• Webinar hosted by OpenText and Cognizant explores how AI is reshaping QA and DevOps
  • Focus on AI-powered test automation, faster delivery, and reducing operational risk
  • Practical guidance for applying AI across CI/CD, shift-left testing and observability

OpenText and Cognizant Webinar: How AI Is Redefining QA and DevOps

Overview

Industry leaders OpenText and Cognizant convened a webinar to examine how artificial intelligence is changing the practice and outcomes of quality assurance (QA) and DevOps. The session highlighted AI-driven automation, smarter test design, and new approaches to shortening release cycles while preserving — or improving — software quality.

Why This Matters

As teams push for faster releases, manual testing and brittle pipelines create risks: missed defects, longer rollback windows, and costly downtime. The webinar framed AI not as a replacement for engineers, but as a force multiplier that helps teams detect issues earlier, automate repetitive tasks, and make data-driven decisions about release readiness.

Key Themes Covered

  • AI-powered test automation: Using machine learning to prioritize tests, generate test cases, and speed regression testing.
  • Shift-left and shift-right integration: Applying AI earlier in the development lifecycle and leveraging production telemetry for smarter test coverage.
  • CI/CD acceleration: Reducing build and validation time by focusing automation on high-risk changes.
  • Observability and feedback loops: Feeding runtime insights into test design to close gaps between development and operations.
  • Risk reduction and governance: Ensuring AI-assisted processes are auditable and aligned with compliance requirements.

Practical Takeaways for Teams

Webinar participants were given a pragmatic playbook for adoption rather than a purely theoretical treatment. Recommended first steps included mapping high-effort manual tests, piloting AI-assisted test generation on a representative service, and integrating production telemetry into test prioritization. Organizers emphasized incremental adoption — pilot, measure, expand — to avoid disruption while demonstrating measurable gains.

Who Should Pay Attention

DevOps leads, QA managers, SREs, and engineering executives were identified as the primary beneficiaries. The session underlined that organizations lagging on AI adoption risk falling behind competitors who use automation to shorten release windows and reduce customer-facing defects.

Final Note

OpenText and Cognizant’s webinar framed AI as a practical toolkit for improving testing efficiency and delivery confidence. For teams seeking to modernize QA and accelerate DevOps without increasing risk, the webinar’s guidance offers immediate, actionable directions — and a reminder that delaying adoption carries competitive costs.

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