Products and services relying upon Artificial Intelligence (AI) have moved from mere concepts to reality. However, challenges still exist in applying AI technologies to traditional industrial and service enterprises. Two central problems are a proper understanding of the opportunities AI could bring to the business processes and making the business logic and data sources transparent to AI experts. As small and medium-sized enterprises (SMEs) are considered the economic backbone of many countries, this paper studies how to support SMEs in understanding the potential of AI in their business and how to prepare their data and requirements for a possible AI project. For this purpose, we first proposed the Cross-Industry Standard Process for Data Mining (CRISP-DM) an industry-proven way to apply AI solutions. The weight was in early business and data understanding. Then, we performed data visualization and developed some machine learning methods for 11 SMEs in South-western Finland as case studies to get more ideas for improving their business using AI. Two surveys probed the possible changes in AI practises of companies.

Human pose estimation has gained significant attention in recent years for its potential to revolutionize athletic performance analysis, enhance understanding of player interactions, and optimize training regimes. Deep learning models, particularly Convolutional Neural Networks (CNNs), have outperformed traditional methods in pose estimation tasks. This study addresses a gap in sports analytics by applying two popular CNN-based frameworks, YOLO and DeepLabCut, to analyze pose estimation in hurdles athletes. Videos of a single female athlete during training sessions were used, and frames were manually annotated to capture three critical foot landmarks: ankle, heel, and big toe. The results highlight YOLOv8l’s superior accuracy, achieving a Percentage of Correct Keypoints (PCK) of 79% for these landmarks, while demonstrating the feasibility of a low-cost setup for practical applications. Visual comparisons further validate the model’s effectiveness in real-world scenarios. Additionally, YOLO predictions were utilized to analyze step progression in the time domain, providing actionable insights into athletic movement. This study underscores that even modest video equipment, combined with CNN-based methods, can equip coaches with powerful tools to analyze and optimize movements and techniques, paving the way for data-driven advancements in sports performance.