AI ACROSS INDUSTRIES #4A BLOG SERIES ABOUT OUR EIGHT INDUSTRIES FOR APPLIED AI AND THEIR USE CASES
By addressing AI across industries in a series of blog posts, we wanted to provide you with great inspiration and insight into how AI can increase business value. We believe, that we have only just begun to see the influence that AI will have on transforming the way we do business. A transformation that covers a lot of industries and areas. In this final post, we will round off this series, giving you insights into how implementing AI can increase business value.
INDUSTRY INSIGHTS | JULY | 2019
Last week we addressed the industries Utilities and Legal & Accounting including their use cases. This week we dive into the last two industries on our list: Public and Manufacturing.
Public institutions are being pressed by private companies and new generations on the labor market to provide better service at a lower cost. At the same time, many public institutions are suffering from legacy IT systems, as well as an extreme focus on the legality of their processes, which makes the adoption of new technology complicated.
AI can improve service for citizens and companies. It can lower costs for the services provided and at the same time raise the satisfaction level. This can be achieved through, for instance, better personalization (portals, communication), fraud detection and prevention or, for instance, channel preference. Utilizing AI at scale (including gathering the right data for analytics purposes, improving data quality and also putting analytical models into production) is the key to future service offerings in the public sector.
The public sector can use machine learning solutions of all types – natural language processing, supervised learning, and unsupervised learning.
AREAS OF AI APPLICATION IN THE PUBLIC SECTOR
- Personalized content.
- Prediction of potential fraudsters or find historical fraud patterns and predict new.
- Sort and categorization of large amounts of documents, cases or text material in general for QA.
- Categorization and predictions on companies or citizens according to the behavior and needs.
LEARN EVEN MORE ABOUT AI IN PUBLIC AND THE INDUSTRY SPECIFIC USE CASES
Modern manufacturers are ideal candidates for sophisticated analytics as they are well-instrumented with sensors, controllers and storage of historical data. However, less automated manufactures can highly benefit from implementing AI as well – sensors can quickly be installed and can run even offline without disturbing existing infrastructure.
Working with manufacturing is a continuous race for establishing sufficient capacity for new or existing products and/or reducing production costs. Traditional optimization entails a lot of data retrieval and cumbersome value stream maps in order to identify areas ideal for optimization. This is time-consuming and data might not be easily available – if available at all.
Subsequently, you apply statistics to understand root causes, and business warehouses and dashboards are perhaps built to help monitor the process in real time – though this is all stationary. Implementing AI you will also use statistics to understand data. But more so, historical data will be used to identify hidden patterns and train AI algorithms so that they will be able to predict outcomes. For instance, you can start predicting how well your equipment will be running, by understanding how input parameters influence the output of products. Based on this, adjustments can be done manually or automatically to maximize output. The new insight will also support you in deciding which long-term improvement initiatives will increase equipment output and what the increase would be.
AREAS OF AI APPLICATION IN MANUFACTURING
- Quality control can be enhanced through vision systems by adding image recognition models based on convolutional neural networks e.g. to inspect and sort for defects.
- Downtime of equipment can be minimized by analyzing data for each manufacturing step and/or for a chain of linked operations e.g. moldings importance on assembly output.
- Predictive maintenance can proactively identify problems related to spare-parts, software, hardware and firmware to eliminate possible points of failure or degraded performance.
- Implementing AI you will be able to initiate optimization projects based on already available data, without having to start from scratch every time and even be able to predict the benefit of such initiatives.
LEARN EVEN MORE ABOUT AI IN MANUFACTURING AND THE INDUSTRY SPECIFIC USE CASES
The areas where various industries can benefit from AI are ever expanding. One way to get started with AI is to look into specific use cases, enlightening you on how AI can benefit your organization and enhance business value.
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