How can companies best use artificial intelligence?

Use artificial intelligence in the company: 5 steps

Artificial intelligence (AI) in the company has long been from hype to competition driver developed. 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 which steps should be considered in an AI project:

1. Identification of a specific application

Due to the numerous 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 have the to look at one's own processes criticallyto get concrete Identify pain points. Experience has shown that there are so many entry points. In order not to get bogged down, 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 identify some specific Success Criteria to be determined. 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 considered 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. This should also include the Data quality be in focus. 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 they are still Need for optimization consists.

They also provide valuable input, especially when it comes to that Training the AI ​​solution goes. They test the solutions and give active feedback. Only in this way does the AI ​​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 the strategic and operational position influence positively and on the other hand Competitive advantages can generate. Implementing the right system in a well-considered and intelligent way in the company is definitely a real one Game changer.

Daniel Fallmann has been dealing with the topics of artificial intelligence, machine learning and deep learning since he was a young boy. He studied computer science at the Johannes Kepler University Linz and founded Mindbreeze GmbH in 2005 at the age of 23. Daniel Fallmann has headed the company as CEO since then. Mindbreeze, with headquarters in Linz, Austria and Chicago, USA, is one of the leading international providers in the field of applied artificial intelligence and knowledge management.