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This past year hasn’t just shown us that AI is here to stay but also that it’s transforming workflows and strategies in DevOps and software quality testing across the board.
At OpenText World in Nashville, hosted by OpenText, AI was a part of every conversation, every presentation, and almost everything else. At the end, everybody couldn’t help but wonder,
“What will AI do to software quality testing in 2026?”
Because of this, almost all the predictions on our list are about the impacts of AI on software quality testing. We asked our team of experts what they think the future of software quality testing is. Read on to hear what they had to say.
1. AIOps Will Emerge as a Critical Operations Strategy
AIOps or using artificial intelligence to manage IT operations, will gain some traction in 2026. We’re predicting a shift from reactive monitoring to systems that predict failures before they happen.
Imagine receiving an alert that says “database connection pool exhaustion likely in 45 minutes” instead of scrambling after users start complaining. That’s where we’re headed. AIOps platforms will correlate signals across logs, metrics, and traces to identify patterns that humans would miss, using AI-driven root cause analysis to surface and fix issues sooner (like OpenText’s Aviator for finding risks early and fixing defects fast).
2. AI Hallucination Testing Will Become Its Own Discipline
Here’s our boldest prediction: 2026 will be the year organizations get serious about testing AI systems, and “AI hallucination testing” will emerge as a specialized field.
Right now, most organizations are deploying AI-powered features without adequate testing frameworks. That can’t continue. Teams will build adversarial testing frameworks specifically designed to trick AI systems into generating false outputs – think fuzzing for large language models.
Prompt injection testing will also emerge as a security priority, similar to how we test for SQL injection today. Organizations will need to verify that malicious inputs can’t manipulate AI systems into behaving incorrectly.
3. Low-Code Testing Platforms Will Democratize Quality Assurance
By mid-2026, we predict low-code and no-code testing platforms will enable business analysts and product managers to create automated tests without writing code.
These platforms (like OpenText’s upcoming Aviator Studio) will use natural language processing to convert plain English into executable tests. Someone writes “verify that premium users can export reports but free users see an upgrade prompt,” and the system generates the test automatically.
This won’t replace QA engineers but it’ll multiply their impact. When people who understand business requirements can directly create tests, we eliminate translation errors. QA engineers will shift to building frameworks, reviewing tests, and tackling complex scenarios that require deep expertise.
Why it matters
2026 will be a pivotal year where AI moves from experimentation to production-critical systems. The organizations that succeed will be those who implement AI responsibly with proper testing, governance, and validation.
At VIP, we’re helping clients navigate this shift by applying proven methodologies to validate AI reliability and build robust testing frameworks. As enterprises seek partners who can help them implement AI safely and effectively, we’re bridging the gap between traditional QA and the unique challenges AI systems present.
If you’re planning your 2026 DevOps and quality strategy, we’d love to continue the conversation. Reach out to us at opentextsolutions@trustvip.com.