Predictive maintenance (PdM) is one of the promising technologies coming along with the fourth industrial revolution being pushed by disruptive technologies like Internet of Things (IoT), Artificial Intelligence (AI), robotics and Augmented and Virtual Reality (AR/VR). Adopting PdM potentially allows companies to reduce equipment downtime, increase the safety of their processes, increase revenue and develop additional business models. Although the promises of the technology are extensive, the successful adoption rate of this technology is still relatively slow. This is stemming from PdM’s multi-disciplinary nature and “hype” that over-promised its ease of implementation. Organizations are now starting to understand what is needed for efficient implementation, and this helps to manage the expectations about this technology. The fundamental problems highlighted in this research are the complexity, unclear vision, lack of knowledge and know-how in adopting AI predictive maintenance technologies inside an organization. According to Bain & Company’s survey companies in the industrial sector indicated that implementing IoT inside their organization proved to be more complicated than anticipated (Schallehn, Schorling, Bowen, & Straehle, 2019). There is a knowledge gap in the scientific literature, where a lack of best practice methods in terms of predictive maintenance implementation can be identified. Based on the problem highlighted and knowledge gap, the main research question was formulated: “How to facilitate the adoption of Artificial Intelligence-based predictive maintenance technology in the manufacturing industry?“. This study follows a phase-wise approach to obtain the research results. In the first phase, a literature study is conducted to identify the current situation about PdM, what information is available about the factors affecting this technology’s adoption and where is the knowledge gap to be filled. Selected factors to focus on with this research are discussed and agreed upon with the researcher and supervisors. In the second phase, the development of the best practices checklist is commenced. The centrepiece of this phase and the research project overall is the set of semi-structured interviews with 11 industry experts with extensive domain knowledge about predictive maintenance to collect best practices in PdM implementation. The insights gathered from the interviews are analysed in-detail in multiple iterations and then that filtered, aggregated information is used to develop the predictive maintenance project reference checklist. In the third phase, expert panel evaluates the practical applicability, generalizability and the validity of the constructed PdM checklist. Efficient implementation of PdM inside the organization could face numerous barriers and difficulties. Most of these barriers related to technologies using big data could be divided into three categories: technical, organizational and people related (S. Li, Peng, & Xing, 2019). Addressing all of these barriers in those 3 major categories would be unwise since that would not provide sufficient depth of analysis for each one of them. Selection of barriers is based on 3 criteria: the barriers must be relevant and applicable to the adoption of the PdM technologies; there should be a noticeable knowledge gap about how to overcome the barriers; the barriers must be complex enough (affecting multiple layers and stakeholders of the organizations) to fit with the Management of Technology multidisciplinary problem-solving perspective. Based on the information from scientific literature and consultancy reports on PdM, 3 relevant barriers to be focused on are chosen: business case building for PdM; trust in AI-based PdM (lack of trust in big data analytical results) and data management for PdM (the challenge of collecting the data, utilizing it and making sense of it). The interviews with the industry experts revealed valuable insights about predictive maintenance adoption, factors affecting the implementation and best practices that other companies have followed during the process of PdM realization. The most notable best practice that all the interviewees mentioned was involving all the relevant stakeholders early on. In addition, taking small steps, maintaining PdM platforms, celebrating small successes, showing a broad picture and providing a range for PdM business case were outlined. Furthermore, key factors that emerged from the conducted interviews influencing PdM adoption are delineated and summarized in this research project. These are useful for both practitioners and academic personnel who have an interest in this domain and want to gain further understanding of the dynamics surrounding predictive maintenance projects. This research project developed best practices reference checklist for predictive maintenance project implementation that supports organizations on high-level in adopting this novel technology by illustrating and bringing awareness to best practices that other organizations have been following during PdM implementation. This reference checklist is constructed to be a holistic, high-level PdM project support tool for the stakeholders proceeding with predictive maintenance implementation for the first time. This means that a detailed analysis of separate nuances is not sought after since that would misalign with the goal of being a wholesome, comprehendible overview of PdM project implementation checklist. Having a clear, structured and holistic perspective allows stakeholders to conveniently follow this checklist commencing and during predictive maintenance projects without being overwhelmed by excessively detailed information. This best practice checklist based on empirical study comprises a five-phase approach where the enablers and barriers in each phase are mentioned and suggestions on how to deal with them are outlined. These 5 phases are as follows: concept, feasibility, data, PdM algorithm development and operation phase. Furthermore, high-level, structured steps in each phase are laid out to support and offer recommendations to organizations with their PdM activities. In the end of each phase, an overview of best practices and barriers is delineated to recapitulate. In the concluding section of this best practices checklist, a compact, five-page adaptation of this reference checklist is devised for a quick overview of this constructed PdM project support medium and it is advisable to resort back to phases in the checklist itself if the more detailed explanation is needed. This compact version is meant for practitioners in the industry who have strict time limitations and wish to receive information quickly in a condensed format. To the best of our knowledge, such kind of high-level compact overview to assess PdM projects was not existing in the scientific literature. This research project directly investigates and provides a best practices checklist to fill this gap. In addition, this research provided design improvement ideas for different stakeholders to incorporate in their processes/products to facilitate better adoption of PdM. Trust factors affecting the implementation process of predictive maintenance are also outlined, helping companies to better communicate with their clients and internal organization about the benefits and usefulness of PdM. The developed research output has been preliminarily validated and evaluated by the expert panel that concluded that this best practice checklist indeed supports organizations in adopting predictive maintenance technologies. Furthermore, it was agreed that the output is clear and understandable with a well-structured approach. Coming from the high-level nature of this research, experts agreed that this research is generalizable to other industries. Main recommendations (for future research) include validating the best practice checklist in practice with multiple organizations inside the industry to correlate usage of this approach and success factor of implementing PdM. Furthermore, the development of additional support tools and frameworks to facilitate efficient implementation of predictive maintenance technologies would yield increased adoption rates of the technology. This research highlighted important factors contributing to the adoption of predictive maintenance technologies from organizational, people and technology perspectives. This helps to create more awareness about what is needed to consider for better adoption of this technology. Furthermore, a high-level structured overview of best practices checklist supporting PdM implementation is contributed to the scientific and practical domain, filling the previously outlined gap in the literature. In addition, coming from the analysed literature, this research complements the scientific literature on the topic of predictive maintenance by providing original content and additional awareness to the overall academic context regarding the dynamics of this technology’s adoption.