What if the detail that seems trivial to you is the constraint keeping the entire project from moving forward?

In this episode of KP Unpacked, KP Reddy sits down with Dr. Barry Clark, CTO of Zero RFI, to unpack why construction projects fail on details nobody thought mattered. A structural beam seems simple: read the line on the drawing, spec the size, done. But the client needs the longest span possible without custom manufacturing (adds cost). The superintendent needs to know when the truck leaves to avoid traffic (adds delays). The permitting team worries about wide-load requirements (adds 90 days). The building supplier tracks lead times and availability. Same beam. Five different perspectives. All mission-critical. The edge case you dismiss is someone else's everyday constraint.

Barry explains why AI's real unlock isn't automating standardized workflows (McDonald's already perfected that). It's mass customization at scale. Every persona on a project looks at the same drawings and sees different risks. AI can now hold all those perspectives simultaneously and optimize for all of them. The conversation also reveals why companies are having a "Facebook moment" with AI (deployed it everywhere, now realizing they don't understand privacy), the three-tier consulting model emerging (billable hours get worst talent, equity gets best), why programming got easy and that's actually good, and why Zero's training spends two-thirds of its time on mental models instead of AI mechanics.

Key questions answered:

• Why do construction projects fail on edge cases nobody thought were important?
• What's the structural beam example that shows five different perspectives on the same detail?
• How does AI enable mass customization instead of McDonald's-style standardization?
• What's the corporate "Facebook moment" happening with AI deployment right now?
• Should you go deep on one AI technology or broad across all of them?
• What are supply chain attacks, and how should executives test their IT teams?
• What are the three tiers of AI consulting: billable hours, risk fees, or equity?
• Why did one consulting firm charge $5M but generate $500M in client outcomes?
• Do employees own their skills files when they leave, or does the company?
• Why did some software engineers quit when their companies adopted AI coding?
• What's the difference between LLMs, VLMs, and physics-informed neural networks?
• Why does Zero's training curriculum focus on thinking frameworks instead of tool mechanics?

If you're an engineer dismissing client requests as edge cases, a project manager wondering why small details derail schedules, or trying to understand why AI matters more for customization than standardization, this episode will show you that everyone's edge case is equally critical to project success.

Listen now.