Why 85% of AI projects fail and how Dynatrace can save yours

Artificial Intelligence (AI) holds significant transformative potential across industries, yet a high failure rate in AI projects, as reported by Gartner, often stems from poor data quality and insufficient understanding of AI requirements. To address these challenges, robust data management and strategic planning, including cloud-based models and large language models (LLMs), are imperative. Data observability, which involves monitoring the quality, lineage, and performance of data systems, is critical for ensuring the success of AI projects. Tools like Dynatrace offer features such as data freshness, volume, distribution, schema monitoring, and lineage tracking to enhance data reliability. Beyond data, AI observability focuses on monitoring model performance, explainability, and drift detection to ensure models operate effectively in real-world applications. Combining data and AI observability provides a comprehensive view of both pipeline infrastructure and model performance, facilitating swift issue resolution and compliance with governance standards. Dynatrace provides organizations with the necessary tools for both data and AI observability, enabling them to develop reliable and transparent AI applications that align with industry standards and ethical guidelines.