Systems | Development | Analytics | API | Testing

The development of software is becoming shorter. What took months is now done in weeks or even days. Traditional tests in high-speed environment have been found to act as bottlenecks, which slows down the software release process cycles. Here is where Artificial Intelligence comes in, not only as a new product, but as a very essential infrastructure of the modern Quality Assurance.

Conversational Analytics in BigQuery in preview brings a sophisticated AI-powered reasoning engine directly into BigQuery Studio.

And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds. This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.

In November 2023, I flew to Germany to speak at Agile Testing Days, one of the leading software testing conferences. My keynote was called “10x Software Testing.” I knew there would be skepticism in the room.

AI is reshaping test automation fundamentals. Features that once required hours of manual scripting can now adapt automatically to UI changes, generate realistic test data on demand, and help teams predict which tests matter most. For QA engineers evaluating automation platforms, understanding how AI capabilities differ has become essential. This comparison examines SmartBear TestComplete, Tricentis Tosca, and Ranorex through their AI-powered features.

While every organization races to deploy AI agents faster, a quieter crisis is compounding in the background, and it will play a large part in determining who survives the agentic era. The numbers are stark. Too many executives see AI governance as a brake on innovation or something to figure out later, after the speed problem is solved. With agentic AI, that's backwards.

While every organization races to deploy AI agents faster, finance departments are watching something alarming unfold—and it will play a large part in determining who survives the agentic era. The numbers are stark: 84% of companies report more than 6% gross margin erosion from AI costs. Within that, 26% report erosion of 16% or more. And only 15% of companies can forecast AI costs within ±10% accuracy—the majority miss by 11-25%, and nearly one in four miss by more than 50%.

Choosing the right AI test case builder requires evaluating integration depth, not just feature lists. Evaluate AI test case builders based on how they enhance your current workflow rather than how many features they advertise. Your QA team is drowning in test cases. Requirements change daily, releases accelerate weekly, and manual test creation has become the bottleneck everyone acknowledges but nobody has time to fix. An AI test case builder seems like the obvious solution.

So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?

Our customers’ worlds are complex, and for good reason. It’s multi-cloud. It’s SaaS plus on-prem. It’s Snowflake, Databricks, AWS, Azure, Salesforce, and more. Underneath every one of those choices is the same constraint: data must be accessible, stay current, and stay controlled. The hard part is getting trusted data where it needs to be, when it needs to be there, with the controls to use it responsibly.