Artificial intelligence is increasingly becoming integrated into the daily practice of pathology, not as a replacement for the pathologist, but as an invisible layer of support throughout the diagnostic workflow. This plenary presentation follows a pathologist through a typical working day to demonstrate how AI can assist across the pre-analytic, analytic, and post-analytic phases of care. Through practical examples in digital pathology, the talk highlights how AI can help prioritize cases, improve quality control, surface relevant clinical context, assist with image analysis and differential diagnosis, streamline reporting, and enhance patient safety through automated quality assurance. Beyond improving efficiency, these technologies also transform routine pathology workflows into valuable sources of structured clinical and research data. Using real-world diagnostic scenarios, the presentation focuses on the practical clinical impact of AI adoption in pathology and argues that the most effective AI is often the least visible - seamlessly supporting pathologists in delivering faster, safer, and more informed patient care.

Learning Objectives:

1. Describe how AI can support pathologists across the pre-analytic, analytic, and post-analytic phases of their workflow.
2. Identify practical applications of AI in digital pathology that can improve patient care and enhance operational efficiency
3. Discuss how AI-enabled workflows can generate structured data for clinical research and real-world evidence development.

Digital pathology will only succeed when it delivers in the real world, not just in the demo room. This workshop explores how open, vendor-agnostic systems can connect the complete digital pathology workflow: scanning, automated QC, case accessioning, LIS/EHR integration, high-performance viewing, with AI models seamlessly integrated for analysis and reporting. We'll then look beyond today's workflow to the next steps for pathologists: intelligent tools that augment, not replace, the pathologist's capacities. From case assembly, history retrieval, and triage, through feature detection, measurement and report completion, AI is already available to save pathologists time and effort, and it can amplify its benefit by making the result of pathologists' work truly computable. Drawing on real-world implementations at leading institutions, and with practical perspectives from technology partners and customers, we'll examine what works at clinical scale now and preview capabilities that will drive the evolution of pathology in the days and years to come.

Learning Objectives:

1. Identify the essential components of an open, vendor-neutral digital pathology workflow
2. Understand why interoperability is key to effective clinical deployment in real world use
3. Appreciate how an emerging "AI exoskeleton" can augment the pathologist across the diagnostic workflow