• 53% cannot detect emerging risks in time
• 73% lack visibility into AI-driven decisions
• 61% need predictive insights on future system behavior

AI-driven systems are no longer deterministic. They generate, adapt, and evolve in production, yet the majority of Quality Engineering organizations are still structured to validate predefined outcomes.

That model does not scale. As a result, organizations face growing exposure to emerging risks, reduced release confidence, compliance challenges, and delayed delivery.

Organizations are beginning to diverge, and the gap is increasing; Some continue scaling traditional QE, while others are redesigning quality for autonomous systems. The opportunity lies in turning the Quality Breakpoint into a catalyst for faster innovation and sustainable competitive advantage.

 Download our free eBook to access:

• A model for measuring release confidence beyond traditional QA metrics
• The emerging role of the Trust Engineer in AI-driven QE
• A structured 4-step playbook to redesign QE for the agentic era

As software becomes increasingly adaptive, organizations need to understand AI-driven decisions, identify emerging risks, and maintain visibility into system behavior. Success now depends on balancing two priorities: using AI to strengthen Quality Engineering, and evolving Quality Engineering to assure increasingly autonomous systems.

Click the button on the right to get our eBook and find out how to move forward with confidence.