CPACT Webinar
on
Importance
of Data Integrity (DI) and Organizational
Readiness
for AI Transformations
Michelle
Wittmer, Hellma
14th
May 2026 at 2pm EDT, 7pm UK time
As PAT models leveraging
machine learning become increasingly desirable for modern manufacturing and
quality control, organizations are encountering new challenges in deploying,
maintaining, and troubleshooting data-driven systems. While advanced analytics
and machine learning promise improved efficiency and real-time decision-making,
many implementations fall short due to gaps in 21 CFR 11 data integrity
compliance.
This
presentation first explores data capture architecture, which encompasses
traditional traceable protocols and the applicability of ALCOA+ in autonomous
systems. In addition, it explores practical
approaches to troubleshooting data-driven PAT models, with a focus on
identifying common design points where root causes of model drift, poor
predictive performance, and inconsistent outputs can be addressed. Emphasis
will be placed on the critical role of data integrity in PAT design phases—spanning
data collection, preprocessing, governance, and lifecycle management—ensuring
reliable and compliant model performance.
Beyond technical
design considerations, this presentation will examine the often-overlooked
importance of organizational alignment in successful AI transformations. Topics
include areas for change management, digital maturity, documentation design,
and the need for transparency in data planning. Attendees will gain actionable
insights into building robust PAT ecosystems that integrate strong data
foundations with organizational readiness, enabling scalable and sustainable
AI-driven process optimization.
This webinar will take no longer than one
hour.
The webinar is for CPACT members only and is free to attend.
Please register at
https://universityofstrathclyde.webex.com/weblink/register/rd277b0168d8257b1737f4780a67594a8