4 STEPS FOR A USEFUL AND PLANNED ENTRY INTO THE WORLD OF ARTIFICIAL INTELLIGENCE

The world of Artificial Intelligence (AI) has kept companies highly engaged in recent months and continues to be seen as a driving force behind innovation and growth. From small and medium-sized enterprises to large corporations, the introduction of AI will change the way of working in almost all industries and, not least, in customer service.

However, the selection process and the path to implementing AI technologies are not always clearly defined, and many companies have so far merely experimented and tested various AI solutions in an uncoordinated manner. So, how can companies professionalize their AI journey and find a meaningful and manageable entry point to utilizing these promising technologies?

One possible approach is to plan the entry through AI prototypes and a structured four-step process:

The first step on the journey into the world of AI begins with a Discovery Phase. This involves becoming familiar with various AI solutions in areas such as text processing, speech processing, process optimization, and data analysis. A multidisciplinary team of internal and external experts—preferably independent of existing traditional departmental structures—should develop ideas and use cases for the potential application of AI in the organization.

It is important to know or establish the relevant framework conditions, such as governance, compliance, security, intellectual property protection, accepted AI platforms, and possible operating models.

The AI solutions should naturally support the goals and desires of users, whether they are the company's own employees or customers, and not work against them (AI alignment). Transparency, traceability, and explainability of AI decisions are crucial to gaining trust in the technology and thus best supporting acceptance and use.

The goal of the Discovery Phase is to develop a clear vision for the use of AI in the organization, to evaluate initial ideas and use cases regarding their feasibility, effort, and expected benefits, and to outline a roadmap including the assignment of respective project team members and their responsibilities.

After identifying the initial ideas and use cases, it is time to prioritize and decide on a first proof of concept or pilot. Here, a heterogeneous team of internal and external subject matter experts and AI experts should collaborate.

The proof of concept should be quickly implementable, not use critical business data, and require no or minimal integration into existing applications. It aims to gain a technical understanding of the AI solution and demonstrate its value in the organization. The results should be analyzed and evaluated in predetermined dimensions, which can be both qualitative and quantitative assessments.

In this phase, various solution providers or AI models can also be compared with manageable effort.

If the proof of concept is positively evaluated, the next step is to create a prototype. This uses real business data and already represents the operational architecture for a later transition to live operation. Therefore, selecting the operating platform and considering further integrations play an important role.

This phase involves deeper learning about the AI solution and further evaluation, especially obtaining measurable results.

If the prototype is successfully evaluated, systematic scaling can begin. Company standards and handover to the IT organization must be considered.

Introducing AI into operational operation often requires a change process. It is important to ensure the acceptance of the AI solution within the organization and among customers. Training, user feedback, and continuous analysis and optimization of the AI solution are crucial, particularly regarding its effectiveness and efficiency.

It is important to regularly reconsider the process in the context of one's business processes and the ever-evolving AI solutions, questioning implemented solutions, and looking for new opportunities for AI deployment.

Overall, a meaningful and manageable entry into the world of Artificial Intelligence is possible if companies follow a structured approach and carefully go through the steps of the Discovery Phase, Proof of Value, Prototype, and Scaling.

This enables them to optimally test and utilize the use and benefits of Artificial Intelligence—thus strengthening their competitive position in the market. Additionally, this approach minimizes the financial and operational risk when introducing new AI solutions and simultaneously increases their internal and external acceptance.