Systems thinking of a manager taking into account artificial intelligence

Systems thinking of a manager begins with the realization that he is not dealing with individual elements, but with a holistic system that has its own relevance and purpose of activity. This means that the manager considers the environment as a complex of interconnected parts that form a single whole environment. This approach allows you to see not only individual processes or tasks, but also their role in achieving the overall goal of the organization or community.

Within this systemic whole, there are different levels of interaction:

• supersystems that define the framework and rules of the game,

• your own system with its subsystems,

• friendly systems that support development,

• competing systems that create challenges,

• hostile systems that threaten.

The systems thinking of the manager is:

• Analysis of the connections of the system as a whole, registration of its elements in a form convenient for highly automated analysis using OLAP, Data Mining, Image Mining, Video Mining, Wave Mining; Dashboads, LLM (Large Language Models) - Gemini, ChatGPT, Copilot, Grok, Deep Seek, etc.) - this is the basis for further analysis.

• Analysis of the dynamics of interaction of the specified elements of the system, moving "from the general to the partial". In this case, the possibility of the reverse operation - synthesis from partial elements of the whole is provided.

• Based on the above, it is necessary to make forecasts of possible options for the future.

• Based on the above, it is necessary to make options for management decisions, their predictable (probable based on fuzzy logic) solutions. At the same time, keep in mind that fuzzy logic decisions are determined on the basis of a certain law of fuzziness (normal, Rayleigh, trapezoidal, triangular, etc.) and may be erroneous or inaccurate.

• On this basis, make and implement decisions that may be relatively correct or incorrect on the basis of fuzzy logic.

• Evaluate the results of decisions.

• Based on the previous one, correct the following decisions.

• Save the previous one in the database to be taken into account when forming future decisions.

This creates the basis for strategic management, where decisions are made taking into account a holistic and simultaneously multi-level picture.

In contrast, traditional logic is oriented towards the movement from the partial to the whole: first, individual elements are considered, and only then a conclusion is drawn about the system as a whole. Such an approach often leads to a fragmented vision and loss of strategic perspective.

Systems thinking, on the contrary, begins with the analysis of the whole and gradually moves to the details, which allows the manager to make more balanced decisions, take into account the context and avoid narrow thinking.

A modern modification of systems thinking involves the formation of system elements in the form of a multidimensional OLAP cube.

In the absence of time for in-depth analysis, automation tools help, expert assessments, and intuition developed based on the results of many decisions.

Improving the system approach based on the transformation of primary data into a multidimensional cube and its OLAP (multidimensional analysis in online mode)

The use of OLAP improves systems thinking, supports analysis in online mode both from general to partial and from partial to general with gradations determined by the structuring of primary data and the needs of the analyst. In some cases, the set of elements for OLAP can be improved using ETL operations (Extract, Transform, Load). 

To simplify the understanding model, this article does not use the disclosure of OLAP content and teaching the content of Data Mining, Image Mining, Video Mining, Wave Mining; Dashboads, LLM (Large Language Models) - Gemini, ChatGPT, Copilot, Grok, Deep Seek, etc).

The fundamental flaw of "data-driven decisions" and the justification for using "clairvoyants"

The "scientific approach", especially the so-called "data-driven decisions" is built only on the basis of HISTORICAL DATA FROM DATABASES. However, in practice "it happens like never before" - this justifies the use of clairvoyants, fortune-telling on cards, numerology, etc. There are historical and mythological examples that confirm this. For example, the predictions of such people as Nostradamus, Baba Vanga, Lev Messing and other mythological and real people - in many cases their predictions are correct or plausible, that is, they require additional interpretation.

Therefore, a full scientific approach in the most important tasks of system analysis may include an appeal to clairvoyants, taking into account the probable fallacy of their judgments and the possibility of external influence on their decisions. For example, there is a well-known mythological example when the enemies of Greece allegedly bribed the "Delphic oracles" to give a false prediction about the possibility of a Spartan victory over the superior Assyrian army of King Xerxes.

In any case, system solutions also have a probabilistic approach.

It should be noted that the relatively gray beard of a young mentor or a mature man is more likely a marketing tool for self-promotion and is often a sign of self-confidence, hides the flaws of the face and lifestyle, etc. This became quite common in Ukraine during the war, where it is often necessary to use a beard as a means of disguise.

Source: publication based on the archive of the Synergy Research and Development Center, 2007. Speech to the organizers of corporate information (information and analytical) work and automators of corporate information (information and analytical) work.