What Decision Making Should Be Proven During an Agentic AI POC

Evaluating an AI or data intelligence platform should prioritize testing real-world decision-making capabilities during a proof of concept (POC), rather than focusing solely on visibility and polished interfaces. A successful POC demonstrates the platform's ability to make autonomous decisions under pressure, such as handling pipeline failures, late data, and policy violations, while balancing automation with necessary human intervention. Acceldata exemplifies this by connecting data health signals to business SLAs, enabling proactive decision-making that prevents disruptions and reduces manual intervention. The platform should show how it evaluates context, weighs risk, and executes decisions, providing transparency and reliability. A POC should include real data and scenarios to test decision quality, focusing on outcomes like resolution time and manual effort reduction. Ultimately, a strong POC builds confidence in the platform's ability to make accurate, scalable decisions without constant human oversight.