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Culture is vital to a company's success, but it's not easy for employers to develop, sustain, and grow it. Although organizational and managerial factors drive a company's competitiveness, corporate culture [...]

The article explores the evolving landscape of leadership development, emphasizing the integration of diversity, equity, inclusion (DEI), and artificial intelligence (AI). It highlights the need for leaders to adapt to diverse workforces and embrace conceptual skills at all organizational levels. It focuses on developing authentic leadership through continuous introspection, unlearning outdated practices, and fostering inclusive environments.

The article offers information on the Magic Spark Model, a reliable approach for developing human capital in companies. Topics include the importance of balancing work and home, the systemic survival approach for organizational success, and lessons from successful companies like Tesla, Apple, and Toyota.

This article emphasizes the importance of focusing on intangible assets for long-term success in organizations. It argues that organizations should invest in new and long-term competencies, such as social capital, organizational capital, and human capital, to gain a competitive advantage and ensure survival in a changing business environment. The article highlights the need for leaders to go beyond financial results and prioritize investments in knowledge management, training and development, and other concepts that enhance intangible assets. It also discusses the significance of organizational resilience and adaptability in achieving long-term survival. [Extracted from the article]

Leadership has been a circumstance, an event, and a mode of operation, and executives follow recipes. Some recipes are used once, and some are thrown in the trash. The ones that are in focus are those that reap the largest benefits to the stakeholders. In practice, leadership is a set of roles that inspire employees and become role models for them. A leader's personality must be also continuously discerned. This requires outside evaluation and suggestions. In this article, we explore leadership through a more detailed lens to define the roles of leaders for the future. [ABSTRACT FROM AUTHOR]

The article discusses the need for a more balanced approach to capitalism, addressing issues such as income inequality, environmental degradation, and the neglect of social welfare and freedom. It advocates for a systemic approach that considers both profit and social responsibility, emphasizing the importance of innovation, government intervention, and corporate social responsibility in revitalizing capitalism for the future.

The article discusses the role of the future US President in shaping the future for the better, particularly in relation to climate change. It suggests that the next president can continue the fight against climate change and make it more effective through sustainable policies and investments in renewable and nuclear energy. The article also emphasizes the importance of addressing deforestation and soil erosion, as well as expanding the use of electric cars. It concludes by highlighting the vital role of American voters in building a better world and the responsibility of the United States President in leading on the global stage. [Extracted from the article]

The article focuses on the importance of effective leadership in digital technologies for organizational success. Topics include the relationship between digital leadership and market value, strategies for integrating digital technologies to reduce costs and increase profits, and the need for training leaders in digital management and business methods.

The article presents the discussion on emphasizing the importance of data quality and availability in implementing an artificial intelligence strategy. Topics include challenges with incomplete datasets, the need for robust data governance, and solutions such as data pre-processing and integration for developing effective data security systems.

The article discusses the use of artificial intelligence (AI) to empower business decisions. Solutions to guarantee data quality are data pre-processing or data cleansing, data as a service and explainable AI techniques. It suggests organizations to leverage cloud computing and distributed systems. Factors for implementing data quality funnel for decision-making are data and organizational culture, trust in machine learning and AI outcomes, and organizational technical resource capabilities.