Hardly anyone can afford to ignore AI. In almost all industries there are more and more use cases that successfully use AI and Machine Learning. The question is therefore rather how fast you have to go this way to start successfully into the future.
A clear objective
AI can help in many areas but not all projects are successful.
Therefore it is important to first work out a clear goal.
Examples of areas in which AI and Machine Learning can be successfully applied are
- Process Automation
- Customer care chatbot
- Machine data evaluation to proactively detect errors
- Selection of the most promising leads and mailing recipients
More about application areas of the AI
Reliability of AI
Machine Learning and AI are very good at processing large amounts of data. However, the results are not 100% correct. Machine Learning and AI need a field of application in which errors or automatic wrong decisions are allowed or can be intercepted sensibly.
With Ki-optimized leads, for example, the error rate is irrelevant. However, errors are not acceptable when an AI decides to grant a loan. This is why the GDPR has also banned automatic decisions which have legal consequences for natural persons.
How reliability can be measured and how to deal with AI failures is part of the preparation of every AI project.
Data quality
AI learns from the data with which the AI is trained. Therefore, great care must be taken to ensure that the data is well prepared.
Otherwise, for example, an image recognition system would not recognize a polar bear, but the environment with snow and ice, and might even mistake a car in a snowy landscape for a polar bear.
In practice, these learning mistakes are less obvious. However, they can destroy the entire benefit of a project.
Most AI systems in use today cannot explain why a particular decision was made. This makes it very difficult to correct and understand.
Communicate with the team
The contact person in the company should be familiar with the emerging technologies and be able to assess how machine learning can solve business problems. He or she should also have knowledge of the systems and know where data necessary for the process is stored. Our services databee can read out and prepare data, so that you too can be successful with AI
Keeping an eye on ethical principles
Many companies do not have the explicit permission to use customer data in their AI projects. Asking for this permission must be part of the data governance. Especially when decisions are made by the AI that have an impact on people, the GDPR requires that the people concerned are also heard by a human being.
One must not forget that AI is something threatening for many people. Think about facial recognition technology, how widespread it has become and what that means for your privacy. Do not harm your business by crossing this line.
Data security
Data is the engine and without it AI will not work. To minimize the risk of data breaches, you need the right IT security solution to be able to react faster to incidents and to better protect your data.
Therefore plan enough time to implement projects. But the most important thing is: Start now!
