azure machine learning and also the impacts they have on employment opportunities.
To introduce the series, this blog will define AI, ML and key related terms, why they are so important and current state of AI/ML in industrials.
Artificial Intelligence – Machines that use algorithms to automate and execute tasks without direct human oversight.
Machine Learning – Machines that use data driven algorithms to draw conclusions and provide insights with human training.
Big Data – Data set that is not easily deciphered with standard methods due to the complexity and size. Big data is a huge driving force behind AI and ML.
Why is it so important?
- Data insights: A large amount of data is generated in industrial settings via machines. Having the capability to use machines to decipher hundreds of thousands of records will consistently give you data to make correlations and draw conclusions. For example, D365 offers demand forecasting which will predict how much demand you will have for a product based on historical data. (We will be going further into detail on this topic within a future blog post.)
- Faster processing times: Processing times can be significantly reduced using machines rather than solely relying on a human. The amount of records produced in industrial processes could take hours or days to review if relying on a manual process. Data evaluation completed by a machine can significantly reduce the time taken to complete a task or draw a complicated conclusion.
- Processes built to adapt: With AI/ML insights can be changed based on the data provided over time. For example, many of us have used a chatbot when trying to reach a company’s customer service department. Chatbots can be coached to answer customer’s questions without human interaction. With the COVID-19 pandemic many people have switched to placing orders online and the wait times for calling customer service desks have risen tremendously. Chatbots can help alleviate the pressure on customer service employees by gathering routine information and/or answering questions.
- Increases profitability: Implementing AI/ML can significantly reduce costs and increase profitability through increased employee productivity and business process evaluations. AI/ML can reduce repetitive, tedious tasks and automate business processes allowing employees to focus on tasks where they can have a huge impact on company initiatives. Big data can also be deciphered quickly allowing employees to identify re-occurring issues with current processes allowing you to change how tasks are being completed.
State of the industry: Industrials
Industrials is among the top industries to be largely impacted by AI and ML because it is highly susceptible to automation and there are a large amount of machines, including robots.
Transportation is a sub-sector of industrials that is currently being impacted by artificial intelligence. A few examples of where AI is used in transportation is self-driving cars, traffic delays and directing traffic.
- Have you been in a traffic jam and wondered how long you are expected to sit in traffic? GPS can now use AI to determine how long you are going to be delayed and what time you should be expecting to reach your final destination.
- AI is used in self-driving cars to recognize objects, including but not limited to cars and road signs, to avoid collisions and determine the best way to reach its final destination.
- AI can also be used to determine which route will be the best option for truck drivers (and regular drivers) to take to reach their final destination. This can reduce the costs that are incurred by shipping goods.
What is next?
In the next series topic, we will be covering the specifics of how AI and ML relate to Dynamics D365 Supply Chain.