Innovation with AI: The Skills You Need

Developing an AI requires a variety of different competencies. Missing certain competencies quickly has a negative impact, potentially leading to project failure. Good preparation is therefore worthwhile. Learn in this article which core competencies you need for developing your AI.

1. Value Creation Focus

Every AI initiative begins with a question about the use case. For which users do you want to develop a solution? What value do you want to create for these users? To answer this, you need a person who understands user needs well and has a sense for where value can be created. For internal projects, this person could be a user themselves, while for customer-facing projects, members of your sales team are well-suited.

2. Developer Power

Software developers take care of databases, user interfaces, and underlying functions. They also ensure that new software is compatible with existing software, and they are on hand when there are operational issues. Good software developers can therefore never be enough.

3. Data and AI Expertise

Data scientists are responsible for making data usable and analyzing it. Using sophisticated algorithms, they search large datasets for unknown patterns and correlations. This can yield new insights, for example, why a certain problem occurred on a machine or when it will lead to failure. Data scientists should logically be complemented by AI developers who transform such insights into repeatedly executable and self-learning AI algorithms.

4. Domain Knowledge

For quick and meaningful data analysis, data scientists must have access to expert knowledge about the application area and adjoining processes. It is only this domain knowledge that makes it possible to view data in the application context and better understand behavioral patterns. AI development can also be significantly accelerated when the appropriate expert knowledge is available during the training process.

5. Software DNA

Modern software and data solutions work fundamentally differently than hardware solutions. While a new hardware solution must fit into an existing and clearly defined environment, software is nearly infinitely flexible. This opens up many possibilities but also harbors a few nasty pitfalls. Ultimately, every AI solution must be embedded in a consistent and future-proof software architecture. Only this ensures that no additional costs arise when the scope and nature of AI usage change.

6. DevOps Specialists

DevOps automates processes around software development and operations. Only a well-configured DevOps environment enables software developers to efficiently roll out AI updates without worrying about quality, availability, and functionality. DevOps expertise is therefore a key competency when an AI solution is introduced to market and scaled there.

And where should all these competencies come from?

With so many competencies, the legitimate question arises: where do you get the know-how to develop your AI product? Should you build your own team within your company or should you source expertise externally? Feel free to reach out to us—we will examine your situation and requirements carefully and provide you with concrete recommendations for action.