Traditional quality engineering is breaking because it was built for slower, more predictable software delivery, AI-assisted development is increasing engineering productivity by 30–50% in many enterprise environments, but testing and validation processes are not scaling at the same pace. As development speeds up, many enterprises are discovering that their existing quality practices cannot scale at the same pace. The result is a growing quality gap between how fast software is built and how confidently it can be released.  

Integrated Evidence Planning (IEP) breaks down when evidence exists across too many systems, teams, and decision contexts to be used in a timely, aligned, and decision-ready way. AI changes that by helping life sciences organizations move from fragmented evidence gathering to continuous evidence orchestration across the asset lifecycle. 

That is the simplest way to understand why Integrated Evidence Planning, or IEP, is changing so quickly. 

Modern enterprises generate massive volumes of data across applications, customer interactions, supply chains, IoT devices, and cloud platforms. Yet, many organizations still struggle to turn that data into real business value because their systems are fragmented, slow, and difficult to scale. 

This is where Azure Databricks comes in.