I can tell you from my practical experience, that you must not confuse artificial intelligence with science fiction. This is one of the most common mistakes when introducing AI in companies.
Excessive expectations reduce acceptance
AI projects must be described in detail and target a clear business benefit.
Artificial intelligence is not a silver bullet. Excessive expectations in the specialist departments reduce the acceptance of the AI. It has to be communicated very clearly, that business processes also change with the introduction – along with new approaches and technologies.
With the definition of a business case (Business process) so it is by no means done. The real challenge is the implementation of the individual steps of an AI project.
Define, testing and training a model go hand in hand with the
Monitoring and the most necessary adjustment of parameters.
Only then can a project be carried out successfully, when technical requirements are compared with the technological possibilities.
Is artificial intelligence new territory for the company or. the corporate division, so is strongly recommended, to gain initial experience with simple and uncritical processes.
A large database has a lot of potential for process improvement
Artificial intelligence reveals, that there is a great deal of potential in data for process improvement and the design of new processes. Ultimately, it is also the basis for new product developments.
It goes without saying, that the use of large amounts of data previously unknown perspectives on products, Reveals developments and customers.
It is very important to pay attention to this in this context, the quality of the data. Questioning and testing with a head and gut feeling are among the most important human tasks, even in the age of computers!
In the first step, artificial intelligence processes the data made available. Right or wrong – logical analyzes can (still) not done. Initially, there is no learning basis for this.
Don't make mistakes in limiting yourself to internal data sets. First the integration of external information, completes your system.
Even if I repeat myself: Selection of the relevant data and high data quality are the two most important parameters for the success of AI in companies.
Pattern recognition and process automation are classic introductory topics
The entry into artificial intelligence can be done in service companies as well as in the processing industry, who focus on pattern recognition and process automation, can be done with relatively little effort.
Simple rule-based and repetitive processes are the very best way to start the AI age.
The implementation requires a manageable use of resources and the results can be seen quickly.
Efficient, Error-free and transparent company processes oppose chaotic and expensive processes. In addition to the improved operating result, stress-free employees are also unmistakable.
Artificial intelligence is often criticized as a job killer. However, these arguments are populist and in no way reflect actual developments.
Chatbots & Co. have become an indispensable part of business
Without question, text also provides added value- and image recognition as well as natural language processing (NLP). Everything that goes beyond the classic readout of forms, makes the crucial difference.
The recognition of language and texts only has to do with artificial intelligence to a limited extent. First to draw the interpretation and appropriate conclusions from it, makes the use of AI.
The insurance industry and the banking sector would no longer be possible in today's fast-moving world without the use of the above systems. Manual testing has long been replaced by artificial intelligence.
