Introduction
With the development of technology, people can take advantage of the fast communication systems or computer algorithms that allow analyzing data, presenting the quality of analysis, and working with the quantity of data unreachable for a single person. An intelligence cycle allows individuals to collect raw data and process it for adequate decision-making. Modern intelligence organizations use technology, which allows them to collect large amounts of data and derive essential insights from it. However, this process requires a structure and approach that would help carry out this task efficiently. This paper argues that the intelligence cycle is useful for modern organizations and can be leveraged through the use of intelligent systems.
Essential Components
Firstly, it is necessary to define an intelligence system and distinguish its main components, which will help understand its applicability in the context of modern intelligence organizations. Levchuk et al. state that in the past, such systems were only imagined by science fiction writers, while currently, they are becoming an integral part of people’s day-to-day lives (67). Usually, these systems can perform preset tasks or gather information.
According to Levchuk et al., “increasing interdependencies between component devices, data, physical systems, and human users prompted researchers and practitioners to explore the implications of emergent device intelligence on broader aspects of our everyday lives” (67).
According to Rause, it is a system connected to the Internet that can gather information and analyze it. However, the distinguishing aspect of these systems is the fact that they can also learn and their security. These systems can form networks of interconnected devices and include technology such as Artificial Intelligence (AI), chatbots, and other types of intelligence systems (Rause). Therefore, an intelligence system allows individuals or organizations to improve task completion and conduct gathering and analysis of large portions of data.
Arguably, adequate decision-making requires one to use information, such as statistics, best practices, or other types of data. The Federation of American Scientists distinguishes five stages of the intelligence cycle – planning, collection, processing analysis, and dissemination of findings (The Intelligence Cycle). As the names of these stages suggest, before making a decision, one has to determine the goal and the specific information that should be collected to complete the task.
An intelligent system usually consists of a device such as a computer, which should be secure and connected to the Internet. For proper analysis, intelligence organizations should have data warehouses for storing the information and data sources (Business Intelligence (BI). A set of such devices forms a network, expanding the capacity of the intelligence system and allowing them to work on complex tasks. Therefore, the essential components of an intelligent cycle are the five steps necessary to plan and carry out the decision-making process based on the intelligence cycle principles.
The examined elements of an intelligence system suggest that it is useful when applied by modern intelligence organizations. This argument is supported by the need to make informed decisions in the context of global competition for businesses and the increasing pressure of social issues for governments and policymakers. The fact that the intelligence cycle allows one to plan the process of making a specific decision and collect the required information is especially important in the modern world, where the amount of data is overwhelming (Trieu 111). From the perspective of an intelligence cycle, intelligent systems can gather and analyze information more effectively when compared to humans, with the latter accounting for the first and final stages – planning and dissemination.
Provision of Intelligent Support
This paragraph will explore the issue of applying the intelligence cycle and intelligent systems in the context of modern organizations. Ozleblebici and Aydin argue that “data that have to be handled for intelligence is much more than ever,” suggesting that the contemporary intelligence cycles require the application of intelligent systems capable of handling large arrays of data (1). However, some researchers and practitioners argue that the approach is outdated and should be changed to correspond with the challenges of the contemporary world.
Ozleblebici and Aydin state that the DIKIW pyramid was developed to improve the understanding of the relationship between intelligence, data, and knowledge in the modern world (1). This hierarchy suggests that intelligence is the middle, between knowledge and wisdom, and argues that intelligence is always purpose-specific.
Notably, the intelligence cycle was developed to aid decision-making, and it can be used within modern organizations. Operational management and decision making, which are the core elements of organizational an organization’s daily life, all require the use of intelligence, more specifically, the application of information for the improvement of work. Intelligent support for operations incorporates the use of intelligence systems when managing the day-to-day work of a company.
Trieu and Fink et al. argue that in the context of business intelligence (BI), intelligent systems allow organizations to collect and analyze data regarding their work (111; 38). BI itself is an approach that allows companies organizations to use facts in their decision-making process. Therefore, BI and the intelligence cycle aim to use information when implementing a new strategy or managing operations.
The examples of finished intelligence applied in the context of intelligence organizations are reports received by the President daily, regarding national security (How Intelligence Works; Intelligence Cycle and Process). This approach is referred to as the current intelligence, while a more long-term outlook is estimative intelligence. In the context of decision-making, the intelligence of the cycle may be initiated to explore a specific problem and reinitiated to explore the aspects of it in detail (Prakash & Sarkar, 1). Using the previous example, The President may require more information regarding a specific issue, triggering a new intelligence cycle.
The following paragraph explores the criteria that make intelligent systems useful in the context of organizational decision-making. Ozleblebici and Aydin state that they can be divided into two categories – “standing and spot requirements” (10). The former category refers to decisions made for long or midterm, while the second is time-specific and usually refers to information analysis needed at a given moment. In the context of the criteria for spot decision making, the traditional intelligence cycle is not useful, because this approach requires time to plan, gather, and review the information.
However, for long-term decision making, where large amounts of data allow for better precision, the intelligence cycle is useful. Therefore, the primary criteria for determining the usefulness of the intelligence cycle are the amount of collected data and the timeframe for decision making. The evidence collected in this report meets the criteria because they outline the processes of the intelligence cycle and highlight the connection between intelligence systems and the intelligence cycle. Overall, this paper examined the applicability of the intelligence cycle in the context of modern intelligence organizations.
Work Cited
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Levchuk, Georgiy et al. “Active Inference In Multiagent Systems: Context-Driven Collaboration And Decentralized Purpose-Driven Team Adaptation.” Artificial Intelligence for the Internet of Everything, 2019, pp. 67-85.
Ozleblebici, Zafer and Aydın, Bahadır. “Is Intelligence Cycle Still Viable?” Conference: ICMSS 2015 (International Conference on Military and Security Studies), Istanbul, Turkey 2015.
Prakash, Nikhil and Sarkar, Aratrika. “Development of an Intelligent System which Assists in Decision Making Based Upon Previous Decisions.” 2015 International Conference and Workshop on Computing and Communication (IEMCON), Vancouver, Canada, 2015.
Rouse, Margaret. “Intelligent System Definition.” WhatIs. Web.
Trieu, Van-Hau. “Getting Value From Business Intelligence Systems: A Review And Research Agenda.” Decision Support Systems, vol. 93, 2017, 111-124.