Systems | Development | Analytics | API | Testing

You open your performance dashboard and notice that your sales pipeline has dropped 15% since last week. You can see the decline, but the numbers alone don’t explain what caused it. Maybe several deals were pushed into next quarter, your team lost a major opportunity, or a campaign generated fewer qualified leads than expected. The answers are likely somewhere in your CRM, project management tool, or conversations your team has been having.

Test automation gets harder when the application portfolio stops looking like a modern web stack. Desktop clients, packaged systems, internal applications, and long-lived business software all create different requirements for how teams build, run, and maintain tests. In an AI-disrupted SDLC, the rest of the stack changes even faster, and the risk concentrated in those systems grows with it.

In September, Anthropic’s CEO argued that AI companies should deliberately slow the pace at which they improve their models, because advances are outrunning researchers’ ability to understand and control them. Sam Altman endorsed the idea within hours, and other leaders of frontier AI labs quickly echoed him. For QE teams, the pace problem is already here. AI-generated change is arriving faster than anyone can review, and it isn’t arriving clean.

Mike Dillon is a Global Platform Leader at IBM. While deeply technical and experienced in enterprise architecture, he is not a traditional hands-on software developer. Yet this spring, Dillon paired his domain expertise with IBM Bob, IBM’s AI coding assistant, to engineer his first full-scale enterprise application: WatsonAuditX. Designed to automate the documentation required for complex SAP audits, the app addresses a familiar operational roadblock.

Quick summary: SAP Enterprise Performance Testing (EPT) by Tricentis now integrates with SAP Cloud ALM, so teams can create, run, and track performance tests in the same hub as the rest of their S/4HANA transformation. For teams managing SAP transformations, most major workstreams are visible in Cloud ALM, but performance testing has long been an exception. It has lived in a separate tool, forcing teams to treat it as a separate workflow that’s often left to the end of a project.

Watch an AI agent run a regression test sometime. Really watch it. It lands on a login page. It reads the entire page structure: every element, every attribute, every nested div someone added in 2019 that nobody has dared to remove since. Then it reasons. It weighs its options. It explains to itself, at some length, that the button labeled "Sign in" is probably the one that signs you in. And then, finally, it clicks. Now multiply that by every step, in every test, in every regression run, every night.

An API is a defined way for one piece of software to request data or actions from another. The Model Context Protocol (MCP) is an open standard that gives AI agents a consistent way to discover and use tools and data sources, so they don't need a custom integration for each one. The short version: MCP doesn't replace APIs. Most MCP servers call APIs behind the scenes. What MCP changes is who the interface is designed for. APIs are designed for developers who read documentation and write code.