5 steps to implementing AI in your business

The term “artificial intelligence” describes a variety of different methods, processes and technologies. Today, AI technologies can be used in almost all areas of a company, for example to make processes more efficient or to take over routine tasks. The areas of application are now so diverse that it is often difficult for companies to find the right starting point.

To ensure that the integration of artificial intelligence succeeds and, above all, achieves the desired success, companies should consider their steps carefully.

1. Identify and determine the use case

Instead of being blinded by the extensive capabilities of all the solutions on offer, it is important to consider where (which use cases) AI could be applied and which known pain points could be improved or eliminated.

You will quickly notice that there are many starting points for an implementation. However, it is not very efficient to work on all of them at the same time. It makes sense to start with a concrete use case from a specific department and to think about how this can be optimized by AI.

2. Define and establish success criteria

Once the right use case has been found, it is also easier to determine the next steps, such as defining success criteria. Not every available solution is suitable for every use case. Therefore, it is important to define so-called “success criteria” in advance.

These consist of:

• Business Needs
• Data Sources and Data Quality
• Semantic Relationships and Extraction
• ROI Calculation

Business needs are defined as the deviation between the actual state and the target state in the case of optimal implementation. After the concrete deviations have been worked out and the requirements for the implementation have been defined, companies must identify which data is needed from which data sources in order to achieve the desired goals.

In order to extract information from this existing data and make it usable, the relationships between the different pieces of information must be extracted, models must be built, and additional information must be correctly interpreted and linked.

In the purpose of impact controlling, it is necessary to define meaningful KPIs (Key Performance Indicators) in advance that are comprehensible to both employees and other stakeholders. These serve as the basis for calculating ROI and measuring success.

3. Hands-on with real enterprise data

A proof of concept (PoC) is an important milestone in the introduction of AI solutions.  It lays the foundation for further decisions and should help to separate the wheat from the chaff among providers.

The customer should test directly with his own data whether the identified requirements can actually be implemented with the solution.

One factor that should not be underestimated is the quality of the existing company data. “Data garbage” can significantly impede the use of machine learning and artificial intelligence (garbage-in-garbage-out principle).

To avoid this, the existing data in the various data sources should be sifted, understood and cleaned up in advance. These steps can be performed in a very short time but are enormously important for the quality of the results.

4. Very important: Involve the users

If the AI solution in the PoC for the selected use case is convincing from a technical point of view, the experts from the relevant department must be involved. Therefore, in the purpose of continuous change management, the employees should already be involved in this phase. They know their processes and can best assess where the solution still needs to be improved or provide valuable input, especially when it comes to training the AI solution.

5. Validate ROI calculation

If the tests are satisfactory, the transition to live operation takes place. All settings from the PoC can be adopted directly and the ROI calculation created at the beginning can be validated. Once the project has been successfully launched, the AI system can be rolled out to other departments or areas of activity until it is finally deployed throughout the entire company, thereby transforming all processes (business process transformation).

When used correctly, AI can dramatically improve a company’s performance, for example by automating recurring business processes, improving customer understanding, increasing efficiency, and making employees happier.

Daniel Fallmann

Daniel Fallmann beschäftigt sich seit frühester Jugend mit den Themen Künstliche Intelligenz, Machine Learning und Deep Learning. Im Jahr 2005 gründete er im Alter von 23 Jahren das Unternehmen Mindbreeze. Dieses zählt heute zu den führenden internationalen Anbietern im Bereich angewandte künstliche Intelligenz und Wissensmanagement mit tausenden Kunden weltweit.