Welcome & Introduction

Rainer Michaeli opens the webinar and welcomes participants to the session on "Structured Team Thinking Methods for CI and Strategy Leaders." He notes that the topic is particularly timely in an era dominated by artificial intelligence — a period in which traditional human intelligence and the methods for deliberately structuring and applying it often receive insufficient professional attention.

Participants are informed that the session is being recorded. Questions may be submitted via the chat function at any time during the presentation. The session format consists of a 20-to-30-minute presentation by Ursula Teubert, followed by a Q&A period of approximately 15 minutes.

Patterns & Human Potential

Teubert opens her presentation with a pattern-based comparison designed to provide historical perspective on the current moment in human intelligence development.

Referencing the cultural and economic climate of the 1980s and 1990s, she recalls the widespread public discourse around mechanical automation and industrial robotics — a period in which it was broadly assumed that increasing mechanisation would eventually eliminate the need for physical effort. In sharp contrast, today people voluntarily train for Iron Man competitions, ultramarathons, and extreme endurance events — a 46-year-old amateur runner recently set a new women's world record by covering 278 kilometres in a 24-hour period.

A second pattern begins in the late 1990s, when computers first defeated human chess champions and, later, Go players. Teubert draws a direct analogy to the present moment: just as 1980s automation initially seemed to render physical effort obsolete, the current wave of AI leads some to conclude that structured human thinking is no longer necessary.

Applying the same 20-to-30-year cycle observed in the physical domain, Teubert proposes that by approximately 2040, a comparable revival may emerge around human cognitive capacities. The central message: humanity is sitting on an enormous, largely untapped capital of human intelligence — and the choice of what to do with it is not predetermined.

Explicit vs. Tacit Knowledge

To illustrate the scale of the opportunity, Teubert introduces a Pareto-based framework distinguishing between two categories of organisational knowledge.

Explicit knowledge represents approximately 10% of the total knowledge present in an organisation. It is formal, codified, and readily accessible — stored in manuals, databases, procedures, and training materials. This is the layer that feeds AI systems, algorithmic decision-making, and automated processes.

Tacit knowledge accounts for the remaining 90%. It resides in the minds, experiences, and judgements of people: intuitions developed over years of practice, context-specific skills, values, and perspectives that resist simple documentation. Current investment in AI tools overwhelmingly focuses on the explicit 10%, leaving the tacit 90% structurally underutilised.

This represents an inversion of the Pareto Principle — directing resources at the smallest fraction of available knowledge rather than the dominant share. The same ratio applies to human cognition itself: the conscious mind accounts for roughly 10% of neurological capacity, while the subconscious holds approximately 90% of expertise, pattern recognition, and experiential knowledge.

Subconscious Mind & Interaction

Two further dimensions of human cognitive potential are highlighted as systematically neglected in contemporary professional practice.

The first is the relationship between the subconscious mind and large-scale knowledge. While digital systems allow unrestricted access to all stored data, human cognition operates through selective filtering: only a fraction of what the brain holds can be consciously retrieved at any given moment. The vast neurological "big data" of the subconscious remains largely inaccessible without deliberate methods to surface it.

The second dimension is the social nature of brain function. Individual cognitive performance is highly context-dependent and relational. The same person working alongside different colleagues may demonstrate meaningfully different capacities for problem-solving, knowledge sharing, and expertise development. This variability has measurable neurochemical and neurological underpinnings.

Motivation is neurologically linked to dopamine responses triggered by meaningful social interaction and the anticipation of purposeful outcomes. The implication for team leadership is direct: the quality of interactions within a team — the questions asked, the degree of respect expressed, the social dynamics at play — directly shapes the neurological conditions under which each member operates. When team dynamics are damaging, a measurable share of available collective intelligence is simply and silently lost.

Why Choose a Thinking Method?

The distinction between "thinking a bit" and making a deliberate choice of thinking method is central to the session's argument.

Teubert draws an analogy to athletic preparation: a gymnast and a marathon runner train entirely differently — in timing, muscle groups, and cognitive demands. The same logic applies to cognitive work. The human brain performs differently depending on time of day and prior cognitive load. In the early morning, processing tends to be slower and associative; by the time a person arrives at work, the mind is more alert and better equipped for analytical tasks.

The practical question for any team leader or knowledge worker is: what is the cognitive objective of this task? Selecting the appropriate thinking method requires honest awareness of:

• The objective at hand — understanding, ideation, or problem-solving
• The complexity and scope of the task — incremental improvement vs. long-range strategic thinking
• The current cognitive state of the team — alert and analytical, or better suited to creative exploration

Structuring how a team thinks is as important as structuring what it thinks about.

Reformulation & Brainwriting

Two practical thinking methods are introduced to demonstrate how structured approaches improve team cognition in concrete, applicable ways.

Reformulation addresses a common but frequently underestimated challenge in heterogeneous teams. When participants come from different professional disciplines — legal, scientific, engineering, leadership development — the same question will often be interpreted in meaningfully different ways. A five-minute reformulation exercise at the start of any collaborative session ensures a shared understanding. Three techniques are useful:

• Echo: repeat what has been said in the same terms
• Mirror: "If I understand you correctly…"
• Synthesis: "Let me attempt to summarise — what I understand is…"

Brainwriting is a creativity and knowledge-extraction method built on the principles of collective intelligence. All participants write simultaneously and silently during timed cycles. The governing rules are:

• No verbal communication during a writing cycle
• Automatic, spontaneous writing — the hand leads
• No reflection, self-censorship, or internal editing
• No criticism of any idea, however preliminary
• Developing ideas means combining, refining, and enriching contributions already on the table
• Continuous writing throughout the cycle

An optional brief meditation beforehand can enhance access to associative and subconscious material. Both methods can be integrated directly into existing management, project, or product development processes.

Cognitive Biases & AI Usage

Teubert turns to the risks associated with cognitive biases and their interaction with AI tools — a combination that, when unmanaged, can systematically degrade the quality of competitive intelligence and strategic analysis.

Cognitive biases evolved as energy-saving mechanisms: in resource-scarce environments, reducing the metabolic cost of thinking and enabling rapid decisions in dangerous situations offered genuine survival advantages. Several biases are particularly relevant in professional analytical contexts:

• Confirmation bias: seeking, interpreting, and retaining only information that confirms existing beliefs
• Conservatism bias: favouring prior evidence over new data; resistance to updating beliefs
• Outcome bias: evaluating a past decision based on its result rather than the quality of the process
• Pro-innovation bias: systematically overestimating the utility and underestimating the limitations of new technologies
• Stereotyping: assigning characteristics to individuals based on perceived group membership rather than observed evidence

The interaction with AI tools amplifies this risk. AI systems trained predominantly on documented, explicit knowledge may reflect and amplify the biases embedded in that data. An uncritical reliance on AI-generated outputs, without subjecting them to structured critical thinking — such as Analysis of Competing Hypotheses (ACH) — can accelerate quality degradation rather than prevent it.

Assumptions & Real-World Impact

Two historical examples demonstrate the professional consequences of unexamined assumptions regarding public and consumer behaviour.

IKEA Netherlands (2015): More than 32,000 people registered via social media for spontaneous hide-and-seek events in IKEA stores in Amsterdam and Utrecht. The retailer's risk management framework had anticipated overcrowding and conventional security threats — but had not modelled the possibility that tens of thousands of visitors might arrive not as consumers, but as game participants. Police were called and faced the operationally complex challenge of distinguishing participants from ordinary shoppers. The reputational impact was significant. IKEA subsequently revised its risk management approach to account for non-consumer uses of its retail spaces.

Pokémon Go (2016 onwards): The launch of the location-based augmented reality game led players to seek virtual objects in physically restricted locations, including police stations and other secured facilities. Motivated by game objectives rather than any adversarial intent, players entered spaces they would not otherwise have accessed — creating legal and security complications the designers had not anticipated.

In both cases, the underlying failure was an incomplete model of human behaviour. For professionals working in innovation, consumer product development, or any domain involving interaction with the public, structured scenario thinking about edge cases and unconventional user behaviours is a professional necessity.

Overcoming Organisational Biases

One of the most significant and underestimated barriers to accessing organisational knowledge is social desirability bias — the systematic tendency of individuals to provide responses they believe will be well received, rather than responses that are fully authentic or accurate.

The dynamics are readily observable on internal knowledge-sharing platforms: a question posed by a junior employee tends to receive dismissive or minimal responses, while the same question posed by a senior executive triggers detailed, well-considered replies — particularly from employees with active career advancement goals. The result is a structural distortion of knowledge flows filtered through social hierarchy.

A straightforward corrective measure is anonymous questioning with identified responding: questions are submitted without attribution, but responses are clearly attributed to the contributor. This ensures knowledge-sharing is determined by the content of the question rather than the organisational status of the questioner.

AI tools offer an additional mechanism. Rather than deploying AI primarily as a knowledge generator — which carries inherent risks of confabulation — AI can be used more effectively as a moderator: identifying relevant internal or external experts, facilitating introductions, and routing enquiries to the right people. Additional structural benefits include:

• Expert identification independent of demographic characteristics, seniority, or social position
• Access to motivated external talent beyond traditional organisational boundaries
• Acceleration of globally distributed R&D collaboration

Trust, Time Windows & Team Input

Two foundational levers for improving the quality of team contributions are identified: mutual trust and deliberate time structuring.

Mutual trust is a prerequisite for genuine collective thinking. In teams where hierarchy is strongly enforced, where certain topics are implicitly off-limits, or where individual contributions are routinely minimised, a substantial share of available expertise never enters the shared cognitive space. An analogy from high-altitude mountaineering illustrates the point: in a rope team on a difficult ascent, the loss of any one member significantly increases the burden on the remaining three. The interdependency is immediate and unambiguous — the quality of collective decisions is directly bounded by the quality of each individual's contribution.

Time windows refer to the deliberate structuring of when and how contributions are collected. Different cognitive tasks benefit from different temporal architectures:

• Synchronous sessions — such as a structured brainwriting cycle — work well when concentrated, simultaneous output is required
• Extended ideation windows — for example, a seven-day open chat channel — allow ideas to emerge across diverse contexts and moments; contributors add whenever inspiration strikes, unconstrained by a meeting slot

The underlying principle is to design knowledge collection processes around the natural rhythms of human cognition, rather than forcing cognitive work into administrative time slots that may not align with peak readiness.

3 Reasons for Thinking Methods

Three interconnected arguments are presented for why CI and strategy professionals should develop competence in choosing and applying structured thinking methods.

1 — Accessing the full knowledge base. The Pareto inversion currently in effect — focusing primary resources on the documented, explicit 10% — leaves the vast majority of organisational intelligence untapped. Structured thinking methods are the primary means by which tacit knowledge, subconscious expertise, and contextual judgement can be drawn into analytical and decision-making processes.

2 — Enabling full cognitive participation. The conditions under which team members operate determine the cognitive output they can produce. Just as restricting a high-performance vehicle to 30 km/h wastes its engineering, creating environments that suppress contribution wastes human potential. Structured interaction, psychological safety, and well-designed thinking processes are the technical conditions for accessing the full cognitive capacity of a team.

3 — Cross-domain insight and innovation. Drawing on more than 25 years of experience in engineering, R&D, and innovation management, Teubert illustrates how deliberate cross-domain thinking generates unexpected solutions. Engineering challenges in high-performance sports equipment and mobility solutions for people with disabilities share significant technical overlaps — when teams from both domains think together, each benefits technically and socially. This cross-pollination is only possible when diverse expertise is actively accessed and structurally enabled to enter the shared problem space.

Separating Data from Information

A conceptual distinction of direct operational relevance — particularly in the context of AI adoption — is the difference between data and information.

Data exists in devices, databases, and systems. It is stored, retrievable, and processable — but it does not inherently correspond to any external reality. It reflects what has been recorded and encoded, which may or may not accurately represent the world outside the system.

Information is data that has been verified against or derived from observable reality. It is measurable, falsifiable, and — in a professional context — legally and analytically defensible. A temperature recorded at a specific location at a specific time is information. An AI model's output predicting conditions several hours later is a probabilistic data output: not verifiable as information until checked against subsequent observation.

By this definition, AI tools produce data as their primary output. That output may become information if and when it is cross-referenced against verifiable, real-world sources — but this verification step is frequently skipped under time pressure. The methodological frameworks developed within competitive intelligence and investigative journalism — source validation, cross-referencing, provenance tracing — provide established approaches to making this distinction in practice.

Technology, Thinking & Human Value

To close the presentation, Teubert invites reflection on the philosophical dimension of the relationship between technology, thinking, and human agency.

Swiss author Max Frisch (1911–1991) defined technology as "the art of achieving more by thinking less." His corollary observation — that a world fully saturated by technology would ultimately be tedious, because it would leave nothing for humans to think about — connects directly to the session's central argument.

American philosopher and activist Emma Goldman (1869–1940) expressed a related insistence on the irreducibility of human experience: "If I cannot dance, it is not my revolution." The juxtaposition of Frisch's efficiency critique with Goldman's demand for lived meaning underscores the tension that runs through every debate about automation and AI: efficiency vs. purpose, convenience vs. cognitive challenge, delegation vs. human agency.

The argument for structured thinking methods is ultimately an argument for taking human intelligence seriously — not as a legacy input to be optimised away, but as a living, developmental resource. Organisations that reduce their people's contribution to feeding data into AI systems, while ignoring the 90% of tacit, experiential, and subconscious knowledge those people carry, are not merely inefficient. They are making a choice about what kind of institution — and what kind of society — they are building.

Q&A

Following the presentation, Rainer Michaeli moderates a question-and-answer exchange based on participant questions submitted via the chat function.

Q1 — Is IQ constant, or does it vary with context?
IQ provides partial, useful information about the speed and general processing capacity of an individual's cognition, but does not capture the full range of what a person is capable of contributing. The same individual working in partnership with different colleagues will demonstrate meaningfully different levels of problem-solving performance, depending on the quality of the interaction, the mutual assumptions in play, and the degree of cognitive complementarity between the parties.

Q2 — How can thinking methods be realistically applied in established teams?
A neurological perspective starts from a more fundamental premise than cultural psychology: while individuals can adapt to cultural norms and learn new behaviours, they cannot alter their underlying neural architecture. Achieving excellence in business requires designing the team environment to work with human neurology, not against it — prioritising the conditions that allow each person's actual cognitive capacity to be expressed.

Q3 — Is AI output data or information?
AI output is, first and foremost, data. It becomes information only when it can be demonstrated to correspond to verifiable, real-world facts. A weather measurement at 7 a.m. is information; the model's forecast for noon is a probabilistic data output. This distinction has direct implications for the defensibility of analytical conclusions and the reliability of strategic decisions built on AI-generated outputs.

Q4 — What is the view on brain-chip technologies in corporate contexts?
Brain-computer interface technologies are currently under research as communication aids for individuals who have lost the ability to speak — a medically grounded application. On the use of AI chips to influence cognitive performance in corporate settings, personal caution is appropriate. Ultimately, this is a matter of individual choice — one that warrants careful consideration of the long-term implications for physical and cognitive autonomy.