More and more business owners are realizing that AI applications can give their business a decisive competitive advantage. But the question of how exactly AI should be used in a particular company is extremely difficult to answer. To tackle this challenge, a certain approach has proven to be particularly effective.
One thing is certain: there are numerous concrete use cases for AI applications in your company as well, which together can have a positive influence on its future development. In principle, however, it is important to proceed strategically in order to actually make good use of the resources available to you. AI per se is no guarantee of success — how and in which areas it is applied is ultimately decisive. In our experience, the following guide is the best way to find out how AI can be used most effectively in your company:
To develop a sound AI strategy, it is essential to first hold an intensive and detailed meeting with all the important departments of your company involved. This is where the essential question of which data is fundamentally available is clarified, but also what expectations should be placed in AI and how they can actually be realized. Management and IT in particular should be present, but also the product development department and other important strategic areas. At this meeting, the first step should be to clarify what AI and machine learning are actually capable of and what possibilities exist through existing data sets and those yet to be tapped. Fundamental objectives can also be defined at such a meeting.
Machine learning is capable of a lot — but it cannot work miracles. Precisely because of the hype that ML and artificial intelligence have experienced in recent years, it is important to educate your employees as comprehensively as possible about the possibilities of AI. Only in this way can a sound basis be created on which realistic, creative solutions tailored to your company can emerge. It is important to tell your employees in advance to approach the topic as openly and impartially as possible.
AI can be applied to almost all types of data. The big question, however, is: for what purpose and at what price? To find this out, it is essential to engage with your company in detail. And this is not just about analyzing your business model. Ask yourself questions such as: What do my customers want? What do they definitely not want? How can I create added value for customers? How can I meaningfully strengthen my team and best support their talents?
Engage with your company's goals as well as its corporate ethics. Include lessons learned from the past. Ask yourself how you want to integrate AI — and how your workforce and your customers might react to it (think, for example, of data protection and the current debates around automation and disappearing jobs). Your idea for using AI may be as original as you like — if it is not in harmony with your corporate values, an ill-considered strategy could have unintended negative effects.
An important question when introducing AI into a company is, of course, the framework within which this should happen. If you engage with the following questions, it will be much easier for you to find the right applications:
With the help of our expertise, determine in which areas the best conditions exist for using AI. It is particularly important to be guided by the following criteria:
Those areas that have the largest data output in your company are usually also the ones best suited to benefit from AI applications, and thus usually a good starting point for the first AI-based experiments. Without the corresponding available data, even the most sophisticated AI algorithm is worthless. The quality of the data naturally also plays a major role: how well structured is this data, and to what extent can it be meaningfully evaluated? A yardstick for the quality of the data can be the extent to which it is already being incorporated into decision-making in your company today.
If decisions are already being made based on data, then especially those processes can be automated through AI applications in which such decisions are made in large numbers and repetitively.
Areas that are already heavily software-supported can usually be further optimized through AI as well.
If there is already an idea of how AI should be used in your company, check whether the data required for it is already available. If this is not the case, evaluate whether the necessary steps to get there are economically viable. Obtain the necessary expertise to find out which steps are required to achieve your goals. We are happy to provide you with assessments based on the experience we have built up over the years.
Based on the points mentioned, create a list of AI applications that make sense for your company. Then move on to the actual decision-making process, which serves to find out which applications should have priority for you. On the one hand, it is decisive which areas actually bring the greatest benefit. On the other hand, it is equally important how quickly you can achieve success given the circumstances. So if, for example, the data required for an application is very difficult and costly to obtain and prepare, you should not rank this project as a priority. It is advisable to start with an application that both delivers great added value and already has the required data available, so that the implementation can reach its goal in a relatively short time.
Once you have decided on a project, it is time to plan the actual implementation in detail. The good thing here is that with AI applications, similar applications have usually already been used in research. Since most of these use cases are extensively documented on the corresponding platforms, there is a good chance that you will not have to start from scratch. And with a bit of luck, you may even find code examples there, saving you some implementation time. Take enough time to identify the most promising AI applications for your company based on the openly available data.
If you now update your list, an application suitable for getting started will emerge relatively easily. The goals can now be finalized, and your team for the first AI project can be assembled in a meaningful way. If you have any questions about use cases in the field of artificial intelligence and machine learning, please feel free to contact our team.
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