Not all processes can be optimized through agility. Areas characterized by low complexity and repetitive tasks benefit more from standardization and clear structures.
With the dawn of the AI era, increased flexibility, speed, and a self-motivated spirit of experimentation are becoming critical factors for success—even in public administration.
The adoption of agile work methods represents a proven path toward a new “operating model” that, above all, counteracts lengthy coordination processes, hierarchical decision-making trees, siloed work where different departments operate in isolation from one another, and a lack of ownership and overall accountability.
And yet, not all processes can be optimized through agility. Areas characterized by low-complexity, repetitive tasks benefit instead from standardization, clear structures, and, consequently, AI-supported workflows. This requires an analysis of the value-added processes:
When complex tasks must be solved in ways that are not yet known and at unpredictable times, agile work methods will become the norm.
Principal & Partner
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In contrast, in situations where complex tasks need to be solved at unpredictable times and in unknown forms, agile working methods will prevail.
These differing requirements have become well known in connection with the distinction between "complicated" and "complex" tasks and are clearly illustrated by the Stacey Matrix.
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