Artificial intelligence (AI) in companies has long since developed from hype into competition driver. Due to the high value creation potential, more and more companies are pushing the implementation of AI-based systems. But finding the right solution is not that easy. Therefore, companies should carefully consider the first steps in order to achieve the desired success.
This article gives a brief overview of the steps to consider in an AI project:
1. Identification of a specific application
Due to the large number of manufacturers, concepts and approaches, it is often difficult to find the right entry point for the introduction of an AI solution.
Before starting the project, it is necessary to take a critical look at your own processes in order to identify specific pain points. Experience has shown that there are so many entry points. In order not to get bogged down, however, the focus should initially be on a very specific use case in a specialist department.
2. Definition of the success and ROI criteria
Once the suitable use case has been found as an entry point, the next step is to define some concrete success criteria. These primarily concern the requirements for the solution, the required data sources, the data quality and the measurement of success.
- Business Needs: What should be achieved with the introduction of the solution?
- Data sources and data quality: Which data and data sources need to be taken into account in order to achieve the previously defined requirements or goals?
- Success measurement: How can the success of the solution be measured? Definition of meaningful KPIs (Key Performance Indicators).
3. Testing with your own company data
A proof of concept (PoC) is an important milestone to check whether the selected solution also meets the requirements. It is advisable to carry out a test with your own data in order to identify any problems at an early stage.
In addition, after a successful test, all settings can be seamlessly adopted for real operation. The focus should also be on data quality. Only a good database with few duplicates, errors, etc. is an ideal basis for the extraction of good results (garbage-in – garbage-out principle).
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4. Include the users
The earlier the employees are involved in the process, the more successful it is. They know their processes and are best able to judge whether there is still a need for optimization.
They also provide valuable input, especially when it comes to training the AI solution. They test the solutions and give active feedback. This is the only way for the AI to constantly learn, expand its knowledge, deliver more precise results and subsequently provide support in day-to-day work.
5. Validation of the ROI
After a successful practical test, the previously defined success criteria are checked.
The introduction of an AI solution for a specific use case often “gets the ball rolling” and other departments recognize the added value.
Artificial intelligence in the company opens up extensive possibilities, which on the one hand have a positive influence on the strategic and operational position and on the other hand can generate competitive advantages. Implementing the right system well-considered and intelligently in the company is definitely a real game changer.