Systems | Development | Analytics | API | Testing

How we utilized all of our customer data to identify churn trends and prevent future loss.

A marketing director sits down ten days after her campaign closed. Six browser tabs are open: LinkedIn Ads, HubSpot, GA4, Mailchimp, an attribution spreadsheet, and a blank doc that is supposed to become the post-mortem narrative. The meeting is in two hours. She knows something broke in the middle of the funnel (pipeline came in below target), but she cannot prove where or why until she reconciles numbers across all six sources.

New Sauce Labs Real Device Cloud Agent — available now in the watsonx Orchestrate Agent Catalog — can enable enterprise teams to trigger real-device tests, manage fleets, and validate app quality using natural-language commands.

There's a pattern we see repeatedly in enterprise AI projects. A team identifies a compelling use case. They build the model. They staff the project. Then they spend the next six to eighteen months trying to solve a problem that was never on the roadmap: their data isn't ready. Not because it doesn't exist. It exists everywhere: in cloud warehouses, on-premises databases, SaaS platforms, and data lakes across multiple regions.

Enterprises are buying AI infrastructure faster than their platform teams can operationalize it. Dell and ClearML are working together to close that gap, giving enterprises a faster, simpler path from Dell AI Factory hardware to a production-grade AI platform. Dell carries the hardware. ClearML provides the AI infrastructure layer on top. Together, the two give platform teams a way to deliver AI as a service to their organization without a multi-year integration project.

AI agents do not just respond. They interpret goals, use tools, follow workflows, apply context, and sometimes change real systems. That means they need a different kind of testing.

Agentic Testing and QA is a practice in which AI agents operate directly on a project — reading files, planning tasks, generating framework code, and interacting with a browser — rather than simply answering prompts inside a chat window. Tools like Claude Code bring this capability to the terminal, giving QA teams a command-line assistant that understands repository context, proposes changes before applying them, and generates test assets across Playwright, Selenium, and API testing workflows.

Automated load testing has reached a turning point in 2026. Artificial intelligence, once a gradual addition, now drives a clear shift in how organizations validate performance. Industry reports project a 15% compound annual growth rate (CAGR) for AI in software testing from 2023 to 2026, underscoring the urgency to modernize testing practices and keep up with rapid development cycles.

We surveyed 2,501 IT decision-makers, QA professionals, and business leaders across six countries for our second annual Quality Transformation Report. Respondents came from organizations with 150-plus employees across manufacturing, energy and utilities, retail, financial services, and the public sector. One of the major findings: confidence in AI agents making release decisions dropped from 48% in 2025 to 34% in 2026. That’s a 14-point decline in a single year.