BarmeniaGothaer
A shared vision for the healthcare partner of the future

October 2026

At a glance

Client:  BarmeniaGothaer (Fusion 2024)
Industry: Insurance, with a focus on health
Challenge: Developing a robust data and AI strategy during an ongoing merger – amidst varying starting points and limited implementation readiness
Solution: Joint post-merger target state for data and AI, strategic operating model, and a structured cluster and use-case logic
Results:  Clear strategic framework, broad stakeholder buy-in, and direct alignment for operational implementation

Challenge
Two worlds, one vision

The merger that created BarmeniaGothaer resulted in more than just a larger insurance company; it established a commitment to rethinking healthcare. At the heart of this effort was a key question: How can data and AI help provide policyholders with faster, more transparent, and more personalized support in the future? Consequently, it was essential to define—at an early stage of the merger—a shared, viable vision for the healthcare division, along with a roadmap for its operational implementation.

From the outset, the merger was planned as a multi-year process, encompassing both technical and organizational integration. Even as structures, systems, and responsibilities were still being aligned, the goal was to simultaneously develop a robust data and AI strategy for the healthcare sector. The pressure was significant: the merger not only expanded the company’s customer base but also increased its responsibility toward those it insures. Delivering faster claims processing, ensuring transparent communication, and providing more comprehensive support as a dedicated healthcare partner—these priorities moved even more sharply into focus.

The development of the AI strategy was particularly compelling due to the different starting points of the two organizations. The task was to seamlessly integrate these established areas of focus: on one side, there were robust processes with a clear emphasis on efficiency; on the other, extensive foundational work regarding data structures and governance. The challenge lay in combining these strengths so that they would collectively contribute to a single goal: providing better, consistent support for policyholders in the healthcare sector.

The use of AI has long been a natural fit for claims and service processes. However, high process complexity and strict regulatory requirements have historically made the seamless integration of such technology a significant challenge. Consequently, at the start of the project, there was a wide spectrum of attitudes—ranging from “we don’t have the capacity for this right now; perhaps after the merger” to “we’re doing just fine without AI.” Given this complex landscape, it was crucial for Comma Soft to bring all stakeholders on board—those who would ultimately be responsible for these processes and tasked with implementing changes in day-to-day operations.

Another classic challenge present at the outset was that evaluating individual initiatives in isolation often leads to fragmented, “siloed” solutions rather than sustainable, long-term change. The objective, therefore, was to establish a framework where innovation functions not as an isolated, sporadic occurrence, but as a structural element—ultimately strengthening the organization’s role as a partner in healthcare. The project’s aim went beyond simply identifying new AI concepts; it sought to create the necessary framework to make data-driven innovation in the healthcare sector truly sustainable.

Proposed solution
Developing a strategy – and structuring it for impact

During the preliminary discussions for the joint strategy project, it quickly became clear that what was needed was not just ad-hoc consulting or a simple workshop format, but rather a **structured strategy process that takes into account the unique context of the merger** and purposefully advances the healthcare division.

“For us, it was crucial that the strategy did not remain abstract, but was instead consistently designed with a focus on implementability and management. That is precisely what made the difference.”

Christian Ritz, Health Insurance Board Member, BarmeniaGothaer

It was anticipated that such a strategy process might not always adhere strictly to the planned timeline; the priority was, instead, to ensure depth of content and practical applicability. To gain a realistic understanding of the starting point, our team conducted discussions across the full spectrum of relevant departments and organizational levels—including two board members, several division heads, and those responsible for operational execution. The objective was clear: it was not about the sheer number of interviews, but rather about conducting the right ones.

Methodologically, the team combined a structured interview component—consisting of standardized questions to assess maturity levels, data culture, and organizational sentiment—with a semi-structured component that focused specifically on topics such as governance, stakeholders, use cases, or management models (depending on the individual’s role). A pre-defined matrix of topics and stakeholders ensured that all pertinent aspects were covered. Within this secure environment, friction points and weaknesses could be addressed openly—providing essential raw material for a robust strategy.
A key outcome of the process was a clearly defined **Strategic Data Operating Model** that bridges the gap between strategic objectives and operational reality within the healthcare sector. The goal was to avoid the common disconnect between vision and execution. A particular focus was placed on the level of individual use cases: rather than producing ad-hoc lists, the approach required structured backlogs, clear prioritization, and defined responsibilities. In our operating model, we intentionally designed governance to span multiple levels—not just at the level of individual use cases, but primarily through functional clusters, core pain points, and overarching objectives. This provides essential guidance for the transformation process without overwhelming the operational teams.
A key component of the approach was moving away from isolated, individual use cases—each with its own standalone business case—toward a “cluster logic.” This framework organizes initiatives into thematic clusters, comprising both high-profile “star” use cases and essential, albeit less prominent, foundational use cases. Within each cluster, teams work on a pipeline of concurrent applications at varying stages of maturity, ensuring a continuous flow of new insights. Evaluation goes beyond short-term monetary gain, focusing instead on “strategic business value”—a metric that encompasses feasibility, cultural impact, strategic alignment, and the actual contribution to a data-driven transformation. For BarmeniaGothaer, this also represented a fundamental paradigm shift: data has evolved from a mere administrative tool into an enabler for better services and more informed decision-making—ultimately benefiting the policyholders and serving as a core value driver.
Our approach was rounded off by the “Data Innovation Flywheel”: greater value generates more data, better data enables better models, and better models lead to better products—which, in turn, create even more value. We brought this principle to life through concrete examples and adapted it specifically for the healthcare sector.

Ultimately, this resulted not in a purely theoretical vision, but in a strategy with a clear structure, a shared language, and a direct, practical link to implementation.

“Collaboratively developing the data and AI strategy helped us define a clear vision for the healthcare sector—and, above all, a realistic path to achieve it.”

Jonas Lichtblau, Head of Digitalization in Healthcare, BarmeniaGothaer

Results and Benefits
A clear vision for the future of health

Even as the strategy phase concluded, key decisions were made that set the course for further development in the healthcare sector:

Clear post-merger target state: A shared strategic framework for data and AI in the healthcare sector, serving as a guide for business units, IT, and management.
Structured use-case landscape: Functional clusters containing concrete use-case ideas—moving away from isolated, individual initiatives—while remaining intentionally flexible for prioritization and further development.
Strategic Data Operating Model: A bridge to execution that connects strategy, governance, and operational implementation.
Broad buy-in: Early involvement of those who will ultimately own the processes ensures acceptance and feasibility of implementation.
Clear path to sustainability: Positive reception at the executive board level, leading to a direct follow-up mandate for steering, prioritization, and implementation support.
Measurability framework: Future AI impact will be evaluated based on “strategic business value”—going beyond traditional business case metrics.

This provides BarmeniaGothaer with a robust foundation—rooted in data and AI strategy—within the healthcare sector, enabling the company to purposefully further develop its role as a healthcare partner for the future and to take the next step from a shared vision toward measurable operational impact.