AI and machine learning will continue to create the IT jobs of the future

IT professions are becoming more and more important, smart technologies are changing the day-to-day work of IT decision-makers and IT professionals. Automation is an important part of the IT industry. The use will increase in the future through the further development of smart and cognitive technologies. Artificial Intelligence (AI), deep learning, and neural networks offer further opportunities to outsource work steps to machines in very different areas.

As a result of these developments, many jobs will initially be lost. Different areas are affected to different degrees. However, market researchers assume that, in the end, more skilled workers will be needed in these areas due to cognitive technologies. According to the market researcher Gartner, around 1.8 million jobs will be redundant by 2020 thanks to AI. At the same time, 2.3 million new jobs are forecast to be created.
 

 

Reduce the high barriers with pre-packaged solutions.

AI is the framework for machine and deep learning models. With the help of a rule base, artificial intelligence (AI) can correctly interpret everything that machine and deep learning generate as results. This is already a reality in the private sector with the assistance systems from Apple, Amazon, or Google. Speech recognition is an essential part of this. Thanks to the experience of Amazon. with the various assistance, systems will soon be created for very different business areas in the business sector.

However, the entry barrier for many companies is still very high. Not only is there a lack of adequate IT infrastructure, but also well-trained and experienced staff. AI cloud services can help lower the entry barrier, especially for small businesses. There are pre-configured and fully trained deep learning models for Optical Character Recognition (OCR) that companies can use, such as sevdesk’s software with automated receipt capture.
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Helpdesk as an entry point

For many companies, the help desk is the entry point to AI. In the first step, chatbots support employees. They relieve them of standard questions. When the customer asks the chatbot a question or searches for a specific item, the chatbots can perform simple tasks in the background, such as presorting user queries or performing simple tasks automatically. In retail, it is already possible for customers to ask chatbots questions or search for specific items. It’s like the helpdesk principle.

Robots can take over simple and repetitive tasks, such as resetting passwords or creating new users. This gives administrators more time that they can invest in more demanding tasks. This can lead to job cuts. In other areas, however, this can also open up opportunities – for example when employees who have become vacant can drive new projects forward.
 

 

AI in security

In security solutions, more and more companies are using AI or machine learning. Today administrators are hardly able to evaluate hundreds of thousands of log files. They train models and define normal behavior. If there are any deviations from this model, the system reports to the security expert, who then decides how to proceed.

A less nice aspect is the fact that hackers and other criminals also use AI. You use them to develop cyberattacks or identify weak points. Security researchers were able to enhance malware with AI. The software could modify itself.
 

Business Intelligence and Artificial Intelligence

Business intelligence also profited from the use of artificial intelligence. Combining data by machine and automatically recognizing patterns is increasingly being optimized by AI. The development of new tools and applications for special user needs becomes easier. In the future, it will be possible to compile applications simply using drag and drop. In the future, employees or IT backgrounds can also work with it. The simplified handling of unstructured data means that these business intelligence applications can be largely automated. This opens up new opportunities in the company, for example, to generate competitive advantages. Due to a large amount of data to be evaluated and the amount of information that can be obtained from it, it is to be expected in the long term that workers will be sought in this area.
 

Application scenarios in financial services

There are many applications for AI, deep and machine learning in the financial services sector.

Classify a customer’s creditworthiness:
As soon as a bank customer applies for a loan, the bank has to check the customer’s creditworthiness due to regulatory requirements. A scorecard serves as the means of choice. With their help, the probability of default for a loan is determined. The scorecard is based on a statistical forecast model that was trained on the basis of historical bank data. Depending on the calculated default probability, the customer receives the loan on certain terms.

Detect insurance fraud:
Attempts at fraud are the order of the day with insurance companies. Insurers try to identify attempted fraud more easily on the basis of the damage reports with the liability insurances. Employees train the software using historical data. The software calculates a probability of fraud for each incoming damage report. A threshold value is stored in the workflow. If this is reached, the system reports. An employee can then manually examine the damage report. Otherwise, the workflow can run completely automatically.
 

 

The use of AI needs framework conditions

In order for AI, machine learning and deep learning to be implemented successfully, there are three important aspects to consider:

• the technological requirements,
• the organizational requirements and
• the mental attitude.

A lot of computing power is also required to ensure that everything runs properly.

Projects dealing with AI should primarily be viewed as a research project. The outcome cannot be determined with certainty. A normal research budget is usually sufficient for machine learning. Projects in the field of deep learning and AI are significantly more complex and therefore correspondingly more expensive.
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