Abstract

An opportunity is a relationship between a company's objectives, the resources at its disposal, and the situation in the environment, such that it favours the achievement of the objectives with the available resources. Opportunities arise in a changing environment. The dynamics of change causes the life cycle of an opportunity to shorten and companies that discover the opportunity and can exploit it gain a competitive advantage. Opportunity discovery means that opportunities exist but the company does not know about them. Opportunity discovery is the subject of several scientific disciplines. They are dealt with by strategic management using the methods of strategic analysis. They are also of interest to agility theory, which focuses on reacting quickly to market opportunities. In addition, entrepreneurship theory deals with them, which addresses the issue of cognitive traits in their discovery. New possibilities in discovering opportunities are created by artificial intelligence. The article aims to present the opportunity discovery model understood as the relationship between three vectors: the company's goals, its resources, and the external environment in which it operates. The discovery of the opportunity consists in finding such a value of the environment vector at which the desired value of the vector of goals is achieved at a given value of the resource vector. The article answers the question of how the knowledge necessary to find such a relationship between these vectors must be structured so that this relationship reflects the opportunity. It was hypothesized that this structure can be modelled using a network of cause-and-effect relationships leading to the determination of the relationship between demand and supply. This hypothesis was verified using artificial intelligence with the Monte Carlo algorithm, Markov chain, and the Metropolis-Hastings algorithm. The results obtained confirmed the validity of the model structuring the knowledge. The article presents the result of a pilot study. The results concerning knowledge structuring are useful for opportunity discovery both in the case of traditional approaches i.e. using the achievements of strategic management theory, corporate agility and entrepreneurship theory, and in the case of using artificial intelligence. In the first case, the target audience for these results is business executives while in the second case, the research community working on this problem and IT companies.

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