Organizations play a pivotal role in the dynamics of social, economic, and ecological systems. Current organizational life-cycle models do not adequately consider the impact of propensities (deeply ingrained preferences and patterns of behavior) on organizational culture and evolution. On a global basis, the predominant thinking modes in organizations are driven by senior executives, marketers, financial experts, legal resources, and the engineers and scientists that create our technology-rich world. Each of these groups has, in aggregate, embedded propensities or tendencies that profoundly shape decision-making patterns and overall social dynamics. Dominant propensities can make organizations vulnerable to risks by inhibiting the level of systems thinking and networking necessary to ensure integration within a global socio-ecological context. The spectrum of propensities within an organization shapes the relative resilience of its human and management systems, and ultimately determines organizational effectiveness. This paper proposes a model for organizational evolution that links the role of propensities to adaptability and resilience. Conscious effort to expand the intelligence of organizations through diversification of propensities better equips organizations to achieve adaptability and sustainability.
The advent of Artificial Intelligence in the corporate world is disrupting existing business processes and changing the way organizations are run. AI is fast becoming a cornerstone of how businesses manage their bottom line, while opening new revenue streams that could provide a boost to their toplines as well. Given the scale of its impact, there is no doubt that AI will also have a severe impact on the science that governs how organizations are run today. I am obviously referring to incumbent management theories and models that govern modern organizational management. In classic terms, management theories are frameworks of wisdom which guide the decisions made by organizational leaders that have survived phenomenally well over the period of the modern enterprise.
This paper presents a typology of multi-organizational structures that emerge from the interaction of several organizations or are deliberatively created by them. Common in political, military, and business worlds, these inter-organizational partnerships create compositional structures which are controlled by several organizations. Multi-organizational structures offer a very interesting framework for the study of the costs and advantages of cooperation. It is shown that these structures can be characterized in terms of three features, which are purpose of partnership, control and cooperation structure, and dynamics of membership. Implications of each organizational structure on its autonomy and performance are discussed.
For some organizations, harnessing artificial intelligence's full potential begins tentatively with explorations of select enterprise opportunities and a few potential use cases. While testing the waters this way may deliver valuable insights, it likely won't be enough to make your company a market maker (rather than a fast follower). To become a true AI-fueled organization, a company may need to fundamentally rethink the way humans and machines interact within working environments. Executives should also consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making. Likewise, AI could drive new offerings and business models. These are not minor steps, but as AI technologies standardize rapidly across industries, becoming an AI-fueled organization will likely be more than a strategy for success--it could be table stakes for survival. In his new book The AI Advantage, Deloitte Analytics senior adviser Thomas H. Davenport describes three stages in the journey that companies can take toward achieving full utilization of artificial intelligence.1 In the first stage, which Davenport calls assisted intelligence, companies harness large-scale data programs, the power of the cloud, and science-based approaches to make data-driven business decisions. Today, companies at the vanguard of the AI revolution are already working toward the next stage--augmented intelligence--in which machine learning (ML) capabilities layered on top of existing information management systems work to augment human analytical competencies. According to Davenport, in the coming years, more companies will progress toward autonomous intelligence, the third AI utilization stage, in which processes are digitized and automated to a degree whereby machines, bots, and systems can directly act upon intelligence derived from them. The journey from the assisted to augmented intelligence stages, and then on to fully autonomous intelligence, is part of a growing trend in which companies transform themselves into "AI-fueled organizations."
Founded in 2006, Smartlogic is a leading San Jose, CA-based computer software company. Smartlogic's Semaphore is an enterprise-grade semantic platform that allows organizations to realize the business value of their information. Bringing structure to the unstructured, Semaphore scales to manage organizational volumes, and supports industry-standard semantic vocabularies. Its model-driven, rule-based semantic approach solves complex business problems that traditional technologies cannot. It integrates into and enhances the capabilities of existing technology to improve time to value for new opportunities.