Articles

AI rarely replaces humans; instead, it creates a partnership where both amplify each other. By choosing between models like "AI advises" or "iterative collaboration," leaders balance speed and judgment. This strategic approach ensures AI moves from experimental use to structural impact, with humans remaining the essential directors.

Data is a strategic asset only when it is unique, scalable, and directly drives decision-making. Without active feedback loops and behavioral change, data remains a passive cost center. True advantage arises when unique data streams structurally improve processes, building a competitive edge that is impossible to replicate.

With guest speaker Dirk van Dierendonck, Roemer Visser explores how servant leadership requires humility, redefines responsibility, and unlocks new possibilities for results.

AI success doesn't require a surge of projects, but rather strategic focus. By categorizing initiatives based on impact and scalability, and limiting priorities to a maximum of three core initiatives, AI transforms from fragmented experiments into a driver for structural value creation and growth.

While AI is widely used in operations, its application in corporate strategy remains limited. This is due to the unique, context-dependent nature of strategic decisions, which contrast with the repeatable patterns AI typically masters. However, AI offers immense value as a strategic partner by enhancing scenario analysis, providing granular data insights, and creating faster feedback loops. Ultimately, AI doesn't set the direction; it empowers executives to make more informed, data-backed choices.

Professor Ron Meyer’s "Personal System Map" explores human development through four internal layers and four external systems. While core traits are stable, growth is most effective "outside-in," where changing one's roles and positions drives meaningful shifts in knowledge, values, and behavior.

Top scores for content, faculty, and career preparation confirm TIAS’ ambition.

The 3A Model (Augment, Automate, Aspire) structures AI initiatives based on value creation rather than technology. It helps organizations choose between enhancing human decision-making, automating tasks, or developing new value propositions. This transforms AI from a collection of isolated experiments into a focused, strategic roadmap.

GenAI is ideal for creativity and variation, but unsuitable for tasks requiring strict logic or factual accuracy. Successful organizations use it strategically for inspiration, while relying on rule-based systems for predictability and consistency.

Anil Seth: “Perception is a kind of controlled hallucination.”

Many AI projects fail because the problem is formulated incorrectly. This article clarifies the difference between classification, ranking, and optimization, and shows how making the right choice is critical for effectiveness, trust, and real value creation with AI.

Predictive AI can improve decision-making and processes, but it is not suitable in every situation. This article provides a practical framework with five criteria to determine whether its deployment is responsible and effective — from representative data and stable patterns to social acceptance and demonstrable value.

AI is evolving from experimentation into a core strategic technology, but not every organization is at the same stage. These five steps help you assess where you are today and what it takes to move AI from pilot projects to lasting, organization wide impact.

Which factors are making my organization more inward-oriented? TIAS professor of Strategic Leadership Ron Meyer presents an insightful tool to kickstart your thinking: Five Company-Centric Forces Model.

Today, most organizations don’t lack AI ideas. On the contrary, use cases, pilots, and proof-of-concepts are piling up. Yet, strategic impact often remains limited—not because the technology falls short, but because ideas are treated too quickly as if they were already decisions.

Jill Bolte Taylor: “We have so much more power over what’s going on inside our brain than we’ve ever been trained to believe.”

AI pilots succeed remarkably often. They deliver working models, accurate predictions, and convincing demos. Yet in most cases, that is where it ends. The step from pilot to structural impact is rarely taken. This is not a technology issue. In most cases, the technology works just fine. The problem lies elsewhere: scaling affects the organization, not IT.

Good AI doesn’t start with data or a model, but with five sharp choices that determine whether it will actually be used later or just sound clever on paper.

Why Governance, Assurance, and Accountability must keep pace with AI innovation”

Barry Posner: “Organizations can make you a manager, but they cannot make you a leader.”