AI Healthcare: Trust, Risk, and AI in Engineering

About the event

AI coding tools are widely available, yet meaningful adoption within engineering teams remains inconsistent. While demonstrations highlight significant productivity gains, real-world usage often stalls due to concerns around quality, trust, and disruption to established workflows.

Senior engineers often resist AI integration where outputs are difficult to validate or control, while organisations struggle to define how AI should fit into development processes without introducing risk.

This session explores why adoption is proving difficult in practice, and how organisations are redesigning engineering workflows to integrate AI effectively.

Join this session to:

• Understand the key barriers to AI adoption within engineering teams
• Learn how organisations are integrating AI into real development workflows
• Explore how to manage trust, quality, and governance in AI-assisted coding
• Examine how impact is being measured across productivity, risk, and output quality