Description
On Tuesday’s show, the DAS crew discussed why AI adoption continues to feel uneven inside real organizations, even as models improve quickly. The conversation focused on the growing gap between impressive demos and messy day to day execution, why agents still fail without structure, and what separates teams that see real gains from those stuck in constant experimentation. The group also explored how ownership, workflow clarity, and documentation matter more than model choice, plus why many companies underestimate the operational lift required to make AI stick. Key Points Discussed AI demos look polished, but real workflows expose reliability gaps Teams often mistake tool access for true adoption Agents fail without constraints, review loops, and clear ownership Prompting matters early, but process design matters more at scale Many AI rollouts increase cognitive load instead of reducing it Narrow, well defined use cases outperform broad assistants Documentation and playbooks are critical for repeatability Training people how to work with AI matters more than new features Timestamps and Topics 00:00:15 👋 Opening and framing the adoption gap 00:03:10 🤖 Why AI feels harder in practice than in demos 00:07:40 🧱 Agent reliability, guardrails, and failure modes 00:12:55 📋 Tools vs workflows, where teams go wrong 00:18:30 🧠 Ownership, review loops, and accountability 00:24:10 🔁 Repeatable processes and documentation 00:30:45 🎓 Training teams to think in systems 00:36:20 📉 Why productivity gains stall 00:41:05 🏁 Closing and takeaways The Daily AI Show Co Hosts: Andy Halliday, Anne Murphy, Beth Lyons, and Jyunmi Hatcher