From Engineering and R&D to CI/MI

Ursula Teubert’s professional roots lie in engineering, with many years spent in research and development and in managing innovation projects. Within this environment, she observed a persistent structural gap: strategic and innovation-related decisions were often made without a sufficiently robust, data-based understanding of markets and value creation.

This gap drew her towards competitive and market intelligence. CI and MI offer frameworks and methods for systematically gathering and interpreting information on competitors, technologies, markets and customer needs. For Teubert, this transition was not a change of discipline but a logical extension of engineering and innovation work, adding a fact-based view of the external environment to technical and internal perspectives.

Strategy Consulting, DCIF & Breaking Silos

Rather than occupying a classic in-house CI role, Teubert applies CI as an integral component of strategy consulting. In this capacity, she uses structured intelligence work to support strategic decision-making, linking market and technology insights directly to corporate strategy.

In parallel, she serves as President of the Deutsche Competitive Intelligence Forum (DCIF), where she contributes to building and connecting the CI community. Across 25 years of industry experience, one conclusion has become central to her thinking: silo-based work severely limits an organization’s ability to create value. When CI operates in isolation from other functions, its insights remain underutilized. Sustainable value creation emerges when CI is interlinked with other expert domains and when communication between these domains is actively facilitated.

Teubert therefore understands a key part of her role as fostering a shared understanding of the organization as a whole, combined with a clear appreciation of each discipline’s specific contribution. CI becomes most effective where it is not a standalone island, but a connecting and enabling function.

Developing Thinking as a Core Skill

Teubert has been involved with the ICI faculty for almost a decade. Her work began with the ICI37 workshop on critical thinking, which she subsequently expanded to include creative thinking. This combination reflects her conviction that both analytical rigor and creative exploration are necessary to fully leverage human intelligence in CI and strategic work.

A recurring pattern she observes is the assumption that intelligence or academic qualifications automatically translate into strong thinking skills. In innovation, it is often taken for granted that “people are intelligent,” and in CI, analysts rely on their education as evidence of their intellectual capability. Teubert challenges this assumption with a simple analogy: having two healthy legs does not qualify a person to run a marathon without training. Similarly, cognitive potential does not automatically result in high-quality thinking. Structured training in thinking methods is required to bridge the gap between raw ability and professional-level performance.

Within ICI37 and related courses, thinking is therefore treated as a learnable skill. Methods are introduced to sharpen perception, challenge assumptions and structure reasoning. The aim is not only to make individuals “smarter” in an abstract sense, but to improve the reliability and depth of their conclusions in concrete CI and strategy tasks.

An essential extension of this approach is the move from individual to collective intelligence. Teubert’s objective is not merely to strengthen the capabilities of single analysts, but to enable teams to operate as high-performing cognitive systems.

In this context, she deliberately contrasts genuine collective intelligence with the “lemming” effect often observed in group settings. When group dynamics reward conformity or deference to hierarchy, the least reflective contributions can shape the outcome, while valuable divergent perspectives remain unheard. By contrast, a well-structured team process can make the accumulated knowledge of all participants more powerful than the expertise of any one individual.

Training in thinking methods, therefore, focuses not only on personal discipline and critical reflection but also on facilitation techniques and group processes that foster balanced participation, constructive challenge and systematic usage of distributed expertise.

Human Intelligence in the Age of AI

These considerations gain additional weight in the current technological landscape, where artificial intelligence plays an increasingly visible role in information processing. It is tempting to assume that AI systems and advanced algorithms will gradually displace human analysis and judgment. Teubert regards this assumption as an expression of technology bias: the tendency to overestimate the capabilities of a technology simply because it is a technology.

AI can undoubtedly support CI and MI work by analyzing large data volumes, visualizing patterns, summarizing content and enabling rapid access to information. However, these capabilities do not replace human thinking; they shift the focus of human contribution. The key question becomes not whether humans or machines are “better,” but how human cognitive strengths can be deliberately deployed in combination with technological tools.

In this view, the deliberate, structured use of human thinking capacity is more important than ever. Rather than relinquishing cognitive responsibility to AI, CI professionals need to refine their own thinking to interpret, contextualize and critically assess what AI systems deliver.

Biases, Group Methods & Tacit Knowledge

A central element of Teubert’s approach is the systematic treatment of cognitive biases. Confirmation bias — the tendency to seek and interpret information that confirms existing beliefs — is one well-known example. Technology bias, as described above, is another. Awareness of these phenomena is a necessary starting point, but not sufficient on its own. Professionals must adopt concrete methods to mitigate the impact of biases on their analyses and decisions.

This requirement becomes particularly pressing in group settings. Traditional brainstorming formats, especially when conducted by a hierarchical superior, often lead to self-censorship and restricted participation. Social desirability and fear of negative judgment can prevent team members from contributing unconventional or critical ideas. The result is a narrow and potentially skewed information base.

Teubert therefore advocates for methods specifically designed to elicit broad and balanced contributions. Such methods structure the flow of input, decouple idea generation from immediate social evaluation and create defined roles for challenge and reflection. In doing so, they aim to maximize the amount of relevant knowledge that enters the discussion.

An additional dimension is the distinction between conscious and subconscious knowledge. Only a small fraction of a person’s expertise can be articulated on demand; much of what experts know is tacit and becomes visible only in concrete situations. If CI work relies exclusively on explicit statements in meetings, this deeper layer of knowledge remains largely untapped.

Structured thinking and group methods can help to access this tacit knowledge more systematically. By presenting realistic scenarios, asking targeted questions and designing processes that trigger associations and experiential recall, organizations can bring a significantly larger share of the available expertise into play. The goal is to make the activation of tacit knowledge a deliberate design choice rather than a matter of chance.

The Future of CI and MI

Looking ahead, Teubert expects competitive and market intelligence not to decline but to gain in importance. From a management perspective, companies are required to create sustainable value, and fact-based decision-making is a key element of this responsibility. CI and MI provide the structured insights necessary for such decisions.

AI will continue to expand its role in collecting, processing and visualizing data. However, data alone does not constitute information, and information alone does not automatically translate into sound judgment. CI professionals play the crucial role of validating sources, interpreting context and distinguishing between meaningful signals and misleading noise, including deliberate disinformation and so-called fake news.

For long-term economic strategy, this interpretive and integrative function is indispensable. The more complex and data-rich the environment becomes, the greater the need for professionals who can think clearly, structure ambiguity and turn raw inputs into reliable strategic insight. In this sense, Teubert argues, the future of CI is not threatened by AI; it is defined by the quality of the human thinking that surrounds and guides the use of AI.