In high-uncertainty environments, this proposal explores a preliminary approach to addressing projects as evolving complex systems. It introduces the development of a Multidimensional Performance Matrix and a Systemic Complexity Index (SCI), aimed at capturing nonlinear interactions and emerging dynamics among critical success factors. Unlike other management models, this methodology seeks to quantitatively identify early signals of cumulative complexity and provide strategic support for decision-making. Although initial applications have been carried out, the model is currently in the phase of comprehensive validation and assessment of its applicability in different contexts.

"In today's context of increasing energy demand, technological development requires high levels of electrical energy and is becoming increasingly integrated into everyday life. To optimize the use of materials and resources, it is essential to design electrical distribution systems that allow the traditional grid to adapt to current needs, promoting more efficient and sustainable solutions. In this regard, the use of digital tools that facilitate data organization, management, and analysis is key to improving the planning and operation of electrical infrastructures. One of the most promising methodologies in this field is Building Information Modeling (BIM), originally developed for the construction sector. BIM enables the three-dimensional modeling of projects, interference detection, and the integration of various disciplines, optimizing costs, time, and resources. Due to its success in construction, BIM has started to expand into other sectors, including the electrical industry. However, challenges remain in its adaptation. This study analyzes the different BIM tools used in construction and evaluates their feasibility for implementation in the electrical sector, identifying advantages, limitations, and opportunities for improvement. "

"Real estate projects are characterized by the collaboration of multiple stakeholders, including owners, investors, designers, production teams, and commercial partners. These projects involve complex, long-term processes that require efficient management to ensure competitiveness. Traditionally, project management has focused primarily on activities, which limits the ability to manage task dependencies, deliverable validation, and milestone tracking. To address these limitations, we propose a dual-layer process management approach. The upper layer focuses on optimizing activity coordination, while the lower layer is dedicated to document management. This model integrates a standardized data structure that automatically generates project schedules, which are distributed through process management software. This software automates task assignments, improving visibility and control across the project. This comprehensive and transparent approach allows stakeholders to reliably track project progress, ensuring that quality, cost, and timeline objectives are met, ultimately safeguarding the projected profitability."

This communication presents a methodology for the automatic generation of electrical projects using generative artificial intelligence, based on single-line diagrams. The proposed approach aims to optimize the process of developing technical documentation in electrical projects, significantly reducing the time and resources required, while ensuring compliance with current regulations.The developed system employs advanced image processing and deep learning techniques to interpret single-line diagrams, extracting crucial information about the topology and components of the electrical installation. In this first stage, the topology is extracted and interpreted, associating characteristics such as power, etc., to each element or line. This allows for calculations to be performed and, ultimately, through natural language models specifically trained in electrical regulations and project standards, to automatically generate the justifying calculations. Results from the system's implementation in real case studies are presented, demonstrating its effectiveness in terms of accuracy, coherence, and compliance with legislation. Furthermore, the implications of this technology for engineering firms are discussed, highlighting its potential to improve efficiency and quality in project development.