Assessment of the impact of artificial intelligence on the economic efficiency of marketing companies

the purpose of the study is to develop and validate a process-level model for assessing the impact of artificial intelligence tools on the economic efficiency of marketing companies. Methods: the research applies economic–mathematical modeling with operationalization through revenue, costs, service quality, automation share, and human capital; process mapping of marketing workflows; scenario analysis of alternative automation levels; and sensitivity checks to input assumptions. Findings: the simulations indicate quality improvement and a reduction in variable costs under AI integration, yielding higher profit under feasible parameter ranges; efficiency depends on the automation share; an interior automation level maximizes economic outcomes given data quality and resource constraints. Conclusions: the proposed model serves as a decision-support instrument for planning AI adoption in marketing agencies; practical use requires calibration to a firm’s service portfolio and available data; further development will address heterogeneity across service lines and the long-run effects mediated by intangible assets.

artificial intelligence, economic efficiency, marketing companies, digital transformation, model