Artificial intelligence can increase salaries by up to 40%

The capacity of organisations to learn, both individually and collectively, has emerged as a fundamental bottleneck in the successful adoption of artificial intelligence. Although technological infrastructure and financial resources are widely discussed in the context of AI implementation, accumulating evidence suggests that organisational learning capabilities are the primary factor in determining whether AI investments generate the expected value.

Organisational learning theory conceptualises learning as the process through which organisations develop, supplement, and organise knowledge and routines around their activities, thereby improving organisational efficiency by enhancing the use of their workforces’ broad skills (Fiol & Lyles, 1985). In the context of AI adoption, however, this theoretical foundation must be extended to encompass both exploratory learning (the acquisition of fundamentally new knowledge and capabilities) and exploitative learning (the refinement and application of existing knowledge to novel technological contexts (March, 1991). Contemporary research shows that effective AI adoption requires engagement with both learning modes simultaneously (Ritala et al., 2024).

Organisations face challenges in identifying precisely which skills require development for specific AI applications, designing training interventions that effectively transfer knowledge to the workplace, and maintaining skills amid the rapid evolution of AI capabilities. Furthermore, the interdisciplinary nature of effective AI implementation, which requires integrating technical, domain-specific, and organisational change management expertise, complicates skill development initiatives (Kumar & Mittal, 2024). Traditional functional training approaches are insufficient for developing the integrative capabilities necessary for successful AI adoption.

Beyond individual skill gaps, organisational-level learning processes often limit the effectiveness of AI adoption. Many organisations lack structured pathways for acquiring, integrating, and institutionalising knowledge regarding AI capabilities (Ritala et al., 2024; Wijnhoven, 2021). This absence can manifest in several ways, such as inadequate mechanisms for capturing and disseminating learning from pilot AI projects, insufficient cross-functional collaboration to enable knowledge integration across the organisation, and limited systematic reflection on AI implementation experiences to refine organisational approaches.

Research shows that AI can improve knowledge management by increasing information processing, pattern recognition, and decision support (Alavi et al., 2024; Olan et al., 2022). However, this integration introduces risks, including over-reliance on AI-generated insights at the expense of human expertise and the propagation of algorithmic biases through organisational knowledge systems. There are also fewer opportunities for tacit knowledge development through human interaction and socialisation.

Addressing these constraints requires reconceptualising AI adoption as an organisational transformation process that entails substantial learning at individual, group, and organisational levels, rather than primarily as a technological implementation challenge. As AI capabilities advance and organisational integration deepens, competitive advantage will accrue to organisations that have developed superior learning capacities, enabling them to adapt continuously and extract value from evolving AI capabilities.

Bibliography:

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This article is part of the project “People and Algorithms in Organisations: Competences to Work in the Digital Environment” (DIGIT_People and algorithms), funded by the NAWA – Narodowa Agencja Wymiany Akademickiej (Polish National Agency for Academic Exchange).

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