The vision of network-centric operations is to increase operational capabilities through networked collaboration. NATO and its member nations state this vision in strategic documents at a very high level of abstraction. While suitable for giving an overall feel, current documentation renders the steps toward implementing those visions largely unsupported. We outline a method, based on agile requirements engineering, for converting high-level strategic visions into capabilities, whose form lend themselves to incremental implementation. We illustrate the use of this method in two cases that concern both operational capabilities and technical capabilities. We also  show how  the method enables one to prioritize  which capabilities to develop first. We conclude that some form of explicit methodology to span the gap between strategic visions and implementing those visions is necessary.

In this article, we give advice on transitioning to a more agile delivery model for large-scale agile development projects based on experience from the Parental Benefit Project of the Norwegian Labour and Welfare Administration. The project modernized a central part of the organization’s IT portfolio and included up to ten development teams working in parallel. The project successfully changed from using a delivery model which combined traditional project management elements and agile methods to a more agile delivery model with autonomous teams and continuous deployment. This transition was completed in tandem with the project execution. We identify key lessons learned which will be useful for other organizations considering similar changes and report how the new delivery model reduced risk and opened up a range of new possibilities for delivering the benefits of digitalization.

Predictions of time (e.g., work hours) are often based on the aggregation of estimates of elements (e.g., activities, subtasks). The only types of estimates that can be safely aggregated by summation are those reflecting predicted average outcomes (expected values). The sums of other types of estimates, such as bounds of confidence intervals or estimates of the mode, do not have the same interpretation as their components (e.g., the sum of the 90% upper bounds is not the appropriate 90% upper bound of the sum). This can be a potential source of bias in predictions of time, as shown in Studies 1 and 2, where professionals with experience in estimation provided total estimates of time that were inconsistent with their estimates of individual tasks. Study 3 shows that this inconsistency can be attributed to improper aggregation of time estimates and demonstrates how this can produce both over- and underestimation—and also time prediction intervals that are far too wide. Study 4 suggests that the results may reflect a more general fallacy in the aggregation of probabilistic quantities. Our observations are consistent with that inconsistencies and biases are driven by a tendency towards applying a naïve summation (2+2=4) of probabilistic (stochastic) values, in situations where this is not appropriate. This summation fallacy may be in particular consequential in a context where informal estimation methods (expert-judgment based estimation) are used.