62.8% of testers identify AI-powered testing as a top priority, but a significant question remains whether AI improves testing or creates new challenges. The industry is also facing an integration crisis, with testers struggling to connect legacy systems with modern tools and frameworks.

Test Guild

Test Guild - hosted by Joe Colantonio has main topic focus on Testing or Automating. Each episode has a different guest. Show notes have comprehensive links and usually a full transcript. Released as audio and video.

• https://testguild.com/
• https://testguild.com/podcasts/automation/
• https://www.youtube.com/playlist?list=PL9AgRtJkydU1jqvx46esyr56BXtm1QEds
• https://www.youtube.com/@JoeColantonio

Episode Details

• Show Notes: https://testguild.com/podcast/automation/a574-joe/
• Published: 2026-01-06T20:40:00Z
• Duration: 12:03
• Author: Unknown

Overview

The podcast outlines key trends in testing for 2026 based on the Automation Guild Survey, emphasizing AI-powered testing as the top priority for testers. While there is enthusiasm for AI’s potential, concerns about its effectiveness and the challenges of integrating it with legacy testing tools remain significant. Experts recommend fewer, more interconnected solutions to bridge the gap between old and new systems. A notable development is the emergence of new roles focused on assessing AI-generated test results, highlighting a growing skill gap in AI testing.

The discussion also introduces “vibe testing,” a concept where AI can detect issues in ways traditional methods cannot, yet human oversight is still considered essential. Testers are increasingly looking for practical AI examples rather than theoretical discussions, emphasizing real-world applications. Compliance with new accessibility and AI regulations is becoming a growing concern. Despite AI’s promise, it is seen as a supplementary tool that still requires human judgment, with many testers only trusting AI outputs after human review. The overall role of testing is shifting towards strategic innovation, tool agnosticism, and AI integration not only in testing but also in production analysis and risk detection.

What If

• What if you created a 30-day AI-auditing training program focused on evaluating AI-generated test scripts?
  Concrete move: Develop a modular training series covering AI bias analysis, test outcome validation, and audit trail documentation.
  Why now? 67% of testers still require human oversight, and the AI skill gap leaves a market opportunity for specialized training.
  Expected upside: Position yourself as a go-to expert in AI testing, enabling clients to adopt AI tools responsibly while reducing compliance risks.

• What if you built a middleware tool to bridge legacy and modern testing frameworks?
  Concrete move: Design an API-first adapter for Jenkins-to-GitHub or Selenium-to-Playwright integration, focusing on minimal code duplication.
  Why now? 62.8% of testers prioritize AI testing, but fragmentation in tools hinders adoption despite clear demand for connectivity.
  Expected upside: Monetize as a tool provider or consultant, filling a critical gap in the industrys shift toward AI-driven workflows.

• What if you demonstrated AI-powered test scripts that navigate sites without using selectors?
  Concrete move: Publish a GitHub repo with open-source examples of AI-driven test cases using natural language input (e.g., Verify login flow for users with visual impairments).
  Why now? The Show Me Era demands practical AI examples, and 75% of testers cite poor requirements as a bottleneckAI can auto-generate testable scenarios.
  Expected upside: Boost your visibility in testing communities, drive adoption of your tools, and align with EU accessibility mandates.

Takeaway

• Incorporate AI-powered testing tools like Vibium to generate test cases without explicit selectors, reducing manual effort and improving efficiency in test script creation.
• Prioritize tool integration solutions (e.g., Playwright, Jenkins, or GitHub) to bridge legacy systems with modern frameworks, minimizing fragmented workflows.
• Integrate accessibility testing into your CI/CD pipeline to comply with regulations like the European Accessibility Act and maintain audit trails for AI-generated code decisions.
• Adopt a hybrid testing model where AI-generated tests and results are reviewed by human auditors to ensure accuracy, especially for critical systems or decision-making processes.
• Use AI to identify ambiguous requirements early in development, leveraging its ability to generate test cases from unclear specifications and improve Shift Left testing strategies.