This research paper examines how Artificial Intelligence (AI) can be used to improve the organisational development of the Tanzania Breweries Limited (TBL) production in Tanzania. The study particularly looks at the role of technology, organisation, and environment in the adoption of AI and the resulting effects on the efficiency of operations, working capacity, and the resulting innovation. The research is based on the Technology-Organisation-Environment (TOE) framework, which makes it a welldeveloped theoretical foundation to comprehend the use of AI in the manufacturing setting. The quantitative research design was used, and the goal was 450 employees working in the TBL production departments. The sample size was calculated using the Yamane formula, and primary data were collected using structured questionnaires. The collected data were also compared through the summated index, descriptive statistics, and hypothesis tests to assess the association between variables and find out the mediating value of AI adoption. The results indicate that AI is significantly adopted in response to technological readiness, such as system maturity, compatibility, and ease of use. The environmental factors, including regulatory compliance, competition in the market, and industry support, have a moderate impact on the adoption decisions, whereas the type of organisational factors, including leadership support, workforce skills, culture of innovation, and resource allocation, have a critical effect on the adoption decisions. The adoption of AI is a mediating variable which converts the elements of readiness into quantifiable operational efficiency, capacity of the workforce, and improved innovation outcomes. The research arrives at the conclusion that successful AI implementation is a multi-faceted process that involves investments in technologies, the ability of organisations to develop, and adaptability to environmental factors. The findings are relevant to the theory due to their support of the TOE framework in the Tanzanian manufacturing environment and practical information to practitioners who want to use AI to improve their operations and strategies. The policy implication is clarity of the regulations, industry-supportive programs, and easy access to technical expertise to make AI adoption easy. Lastly, the paper gives future research for studies such as longitudinal studies on the long-term effects of AI and investigations into newer technologies like the IoT and machine learning in improving organisational development.