Stopping Shadow Tech Debt
TOPIC
Stopping Shadow Tech Debt: Responsible AI Adoption at the Leadership Level
In just a few years, generative AI has evolved from experimental copilots to autonomous agentic systems. The technology is transformative, but has created a critical verification debt: AI agents are generating code faster than human experts can validate its structural integrity. Gartner predicts that the shift toward “”prompt-to-app”” development will lead to a 2500% increase in software defects by 2028, requiring rigorous architectural controls.
For technical leaders, adoption is a competitive necessity, but it requires rewiring the enterprise to ensure that today’s productivity gains don’t, unnoticed, become tomorrow’s agility-killing technical debt. Navigating this shift requires a focus on:
Striking the balance between experimentation and tool sprawl
New metrics for assessing codebase health in the age of AI
Governance policies and technical guardrails
Strategies for scaling AI programs beyond the pilot stage
WHO IS ATTENDING?
Join leaders interested in governing responsible AI adoption by building the architectural controls, governance frameworks, and codebase health metrics needed to scale AI-generated code without accumulating hidden technical debt.
Job titles may include CAIOs, CTOs, VPs, Heads of AI, Software Engineering, Technology and other senior decision-makers.
AGENDA
6:00 PM Arrival of guests
6:30 PM Welcome address by The Ortus Club
6:40 PM Short address by JetBrains
6:45 PM Discussion instigated by the moderator and continued
7:45 PM Discussion brought to a close and guests encouraged to continue networking