ISO/IEC 17025 Beyond Compliance: The Hidden Drivers of Laboratory Credibility
What This Webinar Covers:
Accreditation to ISO/IEC 17025:2017 is often treated as proof that a laboratory has “arrived,” yet many accredited labs still struggle with customer confidence, technical consistency, and data reliability. This webinar looks at what separates simple standard compliance from genuine laboratory credibility, exploring the technical, operational, and cultural factors that determine whether the market actually trusts a lab’s results.
Drawing on real assessment experience, the session examines recurring weaknesses seen during accreditation audits, including gaps in personnel competence, confusion between method verification and validation, underused measurement uncertainty practices, and emerging digital and AI-related data integrity risks. The goal is to help laboratories move beyond meeting the minimum requirements of the standard toward building lasting technical credibility.
What You’ll Learn:
- Why compliance with ISO/IEC 17025 alone does not guarantee credibility
- What truly builds laboratory credibility, from staff competence to consistent operations
- Hidden technical and operational risks created by routine work and infrequent oversight
- The real difference between method verification and method validation
- How measurement uncertainty and metrological traceability support confidence in results
- How digitalization, automation, and AI are introducing new data integrity risks
Key Takeaways:
- Long experience does not automatically equal competence; skills and knowledge must be demonstrated, not assumed.
- Confusing method verification with validation is one of the most common weaknesses found during assessments.
- Measurement uncertainty is a low-cost internal tool for checking the consistency of staff, instruments, and testing conditions, not just an accreditation requirement.
- Unverified reference materials, incorrect calibration intervals, and incomplete traceability chains quietly undermine confidence in results.
- As automation and AI take on more of the testing process, staff must still be able to explain the science behind a result and recognize when something is invalid.
Presented By:
Dr. George Anastasopoulos
Technical and International Business Development Manager of PJLA