With the AI Value & Performance Framework we present a structured model that helps compa-nies move from initial quick wins to in-depth implementation of AI.

Many companies invest heavily in Artificial Intelligence – yet in most cases, business success re-mains missing. A recent study by MIT shows that only 5% of companies manage to integrate their AI initiatives in a way that generates real added value, while the majority remain stuck in the pilot phase and fail to achieve a measure ROI (Source: State of AI in Business 2025, MIT, 2025).

The key challenges that hinder the implementation of AI initiatives include the following:

  • Lack of prioritisation: Without a clear selection and focus on high impact cases, initiatives fall short in their potential
  • Unclear objectives: Given the numerous possible use cases, companies often lack clarity about where to start – and thus also the focus on how AI can quickly create added value
  • Lack of responsibilities and structures: If clear roles, processes and responsibilities are miss-ing, AI remains without direction within the organization and risks cannot be effectively ad-dressed. Especially after initial initiatives, this often leads to open issues not being addressed consistently – instead of using them as learning opportunities, many companies lose momen-tum.
  • Overwhelmed by technology: If the focus is too strongly on technical elements without taking organisational and human factors into account, friction losses and a lack of acceptance arise. 

This brings a key question to the fore: How can companies successfully move beyond the concept and planning phase and quickly generate tangible benefits with AI? The key is to bring technology, organisation and people together in such a way that barriers are broken down, rapid value contributions are realised, and long-term competitiveness is secured.

This is where the AI Value & Performance Framework comes in. It offers practical guidance with four key dimensions that structure the successful use of AI in companies:AI Value & Performance Framework

  • Components: Developing a clear leadership vision and defining goals for the use of AI; promoting a culture that is conducive to innovation and cross-departmental collaboration; transparent communication
  • Added value: A clear vision and a culture of innovation create orientation and acceptance. This makes the benefits of AI visible early on and gives employees and managers confidence in the new technology.
  • Components Establishing a stable technological basis with high-quality data, integrated IT systems and security. Implementing a flagship project as a visible starting point.
  • Added value: A lighthouse project speeds up implementation, delivers initial quick wins and demonstrates the tangible business benefits of AI. At the same time, the stable infrastructure creates the basis for sustainable scaling.


  • Components: Systematic competence building through training and change management; closing skills and performance gaps; introduction of measures to increase efficiency
  • Added value: Employees are empowered to successfully use AI in their everyday work. Early results promote acceptance, while efficiency gains highlight the direct value contribution. This gradually creates the basis for broad acceptance.
  • Components: Establishing structures for responsible control; ensuring compliance and risk management; developing a roadmap from initial projects to the transformation of entire business areas.
  • Added value: A clear roadmap and binding structures ensure that AI initiatives do not remain isolated but are scaled effectively. This turns individual projects into sustainable business impact.


The AI Value & Performance Framework provides the necessary orientation to develop artificial intelligence beyond individual initiatives into a real value driver within the company. It combines strategic leadership with governance, technology and skills – and ensures that investments in AI do not remain in the pilot stage but enable sustainable business success. In this way, AI evolves from an experimental tool to a strategic success factor that strengthens growth, competitiveness and trust in the long term.


This article was written by

Philipp Tiedt
Philipp Tiedt
Partner, Advisory, Management Advisory, Head of AI Strategy & Implementation Service
Max Lembke
Max Lembke
Manager, Management Advisory, AI Strategy & Implementation Services & Customer Experience