The past two years have seen radical digital transformation. Companies and industries that have traditionally been hesitant to adopt new technology suddenly embraced their digital transformations--they needed to find new ways to work. Interestingly, many experts believe that these radical shifts are only the beginning. In a recent Deloitte survey, three-quarters of executives stated that they expect more changes in the next five years than there were in the past five years. The rate of change only increases as organizations are more open and willing to make the changes they need to keep up with the competition. Digital transformation (DX) encourages business organizations to adopt new technologies in order to deliver better value to their customers.

DUBAI: The idea of artificial intelligence (AI) has been around for a long time, but in recent decades it has gone from being the stuff of science fiction to something tangible and beneficial. Sir Michael Brady is the interim president of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), an Abu Dhabi-based AI-focused university -- the first of its kind in the world. "Stephen Hawking, Elon Musk, Microsoft, Google, Facebook and many more have contributed to AI's position in today's spotlight," he said. He added: "We have moved from the use of AI in large-scale industry or ground-breaking circumstances, such as NASA space exploration or factory robotics, to commonplace applications such as advertising algorithms or Netflix suggesting what show to watch next." As society transforms under the impact of technology, AI is also rapidly evolving.

Expert estimation of objects takes place when there are no benchmark values of object weights, but these weights still have to be defined. That is why it is problematic to define the efficiency of expert estimation methods. We propose to define efficiency of such methods based on stability of their results under perturbations of input data. We compare two modifications of combinatorial method of expert data aggregation (spanning tree enumeration). Using the example of these two methods, we illustrate two approaches to efficiency evaluation. The first approach is based on usage of real data, obtained through estimation of a set of model objects by a group of experts. The second approach is based on simulation of the whole expert examination cycle (including expert estimates). During evaluation of efficiency of the two listed modifications of combinatorial expert data aggregation method the simulation-based approach proved more robust and credible. Our experimental study confirms that if weights of spanning trees are taken into consideration, the results of combinatorial data aggregation method become more stable. So, weighted spanning tree enumeration method has an advantage over non-weighted method (and, consequently, over logarithmic least squares and row geometric mean methods).