Why AI Is Slowing Down Experts Before It Speeds Up Work (Brooker, Painter, Deakin, McKenzie)

From the Travel Tech Podcast newsletter for episode 10, Why AI Is Slowing Down Experts Before It Speeds Up Work.

Listen to the episode on Apple Podcasts, also streaming on Spotify and YouTube.

In this episode, I sit down with Ian Painter, Oli Deakin, and Adrian McKenzie, who bring deep experience across aviation data platforms and enterprise engineering leadership. We explore why AI adoption inside teams is not following expected productivity narratives, particularly in environments defined by accountability, verification, and system-level responsibility. We also unpack how trust, proof, and team structure constraints are shaping real deployment, slowing initial gains, and shifting engineering work toward system assurance rather than code production.

Key Takeaways from the Episode

• Productivity Transition Dynamics: AI adoption can initially reduce output for experienced engineers due to context switching and disruption of established workflows.
• Operational Trust Requirements: Deployment in high-stakes environments depends on repeatability, validation, and accountability rather than theoretical capability.
• Human Accountability Constraints: Responsibility for system behavior remains with individuals who must validate and sign off outputs in regulated contexts.
• System Assurance Skill Shift: Engineering roles are moving from writing code toward validating behavior, architecture, and system outcomes.
• Junior Talent Pipeline Risk: Automation of entry-level tasks disrupts traditional pathways for developing system-level expertise and future senior engineers.

We hope you enjoy the conversation.

Alex Brooker, Host of the Travel Tech Podcast, Founder of Airside Labs