AI, Data & Tech

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.

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.

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.