AI coding assistants such as Claude Code and Codex are accelerating software development, yet many teams still face bottlenecks and challenges in delivering software quickly. In CXOTalk episode 908, Bill Staples, CEO of GitLab, explains why AI can create downstream delays in software reviews, testing, security checks, and incident response, even as raw coding output increases. He says the solution is "Intelligent Orchestration" of software delivery and the software development lifecycle.
Staples calls the slowdown the “AI paradox”: despite faster code production, the overall pace of software delivery has declined. Fragmented tools force developers to switch contexts constantly. Governance is overwhelmed, and trust, residency, and cost control have become key challenges for enterprise technology leaders.
In this important conversation, Staples offers practical guidance for CIOs and CTOs to realize the benefits of AI-powered software development while minimizing the downsides. He also explains key metrics that reflect actual delivery performance: cycle time from idea to production, deployment frequency, change failure rate, MTTR, and security and compliance readiness.
Watch this conversation and take back control over your development process!
Key Takeaways
Measure Delivery Flow, Not Code Output
- Track end-to-end cycle time from idea to production and from merge request to deployment to expose the true bottlenecks.
- Add quality, reliability, and risk measures such as change failure rate, pipeline failure rate, mean time to recovery, security vulnerability trends, and compliance readiness: link these metrics to developer satisfaction and business outcomes.
Fix the AI Paradox by Reducing Tool Fragmentation
- Consolidate around a platform where planning, code, tests, security, and pipelines share a single source of truth, reducing context switching and rework for teams.
- Design an inner-loop architecture where agents run close to the data they need, reducing orchestration overhead and improving agent results.
Put Guardrails and Cost Control Ahead of Scale
- Set standards for identity, data access, approvals, and data residency before adopting agents at scale, and apply the same policy gates to both human and agent changes.
- Log and trace every agent action with a durable audit trail, and track usage-based AI spend across vendors to prevent uncontrolled tool sprawl and budget surprises.
Episode Participants
Bill Staples is the CEO of GitLab. He is passionate about developers and has spent nearly 30 years building developer platforms and tools for them. He is an execution-focused leader who loves to build and scale businesses. Bill believes we're still in the early stages of a software transformation, and AI will accelerate how software changes the human experience in the coming decade. He believes there has never been a better time in history to be in the software business, serving developers and helping improve their work and lives, ultimately reaching billions of people around the world in profound ways.
Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep business transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.
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