Systems | Development | Analytics | API | Testing

You can get an AI Analyst from most of the players, or you can try to build one yourself. Fewer let you decide what it knows, what it can touch, and who can use it. And even fewer still let you define what it won't do. You need all three before you hand one to a team or put one inside your product. In most of the product experiences, your AI analyst's context had two homes: one expert's head, or org-wide instructions.

AI is no longer on the horizon. It is already reshaping how software is built, tested, and released, and it is moving faster than most quality processes were designed to absorb. Developers are shipping AI-written code at speed. Test teams are generating test cases, maintaining suites, and triaging defects with AI. But speed cuts both ways. AI magnifies the process it enters. Strong quality practices can become faster and more scalable.

I'll give you the takeaway from this blog right now: the part of a test worth keeping is what it's meant to prove, not the script that proves it. Scripts have become cheap to generate, so we should stop treating them as something precious to maintain. Keep the intent. Throw away the script. Last time I wrote that the first time an AI agent runs a manual test case, it can leave an automated test behind, and every later run can use that script instead of the natural-language steps.

‍ Multi-factor authentication verifies user identity through two or more distinct authentication factor categories. These include something users know, possess, or inherently are, such as biometric characteristics. The multi-factor authentication healthcare organizations deploy protects electronic health records, remote access, and clinical applications. Healthcare authentication must accommodate shared workstations, frequent user changes, and time-sensitive clinical tasks.

Multi-region deployment fundamentally changes performance expectations and testing priorities. Unlike single-region or multi-AZ setups, distributing workloads across continents introduces higher latency, longer failover times, and asynchronous replication. These realities require a new approach to load 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.