Build for the wave you can't name

The web, social, mobile, UX, product, data and AI all arrived with different vocabulary and surprisingly familiar organizational behavior. In the final episode, Berk Bayri argues that prediction is overrated and adaptation is the more durable capability. The previous eight episodes — problem selection, use cases, pilots, handoffs, ownership, teams, judgment and memory — resolve into one operating idea: the ability to learn on purpose. The goal is not “full AI adoption,” but an organization that can form a reliable opinion about something new, test it cheaply, stop intelligently, scale deliberately and begin the next decision from what it already learned. Featuring a Satya Nadella excerpt on seeing the web shift while missing where the value layer would emerge.

References

Stanford HAI, 2026 AI Index Report — Economy. Organizational AI adoption reached 88% of surveyed organizations in 2025; generative AI was used in at least one business function by 70%; agent deployment remained comparatively early.

Deloitte AI Institute, State of AI in the Enterprise 2026. Survey of 3,235 business and IT leaders across 24 countries. The report describes a continuing gap between broad access/experimentation and deeper process redesign, transformation and production scaling.

Thabit Atobishi and Saeed Nosratabadi, Dynamic Capabilities for AI-Enabled Exploration: Antecedents, Mechanisms, and Innovation Outcomes (2026 preprint). Survey-based study of 245 senior executives; proposes a mediated relationship in which organizational readiness and technology compatibility support sensing capability and AI-enabled exploration, which in turn relate to innovation performance. Treat as emerging academic evidence, not settled universal causality.

Alexander Ettinger, Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI Adoption (2025). Literature review plus 16 expert interviews; highlights strategic alignment, governance, organizational agility and data-governance maturity in scaling GenAI.