Artificial intelligence has moved from a promising experiment to a business imperative, transforming how enterprises compete and innovate. IDC forecasts that by 2028, AI will represent 16.4% of IT spending, fueling advancements across security, operations, development, data engineering, and data science — especially with agentic AI use cases.
Despite this potential, most AI projects stall before reaching production. The complexity of operationalizing AI — intertwining data pipelines, infrastructure, and teams — outpaces traditional software delivery methods. This challenge is compounded by a critical gap in governance, with a lack of comprehensive model lineage tracking that renders audits, compliance, and reproducibility extremely difficult.
Fragmented operations expose enterprises to security vulnerabilities and compliance blind spots, threatening to erode trust, inflate costs, slow innovation, and ultimately undermine the transformative value AI promises at scale.
This ebook will explore:
- Top AI hurdles and practical strategies for IT leaders to operationalize AI securely and sustainably
- The innovation-versus-control dilemma and how modern enterprises can balance rapid growth with necessary oversight
- The JFrog and Google Cloud partnership, highlighting how they work together to unify and govern the software supply chain
- ML lifecycle acceleration techniques that ensure AI adoption is both scalable and successful across the enterprise
