Over the past months, we’ve explored the foundational principles behind effective Data & AI transformation. They offer organizations new ways to think and act—spanning structure, behavior, direction, and mindset.
Each principle is valuable on its own. But their full potential only unfolds when they’re seen as part of a larger cultural shift—one that moves beyond isolated actions or individual efforts.
That’s where We Over Me comes in.
As the ninth and final principle, it connects and completes the others. It reminds us that sustainable change doesn’t come from tools or templates alone, but from shared ownership and collaboration—across roles, departments, and even company boundaries.
And that’s exactly why we’re launching the Datentreiber Community: a space for collective learning, co-creation, and continuous progress. Because the transformation doesn’t end with a strategy—it starts with a shared commitment to shaping it, together.
Foundation
Systems Over Objects gives us the holistic lens.
Resilience Over Strength keeps the organisation adaptive.
Motion
Practice Over Theory creates evidence loops.
Pull Over Push fuels intrinsic motivation.
Navigation
Compasses Over Maps sets direction without rigidity.
Autonomy Over Authority shortens the path from insight to action.
Mindset
Learning Over Education turns every project into an opportunity to grow.
Disobedience Over Compliance safeguards curiosity and critical thinking.
Each of these principles solves specific challenges—but it is their interplay that builds the capability to move, decide, and adapt in complex environments. We Over Me is what ensures that this capability doesn’t remain fragmented—it makes it collective.
Central Challenges in Data & AI
Why do so many data and AI projects fail? Primarily, it’s due to the gap between expectations and reality. Often, responsibilities between business units and data teams remain unclear, the actual problems are misunderstood, and data quality and availability are inadequate.
Data and AI projects are inherently hypothesis-driven and difficult to plan in advance. The data collected today shapes tomorrow’s opportunities; missing out on critical data collection today inevitably jeopardizes future projects. Data projects occupy a challenging “middle ground”: teams face intense management pressure for quick successes, yet must remain realistic about technical feasibility.
This situation frequently breeds frustration: databases and data catalogs remain poorly maintained, data silos persist, models remain misunderstood. KPIs lose connection to reality, becoming ends in themselves. Agility is often just a buzzword rather than genuine practice. Technically unrealistic management goals persist, and candid discussions about AI limitations rarely happen.
The root cause of these issues is not technological, but cultural. “We Over Me” tackles this by replacing silos with shared responsibility, isolated expertise with interdisciplinary collaboration, and opaque models with transparent communication.
Concrete Mechanisms of the “We Over Me” Principle
The “We Over Me” principle becomes truly actionable when translated into tangible mechanisms that address real-world friction in Data & AI projects—particularly those rooted in siloed thinking, unclear responsibilities, and low adoption. These three mechanisms reflect different phases of the data value chain and embody the collaborative logic that underpins successful transformation:
- Networked Value Creation: Data only becomes valuable when it is accessible, interpretable, and usable across departmental boundaries. Predictive models, analytics platforms, and even simple dashboards depend on inputs from diverse systems and teams. If insights are hoarded or access is restricted, opportunities for learning and value creation collapse. Shared responsibility across domains—marketing, IT, ops, legal—lays the groundwork for a functional, living data ecosystem.
- Hypothesis Testing and Transparency: Unlike classical engineering projects, AI initiatives are inherently uncertain and iterative. They are not built on certainties, but on hypotheses—what if we could predict churn? what drives this KPI?—that need to be tested and often disproven. This requires not only technical agility but a culture of transparency. Interdisciplinary teams that regularly share assumptions, failures, and interpretations are the only ones able to steer productively through these cycles without losing trust or momentum.
- Acceptance and Accountability: Even the most elegant model is useless if no one applies it. Co-creation ensures that those who will later use or be affected by data solutions are involved from the start. This fosters trust, accelerates deployment, and avoids the common “last mile” failure where outputs gather dust because business stakeholders were never part of the process. When teams own the solution, they also own its outcome—and with it, the willingness to improve, iterate, and sustain impact.
Likewise, each of the other eight principles ultimately relies on collective intelligence and mutual trust—making “We Over Me” not just the final piece, but the connective tissue that enables them all to work together.
Practical Application at Datentreiber: How “We Over Me” is Implemented
Datentreiber systematically and practically implements the “We Over Me” principle. The “train. think. transform.” steps in our portfolio accelerate the journey from idea to execution through collective collaboration. How does this look like, in short:
Train: Interdisciplinary teams immediately acquire a shared language and methodology, establishing clear understanding from day one.
Think: Joint workshops quickly lead to concrete, cross-departmental strategies. Issues are identified early and collaboratively resolved.
Transform: Roadmaps and execution plans are collaboratively created onsite. Clear and transparent responsibilities lead to faster decision-making and rapid implementation.
The Data & AI Business Design Community: A Crucial Space Beyond the Workplace
Why is a dedicated community essential beyond our daily workplace interactions? Today’s professional environment is dominated by platforms like LinkedIn, driven by news cycles, algorithms pushing trending topics, and simplified messages rewarded by clicks. Attention is commodified and driven by volume rather than depth, often leaving critical discussions superficial and noisy.
Precisely because of this, especially when internal company structures haven’t yet fully embraced these nine principles, professionals need a quieter, more thoughtful space. They require an environment that prioritizes genuine exchange over simplified narratives, enabling meaningful conversations about the practical implementation of these transformational principles.
The Data & AI Business Design Community run by Datentreiber aims to provide exactly this: a dedicated, distraction-free environment where like-minded individuals across different organizations can connect and collectively advance the principles that Datentreiber stands for—integrative approaches, interdisciplinary co-creation, hypothesis-driven strategies, transparent communication, and sustainable, long-term actions. It wants to serve as a critical hub for sharing practical experiences, tackling complex challenges, and making abstract concepts tangible and actionable.
In essence, the community should accelerate real progress by connecting professionals who are actively reshaping how data and AI strategies are understood and implemented.
Now is the time to actively embody the “We Over Me” principle. The Data & AI Business Design Community is your concrete next step toward achieving genuine and lasting success with data & AI.
