17. The second DEX analysis
Measure impact, identify potential, develop in a targeted way
After the go-live, the phase begins in which it is decided whether AI has not only been “introduced” but has actually become effective. This is exactly why the DEX analysis is carried out a second time. It is not a formal conclusion, but a deliberately set measurement point to compare the state after the introduction with the initial state.
The first DEX analysis made the actual state before the AI introduction visible: processes, information flows, bottlenecks, friction losses, knowledge dependencies, and system breaks. The second DEX analysis now maps the new actual state – after the introduction. From these two measurement points, a reliable “before–after” comparison is created, which is not based on feeling, but on a structured, comprehensible assessment.
What happens in the second DEX analysis?
In the second DEX analysis, we again look at the same relevant areas as the first time – but now under real conditions of live operation. We analyze how processes and workflows have changed, how knowledge is used, how communication channels have developed, and how employees actually use the system.
It is not only measured whether something has become faster, but also whether it runs more stably, securely, and consistently. It is also examined how dependencies have changed, whether key people have been relieved, and whether the quality of decisions, information, and documentation has increased.
The crucial point is: We are not comparing “theory against theory,” but the real state before the introduction with the real state after the introduction.
Why this step is so important
Many AI projects end with the go-live – and then remain vague. It is assumed that AI “somehow helps,” but it remains unclear how strong the effect really is, where it arises, and where it does not.
The second DEX analysis provides clarity here. It shows which improvements have actually been achieved, where expectations have been met, and where there are still gaps. This not only creates transparency but also controllability. Afterwards, you not only know that AI has been introduced, but also what measurable contribution it really makes in everyday life.
What you get out of it
The before–after comparison makes the benefits visible and demonstrable. You can clearly see which processes and areas have improved and where AI has the greatest impact. This strengthens internal acceptance, supports management decisions, and creates a reliable basis for further investments or expansion stages.
At the same time, the second DEX analysis is a powerful tool for further development. Because it not only reveals successes, but also new optimization opportunities: areas that have become visible through the initial expansion, new bottlenecks that have shifted, or additional knowledge and process potentials that were not previously in focus.
This creates a continuous improvement cycle: The AI keeps getting better – just like your company. A one-time introduction becomes a sustainable development that can be expanded, stabilized, and optimized step by step.
In short:
The second DEX analysis not only shows what AI has delivered. It also shows what is possible next.
1. The introductory meeting
The introductory meeting – the first step towards effective AI The use of artificial intelligence in companies does not begin with technology, but with understanding.That’s why every collaboration with Vimmera AI starts with a structured introductory meeting. This meeting serves to truly understand your organization, your challenges, and your goals. It is not a sales […]
2. The analysis discussion
The analysis meeting – from understanding to structure After the introductory meeting, the next decisive step follows: the analysis meeting.This is no longer about a first mutual introduction, but about a systematic, joint understanding of your organization. In this meeting, we begin the structured company analysis based on DEX (Digital Experience & Execution) – the […]
3. The DEX analysis
The DEX Analysis – Clarity About Your Organization The DEX Analysis (Digital Experience & Execution) is the foundation of every successful AI implementation with Vimmera AI. It creates an objective, reliable picture of how your organization actually works – not just on paper, but in real everyday life. After the analysis meeting, we can, if […]
4. The knowledge collection
The collection of company knowledge – making everything visible Before AI can use, understand, and reliably provide knowledge, this knowledge must first be fully and correctly captured. That is precisely why the systematic collection of company knowledge is one of the most important steps on the way to an effective AI solution. This is expressly […]
5. Data preparation
Data preparation – from raw material to networked corporate knowledge Once the corporate knowledge has been fully collected, the step begins that decisively determines how powerful, reliable, and useful the later AI will actually be: data preparation. In this phase, a large number of individual files, texts, media, system extracts, and experience reports are, for […]
6. The data verification
Data Verification – Creating Trust in Knowledge and AI After company knowledge has been collected and structured, cleaned, and linked together during data preparation, the step follows that turns information into truly reliable knowledge: data verification. In this phase, it is decided which content may actually be considered valid, binding, and actively usable. Because even […]
7. Data embedding and data vectorization
Embeddings & vector search – enabling AI to find and use knowledge precisely After knowledge has been collected, processed, and verified, the next step ensures that the AI can later access this knowledge quickly, precisely, and in the right context: chunking, embedding, semantic processing, and the creation of vector databases (vector stores). This step is […]
8. The selection of the LLM or LLMs
The selection of LLMs – the right AI brain for your tasks After knowledge has been structured, verified, and technically prepared so that it can be found and used precisely, the next central step follows: the selection of Large Language Models (LLMs). LLMs are the “thinking machines” behind AI – they determine how language is […]
10. Implementation of security mechanisms
Additional security mechanisms – protection for knowledge, systems, and results In addition to selecting and combining the appropriate LLMs, another important step is implemented depending on the requirements and area of application: the implementation of additional security mechanisms. Because your knowledge is valuable, and it deserves the same protection as any other business-critical system. Vimmera […]
9. Prompt Engineering
Prompt Engineering – Giving AI a Clear Identity After the company knowledge has been collected, processed, and verified, another central step follows: prompt engineering. In this phase, it is determined how the AI thinks, responds, and acts. Vimmera AI takes this step in close coordination with you, because this is not about technology, but about […]
11. Additional data sources and interfaces
In addition to the knowledge base from documents and verified company knowledge, it may be useful or necessary—depending on the application—to connect additional data sources. This includes the additional implementation of databases, business systems, and other data interfaces, for example via modern integration standards such as MCP. The background is simple: Not everything an AI […]
12. Customization of the user interface
Frontend & User Interface – AI where people really work For AI to be effective in a company, it is not enough for it to work technically. It must be available where employees actually work – simply, reliably, and without additional hurdles. That is why Vimmera AI generally relies on browser-based frontends. Browser-based solutions offer […]
13. Rollout of the AI systems
Rollout & Introduction – Bringing AI into Everyday Work Step by Step After all technical, content-related, and organizational foundations have been established, the rollout phase begins. In this step, the AI is not simply “activated,” but is introduced into real everyday work in a controlled, guided, and coordinated manner. The goal is for the AI […]
14. Change Management
Change Management – Safely Guiding People Through Change The introduction of AI is not just a technical project, but a profound organizational and cultural change. New systems, new ways of working, and new possibilities trigger uncertainty, questions, or even fears in many people. Vimmera AI knows this – and that is precisely why change management […]
15. Fine-tuning before the actual launch
Joint fine-tuning before go-live – ensuring quality before it counts After AI has been introduced in initial teams or departments, a targeted phase of joint optimization, adjustment, and error correction follows – even before the system goes live company-wide. This phase is crucial to turn a functioning solution into a truly robust, practical AI. Pilot […]
16. Go-live
the productive start of your AI After the successful pilot phase and joint fine-tuning comes the decisive moment: the go-live. In this step, the AI is officially activated for productive use within the entire defined scope. From now on, it is no longer just a pilot project, but a permanent part of your working environment. […]
17. The second DEX analysis
Measure impact, identify potential, develop in a targeted way After the go-live, the phase begins in which it is decided whether AI has not only been “introduced” but has actually become effective. This is exactly why the DEX analysis is carried out a second time. It is not a formal conclusion, but a deliberately set […]
18. Care, support & further development
so that your AI remains valuable in the long term With the go-live and the second DEX analysis, the AI has successfully arrived in your company. But just like your company itself, your AI does not stand still. Products change, processes are adjusted, new insights emerge, markets and requirements continue to develop. At the same […]
19. Documentation
Transparency, Security and Long-Term Usability A professional AI system is only as good as its traceability. That is why documentation at Vimmera AI is not a by-product, but a central component of every solution. It ensures that your AI system not only works today, but also tomorrow, in a year, and in a changed organizational […]