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Many AI projects fail not because of the technology, but because of the maturity of the organization. Above all, the right mindset is crucial; appropriately adapted corporate structures are a must. Katja Koch and Judith Borgmann (MHP) explain in an interview why a successful AI introduction requires agile principles – and why managers must be prepared to rethink responsibility.
Executive Summary
Agile AI transformation: Why companies need to rethink leadership, mindset and organization
- The challenge: Many companies are currently investing in artificial intelligence, but many initiatives remain stuck in the pilot stage or do not have the expected impact. Often the problem lies not in the technology itself, but in inadequately prepared organizational structures, a lack of skills and a mindset that does not keep up with the speed of technological change.
- The solution: Successful AI implementation requires more than new tools. Agile principles, cross-functional collaboration and clear governance can help connect technology, processes and people. At the same time, companies must realistically assess their organizational maturity, empower employees and redefine leadership – away from control and towards trust, experimentation and shared learning processes.
- Your benefit: Katja Koch and Judith Borgmann from MHP explain how companies can design an agile AI transformation, what role agile working methods play and why managers have to learn to redesign responsibility and decision-making processes.
- Focus: Agile AI transformation, agile organization, AI introduction in companies, leadership in the AI age, organizational development, human-AI collaboration.
How are artificial intelligence and agile working related?
Katja Koch: We find that companies that already work in an agile manner find it easier to introduce AI. In our opinion, the reason is that AI transformation is very complex and cannot only be approached technologically. It requires a high level of flexibility and adaptability from employees. AI will change roles and ways of working. And cross-functional collaboration is becoming increasingly important.
Judith Borgmann: In other words: It’s all about having the right mindset. Agility creates the basis for an open, learning-oriented mindset. Those who work agile don’t think in terms of rigid processes, but rather in solutions that can adapt. Agile teams often think more holistically, are braver and more willing to experiment. You don’t just see tools, but the big picture. It is precisely this attitude that makes dealing with AI easier.
Agile working is therefore a success factor for transformation.
Katja Koch: Also, but not only. We are convinced that the success of artificial intelligence depends on the success of the transformation. This means that processes, structures and culture must be ready for AI. So we have to deal with the organizational maturity level before introducing AI. It is also crucial to understand what impact AI has on people and collaboration. It is precisely this aspect that often falls by the wayside for companies. They don’t take enough time for know-how, development and the concrete use of AI in everyday work.
Judith Borgmann: There is also the emotional dimension: many employees worry about their future and approach AI with uncertainty. That’s why companies must make change transparent and enable participation – not just for management, but for everyone. Transformation only succeeds if people understand why it is important and how they can actively help shape it themselves.
What does it mean to know your own maturity level?
Katja Koch: It’s about recognizing where the organization currently stands. Many companies want to use AI, but often don’t know whether their structures, processes or data landscapes are sustainable enough and whether their employees are ready for it. A maturity assessment creates exactly this clarity: It shows which skills are already there, which are missing and what can realistically be achieved. Only when this picture is available can the AI introduction be designed so that it does not get stuck in the pilot, but rather has an impact.
What does this all mean for the topic of leadership?
Judith Borgmann: Leadership is changing fundamentally: AI is not a tool, but an actor in decision-making processes, keyword “AI agents”. Decisions are becoming more decentralized and data-driven. Managers must learn to rethink responsibility. This often means less control, more trust. Questions that companies should ask themselves in this context are: Who makes decisions, who takes responsibility, who leads disciplinarily and who leads professionally, what takes place centrally and what takes place autonomously?
Katja Koch: Agile principles and practices can help here too. Because agile leadership is very much about values and role-based leadership. For example, as a coach, change manager, communicator, coordinator and enabler – also in the sparring model. This means that managers set the framework within which employees can experiment with AI and develop new skills. This also includes redefining collaboration models: How do AI agents and humans work together and who makes which decisions? It is also important to clearly link AI projects with the strategic corporate goals. An effective tool for this is the agile framework OKR.
Judith Borgmann: In order to be able to achieve this, the company’s managers themselves must of course be empowered to actively manage this change of role. This is about aspects such as trust, psychological safety, but also classic leadership development in the sense of personal leadership skills.
Katja Koch: This is an important aspect, after all, managers also lose power, influence and control in this process – and not everyone is happy to give that up.
Which corporate structures favor this approach?
Judith Borgmann: In our experience, this includes the use of cross-functional teams and the integration of AI-specific roles. This is automatically accompanied by different training and expectations, which are also perceived in the organization as a whole. But structures alone are not enough; they must be linked to a clear enablement strategy. This defines which skills are important for which roles, which further training is relevant and how employees are practically empowered. This creates an environment in which learning and trying things out are a given.
Katja Koch: Companies should create appropriate governance structures around this, for example in the form of a competence center – “AI Center of Excellence” – in which employees can participate and exchange ideas. Here too, please think holistically, not in silos, and involve the works council.
Which agile principles still need to be emphasized in the context of AI?
Katja Koch: I would like to highlight two principles from the Scaled Agile Framework (SAFe), which is based on the Agile Manifesto and Lean Management. The first principle, “Take an Economic View,” is about making decisions based on economic benefit and not on gut feeling. In my opinion, this is exactly what is important when introducing AI: it must create measurable economic added value and bring benefits at an early stage.
An iterative approach supports this because it makes initial results quickly visible. The second principle is “systemic thinking”. When developing or introducing AI, many elements have to mesh like gears. Data must be available, processes must run smoothly and people must work together across departments. AI can only have its full effect if the entire system works.
Judith Borgmann: It is important to also integrate the human perspective. Workshops, usage data, interviews and feedback loops ensure that AI not only works technically, but is also accepted. The iterative approach helps to test and adapt prototypes early on. This creates trust and real effectiveness.
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