12.02.2026 | News Seven steps to GenAI happiness
Artificial intelligence can be a blessing for companies. This is especially true when it is integrated into the infrastructure and processes in a meaningful way. A particularly effective way to use generative AI and chatbots is to implement these technologies in conjunction with an enterprise search solution for chatting with company data. Employees can then receive answers to internal processes in natural language on demand, resolve support tickets more quickly, and get help from AI with report writing and more. The secure use of generative AI without company data – for example, to generate email responses, translate texts, or analyze files – also brings noticeable benefits. In any case, a well-thought-out strategy is needed to successfully introduce generative AI into a company. IntraFind explains what such a strategy should look like.
Step 1: Evaluate clear use cases
Generative AI, or GenAI for short, is the order of the day in many companies and is considered mandatory. And rightly so. However, GenAI should never be an end in itself. Instead, companies should work with specialist users to define specific use cases that deliver measurable added value and a clear return on investment. Examples include time savings in information retrieval, digital assistance in HR, or faster customer communication. The clearer the use case, the easier it is to define requirements and priorities. The basis for this is an analysis of existing processes to identify the biggest pain points and potential.
Step 2: Get the workforce on board
It is essential to involve the future users of the GenAI solution. Since there is sometimes still skepticism toward artificial intelligence and certain fears—such as that it will replace jobs - it is important to bring employees along. This is the only way to ensure a smooth cultural change and entry into the AI age. Good management at this point means finding out what employees really need, what they expect, what costs them time, and what they want from an AI solution. Getting future users on board increases the chances of success for the GenAI project.
Step 3: Define KPIs and must-haves
Realistic expectations are also an important part of successful AI projects. Companies often overlook the fact that success can only be measured if they also define appropriate key performance indicators (KPIs). Together with the stakeholders of the GenAI solution, it is therefore important to define not only the problem to be solved in advance, but also these metrics and must-have criteria. This includes, for example, ensuring that the AI does not hallucinate and that it complies with all data protection requirements.
Step 4: Conduct proof of concepts
Despite all the "AI euphoria," companies should remember to validate feasibility of implementation. Before embarking on the GenAI journey, it makes sense to create a proof of concept (PoC) and, before investing in specific technologies or products, to check whether the use case can even be implemented within a predetermined cost and effort framework and whether it will produce the desired outcomes. Typical use cases that can be tested via PoC include the automation of onboarding processes, contract reviews using AI, chatbot responses to support requests, or connecting AI to internal and external knowledge libraries.
Step 5: Consider data protection and compliance from the outset
Since not every employee and every external user (in the case of a customer support chatbot) is allowed to access all company information, data protection and compliance must be a central issue in GenAI implementation from the outset. Permission-based access control can be implemented securely when generative AI operates in the context of an enterprise search solution. An intelligent enterprise search has native access control mechanisms on board and can apply them seamlessly to generative AI. This ensures that each user only receives the answers they are authorized to see.
Step 6: Select and host the appropriate LLM
When it comes to LLM selection, it can be helpful to seek advice to ensure that the chosen model is optimally suited to the use case and that factors such as data protection, costs, and technological requirements are taken into account. In addition to deciding on a specific large language model (LLM), it is also important to choose the right infrastructure. On-premises approaches, i.e., hosting the GenAI solution in your own data infrastructure, offer maximum data control. Scalability is primarily a strength of pure cloud approaches. With hybrid cloud concepts, companies can run sensitive data locally and less critical processes in the cloud.
Step 7: Rollout and communication
Once everything is prepared and all eventualities have been clarified, it is time to move from PoC to production. Before going live, companies should plan sufficient resources for informing and training their workforce. This also includes internal communication in the form of announcements, creating a FAQ catalog, and providing points of contact for questions. IT teams must also continuously and proactively gather feedback from users, keep the system up to date, and constantly monitor performance. Only in this way will companies be able to benefit from generative AI in the long term.
