INTEGRATION OF ARTIFICIAL INTELLIGENCE IN GLOBAL CORPORATIONS AND ITS IMPACT ON INNOVATION MANAGEMENT

The integration of artificial intelligence (AI) in global corporations has evolved from an optional technological upgrade into a strategic necessity driven by accelerated digital transformation, heightened competitive pressure, and expanding regulatory requirements. This paper examines how AI influences innovation management, with particular attention to its role in due diligence processes where speed, accuracy, transparency, and risk identification are critical for corporate decision-making. Building on the premise that AI is not merely an automation tool but a capability that changes how organizations generate knowledge and coordinate action, the study evaluates both measurable performance effects and organizational implications.
The research objective is to assess the adoption of AI in multinational corporations and to determine its impact on (i) innovation efficiency and outcomes, and (ii) due diligence performance and risk management. A mixed-methods design was applied. Quantitative analysis draws on operational indicators from 25 multinational corporations (technology, finance, manufacturing), including processing time for due diligence reports, frequency of human errors in compliance procedures, R&D productivity expressed through prototype development, and financial indicators linked to AI-enabled innovation projects. Qualitative insights were collected through 30 semi-structured interviews with managers and employees involved in innovation management, compliance, and digital transformation initiatives.
Findings indicate that AI adoption is associated with substantial reductions in processing time and error rates, supporting more reliable and faster decisions in compliance and due diligence activities. Simultaneously, AI contributes to innovation management by enabling advanced analytics, shortening development cycles, and supporting cross-border collaboration through shared data and project platforms. However, the study also identifies persistent barriers that shape implementation outcomes: ethical concerns related to privacy and bias, integration constraints with legacy systems, and ongoing demands for training and reskilling.
The paper concludes that the strategic value of AI depends on governance choices and organizational readiness. Corporations that combine technological investment with human capital development and clear ethical frameworks are more likely to realise AI’s benefits while limiting risks. The contribution of the study lies in consolidating evidence on AI’s dual role—enhancing innovation performance and strengthening due diligence—while highlighting the organizational conditions that enable sustainable, trustworthy, and scalable adoption.