Enterprise Agile Planning
The Trust Problem in AI Test Failure
In Stack Overflow’s 2025 Developer Survey, 84% of developers said they use or plan to use AI tools in their workflow, up from 76% the year before. In the same survey, 46% said they actively distrust the accuracy of what those tools produce, against 33% who trust it. Adoption went up, trust went down, at…
The Spreadsheet Is the Tell
Somewhere in your organization there is a spreadsheet. Someone built it because a director asked a question the testing platform could not answer directly: which projects are actually using the device lab, or how much testing a team ran last quarter. They exported what they could, pasted it into a sheet, wrote a few formulas,…
AI Is Accelerating Cryptanalysis. Cryptography Must Learn to Adapt
In July 2026, Anthropic reported two cryptanalysis results produced with Claude Mythos Preview. One improved an attack against HAWK, a proposed post-quantum signature scheme. The other improved an attack against a seven-round version of AES. Anthropic was explicit about the limits: neither result affects production systems today. That caveat is the right place to begin….
How to Build Support-Ready Products – Lessons from Real Customer Issues
It’s 2 a.m. somewhere in the world, and a release manager at a global enterprise is staring at a red pipeline. Hundreds of automated tests were supposed to run overnight against a mobile device cloud. Instead, they failed with errors that don’t explain themselves. By the time the ticket lands in support queue, the customer…
Parallel Testing Done Right: Why Your Pipeline Fails (And How to Fix It)
Every QA tester knows the crushing feeling of watching an automation suite grow over time. What starts as a nimble 5-minute smoke test gradually evolves into a sprawling 2-hour execution run. Each new feature brings a handful of additional Appium mobile scripts or Selenium browser flows; before long, developers are tapping their feet, release managers…
Why Faster Release Risk Triage Starts with Better Operational Context
Enterprise release teams rarely lack data. They lack a fast, reliable way to turn scattered release signals into a clear operational picture. A production-bound release may have a risk score, a failed task, an overdue approval, a delayed dependency, and a schedule variance, yet those signals often live in different views and require different people…
Automation Frameworks beyond Appium & Selenium
A team ships a React Native app and a marketing site in the same sprint. The mobile suite runs on Espresso and XCUITest; the web suite runs on Playwright. Nobody on either team wrote a line of Appium or Selenium — not because those tools failed them, but because neither team ever needed a cross-platform…
AI and The Future of Enterprise Agile Planning
Artificial intelligence is moving quickly into the way teams plan, refine, and manage work. But for enterprise Agile organizations, the opportunity is to use AI where it can reduce friction while keeping context, accountability, and judgment firmly in human hands. Digital.ai Sage is designed as an assistive capability within Agility. Enabled users can access Sage…
Digital.ai Agility 26.1: Connecting Strategy to Work
Every enterprise has goals. The harder problem is keeping those goals connected to the work teams plan and deliver. Objectives may live in one system, roadmaps in another, and execution in team backlogs. When those layers drift apart, leaders can see activity without knowing whether it is advancing strategy, while teams can deliver successfully without…
Digital.ai Agility 26.2: Connecting Release Planning to Delivery
Digital.ai Agility 26.2 focuses on making that connection easier. The release expands visibility into release scope and progress, brings release and OKR information into the Data Mart, and strengthens Agility Sync for organizations coordinating work across multiple systems. The result is a more connected planning experience that helps teams spend less time reconstructing status and…
